DEV Community: Fabio Lauria

AI for HR: The Complete Guide to Enhancing Human Resources

Fabio Lauria — Fri, 03 Jul 2026 10:51:18 +0000

Are you using AI to streamline HR work, or are you delegating decisions to an algorithm that it should never make on its own? This is where the discussion about AI in HR gets serious. In Italian SMEs, the issue isn’t whether artificial intelligence is useful. It is. The issue is understanding where it generates real value and where, on the other hand, it introduces opacity, bias, and regulatory risks.

As an entrepreneur, I’ve seen how tempting it is to automate the most tedious tasks. If you have hundreds of resumes to review, internal surveys to summarize, or employees who keep asking the same questions about vacation time and company policies, AI saves you time right away. But I’ve also seen the other side of the coin. A compatibility score generated by a model seems objective, and precisely for that reason, it can be more dangerous than an explicitly subjective human assessment.

The correct way to look at this isn’t “AI yes” or “AI no.” It’s about finding the right balance between automation and human responsibility. For those looking for a very practical take on SMEs, I also recommend AI in HR for SMEs.

Introduction

The right question isn’t whether AI can help HR. The right question is whether it can truly select your next top talent without skewing the process.

In practice, AI is already being used today in resume screening, internal chatbots, survey analysis, onboarding, and document generation. It’s a particularly useful technology when the operational workload is high and speed delivers immediate value. But in human resources, every decision affects real people, real careers, and real rights. That’s why its adoption requires a different approach than when you bring in a “co-pilot” to write emails or summarize meetings.

Efficiency matters. When it comes to decisions about people, however,being quick isn’t enough .

In the Italian market, this issue is even more sensitive. The GDPR and the European AI Act significantly limit the margin for error when an automated system influences hiring, performance evaluations, and personnel management. If you’re considering AI for HR , here’s a simple rule: automate routine tasks, but keep decision-making in human hands.

What AI Actually Does for Human Resources Today

AI in human resources isn’t science fiction. It’s already part of our daily work. Today, many companies use it to streamline repetitive tasks, speed up processes, and give the HR team more time for work that requires context and judgment.

According to Yomly data on AI adoption in HR functions, 44% of companies are already using it for recruiting. AI tools can reduce time-to-hire by about 50% and automate nearly 40% of repetitive tasks.

Recruiting and Initial Screening

The most common use case is the initial screening of applications. An LLM reads resumes and job descriptions, compares skills, experience, and semantic cues, and then compiles a ranked shortlist.

In practice, it works well when the role is fairly standardized. I’m thinking of administrative positions, customer support, inside sales, and software development with a defined tech stack. If you describe the requirements clearly, the model greatly speeds up the first step.

It doesn’t work as well when the factors in question are difficult to extract from a resume.

Nonlinear experiences may be penalized, even if they are highly relevant.
Soft skills such as independence, leadership, and adaptability remain difficult to assess automatically.
Whether something fits within the corporate context almost never becomes clear from a simple textual analysis.

Rule of thumb:Use AI to narrow down 500 CVs to a more manageable list. Don’t use it to decide on its own who deserves a final interview.

Employee Support and HR Operations

The second use case is less obvious, but often more useful. HR teams spend a large portion of their time on repetitive tasks. According to Tommaso Maria Ricci’s analysis of AI in human resources, HR teams devote between 40% and 60% of their time to tasks such as vacation requests, payroll, and company policies. HR chatbots can free up as much as 2–3 hours per day for more strategic activities.

The value here is immediate. An internal chatbot answers questions about remaining vacation days, documents, procedures, expense reports, policies, and administrative onboarding. The benefit isn’t just the time saved by the HR team. It’s also the quality of the employee experience — employees get a quick response instead of waiting for an email.

Surveys, Onboarding, and Skills Mapping

Where AI really shines is in the analysis of long, rambling texts. Internal surveys are a perfect example. Instead of manually reading through hundreds of open-ended responses, the model identifies recurring themes, sentiment, emerging issues, and patterns worth exploring further.

The most useful applications I see in small and medium-sized businesses are these:

Job descriptions and policies

The AI generates a coherent first draft, which the HR team then revises to ensure legal and cultural compliance.

Customized Onboarding

It can tailor content, materials, and sequences based on role or department.

Skill mapping

Helps map existing skills and training gaps, especially when data is scattered across resumes, performance reviews, and managerial notes.

Climate Analysis

Transforms unstructured text into useful insights to help identify where action is needed.

There is also a growing distinction between generalist models and vertical models. On the vertical side, Wisq has built HRLM as a model specifically for HR. On the generalist side, GPT, Claude, and Gemini are already being used in many companies for operational HR tasks with well-designed prompts. The difference, however, lies not only in the quality of the output. It lies in governance.

The AI Laffer Curve for Finding the Optimal Point

The worst way to implement AI in HR is to think in absolutes. Zero automation leaves you with slow processes, an operational backlog, and decisions based on incomplete information. Total automation leads you to the opposite mistake: treating people and job applications as tickets to be sorted.

The Problem of Extremes

The Laffer curve metaphor works well here, too. At first, every instance of AI adoption generates efficiency. Automate internal FAQs, first drafts of documents, text analysis, and preliminary CV rankings. The value increases.

Then you reach a tipping point. If you keep entrusting the algorithm with increasingly sensitive tasks, its value begins to decline. Not because the model is useless, but because the risk increases faster than the benefit.

According to Workday’s overview of AI in HR, the main reasons for adoption are improved decision-making (41%) , automation of repetitive processes (35%) , and improved retention and employee experience (32%). These figures clearly explain why AI is so appealing to HR. But they don’t tell us where to draw the line. This is the point that’s often missing from the discussion.

The greatest value doesn’t lie in replacing the HR team. It lies in** helping them work more efficiently and quickly** on the right tasks.

How to Position Your Cursor in Your Small or Medium-Sized Business

To find the optimal point, I use a simple distinction between mechanical tasks and decision-making tasks.

If you run an SME, the optimal approach is usually not technical. It’s organizational. You need to clearly decide where AI should make suggestions , where it should issue commands , where it should summarize , and where it should not make decisions.

Three questions can be very helpful:

Is the mistake reversible? If you get a FAQ wrong, just correct it. If you reject the right candidate, the damage is done.
Is the task repetitive? The more repetitive it is, the better the AI tends to perform.
Does the decision affect a person’s rights or career? If so, human intervention is not optional.

The Hidden Risks of Bias, Privacy, and Regulatory Compliance

The most dangerous aspect of AI for HR isn’t the technology itself. It’s its false aura of neutrality. When a recruiter evaluates a candidate, everyone knows that evaluation involves some degree of subjectivity. When a system assigns a score, many people stop asking questions.

The Myth of the Objective Algorithm

This is the crux of the problem with algorithmic bias. If you train or configure a system using historical hiring data, the system tends to replicate the patterns that already existed in that data. If the company’s history has favored certain profiles and penalized others, the algorithm can do the same thing more quickly and in a less obvious way.

The Amazon case has become emblematic precisely for this reason. The company had to withdraw a resume-screening system that disadvantaged female candidates. This is not some isolated, curious incident. It is the predictable consequence of an approach that uses the past as a model of merit.

In Italy, the picture is far from reassuring. According to data published by ELECTE on this topic, only 12% of HR companies with AI systems have implemented systematic bias audits.

A better model does not solve the problem if the data, criteria, or organizational context remain skewed.

The GDPR and the AI Act in the Italian Context

For those operating in Europe, this is not just an ethical issue. It is a legal issue. Article 22 of the GDPR grants candidates the right not to be subject to decisions based solely on automated processing when such decisions have significant effects on the individual. HR decisions fall squarely within this sensitive area.

In addition, the European AI Act classifies recruitment and personnel management as high-risk uses. This means much stricter requirements for documentation, transparency, oversight, and risk management compared to the general use of AI for individual productivity.

For an Italian company, the practical implications are clear:

Do not use black boxes to make decisions on your own regarding hiring, promotions, or dismissals.
It documents the human role in the process.
Assess the processing of personal data and the legal basis.
Keep track of the checks performed on the system and the criteria used.

Anyone who is seriously working on these issues should also look into companies’ compliance with the AI Act.

General-Purpose Tools and Vertical Models: Which to Choose

The market is splitting into two very different categories. On one hand, there are general-purpose LLMs like GPT, Claude, and Gemini. On the other, specialized models designed specifically for human resources — such as Wisq’s HRLM — are emerging.

When a General-Purpose LLM Is Enough

For an SME, a general-purpose model is often sufficient. If you need:

generate a draft job description,
summarize open-ended feedback,
create internal FAQs,
create an initial sorting of the resumes,
support onboarding and internal communications,

A good LLM with well-written prompts can work very well.

The advantage is practical. You can get started right away, spend less, and test quickly. For small HR teams or companies with relatively simple processes, this approach is often the most sensible way to begin.

There is, however, a limitation. General-purpose models are not designed with HR logic in mind, nor do they come with policies specific to your context, nor do they offer implicit guarantees of compliance simply because they are powerful.

When Is a Vertical Model the Best Choice?

If you handle higher volumes, more sensitive processes, or an organization with multiple levels of authorization, vertical models make sense. Not so much because they “understand everything better,” but because they are designed for a narrower scope.

They are usually the preferred choice when:

more precise HR taxonomies,
workflows integrated with internal systems,
better controls over auditability and governance,
stricter standards for traceability and explainability.

For an SME with 50 employees, the goal isn’t to buy the most sophisticated system. It’s to choose a system that the team knows how to use, monitor, and challenge when it makes a mistake.

The right question isn’t which model is more advanced. It’s which model best suits your operational risk. If the task is low-impact and high-volume, go with a generalist model. If the process involves sensitive decisions and requires structured oversight, the vertical model is worth considering.

A Practical Roadmap for Integrating AI into Your HR Department

The best implementations don’t start with predictive recruiting. They start with everyday friction. That’s where AI builds internal trust and shows whether the team is truly ready to manage it.

Start with the right tasks

The first step is only trivial at first glance. You should start with high-volume, low-risk activities. If you start there, you’ll immediately see the benefit and limit your exposure.

Three sensible examples:

Internal HR chatbots for frequently asked questions about vacation, policies, and procedures.
Automated generation of documents such as job descriptions, onboarding emails, and internal policies.
Automated analysis of surveys to identify themes and issues.

This approach has a positive effect. The HR team stops viewing AI as an abstract threat and begins to treat it as an operational tool.

Define governance and controls

The second step is more important than the first. You need to clearly document where the AI makes recommendations and where a human makes decisions.

Minimum governance in SMEs should include:

Decision-making boundary

AI can classify, summarize, and flag items. The manager or recruiter then approves, rejects, or investigates further.

Review Process

Every high-impact output must be reviewed by a designated person.

Pre-release bias testing

If the system is used for recruitment or personnel evaluation, it must be tested using representative datasets and documented controls.

Internal Transparency

Employees and candidates must know when AI is used to support the process.

An SME that skips inspections isn’t speeding things up. It’s just pushing the risk further down the line.

The third step is to scale up gradually. A pilot project focused on a single HR process yields more insights than a broad rollout. First, validate the task; then, the team’s behavior; and finally, the regulatory scope.

For those who want to organize their work effectively, it’s helpful to follow a clear roadmap for AI integration rather than conducting scattered experiments.

Measuring Success with Concrete Examples

To measure the success of AI in HR, it’s not enough to look at speed alone. We need to understand whether it improves the quality of decision-making without introducing risks, errors, or opaque processes.

In SMEs, the most useful criterion is simple: Is AI moving the HR team toward the right point on the Laffer curve, or is it automating tasks that still require human judgment too soon? If the time saved increases but so do disputes, reviews, or doubts about the fairness of the process, the gain is only apparent.

Proper Use

A concrete example is the analysis of internal satisfaction surveys. In many companies, HR manually reviews hundreds of open-ended responses and identifies the main themes — a process that takes a long time and varies somewhat from person to person. With a well-configured LLM, thematic clusters, recurring patterns, and anomalies emerge more quickly.

Here, the real benefit isn’t just operational. The team stops wasting hours on summaries and can focus on priorities, follow-ups, and working with managers.

In this case, the useful metrics are few and specific: average analysis time, the consistency of the summaries compared to a random sample of human reviews, and the number of insights that lead to actual actions. If the AI produces quick but overly generic summaries, you’ve already gone past the optimal point.

Incorrect use

The opposite scenario is more delicate. A chatbot that conducts the initial interview and assigns a preliminary score without human review may seem efficient, but for an Italian SME, it creates a serious methodological problem — even before we consider the technology.

There are three risks. You may reject qualified candidates based on unclear criteria. You may find it difficult to explain the decision transparently. You may expose yourself to GDPR compliance issues and, in high-impact cases, to the stricter obligations imposed by the AI Act for systems used in the workplace and for employment access.

As I’ve observed in the workplace, the right test is this: Is AI helping us make better decisions, or is it just speeding up a flawed decision? An analysis by ELECTE highlights this very point. Recruitment processes managed solely through automation tend to worsen the actual fit between a person and a role, while final human validation reduces the most costly errors.

Measuring effectively, therefore, means considering four indicators together: time saved, output quality, human correction rate, and compliance risk. If you measure only one of them, you’re usually misjudging the project.

Key Takeaways

Start with the operations team. Internal FAQs, documents, surveys, and pre-screening are the best places to begin.
Do not automate the final decision. High-stakes hiring, promotions, and evaluations must remain in the hands of people.
Test for bias before release. If the system affects job applicants or employees, this check is not optional.
Think in terms of governance. Roles, responsibilities, human review, and documentation are just as important as the model itself.
Choose the tool based on the risk. Use a general-purpose tool for simple tasks, and a specialized tool if you need precision, traceability, and stricter controls.

Conclusion

AI for HR really works when it handles the routine tasks and leaves the most difficult task to humans: interpreting context, motivation, potential, and consequences. That’s the sweet spot. Not no AI, not total automation.

For an Italian SME, the priority is not to chase the latest and greatest innovation. It is to build a system that improves efficiency and quality without conflicting with the GDPR, the AI Act, and sound managerial judgment. If you apply this logic, AI becomes a useful multiplier. If you use it as a substitute for judgment, it becomes a risk.

If you want to turn operational data and organizational signals into clearer insights, ELECTE — an AI-powered data analytics platform for SMEs — helps you analyze complex information, automate reports, and make better decisions. To see how it works in practice, you can watch the platform in action and assess whether it fits your processes.

Originally published at https://www.electe.net .

Originally published on Medium

Reading and Analyzing XML Files: A Practical Guide for SMEs

Fabio Lauria — Thu, 02 Jul 2026 10:51:38 +0000

You receive an XML file via certified email (PEC). You open it in your browser, see a wall of tags, and think the problem is “reading” it. In reality, that’s just the first hurdle. The real problem in your company is something else: figuring out whether that data is correct, consistent, and ready to be included in your reports.

For many Italian SMEs, this issue is no longer strictly technical. Since electronic invoicing became mandatory, XML has become part of the daily work of administration, management control, and analysis. It’s not enough to simply view the document. You need to be able to distinguish between a readable file and a reliable one. You need to understand when a quick check is sufficient and when parsing, validation, and normalization are required before loading the data into Excel, a BI system, or an analytics platform.

If you’re looking for a practical guide on how to read XML files, this is the right approach: start with simple methods, figure out where they break down, and then build a workflow that transforms raw XML into data useful for business. That’s where you minimize errors and shorten the time between “I have the file” and “I have a usable insight.”

What Is an XML File and Why Is It Essential for Businesses?

An XML file organizes data into a hierarchical structure. There is a root element, nested sections, and each block describes a piece of information with a specific meaning. For those who manage administrative processes, this detail makes the difference between data that is readable and data that is truly usable.

The point isn’t to “open” the file. The point is to determine whether that file can be integrated into the control, accounting, and analysis workflows without errors.

Understanding the Structure Without Being a Developer

Let’s take an electronic invoice as an example. The same file contains supplier data, customer data, taxable amounts, VAT, item lines, payment terms, order references, and often exceptions that make it difficult to read. In XML, this information isn’t listed one after another as it would be in a regular document. It’s placed in specific locations, and that location explains what each piece of information represents.

For a manager, the useful distinction is not between tags and attributes in a theoretical sense. It is between isolated data and reliable data. Seeing “1000.00” out of context is of little use. Seeing it in the correct place in the file allows you to understand whether it is the document total, the taxable amount, the tax, or the value of a single line.

This is where the first operational advantage comes into play. XML preserves the context of the data.

Rule of thumb:To properly read an XML file, you need to verify the meaning of the value, not just the value itself.

Why XML Is a Key Issue for Administration, Finance, and Analytics

In Italy, this issue has become a reality with the widespread adoption of electronic invoicing. In the FatturaPA format, XML has become the standard for tax documentation. As a result, interpreting these documents is no longer just an IT matter. It involves administration, management control, procurement, and anyone who needs to use that data to make decisions.

In practice, I always see the same problem. The file exists, the data is there, but the time it takes to turn it into useful information drags on too long. Someone opens the XML file, checks it visually, copies values into Excel, corrects inconsistent fields, renames suppliers listed in different ways, and tries to reconstruct expense categories that the file doesn’t present in a format ready for analysis. The cost isn’t just operational. It’s lost time-to-insight.

With FatturaPA, the risk is even more apparent. Two formally correct files can cause the same analysis problems if one uses very vague line item descriptions, if order references are incomplete, or if supplier master data is entered with different variations. At that point, the problem isn’t reading XML. The problem is preventing valid tax data from becoming unreliable management data.

A common mistake is to treat XML as an attachment to be viewed. In a business setting, it’s better to think of it as a structured data source that needs to be validated before it feeds into reports, dashboards, and expense models. If this step is handled poorly, the finance team ends up discussing numbers that appear accurate but are based on inconsistent classifications.

The right questions to ask at the beginning are these:

Is the field I’m reading actually relevant to the process I need to manage?
The file is formally valid
The data are consistent across different sections of the document
Information can be extracted without losing context
The data records and descriptions are clear enough for analysis

These are very practical checks. They help prevent duplicate suppliers in reports, misinterpreted VAT, incompletely populated cost centers, and slow month-end reconciliations.

This is where the gap between technical interpretation and business value becomes apparent. A parser reads the file. A well-designed process produces clean, comparable data that is ready for analysis. Platforms like ELECTE were created to bridge this very gap, reducing the manual work involved in transforming the received XML into insights that enable better decision-making.

Quick Ways to View XML Files Without Writing Code

For quick checks on a single file, you don’t need parsers or libraries. You need to determine whether you’re performing a visual check of just a few fields or whether you’re already handling data that will end up in accounting, reporting, or management control. The difference matters, especially with FatturePA. A check done hastily today could result in an incorrect row in the supplier dataset tomorrow.

When a quick glance is all it takes

Browsers, text editors, and dedicated viewers solve a specific problem: quickly reading content without setting up a technical workflow. For a single file, this is often enough. You can open an XML file in Chrome, Edge, or Firefox to view its structure, or use Notepad, WordPad, or TextEdit if you want to inspect the tags directly. In the case of electronic invoices, a dedicated viewer makes the header, document lines, taxable amount, and VAT easier to read.

The bottom line is this:

If you need to verify the document date, VAT number, invoice total, or whether there are any attachments, these tools are suitable.

If, on the other hand, the goal is to compare suppliers, categorize expenses, or populate a dashboard, simply viewing the data slows down the work and leaves too much room for manual errors. It’s the classic gap between looking at a file and arriving at reliable data in a timely manner.

Opening an XML file does not mean that the data you will use in your reports has been validated.

Another practical consideration concerns volume. Ten files can still be checked manually. Hundreds of FatturePA invoices, however, cannot. In that case, it makes sense to start thinking about a repeatable workflow or tools that can read the content in a structured way — for example, using an API to capture and manage tax documents in an integrated manner.

The Special Case of Signed XML Files

In Italy, the recurring problem isn’t opening a .xml, but figuring out what to do when a .xml.p7m via PEC. It is important to distinguish between simple XML files and digitally signed files. The latter requires tools capable of reading the signature, extracting the content, and displaying the correct XML, as explained by This guide on XML and XML P7M in PEC.

Here, mistakes cost time:

If you receive a signed file , check the file format and the signature first.
If you’re using a viewer , make sure it supports P7M as well, not just XML.
If the document is archived or undergoes a compliance process , the digital signature is part of the document control process.

For an administrative assistant, the most useful sequence is simple:

Open the PEC email and identify the type of attachment.
If it’s a simple XML file, do a quick check of the key fields.
If it’s a P7M, use a tool that displays the signed content in a readable format.
If that data is to be used for analysis or reconciliation, simply reviewing it visually is not enough.

These methods work well for first-level checks. They do not solve the real problem facing the company: transforming tax XML files — which are often irregular or inconsistent — into clean, comparable data without increasing the time it takes to extract useful information from the received document.

Reading and Processing XML Files Through Programming

When files start to pile up, manual work becomes unsustainable. At that point, reading XML files with code is not an elegant solution. It’s the first step toward avoiding repetitive tasks, copying errors, and inconsistent datasets.

The technical flow that stands the test of time

A solid approach to reading XML always follows the same logic: parsing, normalization, and targeted extraction. In Java and Android tutorials, the correct workflow involves parse(), by normalizing the tree with doc.getDocumentElement().normalize() and then by restoring the fields with getElementsByTagName, a more reliable method than simply viewing the file in a text editor, as shown This technical tutorial on reading XML data.

This sequence is more important than the language you choose. If you skip normalization, if you search for nodes in a way that’s too naive, or if you assume that a tag always appears only once, your script will work on some files but fail on the very ones that matter.

For projects that need to interface with external systems, it can be helpful to establish a replicable and well-documented data extraction workflow. If you’re working on application integrations, the ELECTE API documentation — which includes a verified Postman profile — is a useful resource, especially for understanding how to connect a cleaned dataset to subsequent processes.

Practical examples in different languages

Below are some simple examples. The goal is not to cover every possible case, but to show you the basic logic: open the file, find a node, and print a value.

Python

import xml.etree.ElementTree as ETtree = ET.parse("invoice.xml")root = tree.getroot()number = root.find(".//Number")if number is not None:print(number.text)

Python is often the fastest choice for prototyping, data transformations, and lightweight pipelines. It’s great when you need to read a lot of XML files, extract a few fields, and save them as CSV or JSON.

JavaScript in the browser

const xmlString = `<fattura><Numero>123</Numero></fattura>`;const parser = new DOMParser();const xmlDoc = parser.parseFromString(xmlString, "application/xml");const numero = xmlDoc.getElementsByTagName("Numero")[0];console.log(numero.textContent);

This approach is useful for quick on-page tests or small internal tools. It works well for lightweight interfaces, but less so for structured back-office workflows.

Node.js with xml2js

const fs = require("fs");const xml2js = require("xml2js");const xml = fs.readFileSync("fattura.xml", "utf8");xml2js.parseString(xml, (err, result) => {if (err) throw err;console.log(result.fattura.Numero[0]);});

If you work on the server side and want to build automations, Node.js remains a practical choice. The advantage is that it allows you to easily integrate XML parsing with the file system, processing queues, and internal services.

Java with DOM

DocumentBuilderFactory factory = DocumentBuilderFactory.newInstance();DocumentBuilder builder = factory.newDocumentBuilder();Document doc = builder.parse("fattura.xml");doc.getDocumentElement().normalize();NodeList lista = doc.getElementsByTagName("Numero");if (lista.getLength() > 0) {System.out.println(lista.item(0).getTextContent());}

Java is often used in enterprise, business management, and middleware contexts. The key point here is not just to read the data, but to do so in a predictable and maintainable way.

R

library(XML)doc <- xmlParse("fattura.xml")numero <- xpathSApply(doc, "//Numero", xmlValue)print(numero)

R makes sense when parsing is part of an analytical task. If your next step is statistical analysis or data preparation, you can keep everything in the same environment.

If your team opens the same files every week and performs the same checks, you’re already in the realm of automation.

The real benefit isn’t “reading XML with code.” It’s freeing people from repetitive work and building a workflow that produces consistent datasets.

Overcoming Advanced Challenges with Complex, Large-Scale XML

The real problems begin when there is more than one file. A single FatturaPA is almost always manageable. The difficulty arises when you have to consolidate months’ worth of documents, different suppliers, inconsistently filled-out fields, and embedded attachments.

When the file isn’t large, but the volume is

In Italian SMEs, the most common scenario is not an isolated “mega file,” but a batch. An annual export of purchase invoices can result in a structure with over 380,000 nodes across 4,200 invoices , including headers, line items, payment details, and base64-encoded attachments. In these scenarios, the challenge isn’t opening the document; it’s transforming heterogeneous XML into a coherent dataset.

This is where a technical choice comes into play that has business implications. In the .NET environment, Microsoft notes that XmlDocument loads the document into memory and is useful for reading and editing, while for large files or read-only operations, it is advisable to opt for more efficient approaches such as streaming parsers or XPathDocument to avoid excessive RAM consumption, as specified in Microsoft’s documentation on reading XML with XmlDocument and XPathDocument.

In practice:

DOM or XmlDocument works well when you need to navigate the tree freely.
Streaming or XmlReader is more suitable when the volume increases and you want to read the data sequentially.
XPathDocument is a good option when you’re only performing queries and want greater efficiency.

The trade-off is simple. The in-memory model lets you develop faster. The streaming model performs better in production when there are many files or large files.

Technical validation and semantic validation

Many teams stop at XSD validation. It’s useful, but it’s not enough. A file can comply with the schema and still produce dirty data downstream.

Typical examples from day-to-day operations:

The most insidious case is this: a “Total Document Amount” that is formally valid but does not match the sum of the line items, perhaps due to rounding rules in the supplier’s accounting system. Or VAT codes that are formally valid but do not match the nature of the transaction.

Even a formally correct file can still skew your reporting.

There is also another well-known pitfall in FatturaPA. The DatiBeniServizi tag contains free-form descriptions. The same cost can appear in many different ways, with text that is clear, abbreviated, or cryptic. If you don’t include a normalization step, any analysis by expense category becomes unreliable.

That’s why, in serious data flows, reading the file is only the first step. The second step is always a set of consistency and cleaning rules. That’s where data quality is safeguarded — not in the parser.

How to Convert XML into CSV or JSON Data Ready for Analysis

An XML file that reads correctly is not yet a useful dataset. It is a structured document. To perform analyses, comparisons, groupings, and create dashboards, you almost always need to convert it into a format that is easier to work with.

Why the XML file is not the final product

This is the point that many processes overlook. The bottleneck is rarely the parsing itself. A decent library can read XML quickly. Time is lost in interpreting the structure, extracting useful fields, cleaning the data, normalizing it, and loading it into an analytics tool.

That’s why converting to CSV or JSON isn’t just a convenience. It’s a key operational step. If you skip this step and work directly on the raw file, you almost always end up with manual checks, improvised columns, and logic that’s difficult to replicate.

A useful resource for anyone who frequently works with XML and spreadsheets is this guide on how to convert XML to Excel in a more organized way.

Two useful resources for analysts

The right format depends on how you’ll use the data later.

CSV for tabular analysis

CSV works well when you want one row per document or one row per invoice line item, and then use Excel, Power Query, or BI.

Python example:

import xml.etree.ElementTree as ETimport csvtree = ET.parse("invoice.xml")root = tree.getroot()with open("invoices.csv", "w", newline="", encoding="utf-8") as f:writer = csv.writer(f)writer.writerow(["number", "date"])number = root.findtext(".//Numero")data = root.findtext(".//Data")writer.writerow([numero, data])

The advantage is simplicity. The limitation is that you have to carefully decide how to flatten the hierarchy. If an invoice has multiple detail lines, you need to make a clear choice regarding granularity and the join key.

JSON for semi-structured data

JSON is best suited when you want to preserve part of the hierarchical structure.

JavaScript example:

const record = {numero: "123",data: "2024-01-15",righe: [{ descrizione: "Servizio", importo: "100.00" }]};console.log(JSON.stringify(record, null, 2));

Use it when your next step is an API, a data lake, or an application that works well with nested objects.

Here’s a helpful rule of thumb:

CSV if your goal is tabular reporting and traditional business analysis
JSON if you need to preserve more complex relationships or pass data to other systems
Both , if the process has an integration phase and an analysis phase

The XML file is the container. CSV and JSON are the formats that make the content truly usable.

If you want to reduce time-to-insight, this is where you should focus your efforts. Not on finding a more user-friendly visualizer, but on defining a stable and repeatable transformation.

From XML to Strategic Insights with an Analytics Platform

Once the file has been read, validated, and transformed, the nature of the work changes. You’re no longer struggling with tags. You’re finally focusing on costs, anomalies, suppliers, expense categories, and operational trends.

The bottleneck is data preparation

In the real world, the value isn’t in the parsing time. It’s in the time it takes to go from the raw file to information you can act on. With a manual workflow, a person has to open the document, understand its structure, extract the fields, clean up the values, normalize the text, and then build reports. It’s a fragile process.

A classic example in FatturaPA is the free-text field in DatiBeniServizi. The same service can be described in many different ways by different suppliers. If you import that data without a consistent mapping, the analysis by cost category produces meaningless aggregations.

For this reason, before implementing the analytics platform, a data preparation layer is needed:

Standardization of Descriptions
Category Mapping
Consistency Checks
Stable structure for imports

When this step is done right, any analytics platform works better. If you want to delve deeper into the decision-making and visual aspects of this step, the resource on how to build stories with data is helpful because it shows how a clean dataset can be turned into a narrative that’s useful for decision-makers.

From the cleaned dataset to the decision

At this point, the XML file ceases to be a technical issue and becomes raw material for insights. A well-prepared dataset can support expense analysis, trend monitoring, identification of variances, and review of exceptions.

To choose a platform suited for this “last mile,” it may help to compare what modern business analytics software offers versus purely manual workflows based on spreadsheets and pivot tables.

The right criterion here isn’t “Can it open XML?” That’s the bare minimum. The relevant question is a different one:

The difference between an immature and a mature process does not lie in the ability to read XML files. It lies in the ability to transform them into a reliable database that does not force the team to redo the same work every time.

Key Points to Remember

If you need to read XML files in a way that’s useful for your business, keep this checklist in mind. It’s more practical than any technical definition and helps you choose the right method without wasting time.

Choose the tool based on its purpose

Don’t always use the same approach. Browsers, editors, and viewers are fine for quick checks. Parsers and scripts are needed when the file must feed into repetitive processes. If you confuse data visualization with data processing, you run the risk of building reports on a shaky foundation.

Treat signed files as a special case

The files .xml.p7m require a specific step in signature management. If the content comes from a PEC, this check is not optional. It is part of the proper processing of the document.

Don’t stop at technical validation

Adhering to a schema does not guarantee a clean dataset. Logical inconsistencies — such as mismatched totals or ambiguous tax classifications — are what most often ruin an analysis. Semantic validation is what distinguishes an “acceptable” file from reliable data.

Convert it to an analyzable format as soon as possible

CSV and JSON aren’t just cosmetic changes. They’re what make XML usable by analytics tools, spreadsheets, pipelines, and reports. The sooner you define this transformation, the sooner you’ll reduce manual work and improvisation.

Remember what the real goal is

Your goal isn’t to read XML files. It’s to gain useful insights without cluttering the system with dirty data. If the data flow doesn’t produce a consistent dataset, the problem isn’t with the final dashboard. It lies much further upstream.

Basically, you can use this mini-checklist before starting any new project:

Determine the intended use before choosing the tool
Manage P7M and XML separately
Valid Structure and Meaning
Normalize the free fields
Export to CSV or JSON before analysis

If you want to turn prepared data into clear, actionable insights, ELECTE helps SMEs move from a clean dataset to intelligent reporting, using an approach that’s accessible even to non-technical teams. It’s the fastest way to bridge the gap between operational data and decision-making.

Originally published at https://www.electe.net .

Originally published on Medium

Multimodal AI Business Applications: A Guide for SMEs

Fabio Lauria — Wed, 01 Jul 2026 11:02:12 +0000

You’ve seen this scenario before. The sales team sends you an Excel file with sales figures. Customer support forwards emails with recurring complaints. The warehouse shares photos of damaged products. The admin team keeps invoices and PDFs in separate folders. Each team sees a piece of the problem, but no one sees the whole picture.

This is where multimodal AI business applications become appealing to an SME. Not because they’re trendy, but because they help integrate data that currently exists in silos: text, tables, images, documents, and operational logs. Multimodal AI analyzes them together, just as a person would when listening to an explanation, looking at a chart, and reading a report before making a decision.

For a manager, the issue isn’t technical. It’s operational. If you connect your data sources in an organized way, you can turn scattered signals into insights that are more useful for forecasting, quality control, customer service, and reporting. If you want to know where to start, a good first step is to get a clear picture of the data sources you can connect within your company.

Introduction: Lighting the Way to the Future with Unified Data

Monday morning. The sales rep checks the CRM, the admin team opens the invoice PDFs, the quality manager reviews photos and reports, and customer service reads emails and tickets. Everyone is looking at the same customer or the same process, but from different perspectives. The result is predictable. Decisions are made too late, or they’re made with a piece of the context missing.

In SMEs, this problem is more common than it seems, because data isn’t stored in a single, organized system. It’s scattered across Excel files, documents, images, chat messages, management systems, and exported reports. Analyzing each source separately is a bit like assessing a store’s performance by looking only at the sales receipt, without considering returns, customer complaints, and photos of the shelves. You get an answer — but it’s not always the right one.

Multimodal AI is designed precisely to piece this picture back together. In practice, it brings together different signals, links them, and interprets them within the same analytical workflow. For a manager, the value does not lie in the technology itself. It lies in the fact that an anomaly can be detected earlier, a priority can become clearer, and a decision can be based on a context that more closely reflects operational reality.

Here’s a point that’s often overlooked. For an SME, adopting multimodal AI doesn’t mean rebuilding the infrastructure from scratch. In most cases, it makes sense to start with existing data sources, connect them effectively, and choose a process where the cost of fragmentation is already apparent — such as document control, customer service, or quality monitoring. A useful starting point is to have a clear overview of the company’s data sources to be integrated, so as to understand where context is lost and where it can generate economic returns.

When sales, operations, and administration teams interpret the same issue differently, the cost isn’t just in terms of information. It translates into wasted time, avoidable errors, and shrinking margins.

That’s why the issue isn’t just about innovation. It’s about decision-making coordination. Unifying textual, visual, and structured data helps reduce manual steps, minimize ambiguity, and better measure the ROI of AI projects — without chasing generic use cases or overly ambitious promises.

What Is Multimodal AI and Why Is It a Game-Changer for Businesses?

From Reading in Isolation to Understanding the Context

A traditional system often operates in a single mode: text only, images only, or numbers only. This approach is useful for specific tasks, but it falls short when the business environment mixes everything together.

Multimodal AI, on the other hand, processes multiple types of input simultaneously. It can combine text, images, audio, video, and structured data to uncover relationships that would otherwise remain hidden. McKinsey explains that multimodal models are particularly well-suited for processing multisensory data and combining text, images, audio, and video. In practice, a multimodal analytics engine can unify CRM feeds, support tickets, invoice PDFs, and product images into a single graph, reducing context loss and improving the quality of predictions because weak signals can be automatically correlated (McKinsey’s explanation of multimodal AI).

For a manager, the practical difference is this:

If sales figures, reviews, and shelf images tell three different stories, unimodal AI interprets them separately. Multimodal AI tries to figure out whether they are actually describing the same problem.

How it translates different types of data into a common language

This is where many readers get confused. It seems like magic, but the principle is straightforward.

The model takes various types of data and transforms them into a comparable representation. It’s like translating Italian, English, and Spanish into a common language before analyzing an international contract. In the world of AI, this translation is similar to the concept of embedding. Text, images, or numerical signals are converted into mathematical representations that the system can compare.

Then comes the fusion. Instead of analyzing each mode on its own until the end, the system combines them to form a single view. At that point, the value does not come from the individual data points, but from the relationships between them.

Rule of thumb:If your business problem can be fully understood by analyzing a single database, you probably don’t need multimodal AI. If, on the other hand, the context is spread across documents, images, and different systems, then everything changes.

How Multimodal AI Works in Practice

The best way to understand it is to follow it through a real-world process.

A simple example from the retail sector

Before. A retailer notices a drop in sales for a product line. The sales team checks the dashboard. The category manager receives photos from stores. Customer service reviews comments and returns. Each team comes up with its own analysis.

Next. A multimodal system collects sell-out data, shelf photos, customer receipts, and product descriptions. If it detects damaged packaging or inconsistent displays in the images, it can link that signal to text-based complaints and a drop in sales. Decisions are no longer made based on three separate meetings, but on a single view.

The same pattern holds true elsewhere as well:

Finance: Compare received documents, text notes, and accounting history to identify inconsistencies.
Customer Care: Combine transcripts, support tickets, and order history to determine whether a complaint is an isolated incident or a sign of a broader issue.
Operations: Review machine logs, technical reports, and images of defects to determine whether maintenance or a process review is needed.

Why do so many SMEs start with the visual aspect?

Not all companies start with sophisticated systems. Many begin with more practical use cases, often involving images and documents. A 2025 overview of the multimodal market indicates that computer vision-based solutions account for 35% of implementations and that the cloud accounts for 57% of deployments , a sign that many companies start with computer vision applications and scalable cloud platforms before expanding their use to documents, dashboards, and more complex workflows ( overview of the multimodal market).

This information is helpful because it takes the pressure off. You don’t have to build everything all at once.

Start with a visual or document-based workflow where manual errors are a significant issue.
Connect a second data source, such as your business management software or CRM.
Check whether combining the two sources actually improves the process.
Only then should you expand the perimeter.

If your small or medium-sized business has a lot of PDFs, photos, tickets, and Excel spreadsheets, you’re already sitting on multimodal data. The point isn’t to create it. It’s to orchestrate it.

Key Business Applications of Multimodal AI

Document Intelligence and Administrative Processes

This is one of the areas where ROI tends to be most transparent for an SME. You have repetitive documentation, well-known rules, and significant hidden costs associated with monitoring, reclassification, and verification.

Multimodal systems combine OCR and NLP to extract data from scans, PDFs, and notes, transforming them into structured data that can be used for processes such as invoices, receipts, and contracts ( SuperAnnotate’s in-depth look at multimodal AI). In practice, the system doesn’t just “read” a file. It compares what it finds in the document with the context available elsewhere.

A concrete example. An SME receives invoices from multiple suppliers in different formats. A traditional approach extracts standard fields. A multimodal approach can also compare the invoice text, the document image, the supplier history, and the order in the ERP system. If it detects inconsistencies, it flags the case to an operator.

The most realistic benefits here are:

Fewer manual entries: The administrative team reviews exceptions, not every single document.
Greater reliability: The system checks multiple sources instead of relying on a single file.
Cleaner reporting: Data enters the analysis workflows in a more structured format.

Risk, Anomalies, and Fraud Control

In risk management processes, the value of multimodality is even more evident. A single source may be misleading, incomplete, or simply ambiguous. Multiple sources, if well-aligned, serve as checks and balances on one another.

McKinsey notes that, in the insurance industry, cross-checking customer statements, transaction logs, and photos or videos of attachments helps reduce fraud. For an Italian SME, this principle also applies outside the insurance sector. Consider expense reports, reimbursements, compliance documents, supplier audits, or credit checks. If free-form text, visual attachments, and operational history are compared together, it becomes easier to identify inconsistencies before human validation.

A good multimodal system does not replace human oversight in sensitive cases. It makes the process faster and more targeted.

But here, balance is key. The risk isn’t just technical — it’s also organizational. If the team doesn’t clearly define which anomalies really matter, you’ll end up with unnecessary alerts or important issues being overlooked.

Customer Service and Operations

In customer service, issues rarely occur through just one channel. A customer opens a ticket, sends a photo, leaves a comment, and may have already experienced delivery delays. If you analyze only the text of the ticket, you miss half the context.

Multimodal AI allows you to view CRM history, support notes, attachments, and operational logs all at once. The benefit isn’t simply “responding with AI” in a general sense. The benefit is better classifying cases, understanding priorities, and identifying recurring patterns.

For example, you can more quickly distinguish between:

Actual product defect , supported by images and return history.
A logistical issue , evident in delivery times and geolocated complaints.
Information error , related to unclear product descriptions or incorrect expectations.

In operations, the principle is the same. When you combine machine logs, defect images, technician notes, and production data, you can better understand the chain of events. You’re not just looking at the final error. You’re looking for the cause that led to it.

Management reporting that better reflects reality

Many business reports are accurate yet of little use. They explain what happened, but they don’t help us understand why.

This is where multimodal AI business applications really come into their own. An executive report becomes more valuable when it combines numbers, operational documents, customer signals, and visual indicators into a coherent narrative. It’s not about replacing traditional BI. It’s about providing more context.

A sales manager, for example, doesn’t just want to know that a category has slowed down. He wants to understand whether the reason is price, inventory, merchandising, complaints, or channel mix. Multimodal reporting brings reporting closer to addressing this managerial question.

Tangible Benefits and Risks to Manage

Where True ROI Comes From

The first tangible benefit is a reduction in context loss. When data remains siloed, people spend time manually reconstructing connections. When data communicates with each other, time is shifted from data assembly to decision-making.

The second advantage is the quality of the assessment. A model that compares multiple sources can detect weak signals, inconsistencies, and probable causes with greater reliability than a single-source approach. This is important in processes such as forecasting, document review, anomaly analysis, and executive summaries.

The third benefit is useful automation. Not the kind of automation that produces more output, but the kind that eliminates repetitive work from low-value steps.

A pre-scale inspection checklist

This is where many initiatives get stalled. Not because the idea is wrong, but because the project starts out too broad.

Milvus highlights three key limitations of current multimodal models: high computational intensity , difficulty in correctly contextualizing cross-modal data, and poor generalization to real-world scenarios not encountered during training. This helps explain why many pilot projects fail to scale and why it makes sense to choose platforms with pre-optimized models and managed infrastructure (current limitations of multimodal models, according to Milvus).

For an SME, the main risks to manage are as follows:

Unaligned data: A photo without a time stamp or a PDF without reliable metadata can cause confusion.
Operating costs: More formats mean more work involved in ingestion, cleaning, and monitoring.
Unrealistic expectations: if a project starts out as “AI that understands everything,” it will almost always disappoint.
Regulatory constraints: If you work with sensitive data, you need clear governance and a careful understanding of the regulatory framework, especially in light of issues such as the European AI Act and its operational impact.

Start with a narrow scope, a clear process, and fairly well-organized data. In multimodal analysis, discipline is more important than the power of the model.

A prudent SME treats its first project as a learning investment. It doesn’t ask AI to revolutionize the company. It asks AI to effectively solve a specific problem.

Roadmap for Implementing Multimodal AI in Your SME

Start with the problem, not the model

The most common mistake is falling in love with the technology and then trying to find a use for it. The correct sequence is the opposite. Start with a process where you’re currently losing time, quality, or visibility.

Rasa highlights a point that is often overlooked: companies don’t just ask themselves what AI can do, but also what data is needed, how to manage the data flow, and which processes to automate first. The most solid approach is to start with simple use cases and then expand functionality, focusing on problems where the context arises from the combination of multiple sources ( Rasa’s practical guide to multimodal use cases).

A good pilot problem has three characteristics:

It happens often.
It comes at a visible cost when it is mismanaged.
It requires at least two sources of information to be fully understood.

Typical examples for an SME:

Invoice verification with PDFs and order history
Analysis of complaints with tickets and images
Inventory tracking with sales dashboards and shelf photos
Check for anomalies using operational notes and management data

Choose a driver who combines at least two sources

Here, it’s best to take a very practical approach. There’s no need to start with text, images, audio, and video all at once. Two well-chosen formats are enough.

A realistic workflow might look like this:

The most challenging part is alignment. If you gather customer tickets and images but can’t link them to the same order, the project gets off to a bad start. If, on the other hand, you have a common ID, a reliable date, or a shared matching logic, the quality of the test improves immediately.

For many SMEs, it’s also helpful to follow a step-by-step implementation guide, such as this 90-day roadmap for AI adoption, because it helps turn an abstract idea into weekly tasks.

Measure, then stretch

The pilot must answer a simple question: Is the process working better now, or not?

It measures both operational elements and the quality of decision-making. For example:

time required to complete an audit
number of manually handled exceptions
managers’ perception of the quality of the reports
reduction in classification errors
the speed at which the team identifies an anomaly

If you don’t first define what you’re going to improve, you’ll end up confusing the activity with the result.

Once the value has been confirmed, expand the scope to adjacent areas. Move from invoice verification to contracts. Move from product images to in-store images. Move from receipts to call transcripts. The right approach isn’t “more AI.” It’s “the same method, applied to another process where the data is already available.”

KPIs and Integration with Analytics Platforms such as ELECTE

The KPIs You Should Really Track

An SME manager doesn’t just need to know whether the model “works.” They need to understand whether the process is less expensive, whether decisions are made faster, and whether the team trusts the outcome. That’s the difference between an interesting prototype and a tool that truly becomes part of day-to-day management.

That’s why the most useful KPIs are those that link multimodal AI to the income statement and operational quality. In practice, it’s worth tracking:

Time saved in the process. How many hours are saved in reading documents, verifying images, comparing data, and manually reclassifying items.
Reducing rework. How many cases are sent back because information was missing or there were inconsistencies between different sources?
Quality of the decision. The faster the team identifies the likely cause of a problem or detects a genuine exception.
Reliability of reporting. How many corrections are needed before a report is considered usable by operations, administration, or management?
Internal adoption. How many people actually use the insights generated and incorporate them into their weekly decisions?

A simple rule of thumb helps prevent mistakes. If a KPI doesn’t influence an operational decision, it’s probably not the right KPI.

On the market front, the message is clear. Investment in GenAI is growing rapidly, and many companies are integrating AI into a wider range of functions — not just isolated projects. For an SME, this doesn’t mean jumping on a bandwagon. It means understanding where the combined use of text, documents, images, and business data can yield a measurable return — without having to rebuild existing systems from scratch.

Why the platform matters more than the individual model

In practice, value isn’t created by the model alone. It’s created at the point where different data sets are collected, cleaned, linked, and made readable to decision-makers. If this step is weak, even a good algorithm produces little value.

An analytics platform functions like a control room. It does not replace ERP, CRM, or document management systems. Instead, it coordinates them. It connects data sources, maintains a consistent interpretation framework, applies access rules, and transforms technical outputs into dashboards and reports that are useful to business leaders.

For an SME, this factor has a significant impact on ROI. Building separate integrations for each data source increases time, maintenance costs, and reliance on specialized expertise. Using a platform specifically designed to unify data and insights reduces organizational friction and allows you to start with a limited scope, then expand the project only where the benefits are clear.

In this context, ELECTE, an AI-powered data analytics platform for SMEs , can be used as a hub to connect diverse data sources, automate pre-processing, generate insights, and produce visual reports without having to build the entire technical stack in-house.

There is also one point that many projects overlook. Integration is not just a technical matter. If administration, operations, and management gain new insights but continue to make decisions as before, the value remains limited. For this reason, it is advisable to accompany the rollout with clear guidelines on how to manage change within the company, especially when the new workflow alters responsibilities, verification timelines, and reporting procedures.

Ultimately, the right question is a practical one. Does the platform help managers spot a problem sooner, better understand its cause, and take action with fewer manual steps? If the answer is yes, the integration is generating real value. If the answer is vague, the project needs to be adjusted before it is rolled out.

Conclusion: Turn Your Data into a Competitive Advantage

Multimodal AI isn’t interesting simply because it combines multiple technologies. It’s useful because it better reflects the reality of your business. Where you currently have separate spreadsheets, documents, images, and operational signals, you can begin to build a single view that more closely mirrors how managers actually make decisions.

For an SME, the sensible approach isn’t to revolutionize everything right away. It’s to choose a concrete process, combine two information sources, measure the results, and scale up only when the value is clear. That way, the ROI becomes measurable and the risks remain under control.

The best multimodal AI business applications don’t come from spectacular demos. They come from real-world problems, readily available data, and a well-structured roadmap.

If you want to learn how to connect your data, automate insights, and turn scattered reports into faster decisions, check out how ELECTE works.

Originally published at https://www.electe.net .

Originally published on Medium

Product Data Sheets: Create Your Own with AI in 2026

Fabio Lauria — Tue, 30 Jun 2026 10:59:29 +0000

You create a new product sheet, open the product manager’s Excel file, then the export from the ERP system, and then the CRM. The weight doesn’t match. The technical description is up to date in a shared folder, but the logistics information is still based on a previous version. Meanwhile, sales, quality, and operations are all asking you the same thing: “Which figure is correct?”

For many companies, the problem with product data sheets doesn’t arise when the document is actually written. It begins much earlier, when no one is really sure which field is reliable. That’s where errors, delays, endless revisions, and duplicate versions pile up.

Italian guidelines treat technical data sheets as serious documents, not as brochures. They must present the product in a clear, standardized, and comparable manner throughout its life cycle, including measurable data, design specifications, certifications, instructions for use, and maintenance information, as noted in the Italian guide to product technical data sheets.

The good news is that this problem can be addressed in a practical way. Not by starting with the template, but by focusing on the quality of the data that feeds the template.

Introduction: Why Your Product Pages Are Full of Incorrect Information

The typical scenario is simple. The technical department updates a measurement in the management system. The marketing department continues to use an old Excel spreadsheet. The sales department copies the data from a PDF presentation. Eventually, the data sheet is released, but no one would be able to explain every single field to a customer, a distributor, or an internal auditor.

This happens because many companies treat the technical data sheet as a form to be filled out, rather than as the final output of a data governance process. When data is generated incorrectly, it circulates even worse. And when it circulates worse, the data sheet becomes merely the point where the error becomes visible.

The same pattern can also be seen outside the manufacturing sector. In any context where authenticity, traceability, and attention to detail make all the difference, value lies in the quality of the information and the ability to interpret it correctly. A useful example — albeit in a different field — is this expert guide on counterfeit Rolex watches, which shows just how much technical detail really matters when you need to distinguish between reliable information and a convincing appearance.

Rule of thumb:If you have to compare multiple files, departments, and versions to complete a form, the problem isn’t the document. It’s the data architecture.

Product data sheets can only be filled out quickly when there is a clear “source of truth” in place. As long as that foundation is missing, every new data sheet is a small manual reconciliation project.

The Structure of an Effective Technical Data Sheet

A technical data sheet is truly effective when it can answer a simple question: Where does this data come from, who validated it, and when was it updated?

This is where many companies get their priorities wrong. They focus on the template, the order of the fields, and the final PDF. Then, at the first serious review, inconsistencies emerge: codes that don’t match, weights copied from old versions, certifications cited without a link to the correct document, and descriptions that vary from department to department. The quality of the data sheet depends first on data discipline, and then on the format in which you present it.

Must-haves

A useful structure starts with fields that have a clear owner and a unique definition. In practice, these blocks are the ones you almost always need:

Product Identification. Trade name, internal code, SKU, version, update date, product category.
Technical description. Materials, components, finishes, configurations, compatibility, intended use.
Measurable characteristics. Dimensions, weight, capacity, tolerances, available sizes.
Logistics data. Packaging, units per package, storage conditions, palletizing, and shipping requirements.
Compliance and Certifications. Applicable regulatory references, available certificates, operational warnings, related documents.
Use and Maintenance. Essential instructions, limitations of use, cleaning, storage, and service life, if applicable.

The common mistake isn’t forgetting a field. It’s mixing static data and frequently changing data in the same field, or using generic labels for information that means different things within the company. “Weight,” on its own, isn’t enough. You need to know whether you’re referring to net weight, gross weight, or shipping weight. The same applies to “dimensions,” “capacity,” “compatibility,” and any certification listed without context.

For this reason, it is advisable to define the field dictionary and the acceptable sources early on, especially if the data comes from ERP, CRM, PLM, or distributed repositories. A well-managed database, fed by linked and verifiable product sources, reduces errors even before the data entry phase.

The Difference Between a Functional Card and a Decorative Card

Even a well-organized record can still be unreliable. This often happens in situations where the document is updated manually and no one checks for consistency across systems.

The structure varies from sector to sector. In fashion, it includes variations, sizes, materials, manufacturing processes, and production notes. In the food industry, it requires ingredients, allergens, shelf life, and regulatory references. In technical retail, compatibility, dimensions, logistics data, and display constraints are key factors. The principle remains the same: if the source data isn’t clearly defined and verified, the product sheet will simply present a jumble of information.

A reliable technical data sheet contains information that is verifiable, traceable, and consistent across departments.

Those who create truly useful forms follow a specific order: they define the fields, assign responsibility for the data, establish validation rules, and only then decide on the layout. In this way, the form ceases to be a document filled out at the last minute and becomes the consistent output of a reliable process.

The Real Bottleneck: The Chaos of Product Data

When a team says that “creating worksheets takes too much time,” they’re almost never talking about the layout. They’re talking about the hunt for the right data. It’s a huge difference, because it completely changes the type of solution that needs to be adopted.

In a real-world example shared by the ELECTE team, a client with a catalog of 340 product listings spent an average of 45 minutes per product sheet just to gather up-to-date data from various sources. With the data already standardized and analyzed, that same process was reduced to less than 10 minutes. The point isn’t that the document writes itself. The point is that you stop wasting time checking whether your ERP, CRM, and local files contradict each other.

Where the process breaks down

The most common breakdowns are very specific:

Separate systems. ERP, CRM, Excel spreadsheets, and shared folders describe the same product in different ways.
Fields with the same name but different meanings. “Weight,” “net weight,” and “shipping weight” appear in the same document without a common definition.
Manual updates. A change is applied to one system but not to the others.
Lack of ownership. Everyone uses the data, but few take responsibility for it.
Disconnected versions. The PDF file outlives the data it contains.

If your teams currently gather information from multiple sources before filling out a form, the priority isn’t to redesign the template. The priority is to clarify the sources of the data and consolidate them. A good place to start is to build a single view of the sources, as in an integrated data-source-oriented approach for the business.

The Operational Cost of Distrust in the Data

When trust is lacking, the workload doubles. The product manager double-checks everything. Marketing asks for confirmation. Sales waits. The quality control department halts the release. No one says outright, “We don’t trust the system,” but the process makes that clear at every step.

If three departments validate the same field at different times, the problem isn’t quality control. It’s that the data isn’t governed.

The consequences go beyond product data sheets. This same disorganization slows down price lists, catalogs, distributor data sheets, e-commerce documentation, and performance analyses. That’s why the data sheet is an excellent indicator. If creating one is a struggle, your product data assets are almost certainly already in trouble.

Practical Examples for the Retail and Financial Sectors

A buyer opens a product sheet and finds that the weight, dimensions, and material are correct. Then, when they check the ERP system, they see a delivery time that differs from the one shared with the sales team. At that point, the product sheet ceases to be an operational tool and becomes a document that needs to be verified.

Retail

In retail, a product spec sheet is useful if it helps with decision-making. It’s not enough to simply describe the product. It must also reflect the actual conditions under which that product is sold, returned, restocked, and compared with other options in the catalog.

That’s why the most useful fields aren’t always the most “technical” ones in the strict sense. Often, information such as the following makes all the difference:

Channel-by-channel turnover. Helps buyers and category managers understand where a product is truly performing well.
Return rate. This highlights issues related to expectations, perceived quality, or unclear customer information.
Margin per unit. Avoid promoting products that drive sales volume but erode profitability.
Availability and average delivery times. These factors directly affect the product’s marketability.

I often see the same mistake here. The team enhances the template but continues to pull data from different sources, each with its own set of rules. The result is a report that is only seemingly more comprehensive. If turnover, inventory, and profit margins aren’t aligned, the document sparks discussions instead of resolving them.

Those who work on product assortment, distribution, and sell-through need to analyze product data and performance data within the same operational context. This need is clearly evident in use cases related to retail and distribution.

The structure of product pages also varies greatly across different verticals. In fashion, factors such as variations, sizes, materials, production notes, and visual references come into play. In the food industry, ingredients, allergens, nutritional values, and regulatory requirements are key considerations. The bottom line, however, remains the same: the more specialized the content becomes, the more expensive it is to manage without a well-organized and governed database.

Financial Services

In the financial sector, the product itself isn’t touched, but the problem is the same. An information sheet, an internal KIID, or support material for the sales force is only useful if it contains data that is consistent across analysis, compliance, and client-facing documentation.

The typical error isn’t a poorly compiled measurement. It’s a risk assessment that has been updated in the system but remains outdated in the document used by those who sell to or assist the customer.

The consequences differ from those in the retail sector. In retail, inconsistent data slows down orders, restocking, or negotiations. In the financial sector, it raises issues of governance, control, and accountability.

For this reason, in regulated contexts, the quality of the entry depends first on the data standards and only secondarily on the document’s format. If the source is reliable, the entry is updated with less difficulty. If the source is unreliable, even the most carefully prepared PDF remains unreliable.

Beyond PDFs: Automating Data Analysis with ELECTE

The limitation of PDF isn’t the format itself. The limitation lies in using it as the final container for data that no one has really structured properly. When a technical data sheet relies on copy-and-paste, attachments, and manual revisions, every update creates a new point of failure.

A very practical question that has emerged in Italian technical documentation is this: How can a technical data sheet be transformed from a static PDF into an automated, up-to-date compliance check? This issue is critical because companies manage multiple document versions, and the predominant approach remains static — not based on structured data — with implications for quality, safety, and legal liability, as highlighted in this article on the relationship between technical documentation and operational compliance.

From Static Documents to Data Streams

Here, the shift in perspective is clear. ELECTE does not automatically generate the technical data sheet and does not replace the marketing team’s or engineering department’s document management tool. Its role is different — and, for many companies, more useful: it makes data available that has already been standardized, analyzed, and verified before anyone begins filling out the document.

The typical workflow is as follows:

Connection to data sources. ERP systems, databases, structured exports, and management systems feed the platform.
Field normalization. Different names, different formats, and inconsistent structures are made comparable.
Automated analysis. Key metrics are highlighted in dashboards and reports that teams can use.
Checking for anomalies. Inconsistencies don’t remain hidden in scattered sheets.
Transfer to the template. The team responsible for creating the form takes the data that has already been verified and enters it into their own layout.

When source data comes from unstructured documents, one of the preliminary steps is to convert the content into a format that can be analyzed. For those who frequently work with technical attachments and locked tables in unstructured documents, it is helpful to better understand the process of converting PDFs to Excel.

What’s changing in our day-to-day work

The biggest difference isn’t aesthetic. It’s operational.

First, here’s how the team works:

Once you have a solid database, the work changes:

The product manager doesn’t chase numbers. Instead, they refer to a consolidated view.
Marketing and technical teams start from the same foundation. Not from different personal files.
Audits are becoming less frequent. Not because they are disappearing, but because they are becoming more targeted.
The card becomes an output again. Not the place where chaos is revealed.

The real leap forward comes when the question shifts from “Who has the latest version?” to “Has the data already been validated?”

For those who manage many product data sheets, this step matters more than any layout automation. If the data is reliable, drafting the document is a straightforward process. If the data is questionable, even the best template will only produce a well-laid-out but fragile PDF.

Your Next Steps Toward Perfect Technical Data Sheets

Companies that truly improve their product data sheets don’t start with the font, the layout, or the software they use to export the PDF. They start with a much more challenging question: Which product fields are reliable, who updates them, and how do we validate them before they’re included in the document?

If your process currently requires constant checks, coordination between departments, and manual data reconstruction, you don’t need another template. You need a clearer data management framework. A technical data sheet works when it reflects a solid system in place upstream.

Actions to Take Immediately

A perfect technical data sheet isn’t the one with the most fields. It’s the one you can defend without hesitation, because every piece of information has a clear source, a shared logic, and a recognizable update history.

If you want to reduce the time spent searching for, verifying, and consolidating the data that ends up in your spreadsheets, ELECTE — an AI-powered data analytics platform for SMEs — helps you centralize various sources, normalize the information, and transform it into reliable insights ready for downstream processes. It doesn’t create the document for you. It empowers you to fill it out with clean, consistent, and up-to-date data. If you’d like to see how it works, you can explore the platform and learn how to bring more order to the decisions you make based on your product data.

Originally published at https://www.electe.net .

Originally published on Medium

Conversion Rate Optimization: A Practical Guide for SMEs

Fabio Lauria — Sat, 27 Jun 2026 10:36:51 +0000

You’re already doing CRO, even if you don’t call it that. Every time you pay for Google Ads, publish SEO-optimized content, or drive traffic to a landing page, you’re buying attention. The problem is that many small and medium-sized businesses stop there. They bring people to their site, but they don’t track where visitors stop, what they read, where they hesitate, or why they don’t take the action that matters.

Conversion rate optimization is exactly what this is for. It’s not just for large e-commerce sites with dedicated teams. It’s the most pragmatic approach you can take when you want to get more out of the same traffic. If your site has a conversion rate of around 2%-3% , you’re in a typical range and have real room for improvement through targeted and ongoing efforts, as Optimizely notes in its operational definition of CRO.

As CEO, I take a simple view of the CRO: every visitor who doesn’t convert is a cost we’ve already incurred. That’s why optimization can’t be separated from analytics. If you’re also working on reducing acquisition costs, these effective CAC reduction methods only make sense if your funnel isn’t losing users at key stages. And all of this must be managed in compliance with data regulations and user consent. If you track user behavior, start with a good Guide to Online Privacy Compliance.

Introduction: How much does each non-converting visitor cost you?

You’ve spent money to drive traffic to your website — through Google Ads, SEO, LinkedIn, word of mouth, and content. Then that visitor arrives, can’t figure out what you do within a few seconds, doesn’t find any credible evidence, doesn’t fill out the form, and leaves. The cost is already real. The result isn’t.

For an Italian SME — especially one that sells services or generates leads — this is the factor that has the greatest impact on the income statement. It’s not just about how many visitors you get. What matters is how many of them turn into qualified leads, scheduled calls, quotes, trials, or orders. If this conversion rate is low, every euro invested upfront yields less than it could.

Conversion rate optimization, or CRO, is designed to improve this performance. In practice, this means optimizing the path from entry to a desired action by reducing friction, doubts, and unnecessary steps. The standard definition of the term is provided by Optimizely in its entry on CRO, but for an entrepreneur, the goal is simpler: to get more results from the same traffic.

Let’s be honest here. On many SME websites with limited traffic, talking right away about sophisticated A/B testing, advanced personalization, or enterprise-grade tech stacks is often a distraction. First come the fixes that really make a difference for the business: a clear value proposition, visible CTAs, lighter forms, fast-loading pages, trust, proper tracking, and a clear conversion funnel. If these fundamentals are missing, the rest just adds complexity before it adds profit.

There’s also the issue of measurement. If cookie consent or tracking is mishandled, you’re making decisions based on incomplete data. That’s why it’s worth getting your privacy and consent processes in order early on-for example, with this Guide to Online Privacy Compliance.

The CRO also has a direct impact on the cost of acquisition. If more visits turn into leads or customers, the CAC decreases without having to chase additional volume. The principle is the same as that described in these effective CAC reduction methods.

The point, then, isn’t to drive more traffic to a site that’s missing out on opportunities. The point is to stop the losses before increasing traffic.

Why a CRO Is the Best Marketing Investment for Your Small Business

You’ve already paid to bring a visitor to your site — whether through Google Ads, SEO, content, trade shows, social media, or word of mouth. If that visitor then gets lost amid a confusing page, an overly long form, or a weak call to action, the problem isn’t the volume of traffic. It’s the return on what you’ve already paid for.

For this reason, for an SME, a CRO often delivers a better ROI than many customer acquisition initiatives. Improving conversion rates lowers the cost per lead, increases the revenue generated from the same traffic, and makes campaigns that are currently barely breaking even more profitable.

This point is even more important in Italy, where many SMEs don’t sell online using a standard checkout process but instead collect inquiries, quotes, appointments, and business contacts. In these cases, there’s a lot of wasted effort: generic service pages, intrusive forms, slow loading times, and a lack of trust. Every well-executed improvement has a direct impact on the sales pipeline.

General benchmarks for conversion rates are often cited in the industry. They’re useful to a certain extent. If you run a lead generation site with just a few hundred qualified visits per month, chasing the industry average matters less than fixing three obvious problems that are blocking conversions. This is where many companies waste time. They look at dashboards and talk about advanced testing, but they don’t resolve the basic friction points.

For an SME website, the financial return is most evident in four areas:

More leads or sales on the same budget. The same amount of traffic generates more opportunities.
Lower CAC. Every euro spent on customer acquisition yields a higher return.
Better margin. Improve profitability without proportionally increasing fixed costs.
Less reliance on ad platforms. If your site converts better, you can maintain your results even when clicks cost more.

There’s also an operational aspect that I see quite often. “Enterprise” techniques are copied out of context. A website with 1,000 visits per month almost never has enough traffic to conduct serious A/B tests on minor variations in headlines, colors, or buttons. The process drags on, the results remain weak, and the team convinces itself that it’s doing CRO when, in reality, it’s just putting off obvious decisions.

For an SME, the correct sequence is more practical:

Get a clear overview of the offer in just a few seconds
Tailor the page to the needs of visitors
Show genuine signs of trust
Simplify the contact or purchase request
Accurately measure the key stages of the funnel
Address obvious bottlenecks first

HubSpot has observed that companies with more landing pages tend to generate more leads, but the key takeaway for an SME isn’t to publish dozens of random pages. It’s to build pages tailored to different services, industries, cities, or user intent, with consistent messaging, evidence, and CTAs. A good landing page for “workplace safety consulting in Brescia” is worth more than a homepage that tries to say everything to everyone.

CRO, therefore, is not a technical discipline reserved for those with high traffic volumes and expensive tech stacks. It’s about managing a website’s commercial performance. If you have traffic but few qualified leads, you’re already paying for that problem every month.

Rule of thumb:Before increasing your advertising budget, check whether your homepage, service pages, forms, and landing pages are actually converting existing traffic into useful business leads.

For many small and medium-sized businesses, the best marketing investment isn’t buying more traffic. It’s stopping the loss of the traffic they already have.

The Conversion Funnel as a Roadmap to Finding Hidden Profits

Many people talk about CRO as if it were just a matter of buttons, colors, or headlines. In reality, that’s not the point. You need to know where you’re losing people along the way — not just whether the site “has a low conversion rate.”

The Stages That Really Matter

A simple sales funnel, applicable to almost all small and medium-sized businesses, can be described as follows:

For an e-commerce site, the steps may include viewing a product page, adding an item to the cart, checking out, and making a purchase. For a lead generation site, the steps may include visiting the services page, clicking on “Contact Us,” opening the form, submitting a request, and receiving a response from the sales team.

The problem is that many companies only measure the final step. They look at how many sales or form submissions they get. But the hidden profits lie in the intermediate steps, where the user shows intent and then gets stuck.

Metrics to Watch Without Complicating Your Life

If you don’t have an analytics team, there’s no need to start with sophisticated dashboards. All you need are metrics that are easy to read and align with the funnel.

Here’s a handy grid:

Awareness

Useful metrics: traffic source, entry page, new vs. returning users.

Typical action: better align campaigns and content with the landing page.

Interest

Useful metrics: visits to pricing, services, and product pages; scrolling; exits from key pages.

Typical action: clarify the value proposition and CTA.

Consideration

Useful metrics: returns to the same page, clicks on FAQs, comparisons between offers.

Typical action: eliminate ambiguity regarding price, timing, and contact methods.

Action

Useful metrics: form opens and completions, shopping cart, trial, checkout.

Typical approach: reduce non-essential fields, steps, and requests.

Customer Retention

Useful metrics: initial usage, renewal, second purchase, lead quality.

Typical actions: work on onboarding and sales follow-up.

If you want to build a stronger foundation for this project, the right read is a guide to marketing KPIs for growing your small business.

A funnel isn’t meant to describe your ideal customer. It’s meant to help you pinpoint exactly where you’re losing money.

When you look at the funnel this way, CRO stops being an abstract discipline and becomes an ongoing audit of your digital sales system.

CRO Techniques That Really Work for SMEs — and Those to Avoid

You get 300 visits a month to your services page — perhaps driven by Google Ads — and you receive two contact requests. In this situation, trying to copy the CRO strategies of companies with tens of thousands of sessions is a waste of time. For an Italian SME, what matters is understanding where the funnel is getting stuck and quickly removing friction — not chasing a “scientific” approach to optimization that requires traffic, a team, and a budget you simply don’t have.

When A/B Testing Makes Sense

A/B testing is only useful in a specific context: sufficient traffic, a page that directly impacts conversions, and a clear variable to test. If you have a landing page that generates leads every week, you can test headlines, CTAs, form structure, block order, or price presentation.

The problem arises when an SME with low traffic spends an entire month testing minor details and ultimately fails to reach a useful conclusion. In that case, the cost isn’t just the time spent on marketing. It’s the lost revenue while the bottleneck remains unchanged.

On service and lead-generation websites, I often see this mistake: people test colors and buttons when the real problem is an unclear offer, a form that’s too long, or a lack of trust in the business. If the message isn’t convincing, testing won’t save the page.

What Works Best When There’s Little Traffic

For many SMEs, the work that yields the highest ROI is less glamorous and more down-to-earth. You need to observe users’ actual behavior and interpret the signals the website is already sending.

They work well:

Heatmaps. They show whether visitors are ignoring the sections you consider important or trying to click where they can’t.
Session recording. These help identify hesitations, confused scrolling, going back, and issues on mobile devices.
Short on-page surveys. A question like “What’s stopping you from contacting us?” often provides more insight than an internal meeting.
Form analysis. Shows which fields users get stuck on and which requests create unnecessary friction.
Feedback from the sales team. If leads are coming in but aren’t closing, the problem may lie in the quality of the inquiry or in the expectations created by the page.
Interviews with lost or undecided customers. They are a direct source of objections, doubts, and phrases to reuse in your copy.

These techniques are well-suited for SMEs because they provide quick results even with small volumes. It doesn’t take months. Often, all it takes is a week of careful observation and a few targeted adjustments.

Techniques to Avoid, at Least at First

There are activities that seem sophisticated but, for a small business, they eat up time and money:

Redesigning the entire site without having isolated the problem
Copying competitors or foreign templates without knowing if they actually convert
Test minor aesthetic changes on pages that have issues with the offer, social proof, or clarity
Running too many tests at once makes it impossible to figure out what made a difference
Make decisions based on internal preferences rather than user behavior

Optimizations that drive profit eliminate friction, clarify the offering, and make the next step easier.

This also applies to traditional sectors, where conversion depends more on trust than on design. In real estate, for example, pages that immediately address specific concerns about timing, the process, and property valuation often perform better. A good practical example is to look at how certain techniques for selling real estate are presented, where clarity and reducing friction matter more than visual effects.

The key question for an SME is simple: What action can I take to increase inquiries, sales, or lead quality within the next few weeks using the resources I have today? If you can’t answer that, you don’t need a more advanced technique. You need to set better priorities.

Building a Data-Driven Optimization Process

A well-executed CRO is not a one-time project. It is an operational discipline. You measure, make a hypothesis, change something, observe the effect, learn, and repeat.

Measure Before You Change It

The first mistake is to change the website because it “doesn’t cut it.” That’s not optimization. It’s a redesign driven by instinct.

A healthy process begins with four steps:

Define the conversion that matters Purchase, trial, quote request, call, demo. One primary metric. Not five.
Create a baseline You need to know the current values by page, channel, device, and user type.
Find the bottleneck Where are they getting stuck? Pricing? Form? Mobile? Checkout? Sales response?
Come up with a clear hypothesis Let’s not “redesign the page,” but rather “remove this friction because users are hesitating here.”

If you want to take a more rigorous approach to experiments, this guide to Design of Experiments is a good resource.

Optimize for profit, not for volume

This is the point that many guides overlook. Increasing conversions isn’t enough. You need to increase conversions that improve the company’s bottom line.

Matomo puts it bluntly: the common mistake is to focus solely on the conversion rate. A mature CRO analysis links conversion improvements to profit, taking into account metrics such as CAC and CLV. Optimizing a low-margin conversion can even worsen a company’s bottom line, as Matomo explains in its focus on the benefits of conversion rate optimization.

That is why a CEO’s perspective on the CRO differs from a purely marketing perspective.

In our day-to-day work on the funnel, the most meaningful metric isn’t the conversion rate of a single step. It’s the ratio of initial contacts to paying customers further down the funnel. If you optimize only one step, you risk shifting the problem to the next step.

Operational insight:One more conversion isn’t always good news. It’s only good news if it improves revenue, profit margin, or operational efficiency.

This also changes priorities. Sometimes the page with the “lowest conversion rate” brings in better customers. Sometimes the most expensive channel generates users who are more valuable over time. Without integrated data linking the funnel to business results, CRO is left in the dark.

Practical Examples for E-Commerce and Service Websites in Italy

Theory matters little if it doesn’t translate into real changes. Here, it’s almost always simple, visible, and measurable choices that make the difference.

What Works on a SaaS or E-commerce Site

One of the most insightful tests we observed wasn’t about design but about the message. A technology-focused headline describes the product. A headline focused on the customer’s problem tends to generate more genuine interest. It’s a recurring lesson in small and medium-sized businesses: functionality piques curiosity, but addressing a recognized problem drives conversions.

Another measure with a significant practical impact is price transparency. When the price is hidden behind a “contact us” button, many visitors interpret that lack of information as a sign of high cost, a lengthy process, or an offer that isn’t right for them. Making pricing clear reduces uncertainty and helps filter traffic more effectively.

Even removing a single field from a form can affect the completion rate. Every additional field adds friction. If you don’t really need it at that moment, don’t ask for it.

What Matters Most in Service Websites

For many Italian SMEs, conversion isn’t a checkout. It’s a request for contact. In this case, CRO is much less glamorous, but much more practical.

Check the following points:

Phone number clearly visible. If a customer wants to call, don’t make them search for it.
A mobile-friendly form. If the form is hard to use on a smartphone, you’ll lose leads.
Response times. A form submitted without a prompt follow-up is a missed conversion.
Tracking the source. You need to know whether the lead comes from SEO, ads, referrals, or direct traffic.
Clear service page. What you do, for whom, through what process, and under what conditions.

For the Italian market, the mobile aspect deserves special attention. An effective CRO analysis must establish baselines by device and constantly monitor exit rates and page load speeds, which are often the most serious bottlenecks compared to minor aesthetic details, as Lucky Orange highlights in its guide to conversion rate optimization.

The problem is almost never the color of the button. More often than not, it’s a combination of slowness, ambiguity, and avoidable friction.

If you run a service-based website, this means one simple thing: check the site on your phone just as a real customer would. If filling out the form requires too much effort, your CRO doesn’t start with creativity. It starts with usability.

Key Takeaway: Your CRO Action Plan

If you want to make conversion rate optimization a useful habit, don’t start with ten tools. Start with a few non-negotiable decisions.

The Five Steps to Take Right Away

Define a single primary conversion Choose the action that has the greatest impact on your business: purchase, trial, quote request, or call reservation. Everything else comes second.
Map out the actual funnel List the steps from the first click to the final outcome. Don’t stop at a submitted form or a started checkout. Go all the way to the sale, activation, or lead quality.
Check the pages that matter Homepage, pricing, service pages, product pages, forms, and checkout. If a page receives qualified traffic but doesn’t drive action, it’s a priority.
Remove friction before adding elements Unnecessary fields, redundant steps, unclear text, hidden prices, confusing CTAs. First remove obstacles, then consider more sophisticated tests.
Measure by device and channel An overall average hides the problems. Desktop and mobile don’t behave the same way. Neither do organic and paid traffic.

The Minimum Checklist for Next Month

If you do this consistently, your website will stop being just a digital business card. It will become a system that learns and improves.

Conclusion: Stop filling a bucket with a hole in it

Investing in marketing without working on conversion rate optimization means continuing to pour money into a system that wastes value. Traffic comes in, but too much of it is lost before it turns into an inquiry, a trial, a sale, or a repeat customer.

The solution isn’t to complicate marketing. It’s to make it more efficient. A clear sales funnel, techniques suited to your scale, a focus on mobile, rigorous measurement, and a focus on profit rather than just volume. That’s how an SME grows without relying solely on increased advertising spending.

If you want to make better decisions based on your business data — not on assumptions — the next step is to equip yourself with a system that makes this analysis continuous and concrete.

If you want to bring together conversions, profit margins, trends, and operational performance in a single view, discover ELECTE, an AI-powered data analytics platform for SMEs designed to transform complex data into actionable insights. It’s a practical way to apply the same data-driven discipline of a CRO to the decisions that really matter.

Originally published at https://www.electe.net .

Originally published on Medium

Drop-down Menus: A Guide to Design, UX, and Implementation

Fabio Lauria — Fri, 26 Jun 2026 10:58:19 +0000

You open an Excel file shared by the sales team and immediately spot the problem. The same customer appears as “Rossi Srl,” “ROSSI SRL,” “Rossi S.r.l.,” and “rossi.” At first glance, these seem like minor details. In reality, they’re the point where a reliable report starts to fall apart.

This happens often in small and medium-sized businesses. As more people enter data — each with their own style — the spreadsheet turns into a room full of different labels for the same items. Then, when you try to sum, filter, segment, or build a dashboard, you spend more time cleaning up the data than reading it. The real cost isn’t just operational. It’s the loss of trust in the analysis.

In most cases, the solution isn’t a complex project. It’s a very simple design choice made at the right moment: using a drop-down menu instead of leaving a field blank. If you validate the input at the source, you make the dataset more consistent, more readable, and much more useful for any subsequent analysis.

This is the true value of the drop-down menu. It’s not just a graphical detail. It’s one of the most practical ways to turn a disorganized spreadsheet into a database that supports sound decision-making.

Introduction: Data Chaos and Its Hidden Solution

In day-to-day work, chaos rarely comes in through the front door. It usually sneaks in through a small, unlabeled text field. One person writes “Finance,” another writes “finanza,” and yet another uses an abbreviation. After a few weeks, the sheet looks full of data. In reality, however, it contains many versions of the same information.

For anyone who manages sales, purchasing, tickets, or master data, the problem is always the same. Aggregate analyses start to yield inconsistent results, filters don’t return all the data, pivot tables multiply for no reason, and every report requires manual corrections. It’s the classic messy spreadsheet: seemingly alive, but difficult to manage.

The drop-down menu solves the problem at its core: data entry. Instead of hoping that everyone will type the same way, it requires users to select from a controlled list. It’s a small difference in the UI, but it makes a huge difference in the outcome.

Clean data doesn’t start in the dashboard. It starts the moment someone fills in a cell.

That’s why the drop-down menu has an impact that extends beyond Excel. When you standardize data entry, you simplify reporting, auditing, and predictive analytics. The quality of future insights often depends on this initial discipline.

What Is a Drop-Down Menu and Why Is It Essential for Data?

A definition that is also useful in everyday work

In the context of computing, the Italian term “menu a tendina” corresponds to “drop-down menu” in English . The Cambridge Dictionary defines a drop-down menu as “a list of choices that appears on a computer screen and remains in place until you choose one of them.” The definition is simple, but it gets to the heart of the matter: a list of predefined choices instead of free-form input.

In a business context, this feature is much more than just a graphical convenience. In Excel, it is used as a data validation tool to restrict the values allowed in a cell. Basically, you first define what is acceptable and then ask the user to select it.

This approach changes the nature of the data collected. You no longer have a sequence of text variants that you need to normalize later. You have a field that is already structured, ready to be filtered, grouped, and compared.

Because it immediately improves data quality

When a team enters data without constraints, the spreadsheet accommodates differences in capitalization, abbreviations, punctuation, and working language. A drop-down menu eliminates much of this variability at the source.

The practical benefits are immediate:

Consistency of values: Everyone chooses from the same list, so the same category truly remains the same.
Fewer typos: The system reduces typos, made-up abbreviations, and inconsistent formatting.
Faster data entry: Selecting an option takes less effort than remembering how to type an entry.
More reliable analyses: filters, pivot tables, and dashboards work on clean categories rather than raw text.

Rule of thumb:If a field is intended to contain a repeatable selection, don’t leave it as a free-text field.

For a company, this isn’t just a matter of organization. It’s a data governance decision. If you standardize the input, you reduce manual work downstream and make everything that depends on that data more stable: reporting, operational control, analysis, and forecasting.

The Different Types of Drop-Down Menus

There is no single “correct” way to use a drop-down menu. The right format depends on the type of data you want to manage, how much it changes over time, and how many options the user needs to manage.

Static menu when the list changes infrequently

A static menu is the simplest type. The options are fixed and almost always remain the same. This is typically the case for fields such as “Yes/No,” approval status, quarter, or month.

It works well when:

There aren’t many options
The list is stable
Maintenance is minimal

It doesn’t work well when the organization grows and categories change frequently. In that case, manually entering values into the data validation becomes unreliable.

Dynamic menu when the list needs to be maintained over time

A dynamic menu links a cell to a separate data source. It’s the right solution when the list changes over time — for example, products, departments, categories, or locations. Practical guides show a clear progression from static to dynamic menus, often using named ranges and, in more advanced cases, the INDIRECT function to link selections and dependent lists. In an Italian tutorial, this approach is also applied to departments such as marketing, finance, and IT — a sign that it’s not just theory but a technique used in professional settings, as shown in the video dedicated to dynamic menus in Excel.

Another useful detail emerges in the practical guides. The examples often start with small, controlled sets — such as a sheet with 5 movies or a database with 50 entries — precisely to show how quickly a dropdown improves data entry and organization when the list is well-designed.

Drop-down menu when one choice depends on another

Here, the drop-down menu really comes into its own. The second list changes based on the first. If you select a region, you’ll see only the relevant provinces. If you select a department, you’ll see only the related cost centers. If you select a product line, only the correct subcategories will appear.

This model avoids a single, overly long list and reduces cognitive error. The user does not have to scroll through irrelevant options. They see only those that are compatible with their first choice.

A helpful summary:

If you have to choose, start with a simple question: Should the user select from a few fixed options or from a changing taxonomy? The answer will save you a lot of trouble down the road.

Designing Effective Drop-Down Menus: UX and Accessibility Best Practices

A dropdown menu can either make your work easier or slower. It depends on how you design it. If the list is too long, the label is ambiguous, or the logic of the options doesn’t reflect the actual process, the dropdown menu stops being a shortcut and becomes an obstacle.

When a Dropdown Menu Really Helps

The first rule is simple. A drop-down menu isn’t always the best solution. If the user has to scroll through a huge list, the advantage is lost. In those cases, it’s better to switch to a filterable search, an autocomplete field, or a cascading structure.

The most widely used guides often focus solely on the technical aspects of creating a dropdown menu. They do not cover operational issues and scalable alternatives as thoroughly when a simple list is no longer sufficient. This gap is also evident in the literature on the subject, where data governance over time and the most suitable solutions for complex data flows are still addressed in a fragmented manner.

Rules That Prevent Mistakes and Friction

In our day-to-day work, these are the practices that work best:

Clear labeling: The user should immediately understand what they are selecting. “Category” is often too vague. “Customer category” is much better.
Logical order: alphabetical, numerical, or by frequency of use. The important thing is that the criterion be clear.
Realistic options: If the team uses “Other” too often, the taxonomy probably does not reflect the actual process.
Nested lists: When a list gets too long, the design needs to be rethought. It’s not enough to just add more items.
Separation of value and description: In the back office, you can use codes, but in the user interface, the user must see understandable labels.

If a user takes too long to find an entry, you haven’t made it easier to enter data. You’ve just shifted the problem elsewhere.

This is where accessibility comes into play in a practical way. A menu must be navigable using only a keyboard, readable by a screen reader, and unambiguously understandable. Anyone working on websites, portals, or applications should consider these aspects from the very beginning, especially in light of regulatory and practical requirements related to digital inclusion. To learn more about this topic, it’s worth reading ELECTE’s guide on widgets for digital accessibility.

A Practical Guide to Implementation in Excel

Excel remains the starting point for a great many business processes. Before data is entered into an ERP, CRM, or analytics platform, it often passes through Excel first. That’s why it’s a good idea to create reliable drop-down menus right in the spreadsheet.

The most robust configuration for a team

Microsoft provides clear instructions for creating a drop-down list in Excel: first, prepare the valid entries in a single column or row without any blank cells, then use Data > Data Validation > Allow: List in the target cell. Microsoft’s documentation also notes that using a table makes the list more robust and updatable, and that you can quickly convert it using CTRL+T , as described in the official guide for creating a drop-down list.

This best practice is even more useful than the command itself: keep your lists on a separate sheet. That way, you won’t mix up the input interface with the reference data.

How to Create a Drop-Down Menu That Remains Easy to Maintain

In practice, a reliable procedure is as follows:

Create a worksheet dedicated to the lists Enter the allowed values in columns, without any empty rows in between.
Convert the list into a table : Use CTRL+T to make the list easier to expand and manage.
Name the range : Instead of referring to scattered cells, give the range a clear name.
Apply data validation In the input cell, select Data > Data Validation > List, and link the source to the range name.
Protect the structure If multiple people are working on the file, restrict who can edit the list sheet.

This approach is much more robust than typing the values directly into the Source box. Even an Italian guide on creating drop-down menus highlights the advantage of placing the entries in a separate worksheet and using a named range , so that maintenance remains centralized and more consistent with the practical explanation of named ranges.

In practice:Always separate the reference data from the input screen. It’s the easiest way to avoid unstable menus.

If you need a ready-made template that you can adapt to your internal processes, it may be helpful to start with these Excel templates for business.

The same principle applies outside of Excel as well

The concept remains the same whether you’re working on the web or in a desktop application. In HTML, you use a selection element; in CSS, you control its presentation; and in JavaScript, you can handle dynamic or conditional logic. The rule remains the same: the source of the options must be separated from the interface, so the system can be updated without having to rewrite the form every time.

Common Mistakes and How to Fix Them with a Data-Driven Approach

The first mistake is to think that a drop-down menu is “finished” as soon as the arrow appears in the cell. In reality, a drop-down menu only truly goes live when it begins to interact with users.

The false solution of always adding a new entry

The most common feedback is always the same: “My option is missing.” The instinctive response is to add it right away. If you do that every time, the menu grows haphazardly, and within a few months it becomes just as confusing as the free-form text it was supposed to replace.

A better approach is to use feedback as a signal, not as an automatic instruction. If you include an “Other” option with a notes field, you can periodically review the values entered and determine whether any new, meaningful categories are emerging. At that point, you can update the taxonomy based on logic, not as a reaction.

This approach works because it treats the menu as a living but controlled entity. Don’t focus on individual requests. Read the operational pattern.

Data Governance in Shared Files

A second, often overlooked issue concerns collaboration. Many tutorials explain how to create a dropdown menu, but devote little attention to managing lists in shared environments, data governance over time, and alternatives when the menu no longer scales well. This limitation is also evident in the general coverage of the topic, which almost always focuses on the technical creation of the menu and much less on the operational implications in discussions of the gap between creation and collaborative management.

When it comes to shared files, there are just a few rules for keeping things organized, but they’re crucial:

There is only one source of truth: the list must be kept in a single location.
Clear permissions: Not everyone should be able to change the lists.
Periodic reviews: Obsolete categories and duplicates should be removed.
Hierarchies when needed: if there are too many options, it’s better to organize the choices into multiple levels.
Alternatives when the menu is overloaded: In some workflows, a filterable search is more useful.

The dropdown menu does not replace critical thinking about the data. It simply makes it possible to apply that thinking consistently.

A well-designed menu doesn’t eliminate all errors. It eliminates repetitive errors, trivial errors, and those that skew the analysis without being immediately noticeable. And that’s already a huge step forward.

How Drop-Down Menus Enhance AI Analysis in ELECTE

The quality of the analysis depends on the quality of the input data. It’s a simple rule, but one that’s often overlooked. If categories, departments, customers, or geographic areas are entered into the system in inconsistent formats, even the best analytical model will be working with flawed data.

From Controlled Input to Reliable Insights

A well-designed drop-down menu reduces unnecessary variability. This makes operations such as segmentation, aggregation, historical comparison, and pattern detection more reliable. If the same region is always recorded in the same way, you can interpret the geographic data with much greater confidence. If products follow a consistent taxonomy, you can analyze performance and product mix without having to manually correct each data extraction.

The passage reads as follows:

When the data is clean from the start, an analytics platform can do its job more effectively. Anomalies are spotted sooner, categories are unambiguous, and reports require fewer preliminary corrections. This is also why it makes sense to invest first in the structure of the input data and then in the sophistication of the charts.

If your goal is to move from spreadsheets to more sophisticated reporting, you can learn more about how to transform data into actionable reports.

Key Takeaways

Standardize at the source: every field that can become a drop-down list is a quality point earned.
Choose the right type: static, dynamic, or cascading — each meets different needs.
Design with the data entry user in mind: if the selection process is slow or confusing, the whole process grinds to a halt.
Maintain a central source: the value of the menu depends on keeping the lists up to date.
Think about the bottom line: a good dropdown menu today saves you from having to clean things up manually tomorrow.

Key Points and Your Next Steps

The drop-down menu may seem like just a minor interface detail. In reality, it’s a strategic control point. If you design it well, you improve data quality right from the start. If you neglect it, you’ll find yourself having to fix reports, dashboards, and analyses much later on, when the cost is higher.

The practical lesson is simple. Use drop-down lists for repetitive fields. Keep the options in a separate source. Switch to dynamic or cascading menus as complexity increases. And periodically review the taxonomy based on actual usage, not on impressions.

For many SMEs, this is the step that separates the “spreadsheet we all fill out” from the dataset you can actually use to make decisions. Clean data isn’t just more organized. It’s what makes business performance understandable, categories comparable, and insights credible.

If you want better analytics, the work doesn’t start with the dashboard. It starts with a cell filled in correctly.

If you want to turn disorganized operational data into clear, actionable insights, discover ELECTE, an AI-powered data analytics platform designed to help SMEs connect data sources, automate reports, and make more informed decisions based on clean data.

Originally published at https://www.electe.net .

Originally published on Medium

Provider Due Diligence for SMEs: The Definitive Guide 2026

Fabio Lauria — Thu, 25 Jun 2026 10:54:08 +0000

The problem with many SaaS purchases doesn’t arise when you sign the contract. It arises months later, when the provider stops responding as promised, changes the terms, makes it difficult to export data, or shifts responsibilities onto you that you thought were theirs. At that point, the initial low price disappears. What remains are operational downtime, legal risk, and exit costs.

Anyone who runs an SME knows this all too well. The sales demo is always flawless, but the contract is far from it. And when a vendor has access to critical data, processes, or sales workflows, a poor choice doesn’t just affect IT. It impacts administration, compliance, customer service, and business continuity.

I speak as an entrepreneur who has experienced real-world disputes with providers that lack transparency regarding GDPR, European billing, genuine support, and unilateral changes to terms. The lesson is simple: provider due diligence isn’t just a procurement formality. It’s the way you assess whether a supplier can become a strength or a structural risk.

Here you’ll find a practical framework for evaluating a provider just as you would evaluate a business partner. It’s not just about price and features, but also the contract, security, operational reliability, portability, and ongoing monitoring.

Introduction: The Phone Call No Entrepreneur Wants to Receive

The website is down on the worst possible day. Orders are getting stuck, the sales team is messaging on three different channels, and customer service doesn’t know what to tell customers. You open a “priority” ticket with your SaaS provider and get an automated response. No technician, no clear escalation process, no estimated resolution time.

That’s when you realize what you’ve really bought.

You didn’t just buy a service. You bought the way that provider handles incidents, liability, data, contracts, and termination. If you didn’t verify these aspects beforehand, you’ve accumulated operational debt. You don’t see it in the demo, it doesn’t appear in the price list, but it all comes crashing down when the provider can’t deliver.

When a provider fails at a critical moment, the problem isn’t just technical. It becomes a commercial, legal, and reputational issue all at once.

Many business owners treat provider due diligence as a mere administrative formality. They check the price, a couple of features, maybe a certification on the homepage, and then they sign. This is a common mistake. The crucial questions are different: Who is responsible for the data? Where is it stored? How can it be exported? Who actually provides support? What happens if the provider changes ownership or alters the terms of the contract?

The downside is that these questions slow down the negotiations. The upside is that they save you months of trouble later on.

What Is Provider Due Diligence and Why It’s a Mistake to Underestimate It

Supplier due diligence helps you understand what risks you’re taking on along with the service. The point isn’t to gather documents just to put your mind at ease when signing the contract. The point is to estimate, up front, how much that supplier will actually cost you if something goes wrong, if the company’s structure changes, if the support falls short, or if you need to walk away quickly in the future.

Anyone who has ever dealt with a forced migration or a poorly managed incident knows this all too well. The problem rarely remains confined to the vendor. It seeps into internal processes, halts sales, ties up the technical team’s time, raises legal concerns, and turns a seemingly affordable subscription fee into a hidden operating expense.

That is why a thorough due diligence process operates on four concrete levels:

Legal identity of the supplier. You need to know which company is signing the contract, where it operates, who controls the group, and which entity is actually liable in the event of a dispute.
Financial and Corporate Stability. A fragile provider creates instability in your service, response times, and your ability to invest in security and business continuity.
Scope of the Agreement and Privacy. This section addresses who bears the risk regarding data, subcontractors, limitations of liability, unilateral amendments, and termination.
True operational reliability. What matters is support, escalation, the quality of documentation, incident management, and the ability to migrate smoothly.

Rule of thumb: If a supplier handles data, payments, customer service, or a critical process, due diligence should be treated as a business continuity check, not as an administrative formality.

In the Italian context, underestimation is even more costly, because the supply chain consists largely of small and medium-sized enterprises, which are often highly dependent on third parties. SMEs account for 99.9% of active businesses and employ approximately 76.5% of the private sector workforce , according to data reported by the Ministry of Enterprise and Made in Italy. In such a system, the supplier’s risk quickly spreads to the customer.

There is also a common mistake. Many companies evaluate a provider without first clarifying what they are actually purchasing: infrastructure, a platform, application software, or a combination of the three. If you want to set up this analysis properly from the outset, it’s best to start by understanding the differences between cloud services.

Underestimating supplier due diligence means treating a business partner as just another expense. This is where the problems arise that no one mentions in the sales pitch: internal processes that are poorly adapted to the supplier, technical dependencies that are difficult to eliminate, liabilities you only discover after an incident, and exit costs that come when you have the least room to negotiate.

A well-done assessment minimizes surprises. A poorly done assessment merely postpones them.

Contractual and Legal Due Diligence That Really Saves You

Most serious problems don’t stem from a technical glitch. They stem from a clause that was overlooked. The contract tells you who’s in control when something goes wrong.

The Clauses That Matter When Things Go Wrong

When evaluating a provider, price is the last thing to consider. The legal scope of the relationship comes first.

Start from these areas:

DPA and GDPR Roles. The Data Processing Agreement must clearly specify who the data controller is, who the data processor is, what instructions are followed, and which subcontractors are involved.
Data Use and Return. If you leave, will your data be returned to you in a usable format, or in an unusable or incomplete export?
Unilateral changes. If the provider can change terms, pricing, or policies simply by posting them on the website, the risk remains yours.
Acquisition, termination, and transfer of the contract. You need to understand what happens to your data and the service if the provider changes ownership or ceases operations.
Jurisdiction, applicable law, time limits for filing a claim. If a dispute becomes unmanageable or falls outside your operational scope, you’ve already lost your bargaining leverage.

Many entrepreneurs view the contract as a document designed to protect the provider. That’s correct. That’s why it should be read as a roadmap to the provider’s incentives.

Questions to Ask Before Signing

In a sales meeting, it’s best to be direct. There’s no need to speak like a lawyer. You need to speak like a company that wants to avoid hidden costs.

Try asking questions like these:

Who processes the data and in what capacity under the GDPR?
Where is the data stored , and what types of data transfers may occur?
How does the cancellation process work , and what does the exit assistance include?
In what format do you export all the data , including logs, attachments, configurations, and useful metadata?
What happens if you’re acquired or if the terms of service change?
Which subprocessors do you use , and how do you communicate changes?
How do you respond to a formal request for access to or deletion of data?

A good contract isn’t one that promises everything. It’s one that leaves little room for ambiguity when the relationship sours.

A classic red flag is a provider that answers business-related questions well but struggles with exit-related ones. Another is a standard DPA that exists but doesn’t really clarify responsibilities, data transfers, and timelines. If you currently work with data, automation, or decision-making systems, it’s also worth reading about the European AI Act as it applies to SMEs, because it’s prompting many companies to formalize governance, traceability, and the role of suppliers more rigorously.

One last practical tip. If the provider finds your questions about data, liability, and portability annoying, that already says something about the kind of relationship you’ll have after signing the contract.

Technical Supplier Audit: Safety Beyond Certifications

A compliance badge helps. But it’s not enough. A certification indicates that a control system is in place. On its own, it doesn’t tell you whether that provider is suitable for your specific context, your data, and your operational exposure.

Practical experience counts for more than a badge

Vendor management frameworks recommend collecting risk questionnaires, financial reports, and certifications such as ISO 27001 and SOC 2 , and classifying vendors by risk level. For high-risk vendors, on-site audits and reviews of the external attack surface are also required, as summarized by Mitratech in its guide to vendor due diligence.

This point changes the way we evaluate a supplier. The question isn’t “Does it have certification?” The question is “What operational evidence can it show me in addition to the certification?”

Backup jurisdiction and attack surface

Data jurisdiction matters more than many people realize. If the provider hosts or transfers data outside the scope you had assumed, your obligations, assessments, and often even the way you handle incidents and formal requests change.

Then there’s the less glamorous, more practical side: backups and disaster recovery. Don’t just ask if they exist. Ask how they’re tested, how they’re documented, and who steps in if data becomes corrupted or the service goes down.

At the same time, assess the reputation of the party you’re dealing with. In some high-risk sectors, checking for public warnings or alerts is a basic precaution. A useful example is the cryptocurrency scam blacklist, which clearly illustrates why reputation screening and third-party verification aren’t just a formality, but a fundamental safeguard when a provider operates in sensitive or opaque areas.

If a vendor only shows you glossy PDFs and no evidence of how it handles incidents, backups, access, and vulnerabilities, you’re evaluating marketing, not security.

Evaluating Actual Performance: The Support and Lock-in Test

The true quality of a provider becomes apparent when you’re in a rush and have little leeway. Not in the demo. Not in the sales pitch. Not on the “enterprise” page.

The demo doesn’t count in critical moments

You should test the support before becoming a customer. It’s a step that almost no one takes.

You can do it easily:

Ask a challenging question. Don’t ask, “Do you offer priority support?” Ask how they handle a formal request for a full export or an incident involving data.
Check the escalation process. Is there a documented procedure, or are tickets being handled in a generic manner without clear ownership?
Read the SLAs carefully. Response time is useful, but the real issue is resolution time and what happens outside of business hours.
Pay attention to who responds. An account manager who promises everything is no substitute for structured technical support.

A reliable provider won’t take offense if you ask these questions. They consider them normal.

Excellent support isn’t about responding quickly when everything is working. It’s about taking charge of a complicated problem, knowing how to escalate it, and providing you with a written record of the decisions made.

The true price is the exit cost

This is where the most overlooked aspect of provider due diligence lies: lock-in.

Effective technical due diligence must include scanning the code and dependencies to build a comprehensive inventory of third-party software, dependency relationships, and open-source licenses, as well as reviewing the architecture, APIs, and databases to assess the risk of technical debt and vendor lock-in, as FOSSA explains in its guide on technical due diligence.

In business terms, you need to understand three things:

Actual data export. Do they provide CSV, JSON, or other open formats, or just dumps that are hard to reuse?
Documented APIs. Can you extract data and configurations without relying on human support?
Hidden dependencies. How many customizations or proprietary components make the release expensive?

If the provider makes it easy to join but hard to leave, you don’t have a partnership. You have a dependency.

When it comes to business continuity, it’s also worth clarifying how the provider approaches data recovery and data loss. If you’re looking for a framework to evaluate these scenarios, ELECTE offers a good reference point on RTO and RPO management.

One simple rule can be very helpful: before signing, ask for a written offboarding procedure. If there isn’t one, the cost of leaving is almost certainly higher than you think.

The Risk-Based Approach: How AI and Data Are Automating Surveillance

The problem with checklists is that they provide a snapshot of the supplier on a specific day. The risk, however, is constantly changing.

From a one-time check to continuous monitoring

A common shortcoming in provider due diligence is precisely this: almost everyone explains what to ask the provider, but few explain how to reassess its risk over time. Yet the context demands it. The Clusit 2025 report indicates that in 2024 , there were 357 cyberattacks against Italian targets — up from 310 in 2023 — with 79% classified as high or critical severity. Furthermore, third-party-related breaches cost, on average, over $370,000 more than internal ones, as reported by SecurityScorecard in its checklist for service providers.

This changes the control logic. It’s not enough to simply approve a provider upon entry. You need to decide which providers require more attention and which indicators trigger a reassessment.

Which indicators should you monitor?

A risk-based approach starts with an internal classification. Not all suppliers are the same. At a minimum, the following factors matter:

Business Risks. If the provider goes down, does your process come to a halt, or does it just slow down?
Sensitivity of the data processed. Analytical data, customer data, regulated data, operational information.
Technical dependency. How difficult is it to replace or decouple it?
Operational history of the relationship. Incidents, delays, policy changes, decline in support.

From there, you can establish effective monitoring, including through data analysis tools: SLA dashboards, tracking of critical tickets, alerts regarding changes to documentation, changes in subcontractors, performance anomalies, or security incidents.

A supplier doesn’t become a risk just because it experiences an incident. It becomes a risk when warning signs accumulate and no one interprets them in context.

For an SME, this is where data translates into practical governance. Not to improve bureaucracy, but to respond more quickly.

Operational Checklist for Your Next Provider Due Diligence

The checklist serves one purpose only: to help you determine whether you’re choosing a supplier that supports your business or one that leaves you with operating debt, legal disputes, and a costly exit. If the document doesn’t help you say no, it’s not a useful checklist.

Legal and Contractual Matters

This way, you avoid the kind of problem that only comes to light after signing.

Clear contractual identity. Verify who is actually signing the agreement, which group companies are involved in providing the service, and which subprocessors have access to data or infrastructure.
A clear and consistent DPA. It covers roles, instructions, transfers, declared technical measures, notification deadlines, and support in the event of requests from data subjects or incidents.
Exit clauses. Insist on clear timelines, transparent costs, usable export formats, removal of residual data, and transition support.
Unilateral changes. Check how they are communicated, how much advance notice you receive, and what contractual remedies are available if the change increases risk, costs, or operational issues.

Technical Area

What counts here is performance. Certifications help, but they don’t show how the provider performs under pressure.

Security documentation. Request evidence regarding access management, backups, logging, patching, incident response, and known vulnerabilities.
Architecture and dependencies. Understand which APIs, databases, third-party services, and proprietary components are essential to day-to-day operations.
True portability. Check whether data, configurations, and logs can be exported in reusable formats without having to rebuild everything manually.
Business Continuity. Review recovery plans, tests performed, internal roles during the incident, and the quality of communication with customers.

Operational Area

Many mistakes arise here, not in the contract.

Real support. Test response times, channels, escalation procedures, and response quality before you commit.
Offboarding. Ask for a documented procedure. If there isn’t one, the lock-in has already begun.
Change Management. Check how the provider handles updates, deprecations, policy changes, and roadmap decisions that could disrupt processes already in production.
Critical subcontractors. Clarify who does what, who can make changes without your consent, and what operational implications this has for you.
Periodic internal review. Assign a person in charge, establish a review frequency, and set clear thresholds that trigger a reassessment of the supplier.

The most common mistake is to stop at the selection phase. The real risk emerges later, when support deteriorates, subcontractors change, exports turn out to be unusable, or a policy change shifts responsibilities onto you that you thought were included. That’s when secondary costs arise.

If you want to boil it all down to a rule of thumb, use this one: evaluate the provider as you would evaluate a business partner. They must be able to weather an incident, a legal dispute, and an orderly separation. If you don’t know how to walk away, you haven’t done enough due diligence.

If you want to transform data on suppliers, SLAs, incidents, and performance into a continuous monitoring system, ELECTE — an AI-powered data analytics platform for SMEs — helps you gather scattered signals and turn them into useful insights for faster, better-informed decisions. It’s a practical way to move from sporadic due diligence to a more mature operational oversight system.

Originally published at https://www.electe.net .

Originally published on Medium

When Washington Talks About AI as the Manhattan Project

Fabio Lauria — Wed, 24 Jun 2026 10:58:33 +0000

For years, we have referred to AI as an industry. Today, given the U.S. stance, it is more accurate to think of it as strategic infrastructure. The issue is not just technological. It is political, industrial, and, increasingly, a matter of national security.

The comparison with the Manhattan Project did not come out of nowhere. The Manhattan Project was formally launched in 1942 and, under the direction of Leslie Groves from 1942 to 1946 , transformed theoretical research, central coordination, and industrial capacity into a program with measurable operational objectives. It involved three main sites, more than 100 secondary sites , and approximately 130,000 people at any given time between 1942 and 1946, according to the Wikipedia entry on the Manhattan Project. This scale helps illustrate a clear logic: when Washington decides that a technology is strategic, it accelerates the transition from research to industrialization.

For an Italian entrepreneur, this is not an academic debate. If the United States treats AI as a lever of sovereignty, the balance of power shifts throughout the entire supply chain. The dominant suppliers change, technological dependencies shift, and the risks related to data, compliance, and business continuity also change. In this context, considerations regarding AI security become central — not only for those who develop models, but for every company that adopts them.

Here, an essential distinction must be made. The metaphor of the Manhattan Project is powerful as a political tool. But to understand what is really happening, we must separate the narrative from the operational structure.

Introduction: AI is no longer just technology — it’s a matter of national security

When a government uses the language of the Manhattan Project to talk about artificial intelligence, it is doing more than just making a rhetorical choice. It is saying that it considers AI an asset to be safeguarded through national priorities, industrial capacity, and central coordination.

This change matters because AI, unlike other recent digital technologies, encompasses software, hardware, energy, data, scientific research, and security. It is not just any vertical market. It is a general-purpose technology that can reshape entire value chains.

Key point: If Washington treats AI as strategic infrastructure, then even those who use AI for forecasting, operations, or analytics indirectly enter that geopolitical arena.

For Italian companies, the key issue is not taking an ideological stance. The key issue is understanding the operational ecosystem they are entering. The topic of the Manhattan Project for artificial intelligence is therefore of interest not only to those who follow U.S. policy, but also to those who must decide today on technology stacks, data residency, and dependence on suppliers.

What Is the Genesis Mission? The Facts Behind the Story

In public discourse, there is talk of a “Genesis Mission” as a major U.S. initiative on AI. The narrative portrays it as a leap in scale. The challenge lies in distinguishing between what is already established and what, at this point, is still being presented as an announcement, a policy direction, or a strategic ambition.

What we can say with certainty

Based on the available information, the Genesis Mission should be viewed first and foremost as an act of industrial policy and national security — not merely as a research program. Its strategic significance lies in the fact that AI is being placed within the same framework through which the United States has historically addressed critical capabilities.

There are a few key elements that clearly define this approach:

The Central Role of the State: The government does more than just regulate. It sets priorities, highlights urgent issues, and works to coordinate critical infrastructure.
Industrial involvement: AI is not treated solely as academic research. It is treated as an ecosystem that requires computing power, supply chains, energy, and integration with private-sector actors.
Broad objectives: The framework does not pertain to a single product or a single laboratory. It pertains to science, competitiveness, and safety.

This approach is reminiscent of the logic behind “mission-driven” programs, as described in the case of the Manhattan Project: a concentration of talent, central coordination, and measurable objectives, as detailed in the Wikipedia entry on the Manhattan Project.

Why Language Matters

The strategic point is not just what will be accomplished. It is what the language enables. If political leaders use a metaphor of national mobilization, they pave the way for decisions that would otherwise seem exceptional: budget priorities, fast-track infrastructure projects, enhanced cooperation between the government and industry, and greater selectivity regarding suppliers and supply chains.

It isn’t necessary for every detail to be worked out for the market to change its behavior. Often, a political signal is enough.

That is why the Genesis Mission must be analyzed objectively. Not as a founding myth, but as an indicator that the United States views AI as part of a systemic competition. For a European reader, the implication is not that “a new Oppenheimer is coming.” The implication is that Washington is positioning itself to turn technological capabilities into a lasting geopolitical advantage.

The Parallel with the Manhattan Project: What Works and What Doesn’t

The Manhattan Project metaphor works because it evokes a rapid, centralized, and top-priority mobilization. But taken literally, it is inaccurate. To truly understand the “Manhattan Project” of artificial intelligence , we need to look less at the Oppenheimer epic and more at the actual structure of the original program.

Where the analogy holds

The Manhattan Project was a program of exceptional scale. The Trinity test on July 16, 1945 , marked the first nuclear test in history and ushered in the atomic age. Available sources also indicate a cost of approximately $2 billion at the time , with initial funding of $500 million and more than half of the funds allocated to the separation of fissile materials, as detailed in this historical analysis of the Manhattan Project.

This is the first key point to keep in mind when thinking about AI. Major breakthroughs don’t come from a good scientific idea alone. They happen when three factors come together:

There is also a second, even more interesting aspect. In the original project, over 90% of the costs were accounted for by buildings and the production of fissile material, with activities spread across more than 30 sites and a strategy described as “parallel” — that is, research, facilities, and organizational adaptation developed simultaneously, as Mimesis Scenari points out.

For AI, this parallel is illuminating. The bottleneck isn’t just the algorithm. It’s the infrastructure, the data, the energy, the industrial processes, and the ability to coordinate everything quickly.

Where the analogy becomes misleading

AI is not a bomb. It is not a single artifact with a single operational objective. It is a family of capabilities that spans software, models, embedded systems, cloud platforms, enterprise tools, and security systems.

Here, the Manhattan metaphor begins to lose its precision.

Nuclear technology could be kept secret. AI, on the other hand, has emerged and evolved within a much more open ecosystem, featuring public research, open source, global talent, and rapid knowledge transfer.
The objective was specific. In AI, there are multiple objectives, and civilian, scientific, commercial, and military objectives often overlap.
Governance is hybrid. Today, the operational center of gravity also lies with hyperscalers, private labs, and platforms that no single government fully controls.

Rule of thumb: The right question isn’t “Who is the new Oppenheimer?” It’s “Who controls computing power, data, the supply chain, and market access?”

For anyone reading about SMEs and artificial intelligence today, the implication is clear. If you take the metaphor too literally, you underestimate what truly determines scale in AI: not the isolated genius, but industrial organization.

The Contradictions in the U.S. Plan

Major national strategies are never straightforward. Even the U.S. strategy on AI contains internal tensions that a European observer must interpret carefully, because they are part of the substance, not mere background noise.

A grand strategy, but not a linear one

The first contradiction is simple. The United States identifies AI as a strategic priority, but any acceleration of this kind must contend with political constraints, budget negotiations, competing industrial interests, and implementation timelines that rarely align with the public narrative.

This gives rise to a phenomenon typical of large-scale technology policies. The statement of intent appears monolithic. Actual implementation, however, is fragmented. Some components are moving quickly, while others are moving more slowly. Some aspects are very clear, such as the geopolitical signal. Others, however, remain unclear, such as operational governance, long-term structures, or the actual scope of priorities.

Why This Ambiguity Matters for Businesses, Too

For an Italian company, this ambiguity is not just a minor detail for observers in Washington. It means that the AI market in the coming months and years could be influenced by decisions that are not purely economic. A provider could gain a stronger foothold because it aligns with a national priority. A piece of infrastructure could become more critical because it is part of a security strategy. A dependency that is “technical” today could become political tomorrow.

Businesses do not operate outside the realm of geopolitics. They are affected by it in terms of their cost structure, the availability of services, and their range of options.

This is even more true when considering competition between blocs. The United States is increasingly treating AI as an asset of sovereignty. China, in its own way, is making a similar choice. Caught in the middle, Europe risks finding itself in a position where it regulates extensively but has less control over key industrial sectors.

Implications for Europe: Caught Between Two Blocs

The problem for Europe is not merely that it is lagging behind in a technological race. It is the fact that the race is turning into a competition between blocs that integrate industry, security, and foreign policy. In this scenario, Europe often approaches the issue primarily from a regulatory perspective.

The real problem in Europe isn’t just a regulatory one

The EU AI Act is important because it defines boundaries, responsibilities, and risk categories. In the context mentioned by Sanoma Italia, generative AI falls under the “limited risk” category when used responsibly. But this, on its own, does not answer the more concrete question: Is Europe also building comparable industrial capacity?

In Italy, the situation remains uneven. Data cited by Sanoma indicate that, according to ISTAT, the adoption of AI in businesses and the public sector is patchy , and that the skills gap is one of the main obstacles, as summarized in Sanoma’s article on the long-term impact of Prometeo. This shifts the focus: the problem is not just regulating the use of AI, but understanding who truly has the capacity to scale it.

What does this mean for an Italian company?

For an SME, this is not just geopolitical theory. It has a direct impact on three operational decisions.

Choosing a provider: Using an AI service also means accepting its infrastructure dependencies.
Data residency: Not all solutions offer the same level of control over where data, prompts, output, and logs are stored.
Strategic continuity: A technology partner can change its priorities, access conditions, or dependency stack faster than a client company can adapt.

If AI becomes a strategic infrastructure for nations, choosing an AI provider is no longer just a matter of procurement. It is risk management.

In this context, it is also helpful to follow the discussion on ELECTE regarding the AI Act, because for many Italian companies, the real challenge is balancing rapid innovation with operational control and compliance with European regulations.

Technological Sovereignty as a Strategic Choice for SMEs

The word “sovereignty” may seem far removed from SMEs. In reality, it describes a very practical need: maintaining a degree of control over technologies that have become central to sales, operations, forecasting, compliance, and reporting.

Five Practical Criteria for Making a Choice Today

If you’re evaluating AI or analytics platforms, I recommend taking a practical approach to the issue of sovereignty. Here are the criteria that really matter.

Where Is the Data?
Ask where the data is processed, stored, and replicated. Don’t limit yourself to just the user interface.
What infrastructure does the service depend on?
A platform may have a European brand but rely critically on non-European technology stacks. The difference is substantial.
How does it handle compliance and auditability?
For functions such as risk management, finance, or management reporting, process traceability is just as important as the quality of the output.
How replaceable is the supplier?
The higher the lock-in, the greater the strategic risk.
What part of the value remains under your control?
If your key decision-making processes depend entirely on external systems, you are transferring operational power outside the company.

From Unit Price to Systemic Risk

Many SMEs purchase AI based on demos, ease of use, and upfront cost. This is understandable, but today it’s not enough. The right question isn’t just “Does this solution do what I need it to do?” The complete question is “Will this solution remain consistent with my operational, regulatory, and strategic constraints if the geopolitical context worsens or changes?”

At this point, the discussion about the Manhattan Project of artificial intelligence no longer seems so far-fetched. If the United States and China treat AI as national infrastructure, every European company should at least ask itself where it fits into that picture.

Management decision: The best AI partner isn’t just the one with the most features. It’s the one that reduces your unnecessary exposure without slowing down innovation.

That is why technological sovereignty is not autarky. It is the ability to make informed choices, spread risk, and maintain control over critical processes.

Conclusion: What to Watch for in the Coming Months and How to Take Action Today

The most useful lesson is not that we are experiencing a repeat of the Manhattan Project. It isn’t. The lesson is more concrete. AI has now crossed the boundary of the tech market alone and entered the realm of national strategy.

Italian entrepreneurs would be well advised to keep an eye on certain developments in the coming months: the actual level of coordination between the U.S. government and industry; how this narrative translates into operational capabilities; the evolution of Europe’s stance between regulation and investment; and, above all, how these dynamics play out in the areas of cloud computing, models, access to computing resources, and data governance.

The most rational choice today is not to wait for complete clarity. That won’t come anytime soon. The rational choice is to develop an AI strategy that balances innovation, compliance, and reducing critical dependence.

In a world where geopolitics is becoming part of the technology stack, choosing the right partners is just as important as choosing the right tools.

If you want to build a more robust AI strategy that’s consistent with the European context, check out ELECTE, an AI-powered data analytics platform designed to transform business data into clear operational decisions, with an approach tailored to the needs of European companies. You can see how it works and assess whether it fits into your tech stack without any unnecessary complications.

Originally published at https://www.electe.net .

Originally published on Medium

Artificial Intelligence for Scientific Research: Mistral

Fabio Lauria — Tue, 23 Jun 2026 11:00:19 +0000

A team of engineers based in Vienna trains the models using physical constraints rather than relying solely on text. Two days later, Paris turns this capability into a strategic move with continent-wide repercussions.

That’s why Mistral Science is more important than many other AI launches that have generated more buzz. Whether you work in research, industry, or data strategy, the real news isn’t yet another assistant capable of speaking fluently about science. It’s the emergence of a European effort to build artificial intelligence for scientific research capable of modeling, simulating, and accelerating discoveries in fields where physics, materials science, biology, and financial systems leave no room for approximation. For Europe, this goes far beyond a single company. It touches on a structural weakness the continent has lived with for years: relying on non-European model providers for critical digital infrastructure.

Mistral’s focus on open-weight models and its entry into specialized scientific AI through Emmi AI suggest a different path — one in which European organizations can inspect, adapt, and deploy models with greater control over data, methods, and downstream dependencies.

The following is the key question behind the headlines: why this shift could mark a turning point for European technological sovereignty, and what it means in practice for researchers, SMEs, and tech leaders who are currently choosing their AI stack.

Introduction: The New European Frontier of AI

Mistral isn’t interesting just because it’s European. It’s interesting because it’s attempting something that Europe has rarely achieved on a global scale: transforming AI from a general-purpose software capability into a strategic infrastructure for research and industry.

The difference matters. A consumer-oriented model can improve individual productivity, writing skills, and access to knowledge. An artificial intelligence platform for scientific research , on the other hand, can shorten discovery cycles, support simulations, accelerate the selection of hypotheses, and transform the relationship between the laboratory, computing, and industrial decision-making.

This issue is not abstract in Italy either. Istat has formalized the use of AI to innovate statistical processes, with activities that include summary data , classifiers , chatbots , and the LAbInn program to automate coding, improve administrative databases, and analyze territory and geospatial imagery, signaling a shift from experimental use to a more structured institutional adoption ( Istat’s approach to artificial intelligence).

Mistral Science should be viewed as a strategic European asset, not merely as a feature.

Beyond Chat: What Mistral for Science Really Is

The first thing to clarify is this: Mistral for Science should not be viewed as an academic version of a chatbot. That interpretation is too narrow and leads to incorrect conclusions.

When a generalist model “talks about science,” it usually recites technical language learned from textbooks, articles, documentation, and code. This can be useful for summarizing, explaining, or proposing hypotheses. But it does not amount to accurately representing a physical system, an engineering dynamic, or a high-fidelity simulation.

A descriptive model is not enough

In scientific research, the challenge isn’t just about saying something that makes sense. The challenge is adhering to real-world constraints.

A general-purpose model can explain aerodynamics to you. An engineering model should help you simulate how a flow behaves under certain conditions. An LLM can summarize papers on materials. A specialized model should help narrow down the range of possibilities to test.

This is why the acquisition of Emmi AI is so significant. The strategic message is clear: Mistral does not want to limit itself to the application layer of language. It is entering a category where the model incorporates the problem structure.

Why the acquisition of Emmi AI changes the scope

So-called Large Engineering Models point in a clear direction. These are not merely models trained on technical documents, but systems designed to operate in contexts where reality is governed by equations, constraints, and simulations.

For a European reader, this changes the very meaning of “AI for science.” The point is not to create a better assistant for researchers. The point is to build a computational engine that speeds up research on real-world problems.

Three practical implications:

In engineering : models of this type can be integrated into simulation, design, and optimization workflows where the “cost of error” is not a figure of speech, but a flawed technical decision.
For industry : if the model incorporates domain knowledge, it can become part of the R&D cycle rather than merely a layer of document support.
For Europe : specialization reduces direct competition with American giants in the realm of pure general reasoning and opens up a field where industry expertise, manufacturing, and applied research matter more.

There is also a second level that is often overlooked. In Italy, the institutional adoption of AI by Istat creates a cultural and operational environment more conducive to this leap forward. If a national statistical agency uses AI for data summarization, coding automation, and geospatial data analysis, the message is that scientific AI is no longer confined to elite laboratories but is entering the formal processes of public knowledge production.

A general-purpose LLM is good at explaining the world. A useful scientific model should help you calculate it.

This is the point that many people miss. Mistral Science isn’t important because it “falls under the umbrella of science.” It’s important because it seeks to position Mistral in a more defensible category, where value stems from the integration of model, domain, and industrial process.

Open-Weight Models and European Technological Sovereignty

The most underrated aspect of Mistral is not the speed at which the company operates. It is its decision to focus on open-weight models. For research and for many European companies, this is a more strategic decision than any demonstration.

A closed model available only via API offers convenience. An open-weight model offers you control. And in Europe, control isn’t a philosophical preference. It’s an operational requirement when working with sensitive data, intellectual property, regulated processes, or critical industrial supply chains.

What really changes for companies and research centers

When the model’s weights are accessible, an organization can do things that remain difficult or impossible with a purely black-box service.

Adapt the model to the domain : technical terminology, internal workflows, proprietary taxonomies.
Choose where to run the model : European cloud, dedicated infrastructure, or environments with specific requirements.
Reducing vendor lock-in : The vendor does not have sole control over the roadmap, pricing, access policies, and data processing methods.
More credible audits : Transparency does not eliminate risk, but it improves verifiability and governance.

That is why technological sovereignty should not be reduced to a buzzword in policy papers. For a company, it means knowing who controls the platform, where the data flows, how customizable the solution is, and how much it will cost to switch to a different approach in the future.

Because sovereignty isn’t just a slogan

If you manage research data, intellectual property, or highly regulated processes, your real question isn’t “Which model is the most popular?” It’s “Which model can I manage without handing over my strategic independence to a single external party?”

This also applies from a regulatory and organizational standpoint. Anyone dealing with AI compliance requirements for businesses knows that it’s not just about the model’s performance. The traceability of decisions, an understanding of the model’s limitations, and the ability to document its use are also critical.

There is also an economic reason that is less often discussed. In academia and among SMEs, the value of open-source software lies not only in its cost. It lies in the opportunity to build local expertise. An accessible model fosters learning, adaptation, and the development of in-house tools. A closed API, on the other hand, tends to concentrate cognitive and operational power in the hands of the provider.

Technological sovereignty begins when you can choose how to use a model, not just when you can buy access to it.

From this perspective, Mistral’s move is clear-cut. If Europe wants to establish a credible position in AI, it is not enough to have startups that simply resell others’ capabilities. We need players who can build models, ecosystems, and adoption standards compatible with the European industrial landscape.

Practical Applications from Materials Science to Finance

To understand where this trajectory might lead, it is worth looking at an operational benchmark already evident in the market. Microsoft reports that Microsoft Quantum and PNNL, using Azure Quantum Elements, have digitally screened over 32 million materials , identifying a new battery material that requires 70% less lithium , with the selection and testing completed in just a few weeks ( AI and high-performance computing for scientific discovery).

This example does not directly concern Mistral. However, it illustrates the value proposition the category is moving toward: combining AI, high-performance computing, and rapid validation to drastically narrow the search space.

The key performance metric to keep in mind

The lesson isn’t that “AI works magic.” The lesson is more practical: the right combination of mass screening, automated prioritization, and targeted testing can reduce both the time and cognitive effort required for research.

When a team stops exploring blindly and begins to better filter its hypotheses, the quality of the decisions made at the outset improves. In this sense, the true promise of artificial intelligence for scientific research is selective, not flashy.

Where scientific models can create value

In practice, an initiative like Mistral Science makes sense in fields where language alone is not enough.

Materials Science The potential benefit here is clear. Specialized models can help rank candidates, simulate properties, and determine what to test first in the lab.
Biology and drug discovery A system that integrates domain knowledge can support the selection of experiments, the structured review of the literature, and the elimination of less promising hypotheses. It does not replace biological validation, but it can make the drug discovery pipeline more systematic.
Physics and Engineering Simulation If the model incorporates physical constraints, its role changes. It is no longer merely a documentary co-pilot. It becomes an integral part of the computational process.
Quantitative Finance The perspective here is nuanced but interesting. In complex systems, what matters is the ability to model dependencies, scenarios, and nonlinear dynamics. A specialized model can be useful if it is integrated into research workflows, not if it is treated as a linguistic oracle. From an applied perspective, it also helps to understand the debate surrounding real-world LLM capabilities.

There is also a less obvious point. The study summarized by Il Bo Live indicates that researchers who use AI tools publish about three times as many articles , receive nearly five times as many citations , and rise to leadership positions more quickly. However, the same study also notes a 4.63% reduction in the collective exploration of topics and a 22% decline in citations between articles referencing the same work ( Italian analysis of the study in Nature).

This finding suggests an uncomfortable but useful conclusion. AI can boost scientific productivity while simultaneously limiting the diversity of exploration. Those who build research platforms and processes will therefore need to optimize not only for efficiency but also for the variety of hypotheses.

An Honest Comparison: Where Does Mistral Stand Today?

The discussion about Mistral becomes unproductive when it veers toward two extremes. On the one hand, there is the automatic enthusiasm for any European player. On the other, there is the tendency to dismiss as irrelevant anyone who doesn’t dominate every general-purpose benchmark.

The reality is more interesting. When it comes to the most challenging cross-disciplinary reasoning tasks, the entire field is still far from achieving truly reassuring results.

An Overview of General-Purpose Benchmarks

An Italian guide to benchmarks notes that NinjaTech’s Deep Research model achieved 17.47% accuracy on Humanity’s Last Exam , a test described as one of the most difficult for multi-domain reasoning. The same guide observes that benchmarks useful for research must also take into account latency , reasoning quality , and network performance when used via API ( AI benchmarks for research contexts).

This figure should be interpreted carefully. It does not prove that any single model is weak. It shows that even advanced models still struggle with problems that require robust generalization. Therefore, it would be naive to describe Mistral today as generally equivalent to the best U.S. frontier models on the most complex tasks.

Where specialization can beat the ladder

But the right question isn’t “which one wins everywhere.” It’s “which architecture and which strategy are best for a specific task.”

Mistral may be less strong in some general areas but much more compelling where it really matters:

Computational efficiency
Adaptability to specific domains
Flexible distribution
Open-weight class
Integration into European research and industry networks

If you view the market solely as a race to the absolute benchmark, Mistral risks appearing to be playing catch-up. If you view it as the development of a European infrastructure for specialized use cases, the picture changes radically. In that context, the goal is not to beat every competitor in the most crowded arena. It is to occupy a high-value segment where the combination of openness, efficiency, and specialization matters more than sheer scale.

To put this development into context, it is helpful to understand the market for large language models, but without limiting ourselves to rankings of general-purpose models.

Mistral’s strategic advantage does not stem from trying to be everything to everyone. It stems from being able to be highly effective in areas where dominance matters more than scale.

There is also a word of caution that the market often overlooks. Italian studies on the use of generative AI in scientific research have highlighted issues with source verifiability, potential copyright risks, and a decline in scientific quality when these systems are misused. Here’s a simple reminder: the more the model’s apparent autonomy increases, the more human methodological discipline must increase as well.

Implications for European Companies: How to Choose the Right AI

For a European company, the conclusion isn’t “always choose Mistral” or “always choose the most powerful model.” That would be the wrong approach. The right choice depends on the type of problem you’re trying to solve.

A simple criterion for deciding

Whether your problem is cross-functional, documentation-related, language-related, or involves general-purpose productivity, a general-purpose LLM might be a good fit.

If, on the other hand, you work with:

regulated processes,
sensitive data,
intellectual property,
technical simulations,
research or engineering workflows,

Then the question changes. In those cases, you need to assess whether a specialized model — or at least one that is adaptable and controllable — delivers more strategic value than a closed-source service that looks more impressive in a demo.

What to Consider Before Implementing a Model

A practical framework can be based on five criteria:

Type of tolerable error. If an error results only in text that needs to be corrected, the risk is manageable. If it could affect a technical or regulatory decision, more oversight is needed.
Vendor Lock-in. Ask yourself how much it would cost you to switch stacks in a year. This applies not only financially, but also in terms of skills and processes.
The Need for Customization. The more specific your domain is, the less practical a completely off-the-shelf solution becomes.
Data Governance. Where the model runs, how its use is documented, and who can monitor its behavior.
Compatibility as Your Competitive Advantage. If the model touches the core of your expertise, transparency and controllability become assets, not optional extras.

Some market players will continue to view AI as a utility. This is a valid approach for many use cases. However, those operating in highly specialized European sectors should start thinking of AI as strategic infrastructure. It is in this shift that initiatives like Mistral Science become significant.

Key Points for Your AI Strategy

The most useful lesson is simple: don’t confuse the appeal of general-purpose AI with the value of specialized AI.

Here are the points to bring up at the meeting:

Distinguish between conversation and simulation : a model that explains a phenomenon well is not automatically the best model for it.
Consider open-weight as a strategic advantage : control, adaptability, and less vendor lock-in can be more important than a more spectacular demo.
Focus on workflows, not prompts : in research and industry, value comes from integration with data, processes, and validation.
A multi-dimensional approach : accuracy alone is not enough. We also need low latency, sound reasoning, and operational reliability.
Think on a European scale : technological sovereignty means being able to build lasting capabilities on infrastructure that you can control.

Mistral Science is not yet the pinnacle of European AI. However, it is one of the strongest signs that Europe has begun to play a smarter game. Rather than merely imitating global leaders, it is choosing where it can create its own competitive advantage.

If you’re looking for ways to integrate AI into real-world decision-making processes without adding unnecessary complexity, check out ELECTE. It’s an AI-powered data analytics platform designed to transform raw data into actionable insights, with an approach that’s accessible even to non-technical teams. You can see how it works and determine which AI architecture is best suited to your needs.

Originally published at https://www.electe.net .

Originally published on Medium

Next-generation voice assistants: why architecture matters more than the response

Fabio Lauria — Sat, 20 Jun 2026 10:45:27 +0000

The most common piece of advice when comparing next-generation voice assistants is also the least useful: comparing which one “responds better.” This is the logic of a consumer test, not a strategic decision. If you look at the market through the eyes of an entrepreneur, an innovation manager, or a compliance team, the right question isn’t which voice sounds smarter, but which system best orchestrates models, data, devices, and actions.

In Italy, the groundwork has already been laid for this shift in perspective. Home adoption of voice assistants rose from 11% of households in 2018 to 15% in 2019 , according to Biblioteche Oggi Trends on voice assistants and smart speakers. We are therefore not talking about a technological novelty, but rather an interface that has already become part of everyday life.

The point today is a different one. The major players are converging on the same foundational building blocks of AI. When the “engine” starts to look alike, the differences shift to architecture, the ecosystem, actual agentic capabilities, and data governance. That is where the future will be decided.

Introduction: The Wrong Question Everyone Asks

For years, we’ve evaluated voice assistants the way we evaluate a game show. Does it understand the question? Does it respond quickly? Does it make few mistakes? Today, that framework is too narrow. A next-generation assistant doesn’t just compete on the answer itself, but on its ability to connect services, maintain context, perform actions, and operate within an ecosystem.

From my perspective, the real mistake is assuming that the underlying language model is still the primary differentiator. It clearly is no longer the case. As more companies rely on external models or shared infrastructure, conversational quality tends to converge. At that point, the competitive advantage lies not in the “brain” itself, but in how that brain is integrated.

The market isn’t just rewarding those who speak best. It’s rewarding those who best coordinate devices, services, context, and data.

For an Italian professional, this changes everything. The comparison of next-generation voice assistants should not be viewed as a gadget ranking, but rather as a choice between platforms with very different business models, technological dependencies, and operational implications.

Beyond AI: The Great Technological Convergence

The public debate continues to treat Siri, Alexa, Google Assistant, and emerging solutions as if each possessed a radically distinct form of intelligence. This perspective is becoming increasingly less useful. The industry is moving toward the commoditization of output : more powerful models, often accessible through shared infrastructure or partnerships, are narrowing the perceived gap in basic conversation.

Understanding isn’t enough

An Italian benchmark is particularly revealing because it distinguishes between two metrics that many people confuse. In Worldline Italia’s test of 800 identical questions, Google Assistant achieved 100% question comprehension and 87.9% correct answers , Siri achieved 99.6% and 74.6% , Alexa achieved 99% and 72.5% , and Cortana 99.4% and 63.4% , as shown by Worldline Italia’s comparative benchmark.

These numbers tell us one thing for certain: understanding almost everything doesn’t mean being able to answer everything correctly. And above all, it doesn’t mean knowing how to act appropriately. The benchmark also highlights differences by task category: Siri outperformed Google on voice commands, while Google dominated in general knowledge questions and informational tasks. So there is no “absolute champion” that exists in a vacuum, detached from the context of use.

Where does the value move?

If multiple assistants reach similar levels of basic understanding, the platform is no longer the main factor in the decision. At that point, I consider four factors:

Model orchestration. An assistant may rely on one or more AI systems, but what matters is who decides when to use which one.
Application layer. The value increases when the assistant does more than just speak — it also accesses services, memory, apps, and automations.
The experience matters. A consistent interface — whether on a smartphone, speaker, car, or smart home — is more important than a slightly better response.
Dependence on third parties. The more the system relies on external factors, the more critical governance and reliability become.

Rule of thumb: If two assistants seem similar in their responses, see what happens when they have to put words into action.

For this reason, a comparison of next-generation voice assistants shouldn’t start with a “who knows more” test, but with a different question: who truly controls the entire chain — from voice to model to integration to result?

Comparing Architectures: The Real Battle for the Future

When the engine tends to converge, the architecture becomes the real battleground. That is where it is decided how an assistant will evolve, how specialized it will become, and how reliable it will be when it has to handle complex actions — not just simple, isolated requests.

Three different architectural approaches

Large companies are taking different approaches, and this difference matters more than any single demo.

Amazon tends to prioritize a more unified experience. Samsung has demonstrated an approach more focused on orchestrating multiple components. Apple, on the other hand, is noted primarily for its ability to credibly rebuild Siri after a long delay perceived by the market. There’s no need to turn these trajectories into slogans. It’s enough to understand that architecture is a strategic choice , not a technical detail.

Why architecture matters more than a feature list

A feature can be copied. An architecture cannot — or at least not quickly. If a competitor launches a new summary, booking, or auto-dial feature, others can replicate it. But the way an assistant distributes tasks among speech recognition, memory, scheduling, external apps, and permission management determines the system’s quality over time.

For those working in the company, the key question is this: Is the assistant designed to perform a reliable sequence of actions , or to impress during a demo?

It’s one thing to ask, “Reserve a table for me.” It’s quite another to have a system manage a sequence of steps involving constraints, authorizations, sensitive data, and verification of the result.

This also highlights the limitations of consumer-oriented AI. Many assistants promise to “do things for you,” but in practice, they perform best in highly standardized areas: music, timers, quick information, smart home controls, messages, and calendars. As soon as the task involves exceptions, policies, corporate data, or operational responsibilities, their capabilities become more limited.

That’s why, when I assess the future of a platform, I don’t just look at what it can do today. I look to see if its architecture is capable of handling:

Persistent and contextual memory
Multi-step processes with confirmations
Routing to different services
Granular permission management
Performance Monitoring and Failures

When comparing the latest generation of voice assistants , the real battle isn’t about which ones sound more natural. It’s about which ones have more convincing models.

From words to action: true agency

The term “agent-like” is used too loosely. These days, all it takes is for an assistant to complete a guided task to be labeled an agent. I disagree. A system is truly agent-like when it can interpret a goal, break it down into steps, interact with different tools, verify the outcome, and handle exceptions without losing sight of the context.

An assistant who carries out tasks is not yet an agent

In the consumer sector, many “actions” are actually well-designed shortcuts. Turning on the lights, starting a playlist, setting a reminder, sending a message. They’re useful, and often very well designed. But they’re actions that take place in relatively closed environments, with little room for ambiguity.

In day-to-day work, the bar is raised immediately. A true professional must be able to connect data, applications, internal policies, and responsibilities. If a manager requests an analysis of a drop in sales, the system shouldn’t just summarize a dashboard. It should cross-reference sources, flag anomalies, distinguish between assumptions and facts, and produce actionable insights.

This is where the difference between a consumer assistant and ELECTE’s AI Agents for business processes becomes clear. It is not a difference in abstract “general intelligence.” It is a difference in design: objectives, data, tools, controls, and auditability.

The practical limitation lies in the add-ons

The real bottleneck in an assistant’s capabilities isn’t just the model itself. It’s the network of integrations that the assistant can activate in the local context. Historical data on the Italian market illustrates this well: a cited survey indicated 2,920 Alexa skills in Italy , compared to 65,901 in the United States and 34,771 in the United Kingdom , as reported in True Numbers’ analysis of home voice assistants.

This gap is no small matter. It means that Italian users, even when using a powerful assistant, operate within a more limited ecosystem of third-party features compared to English-speaking markets. And if the ecosystem is more limited, so too is the ability to “take action.”

Three practical implications:

The functionality depends on the available connections Without integrated services, the assistant remains a good conversational interface with limited functionality.
Localization is just as important as the model An excellent system in English may be of limited practical use if it lacks local services, content, and workflows relevant to Italy.
True agency requires process control The more important a task is, the more it requires checks, logs, authorizations, and the ability for human intervention.

An assistant who “gets things done” at home isn’t automatically ready to “get things done” at work.

That’s why, when comparing next-generation voice assistants , I always distinguish between three levels: conversation, guided execution, and reliable automation. Marketing tends to lump them all together. Anyone making a serious investment should carefully distinguish between them.

The ecosystem is the real competitive advantage

If basic intelligence becomes standardized, the competitive advantage shifts from the model itself to the network of connections. This is where many public debates miss the point. They treat the assistant as a finished product, when in reality its value depends on what it can enable around it.

Localization is more important than branding

In the Italian market, a strong brand isn’t enough. An assistant may look excellent on paper, but if the local ecosystem lacks depth, its practical value in everyday use is limited. This applies to smart homes, apps, local services, payments, and vertical integrations.

According to GMI Insights’ report on the voice user interface(VUI) market, the market was valued at $16.5 billion, with North America accounting for over 30% of the global market in 2023. For Italy, the same industry landscape helps reveal a concrete trend: the main assistants available are Siri, Google Assistant, and Alexa, but the practical choice often revolves around the ecosystem, multi-device compatibility, and home automation integration.

For business, it’s the entire supply chain that matters

For a professional team, the ecosystem is more than just a list of compatible tools. It’s a complete ecosystem:

Input. How the request is made, in what context, and with what permissions.
Routing. Which engine or service handles the task.
Execution. Which applications or databases are being queried.
Verification. Who checks the results, where records are kept, and how errors are corrected.

A rich ecosystem reduces friction. A fragmented ecosystem creates dependencies, exceptions, and blind spots.

The more interchangeable the models become, the more the ecosystem becomes the product.

This is why a comparison of next-generation voice assistants should be viewed as an evaluation of the platform. You’re not just choosing a voice. You’re choosing an ecosystem of integrations, technology partners, and operational capabilities. And for a business, this ecosystem often matters more than the brilliance of a single response.

Privacy and data sovereignty: Who is listening in on your conversations?

The most overlooked topic in reviews of voice assistants is also the most important one for a business audience. Almost all reviews focus on features, accuracy, voice quality, and smart home capabilities. Very few actually delve into data governance.

The Most Underestimated Information Gap

An Italian source puts it plainly: most analyses of voice assistants in Italy overlook privacy, compliance, and data sovereignty, creating an information gap for companies. This is the key point highlighted by Hello Uniweb in its analysis of voice assistants.

To a consumer, this omission may seem minor. To an SME, a finance team, or a compliance officer, it is anything but. If a voice request traverses cloud infrastructure, third-party services, and external application chains, the question is not just “Is the response correct?”, but also:

Where is the request processed?
Who can access the metadata
Which permissions are actually enabled?
How to handle deletion, anonymization, and logging
If the use is consistent with internal policies and the GDPR

To explore this topic from a broader perspective, it is also worth reading ELECTE’s analysis on listening, data, and information risk in AI systems.

How to Assess Operational Risk

When a voice assistant is introduced into a professional setting, I suggest evaluating it as you would any technology that involves data and processes-not as a mere gadget.

The bottom line: In business, it’s not the nicest assistant who wins. It’s the one who reduces friction without increasing operational risk.

This changes the very nature of the comparison between next-generation voice assistants. If you’re a European professional, the quality of the conversation is just one of the criteria. The other factor — often the more important one — is actual control over the data. And on this front, the market is even less transparent than marketing communications would have you believe.

Conclusion: Choose the orchestrator, not just the voice

The voice assistant market is entering a new phase. The key question is no longer which platform looks the most impressive in a demo, but which one is best at orchestrating models, integrations, context, and governance. That’s where the real advantage lies.

What sets it apart isn’t just the quality of the conversation. It’s the architecture underpinning the experience, the depth of the ecosystem that enables actions, the maturity of the agent’s capabilities, and the level of control over data. For a business user, these four factors matter far more than a witty reply or a command executed in a matter of seconds.

Those looking ahead should think in terms of orchestration. It is the same logic that is redefining not only consumer assistants but the entire new generation of operational AI systems. A useful read on this topic is ELECTE’s analysis of AI orchestration and the role of integrations in real-world workflows.

If you want to turn data, signals, and workflows into concrete operational decisions, try ELECTE, an AI-powered data analytics platform for SMEs. It’s the most straightforward way to see how a business-oriented AI agent differs from a consumer-focused assistant: less conversation for its own sake, and more analysis, automation, and real support for decision-making.

Originally published at https://www.electe.net .

Originally published on Medium

Financial Statement Analysis Using Ratios: A Practical Guide to Informed Business Decisions

Fabio Lauria — Fri, 19 Jun 2026 11:28:56 +0000

Financial statement analysis using ratios is the process of transforming raw financial data — such as that from the balance sheet and income statement — into simple, easily understandable indicators. In short, it is the art of letting the numbers speak for themselves so you can instantly grasp the health of your business: its liquidity, financial strength, profitability, and efficiency.

In this guide, we’ll walk you through the process step by step. The goal is to transform complex calculations into practical, actionable insights. You’ll learn how to interpret key financial and economic indicators so you’re no longer overwhelmed by numbers, but can start using them to your advantage to make faster, smarter decisions.

Transforming balance sheet figures into strategic decisions

Every SME owner constantly faces a dilemma: should they trust their instincts or rely on data? All too often, financial statements are seen as a tedious tax obligation — a pile of numbers to hand over to the accountant and then file away until the following year.

What if those numbers could tell the story of your company, revealing its strengths and, more importantly, flagging potential issues before they turn into emergencies?

This is where financial statement analysis using ratios comes into play — a methodology that transforms cold, static accounting data into a true strategic compass for navigating the market.

Beyond Tax Compliance: A Guide to Your Growth

The idea that financial analysis is the exclusive domain of expert analysts or multinational corporations is a myth that has long since been debunked. Today, thanks to accessible platforms, these indicators are an essential tool for any manager or entrepreneur who wants to make decisions based on facts, not gut feelings.

Thinking of your financial statements solely in terms of taxes is like having a treasure map and using it as a coaster for your coffee cup. Hidden within those documents are the answers you’re looking for to grow your business.

The goal of ratio analysis is not merely to “interpret” the past, but to use that knowledge to build a more solid and profitable future. It serves as the bridge connecting accounting to strategy.

Thanks to this process, you can finally get clear answers to questions that are critical to your business:

Cash Flow: Will I have enough cash to pay salaries and suppliers next month?
Financial stability: To what extent do I rely on banks compared to the resources I have generated internally?
Profitability: Is the capital I’ve invested yielding a sufficient return, or would it have been better to allocate it elsewhere?
Efficiency: How quickly can I turn inventory into cash in my bank account?

Often, the first step is simply to get the data into a usable format; in this regard, you might find our guide on how to convert PDF files into Excel spreadsheets helpful.

Assessing financial stability using liquidity and solvency ratios

Think of your business as a ship sailing the seas of the market. To navigate safely, you need two essential things: enough fuel for the immediate journey ( liquidity) and a sturdy hull to withstand unexpected storms ( financial strength).

Liquidity refers to your business’s ability to meet immediate financial obligations, such as paying salaries, suppliers, and taxes. Solvency, on the other hand, concerns the long-term balance between your assets and liabilities, determining the company’s structural resilience in the face of economic shocks.

These aren’t abstract concepts. They can be accurately measured through financial statement analysis using ratios , turning numbers into a strategic compass. Let’s take a look at the key indicators for assessing the financial health of your small business.

Liquidity ratios for measuring a company’s financial health

Liquidity ratios answer a very practical question: “If I had to pay off all my short-term debts today, would I have enough assets that could be easily converted into cash to do so?”. They serve as the first, crucial warning sign for preventing cash flow crises.

The two most commonly used ratios are the current ratio and the quick ratio.

Current Ratio

This ratio compares current assets (cash, accounts receivable, inventory) with current liabilities (accounts payable, short-term tax liabilities, upcoming mortgage payments).

Its formula is straightforward:

Current Ratio = Current Assets / Current Liabilities

A ratio above 1.5 is generally a good sign. It means that, for every euro of short-term debt, you have at least 1.5 euros in readily liquid assets to cover it. If it falls below 1, that’s a serious cause for concern.

Quick Ratio (Acid Test)

The Quick Ratio is a more conservative version of the Current Ratio. The reasoning behind it is simple: inventory might not be so easy to sell quickly without having to sell it at a loss. For this reason, it excludes inventory from the calculation.

The formula becomes:

Quick Ratio = (Current Assets - Inventories) / Current Liabilities

This ratio tells you whether you can pay off your short-term debts using only your most liquid assets. A value greater than 1 is considered optimal, because it means you can cover all your immediate obligations without having to dip into inventory.

Practical example:A company has €200,000 in current assets (including €80,000 in inventory) and €120,000 in current liabilities.

Current Ratio: 200,000 / 120,000 = 1.67 (Positive)

Quick Ratio: (200,000–80,000) / 120,000 = 1.0 (Balanced, but worth keeping an eye on)

Strength indices for evaluating the hull structure

If liquidity is the fuel, financial strength is the ship’s structure. These ratios measure how much your company relies on external capital relative to its own resources. Excessive reliance on debt makes it more vulnerable to rising interest rates or a credit crunch.

Debt-to-Equity Ratio (Leverage)

This is the key indicator of financial strength. It compares the company’s total liabilities with its equity.

The formula is:

Leverage = Total Liabilities / Net Assets

This figure tells you how many euros of debt you have accumulated for every euro of capital invested by shareholders.

**Value < 1: **The company is financed primarily with its own funds. This indicates a very solid financial position.
Value between 1 and 2: Debt exceeds equity, but is still within a manageable range.
Value > 2: Liabilities are more than double the equity. This is a red flag indicating a high degree of dependence on third parties and significant financial risk.

A recent analysis has shown that Italian corporations have strengthened their financial structure. According to the data, the equity ratio improved, rising from 43.9% in 2022 to 45.4% in 2023 , a sign of their growing ability to self-finance. You can explore these figures in more detail in the Italian Corporate Financial Statements Observatory.

To keep formulas and definitions handy, here’s a summary table that you might find useful.

Formulas and meanings of liquidity and solvency ratios

A summary table for quickly calculating and interpreting key liquidity and capital adequacy ratios, along with their ideal benchmark values.

Always remember that these ratios, however fundamental they may be, should never be interpreted in isolation. Their true value becomes apparent when you analyze them over time and compare them to the average for your industry. Only then does financial statement analysis using ratios transform from a simple numerical exercise into a powerful tool for strategic decision-making.

Discover true efficiency with profitability ratios

A company may be financially sound and have cash on hand, but if it doesn’t generate profits, it’s like a powerful engine idling at a traffic light: it’s not going anywhere. Profitability ratios serve as the dashboard that measures that engine’s efficiency, answering the most important question of all: is the capital you’ve invested generating real value?

While liquidity and solvency ratios ensure that your business is staying afloat, profitability ratios verify that it is also capable of thriving. Financial statement analysis using profitability ratios allows you to understand not only whether you are making a profit, but more importantly, how and where you can increase your profits.

The map below illustrates this concept well: liquidity and financial strength — which we’ve already discussed — form the foundation. Only on a solid foundation can we build long-term profitability.

This image reminds us that only a financially stable company, with strong cash flow and a solid balance sheet, can truly aim for sustainable profitability.

ROE (Return on Equity): the metric preferred by investors

Return on Equity (ROE) is perhaps the metric most closely watched by shareholders and investors. Its purpose is clear and straightforward: to measure how much return is generated by every single euro of equity invested in the company.

The formula is simple:

ROE = Net Profit / Shareholders' Equity

A high ROE is a strong indicator: the company is creating wealth for its investors. For example, an ROE of 15% means that for every 100 euros invested by shareholders, your company has generated 15 euros in net profit.

But be careful. A very high ROE can sometimes hide a catch: high debt (the so-called “financial leverage”). If a company relies heavily on debt to finance its operations, its equity decreases and the ROE is artificially “inflated.” That’s why it should always be analyzed in conjunction with other ratios.

ROI (Return on Investment): a measure of operational efficiency

Return on Investment (ROI) shifts the focus from profitability for shareholders to overall management efficiency. In practice, it tells us how well your company generates profit from the total capital invested, whether it comes from shareholders or banks.

Here’s how to calculate it:

ROI = Operating Profit (EBIT) / Total Capital Invested

ROI shows you how effectively you’re using your resources (machinery, warehouses, raw materials) to generate revenue, regardless of how much you paid for them. It’s the true barometer of your core business’s performance.

Practical example:A company with a** 10%ROI and a cost of debt of 4%is creating value. It’s simple: it earns more than it costs to finance itself. If the ROI dropped to 3%**, the situation would be reversed: the company would be destroying value. For more information, check out our practical guide to net invested capital .

A healthy and stable ROI over time is one of the surest signs of efficient and well-managed business operations.

ROS (Return on Sales): the profitability of what you sell

Finally, Return on Sales (ROS) goes into even greater detail. It focuses on a company’s ability to convert revenue into profit. This metric measures the percentage of operating profit remaining from every euro of sales.

Here is the formula:

ROS = Operating Profit (EBIT) / Sales Revenue

An operating margin of 12% means that for every 100 euros of products or services sold, your company retains 12 euros in operating profit after paying all production and operating costs.

It is a key indicator for determining whether you are competitive in the market and whether your pricing strategies are effective. A declining ROS, for example, may signal that margins are being squeezed by the competition or that costs are spiraling out of control.

Ultimately, that is the purpose of financial analysis: to uncover a company’s true efficiency. But analysis for its own sake is not enough; the goal is always to improve — for example, by learning how to maximize profits in the management of a lodging facility.

Putting the pieces of the puzzle together

The true power of financial statement analysis using ratios becomes apparent when you look at these three indicators — ROE, ROI, and ROS — together, as if they were telling a story.

A high ROE may seem great, but if it’s driven by excessive debt and a low ROI , the company is like a house of cards. Risky.
An excellent ROI , combined with rising ROS , is the best indicator: the company is not only making good use of its capital but is also improving its profit margins.
If the ROI is good but the ROE is low, the cost of debt is likely too high and is “eating into” all the profitability intended for shareholders.

Using these three metrics together gives you a comprehensive view of your company’s performance. It transforms simple numbers into a detailed roadmap to guide your strategic decisions toward profitable and sustainable growth.

Optimizing Cash Flow with Inventory Turnover Ratios

If profitability is the engine of your business, cash flow is the fuel that keeps it running every day. It’s not uncommon to see companies that are profitable on paper go under due to a cash crunch. That’s why financial statement analysis using turnover ratios is essential: it shifts the focus from “how much you’re earning” to “how quickly you’re collecting payments.”

This set of metrics doesn’t measure profit, but rather how efficiently you manage day-to-day operations. In other words, they tell you how quickly you can turn your resources — such as inventory or accounts receivable — into cash. An old adage in finance goes: “Profit is an opinion; cash is a fact.” These indicators are the tools for turning this maxim into a concrete strategy.

The inventory turnover ratio

For many small and medium-sized businesses, inventory is one of the biggest investments. And idle inventory is, in effect, money tied up that isn’t working for you. The inventory turnover ratio measures exactly that: how many times a year you are able to sell and completely restock everything you have on the shelves.

The formula is simple:

Inventory Turnover Ratio = Cost of Goods Sold / Average Inventory

A high figure is a very good sign: your products are moving, and sales are strong. A low figure, on the other hand, is a red flag. It could mean you have obsolete inventory, an ineffective purchasing policy, or — worse yet—products that the market no longer wants.

This metric forces you to ask yourself some critical questions: Am I tying up too much cash in inventory? Which products are slowing down my cash flow? Is my purchasing policy aligned with actual customer demand?

For an even more concrete figure, you can calculate its twin: the average days in inventory.

Days in Stock = 365 / Inventory Turnover Ratio

This figure tells you, on average, how many days an item sits in inventory before being sold. The goal? To minimize this time, of course without running the risk of running out of stock for customers.

Collection and payment terms

There are two extremely powerful — and often underestimated — tools for managing cash flow: accounts receivable and accounts payable. Taking proactive steps on these two fronts can free up vital resources without having to turn to the bank.

Average days to collect from customers (DSO)

DSO is a metric that measures the average amount of time that elapses between when you issue an invoice and when the funds actually appear in your account. Needless to say, a low DSO is a sign of excellent financial health.

DSO = (Trade Receivables / Sales Revenue) * 365

Every single day you can shave off your collection cycle translates into immediate cash flow for your business. If your DSO is 60 days , that means you’re effectively financing your customers for two months. Reducing it to 50 days can make a huge difference in your bank account.

Corrective action: You could review your payment terms, offer small discounts to encourage early payments, or simply implement a more rigorous reminder system.

Average days to pay suppliers (DPO)

Similar to the DSO, the DPO measures the average time it takes your company to pay its suppliers.

DPO = (Trade Payables / Cost of Goods Sold) * 365

Here, the situation is reversed. A longer payment term — while still adhering to agreements and maintaining good relationships with partners — allows you to keep cash on hand longer, using it to finance operations.

Strategic action: It is worth negotiating longer payment terms with key suppliers or planning payments more strategically to optimize cash flow.

The Cash Cycle as a Final Indicator

Let’s put the pieces together. By combining these three metrics, we arrive at the Cash Conversion Cycle (CCC). This figure, expressed in days, tells you how long it takes your company to turn its investment in inventory and other resources into actual cash.

The formula summarizes the flow of money:

CCC = Days in Stock (inventory) + Days Sales Outstanding (DSO) - Days Payable Outstanding (DPO)

Let’s look at a practical example:

Your inventory turns over every 45 days.
On average, your customers pay you after 60 days.
You pay your suppliers after 30 days.

Your cash cycle will be: CCC = 45 + 60 - 30 = 75 days.

What does that mean? It means that your company has to be self-financing for 75 days. In other words, it has to cover all operating expenses (salaries, rent, utility bills) before seeing any revenue from sales. Shortening this cycle — even by just a few days — has a direct and incredibly positive impact on your available cash flow.

Financial statement analysis using turnover ratios is not merely an accounting exercise. It serves as the true control panel for optimizing working capital and ensuring that operational efficiency translates into healthy, solid, and financially sustainable growth.

Looking Beyond the Numbers: Benchmarks and Historical Analysis to Make Sense of the Data

Calculating your company’s ratios is an excellent first step. But it’s a bit like knowing how fast you’re running without knowing whether you’re in a sprint or a marathon. A number on its own, no matter how precise, lacks context. Financial statement analysis using ratios only becomes truly powerful when we start making comparisons, pitting that number against two key factors: your past performance and your competitors.

It is precisely through comparison that true value emerges. Let’s take a look at two fundamental techniques for transforming raw data into strategic insights: historical analysis and industry benchmarking. These two approaches will help you understand not only “where you stand,” but also “how you got there” and “how you compare to others.”

Historical analysis to identify trends

The first — and perhaps most important — form of comparison is the one you make with yourself. Historical analysis simply involves comparing today’s financial ratios with those of previous years. This exercise may seem trivial, but it can actually reveal trends, dynamics, and warning signs that a single-year analysis would completely overlook.

An ROI that drops from 12% to 9% over three years isn’t just a decline — it’s a red flag signaling a likely structural efficiency problem. Conversely, a current ratio that steadily improves indicates increasingly careful and sound liquidity management.

This analysis helps you answer questions that are crucial to your strategy:

Are we improving? Are our margins (ROS) growing, or is competition eroding them?
Are there any warning signs? Is our leverage increasing at an alarming rate?
Did our efforts pay off? Did that new inventory management policy actually reduce days in stock?

Comparing data over time transforms a static snapshot of a single financial statement into a dynamic snapshot of your company’s performance. It helps you understand the direction you’re heading in and make course corrections before it’s too late.

Benchmarking to measure your competitiveness

While historical analysis tells you how you’re performing compared to your past, benchmarking tells you how you’re performing compared to the rest of the world. In practice, this involves comparing your metrics to the averages in your industry.

Are you more or less profitable than your direct competitors? Are your collection times in line with market standards? Without these comparisons, you risk celebrating a 5% ROE when the industry average is 15% , or worrying about inventory that turns over four times a year when the norm for your competitors is three.

Fortunately, finding this data is no longer an impossible task. Authoritative sources such as chambers of commerce, trade associations, and platforms specializing in financial analysis provide aggregated data by sector (ATECO code) that you can use as a reference point.

Using benchmarks allows you to:

Set realistic goals: Instead of aiming for a vague “improvement,” you can aim to match or exceed the industry average.
Identify your weaknesses: If your ROS is significantly lower than average, you know exactly where you need to focus your efforts: on margins or on costs.
Assess your competitive position: An above-average ROI is a strong indicator of a solid and sustainable competitive advantage.

By combining historical analysis and benchmarking, financial ratio analysis ceases to be a dry accounting exercise. It becomes a powerful tool for competitive intelligence, capable of transforming simple numbers into a clear strategic advantage for your small business.

Automate financial statement analysis with AI-powered platforms

https://medium.com/media/30f2d3336d1ddbb90ccd6e963d898a59/href

Anyone who has spent hours on a spreadsheet analyzing financial statements knows the drill: it’s a slow, repetitive process riddled with pitfalls. All it takes is one incorrectly entered data point or a formula that doesn’t update to undo hours of work. That’s precious time you could have spent on strategy, not filling in cells.

Fortunately, there is now a smarter and faster way.

AI-powered data analytics platforms, such as ELECTE, are revolutionizing financial statement analysis using key ratios for small and medium-sized enterprises (SMEs). Say goodbye to manual work. These systems connect directly to your data sources — such as your ERP system or accounting files — and calculate dozens of key ratios in real time.

From endless spreadsheets to clear and concise dashboards

The real leap forward isn’t just about speed — it’s about clarity. Instead of getting lost in a maze of numbers and formulas, you have interactive dashboards right in front of you that show you the health of your business at a glance.

In short, these platforms allow you to:

Keep your KPIs under control at all times: Imagine being able to track the trends in ROI, ROE, leverage, and other key metrics with data that updates automatically, without you having to lift a finger.
Create reports with a single click: Do you need a financial report for a meeting with shareholders or a meeting at the bank? You can generate a customized, professional report in just a few seconds.
Spot anomalies before they become problems: AI does more than just crunch numbers. It can detect unusual trends or outliers that might escape the human eye, alerting you before the situation becomes critical.

This transforms financial statement analysis from a periodic, laborious task into a continuous monitoring process — almost like a strategic co-pilot for your company.

The point isn’t to get data entry done faster. It’s to free up your time so you can focus on what really matters: interpreting insights to make better decisions, faster.

Beyond Analysis: The Predictive Power of AI

But the real turning point comes when you stop looking only to the past. The most advanced platforms use artificial intelligence not only to assess the current situation, but also to anticipate what might happen tomorrow.

An AI system can analyze your historical cash flow data and your customers’ payment habits. The result? An accurate forecast of any potential cash flow issues in the coming months. Having this information allows you to take proactive steps, rather than reacting once the problem has already arisen.

Automation, therefore, is not just a matter of efficiency. It is a genuine strategic advantage. It provides SMEs with analytical tools that, until recently, were a luxury reserved only for large corporations.

If you want to better understand how these systems work and how they can help boost your growth, you can read our in-depth article on Business Intelligence software.

Key Takeaways: Your Next Steps

We’ve seen how financial statement analysis using ratios can turn your accounting data into a strategic compass. Here are 4 key steps to start using this information right away to grow your business.

Start with liquidity and solvency ratios: Before you think about moving forward, make sure your “ship” is solid and has enough fuel. Calculate the current ratio, quick ratio, and leverage to get a clear picture of your financial stability.
Measure your efficiency with ROI and ROS: Find out if your operations are creating value. A solid ROI and a rising ROS are the best indicators that your business strategy is working.
Optimize your cash cycle: Don’t underestimate the power of turnover ratios. Shorter customer collection times or inventory holding periods — even by just a few days — can free up valuable cash to fund growth.
Consider the context (both historical and industry-specific): An index, on its own, means nothing. Compare your results with those from previous years to identify trends, and with industry averages to assess your competitiveness.

Turn your data into growth

Financial statement analysis using ratios isn’t just a theoretical exercise — it’s the most powerful tool you have for making informed decisions and steering your small business toward a successful future. Turning raw data into clear insights allows you to anticipate problems, seize opportunities, and optimize your resources with precision.

Today, thanks to AI-powered platforms like ELECTE, you no longer need to be a financial expert to reap these benefits. You can automate calculations, view your KPIs on intuitive dashboards, and free up valuable time to focus on strategy. It’s time to stop viewing your financial statements as a chore and start seeing them as your best ally for growth.

Are you ready to turn numbers into strategic decisions, without the hassle of spreadsheets? Find out how ELECTE help you grow and start making smarter decisions today.

Originally published at https://www.electe.net .

Originally published on Medium

Data Lake vs. Data Warehouse: A Guide for SMEs 2026

Fabio Lauria — Thu, 18 Jun 2026 11:19:59 +0000

You’ve probably found yourself in this situation before: you have an ERP system, maybe a CRM, a few Excel files being passed around via email, and then someone tells you that to “do serious analytics,” you have to choose between a data lake and a data warehouse. At that point, the conversation immediately shifts to technology, but the real issue is something else entirely. Do you really need a new data architecture, or do you simply need to make the data you already have readable and useful?

For an SME, this distinction matters more than just the terminology. Making the wrong choice doesn’t just create technical complexity. It leads to lengthy projects, reliance on consultants, late reports, and investments that struggle to translate into better decisions. Doing nothing, however, leaves the company flying by the seat of its pants.

The point isn’t to learn vendor jargon. The point is to figure out which solution is the right fit for your business, your budget, and the skills you actually have in-house. Here’s a practical guide to understanding the data lake vs. data warehouse debate from the perspective of someone who needs to balance costs, accessibility, and operational return.

Introduction: The Dilemma of Choosing Between a Data Lake and a Data Warehouse

The pressure to “do something with the data” is very real today. The volume of data is growing, data sources are multiplying, and managers are demanding faster forecasts, dashboards, and alerts. Meanwhile, new terms are popping up that seem to force you into making an immediate architectural decision.

For many SMEs, however, this is precisely where the trap lies. They convince you that the first step is to choose between two infrastructure models, when often the real issue is much more practical: scattered data, inconsistent formats, manual reporting, and no one who has the time to bring order to it all.

There are other questions you should be asking. Do you really have an architectural problem? Or is it a data accessibility issue? If you choose the wrong solution, you risk funding a technical project instead of improving your control over the business. If you don’t choose anything, you’ll keep making decisions based on incomplete information.

Someone running an SME doesn’t need a college lecture. They need a simple guideline to figure out what’s necessary, what isn’t, and where the real costs lie.

Data Lake vs. Data Warehouse: The Difference Explained Simply

The most useful distinction can be understood with the help of two very practical examples.

A data warehouse is like a well-organized library. Every book arrives already cataloged, classified, and placed on the correct shelf. When you look for information, you find it quickly because the order has been established in advance. A data lake , on the other hand, is like a large warehouse where boxes of all kinds arrive. You put in organized files, logs, PDFs, images, exports from the management system, and web data. You apply the order later, when you need to analyze them.

The key difference between schema-on-write and schema-on-read

This is the only technical detail that’s really worth mentioning.

Schema-on-write means that the data is cleaned, structured, and organized before it is loaded.
Schema-on-read means that the data is stored in its native format and interpreted when someone uses it.

This distinction also reflects their historical origins. The data warehouse was originally designed for business analytics on data that had already been cleaned and structured, while the data lake came later to store raw data in heterogeneous formats. For this reason, the warehouse is better suited for reporting and KPIs, while the lake is more flexible for exploration and machine learning, as explained in this analysis of the differences between data warehouses and data lakes.

A data warehouse performs well with known queries. A data lake is useful when you know the data might contain value, but you don’t yet know in what form.

What does this mean for an entrepreneur or manager?

If your goal is to track sales, profit margins, orders, inventory, delays, sales performance, and monthly comparisons, the warehouse is conceptually better suited to your needs. It provides a reliable foundation for standard reports, consistent SQL queries, and repeatable results.

If, on the other hand, you work with highly diverse data types — such as application logs, PDFs, emails, text files, images, or machine data streams — the data lake offers greater flexibility. IT teams can centralize heterogeneous data sources, while those responsible for reporting continue to prefer structured environments for fast and consistent queries. This approach also ties into the broader concept of data-driven business decisions, which require accessible data even more than sophisticated technologies.

The point that is often overlooked

In the data lake vs. data warehouse debate, many people confuse flexibility with immediate utility.

A data lake can store almost anything. But storing data doesn’t mean it’s immediately ready for analysis. A data warehouse is less flexible when it comes to data ingestion, but more useful when you need quick, standardized answers. For an SME, this difference matters more than just in theory. Because the issue isn’t about storing more data. It’s about making better decisions.

Architecture Comparison: Structure, Data, and Processes

Two companies can start with the same data and end up with very different results. The difference often lies not in the amount of data collected, but in how they organize it, prepare it, and make it accessible to decision-makers.

Data Warehouse vs. Data Lake: A Quick Comparison

ETL and ELT are transforming day-to-day work

In a data warehouse , the standard process is ETL: extract the data, transform it, and then load it. It requires more work upfront, but reduces friction down the line. Anyone looking at a dashboard will find consistent fields, stable definitions, and KPIs whose meaning doesn’t vary from one department to another.

In a data lake , the workflow is often ELT: extract, load, and transform only later, if necessary. This approach offers greater technical flexibility, but it defers part of the work. For a small or medium-sized business, deferring work often means letting tasks pile up, which then falls on the team at the worst possible moment — namely, when a quick response is needed.

Rule of thumb:If multiple people need to review the same document and make operational decisions, establishing a clear structure before uploading it helps reduce errors, unnecessary discussions, and wasted time.

Performance and Predictability

From an operational standpoint, a data warehouse is designed for repetitive queries, frequent reports, and dashboards used on a daily basis. A data lake handles large volumes and diverse formats well, but response times and ease of use depend heavily on how the data has been cataloged, prepared, and governed. A technical comparison published by CloudOptimo sums this up well: the warehouse focuses on predictability, while the lake focuses on flexibility.

For an SME, this is no mere academic exercise. When the sales manager opens the morning report, they want consistent numbers and quick results. If, on the other hand, the technical team needs to analyze files, logs, or diverse documents, they may be willing to accept some latency in exchange for a broader range of data.

Where architecture really makes a difference

The practical difference isn’t just technical. It’s about who can use the data without having to ask for help every time.

A well-designed data warehouse brings data closer to the business. A data lake, on its own, more often brings it closer to the technical team. That’s why many SMEs discover an uncomfortable truth too late: the real choice isn’t between two technologies, but between a system that makes data accessible and one that simply stores it without turning it into better decisions.

Anyone evaluating these options as part of an IT modernization project should also consider the operational model, not just the repository. Cloud solutions for SMBs help clarify this very point: where the infrastructure ends and where costs, required skills, and day-to-day responsibilities begin.

The Hidden Cost of Flexibility

Data lakes are often touted as the most cost-effective option because they store raw data and reduce the initial workload. This is only partly true. Without a catalog, access rules, consistent naming conventions, and basic quality controls, the initial savings turn into wasted time spent searching for files, reconstructing definitions, and verifying which data is reliable.

For this reason, in many SMEs, the right comparison isn’t “data lake versus data warehouse” in the abstract. The useful question is a different one: is it really necessary to build one of these comprehensive architectures, or is it better to start with a lighter-weight solution that delivers quick insights without immediately taking on all the complexity?

The Truth About Costs and Complexity for SMEs

For an SME, the most costly mistake often stems from a poorly framed question: “Is a data lake or a data warehouse cheaper?”. In a company, the real cost becomes apparent later. It becomes clear when data systems aren’t interoperable, reports break every time the ERP system is updated, and every request goes through consultants or developers instead of the team that needs to make the decision.

Where the real costs come from

Storage is less of a burden than it seems. What really takes up the most effort are the tasks that make data reliable and usable: modeling, integrations, permissions, quality assurance, monitoring, error correction, and user support.

A data warehouse requires some initial effort. You need to define metrics, build pipelines, align data sources, and keep everything organized when ERP systems, CRMs, or business rules change. In return, management gets more consistent data, and reporting tends to become more predictable.

A data lake often starts out with a more modest promise. You load in different types of data and defer some of the structural decisions. The problem is that deferring these decisions doesn’t eliminate the work. It just pushes it further down the line, where it manifests itself in the form of cataloging, security, computational costs, data duplication, inconsistent versions, and constant verification of which data is truly reliable.

The risk for an SME is having to pay twice: first to collect the data, and then to finally make it readable.

The point that many small and medium-sized businesses realize too late

The real complexity isn’t technical. It’s operational.

If every new report requires manual intervention, if the controller and the sales representative use different definitions for the same metric, or if the business owner has to wait days to get a reliable figure, the data project is already eating into profits — even if the infrastructure looks modern on paper.

That’s why it’s important to evaluate the management model as well, not just the architecture. Cloud solutions for SMBs help clarify this distinction: what you’re actually buying, how much maintenance remains in-house, and how much you rely on specialized expertise each month.

The Italian market favors understated designs

In the Italian market, investors in analytics are looking for tangible results: a reduction in manual work, faster deal closings, and better control over sales, margins, inventory, and cash flow. They don’t want a sophisticated platform that remains in the hands of only a few.

This changes the criteria for making a choice. An SME shouldn’t ask itself which architecture is more appealing or more flexible in theory. It should ask itself how long it takes to develop reliable dashboards, how many people are needed to maintain them, and how quickly the project delivers value.

Two very concrete examples

In retail , hidden costs quickly come to light. If sales, returns, promotions, and inventory data come from different systems, all it takes is a single misinterpretation of “margin” or “net sales” to undermine confidence in the reports. At that point, the problem isn’t the database you chose. It’s that the owner goes back to making decisions in Excel.

In finance , the cost of error is even more apparent. Reporting, reconciliation, management control, and variance analysis all require consistent and traceable data. If every review sparks debates about where a figure came from, the project loses its ROI before it’s even finished.

For this reason, in practice, many SMEs do not need to build a data lake or a full-fledged data warehouse from scratch. They need a system that is more lightweight, manageable, and decision-oriented.

Hidden cost number one: reliance on consultants or key personnel who are difficult to replace.
Hidden cost number two: management time consumed by a project that is supposed to simplify things.
Hidden cost number three: reports that are rarely used because accessing the data remains too technical.

If you can’t maintain data quality, access rules, and shared definitions over time, the problem isn’t whether to choose a data lake or a data warehouse. The problem is that you’ve bought into complexity before having a use case that justifies it.

Practical Use Cases: When to Choose One or the Other

The right question isn’t which architecture is “best” overall. The question is: what problem do you need to solve tomorrow morning?

When a Data Warehouse Makes Sense

In retail, the warehouse runs smoothly when you consistently have to answer the same operational questions:

Sales by period and category: ideal for daily or weekly dashboards.
Inventory control: useful when you want reliable and comparable inventory data.
Promotion analysis: effective when comparing campaigns using standard metrics over time.
Executive reporting: perfect for meetings where everyone needs to review the same figures.

The same applies to the finance sector. Whether you need to consolidate structured data, generate periodic reports, analyze portfolios, or track economic trends using consistent criteria, a data warehouse remains the natural choice.

When a Data Lake Can Really Be Useful

A data lake makes sense when your company collects a wide variety of data and you either don’t want to or can’t define everything in advance.

A realistic example is that of an energy company that combines:

time-series data from smart meters,
PDF reports from distributors,
emails and support tickets,
external data such as weather or other diverse feeds.

In this kind of situation, a traditional warehouse forces you to first map out relationships between data sources that you may not yet fully understand. A data lake allows you to centralize everything and apply structure only when needed for a specific analysis. This is the kind of scenario where the flexibility of a data lake truly adds value.

A data lake isn’t just a “more modern” option. It only makes sense when the variety of data justifies the complexity you’re bringing into your organization.

The most common scenario in SMEs

Most SMEs don’t operate in that kind of environment. They primarily deal with data from ERP, CRM, e-commerce, accounting systems, CSV exports, and Excel. In these cases, the challenge isn’t managing video files, application logs, or unstructured text on a large scale. The challenge is ensuring that the data is clean, consistent, and understandable to non-technical users.

Let’s be clear about this: often, you don’t need either a data lake or a traditional data warehouse.

What is needed instead is:

centralize the truly relevant sources,
standardize names, fields, and definitions,
make reports accessible to decision-makers,
introduce forecasts and alerts where they are operationally useful.

What about the lake house?

The lakehouse aims to bridge the two worlds. It promises the flexibility of a lake and some of the features of a warehouse within the same environment. It’s an interesting approach, especially for companies with mixed workloads spanning BI, AI, and data science.

For an SME, however, the question remains the same: do you really have a problem that warrants all of this? If your goal is simply to gain a better understanding of sales, margins, cash flow, or forecasts, a sophisticated hybrid solution may still be out of proportion to the expected value.

The Hybrid Evolution: What Is a Data Lakehouse, and Do You Really Need One?

The data lakehouse was created to overcome the rigid separation between data lakes and data warehouses. The idea is simple: retain the flexibility of a large, open storage system while adding structure, performance, and analytical capabilities more akin to those of a data warehouse. Technologies such as Databricks and Delta Lake are prime examples of this approach.

In theory, it’s very appealing. You use the same database for BI, advanced analytics, and machine learning, avoiding the duplication of too much data across different systems. For large organizations or mature data teams, it’s a logical solution to an ecosystem that has grown increasingly complex over time.

The point of interest to an SME

In academic benchmarks, the data lakehouse architecture is evaluated using metrics such as throughput, latency, and metadata overhead. This demonstrates that the comparison with the data warehouse is not only functional but also performance-based, particularly in scenarios where even small differences in performance have a significant impact, as highlighted in this academic presentation on lakehouse benchmarks.

In business terms: The Lakehouse addresses challenges faced by organizations that have already reached a certain level of scale, complexity, and specialization.

Five questions to ask yourself before evaluating it

Do you have a wide variety of data sources? If you work almost exclusively with ERP systems, CRMs, and structured spreadsheets, probably not.
Do you have a technical team capable of managing it? Without in-house oversight, the promise remains merely theoretical.
Do you need both reliable business intelligence and advanced data exploration? Not all small and medium-sized businesses have this dual need.
Are you facing a real architectural limitation? Or are you just dealing with slow reports and disorganized data?
Does the project improve a specific decision? If you don’t know which decision it will improve, you’re just adding complexity.

If you didn’t really need either a data lake or a data warehouse, you probably don’t need a system that combines both.

The Pragmatic Solution: Gaining Insights Without Building an Infrastructure

For most SMEs, the most useful question isn’t “Which architecture should I choose?”, but “How can I obtain reliable analytics without turning the data project into a never-ending construction site?”.

This is the third key point that’s often overlooked in many data lake vs. data warehouse comparisons. Don’t build a new proprietary infrastructure. Instead, add a layer of analytics on top of the systems you already use, shifting the technical complexity outside the company’s operational scope.

What Really Works in an SME

In practice, the best approach is this:

Start with existing systems: ERP, CRM, accounting, e-commerce, and exported files.
Standardize key data: customers, products, orders, time periods, and cost centers.
Automate recurring reporting: this way, the team stops chasing after Excel.
Implement forecasts and alerts only where they have an impact: sales, inventory, risk, and variances.
Giving managers access without technical jargon: if only a consultant can interpret the data, the project is vulnerable.

When Accessibility Trumps Architecture

I’ve seen more than one small or medium-sized business invest months in a traditional warehouse system and then hardly use it at all. Not because it was poorly built. It was because no one in the company knew how to query it on their own. The bottleneck wasn’t the database. It was accessibility.

This is a point that is often overlooked. An elegant architecture that always requires a technical intermediary reduces the practical value of the data. A simpler solution — one that management can understand — often leads to better decisions more quickly.

A helpful checklist before investing

Clarify your goal: Do you want to reduce manual work, gain more control, improve forecasting, or ensure compliance?
Count your actual sources — not the theoretical ones. The ones you actually use every week.
Determine who will read the reports: management, finance, operations, and sales.
Assess the technical dependency: how many tasks require a data engineer or a consultant.
Choose practical tools: in many cases, usability and speed matter more than theoretical power.

That’s why many companies get more value from well-designed business intelligence software for SMEs than from an oversized infrastructure solution. Their goal isn’t to own a data warehouse; it’s to understand their business better and sooner.

The right infrastructure is the one your team can actually use, maintain, and turn into decisions. Not the one that looks impressive on a technical slide.

Conclusion: Focus on Value, Not Architecture

The data lake vs. data warehouse debate is useful, but for an SME, it often starts with the wrong question. Before choosing an architecture, you need to figure out whether you really have a problem with data scale and variety, or a much more common issue: scattered data, manual reporting, and poor accessibility.

The data warehouse remains the best choice when reliable reporting, consistent KPIs, and predictable performance are required. The data lake makes sense when the variety of sources justifies greater flexibility and complexity. The lakehouse is an interesting evolution, but it is rarely the right first step for an organization that prioritizes operational control and ROI above all else.

The smartest choice isn’t necessarily the most advanced technology. It’s the one that’s tailored to the actual problem, the skills you have on hand, and how quickly you want to turn data into decisions.

If you want to turn your business data into reports, forecasts, and actionable insights without building a complex infrastructure, check out ELECTE, an AI-powered data analytics platform for SMEs. You can start with the data you already have, reduce manual work, and make analytics accessible to your team with a much more streamlined approach.

Originally published at https://www.electe.net .

Originally published on Medium