DEV Community: Memona

Who Builds AI Tools and IoT Systems? A Developer's Perspective

Memona — Thu, 22 Oct 2026 16:58:31 +0000

Imagine constructing a system that monitors industrial equipment, collects sensor readings, detects unusual behavior and alerts a maintenance team. Writing the application is only one part of the work. The complete system may also require hardware integration, reliable data pipelines, networking, and machine learning.

An important question is then, do software companies build AI tools and IoT systems, or do these projects require specialized engineering teams?

The answer is not a clear-cut, but many software companies indeed develop these solutions. A complex system often requires expertise from several areas.

Who builds AI tools? A closer look

An AI development often extends beyond merely plugging an application together with a model. Depending on the use case, a team may need

software developers to build applications, APIs, and user interfaces,
machine learning engineers to integrated, evaluate, deploy, and maintain the models,
data engineers to build reliable pipeline for delivering the data,
and cloud and infrastructure engineers to manage the deployment, scaling, and monitoring.

A document summarization application may for example solely use an existing AI model through an API, whereas a predictive maintenance system may require historical equipment data, feature engineering, model evaluation, and integration with operational software.

Building a model from scratch is sometimes not beneficial, and for many projects the main engineering challenge consists of utilizing an existing model into a reliable application.

Who builds IoT systems? A closer look

The Internet of Things (IoT) concerns interconnected physical devices, allowing them to collect and exchange information

An IoT project may involve

embedded developers who write firmware for microcontrollers and connected devices,
IoT engineers who configure device connectivity and communication protocols,
hardware engineers who construct sensors, circuits, and other electronic components,
backend developers that receive and process the data from devices,
and security engineers who safeguard devices, networks, and information.

The IoT technology stack is highly varied depending on the project, and can span from the MQTT protocol for messaging between devices and services, HTTP-based APIs for interacting with an application backend, databases for storing device telemetry, edge or cloud-based processing of the information.

The choices made in the technology stack depend on latency requirements, connectivity, power constraints, security, and operating costs.

How do AI and IoT work together? A closer look

When AI is combined with IoT, the result is often referred to as AIoT, or Artificial Intelligence of Things. For example, an industrial monitoring system may have the following elements

1. Sensor data collection — sensors that keep track of equipment conditions,

2. Information communication — a device or gateway that forwards the information to a processing system,

3. Data processing — validate and enrich the data, and store the valuable readings in a database,

4. AI inference — use a model to identify unusual patterns,

5. Operational response — the application acts upon the recommendations from the model, for example, send an maintenance alert.

Each of these steps introduces their own engineering challenge. The sensor data may be noisy, the communications interrupted, and an AI model produce false positives. A production system therefore requires data validation, monitoring, error handling, and an evaluation system for the model. Predictions should also be weighted according to their impact and risk before initiating an action request — in industrial settings such manual review may be prudent before scheduling a maintenance.

Should everything always run in the cloud? A closer look

Depending on requirements, a distributed processing architecture may be preferable, especially when devices need to respond to events with low latency a constraint the hardware may impose.

Edge computing keeps data processing close to the devices, and can be utilized to reduce communication overhead, or avoid bandwidth-intensive raw sensor data to be sent to another processing system.

Cloud-based processing offers powerful computational resources and storage, but have communication and security requirements of their own.

A hybrid architecture allows a system to both perform computations on the device or at the edge, while also storing data in the cloud for long-term storage, or more involved processing later. An edge gateway may for example filter sensor readings, whereas the cloud keeps a copy of the data for later big-data analysis.

The best architecture is determined by the requirements of the system at hand — not necessarily running everything in the cloud is often a sensible decision.

Do businesses need separate AI and IoT teams? A closer look

It depends on what is being built, but a small AI application may only require some software and AI expertise, whereas a connected device product may benefit from embedded engineering, hardware integration, and backend developers. More involved industrial platforms may require all of the above, including cybersecurity and systems engineering capabilities. Some companies specialize in isolated technical disciplines, whereas others seek to combine several. An example is (https://apertureventurestudio.com/), that focus on developing technology ventures at the intersection of AI and IoT for industrial applications.

The important consideration is understanding the problem, the technical requirements, and how the system will operate to fulfill an objective.

What should developers consider before starting an AIoT project? A closer look

Before choosing a framework, model, or hardware platform it is often prudent to outline the problem, and system requirements.

The following points are useful to assess before developing any technical solution:

What information must the system collect?
What sensors or devices can provide this information?
Is AI necessary, or can conventional software solve the problem?
Where should data be processed: on the device, edge, or cloud?
How will the system handle missing data and network interruptions?
How will security, monitoring, updates, maintenance, and scalability work?
How will the team evaluate whether the solution has value?

These considerations help avoid overly ambitious technical solutions, and provide clarity in design decisions.

Final thoughts

AI tools and IoT systems are built by software developers, AI specialists, embedded engineers, hardware professionals, and infrastructure teams. Software offers the applications and integrations, IoT links digital systems to the physical world, and AI provides data interpretation when intelligent analysis is beneficial. The challenge is that all these elements must work together reliably. For aspiring developers venturing into these fields, understanding the complete system is arguably more important than knowing a particular programming language, framework, or protocol.

Discussion: When designing an industrial AIoT system, do you prioritize data collection, processing, or AI inference first? What trade offs would you make?

Inventory Management Software: How do You Pick the Right Solution for Your Business Essay

Memona — Thu, 22 Oct 2026 15:48:49 +0000

Your inventory management tool indicates that a product is in stock, but an employee has just informed out that the shelves are, in fact, empty. This is a simple example of why inventory management software is often more than an inventory counter.

Organizations must invest in inventory management solutions that can create accurate histories regarding the stocks purchase, storage, and sale.

It is also advisable to consult the business’s needs before looking for inventory systems to identify if there are additional advantages of using particular programs over others.

Features of Inventory Management Systems

Inventory tools typically focus on tracking inventories as they move from purchase to storage and finally sale.

Some common inventory software features include

• tracking, counting, and recording of stocks,

• purchase order processing,

• barcode scanning,

• alerts for low stock,

• multi-warehouse inventory management,

• inventory movement reports,

• and integrations.

There are considerable differences in features even among online inventory management systems. It is worthwhile to ensure that the selected software product accommodates the organization’s necessities.

Types of Inventory Systems Used in Businesses

Inventory management systems typically differ according to the problems they aim to solve.

1. Inventory Systems

These problem solving tools focus on tracking inventory levels, items, and related details regarding purchase, stock, and sales.

Inventory systems typically generate better records and forecasts regarding replenishment needs.

2. Warehouse Management System (WMS)

This software is generally more complex than inventory management systems.

A warehouse management system typically assumes the logistics aspect, including storage, transport ,put away, pick, pack, and ship.

For instance, firms that find themselves in need of advanced warehouse management tools usually operate more than several products in different storage facilities.

3. Enterprise Resource Planning (ERP)

Enterprise resource planning is generally similar to inventory management solutions, though the former typically spans the whole supply chain, including finance, functions in the broader network, operations, and purchasing.

ERP programming is necessary when the organization needs to track finances and requisitions in one location rather than several distinct applications.

4. Barcode and Radio Frequency Identification Devices (RFID)

Barcode scanners generally involve using a reading device to look at a scannable label. Meanwhile, radio-frequency identification usually utilizes an RFID reader to access information from RFID tags. Both devices are necessary for barcode tracking or RFID inventory tools, and the selection usually depends on particular environment, scanning and reading necessities, product attributes, infrastructure, and budget constraints.

Some firms in inventory management software usually choose from applications such as Zoho Inventory, Odoo, SAP, NetSuite, Oracle, QuickBooks, or similar options. The chosen solutions usually differ both in scope and budget, and it is necessary to analyze these tools in depth before finalizing the decision.

How to Pick the Right Inventory Management System Essay

There are numerous ways to compare inventory programs, but some are considered more efficient because they entail working backwards. Instead of examining features, the organization should initially assess the issues the inventory management software is supposed to address.

The firm should, first of all, create as comprehensive description of the current process as possible. In this stage, the emphasis should be on tracking how various products are purchased, stored, counted, and received. After that, organizations should generate a list of requirements, including must-have features. Such tools might include multiple warehouses or barcode inventory tools, depending on the organization’s needs. The ability to process purchase orders, reports, and integrations should also be taken into consideration. Finally, the software application should be able to address a few key problems the company experiences, ranging from stock inaccuracies to shipping orders difficulties.

What Inventory Features does a Business Require?

An organization should start assessing the application inventory system possibilities while taking into account existing integration tools and budget constraints. The inventory management tool should also include a set of reports and analyses the company will likely utilize frequently. Users should examine the features of a final shortlist in-depth to ensure that they are aligned with needs. It might also be useful to consider programs that provide enhanced customer interactions, streamline existing procedures, eliminate errors, simplify operations, and improve records. Each company contemplating inventory management software options should realize that the tool will be useless unless it helps operations simplify and become more accurate.

Companies should also estimate current and future scalability demands to ensure that they pick the right inventory management system. After that, it is time to take a closer look at the software application inventory systems and finalize key requirements before starting the shortlist. There are certain financial features to examine while picking the inventory management software. Most importantly, it is advisable to estimate the full lifecycle cost. A cheap inventory software might hide significant expenses, including implementation, training, integrations, and additional costs.

An Illustrative Example of Inventory Software Analysis

Let’s suppose that there is a product discrepancy, although the tool indicates that there are 120 products in inventory. However, in reality, the product shelves only have 112 items. It is not possible to simply adjust the records to reflect the 120 items in stock. One should attempt to identify what accounts for the discrepancy, ranging from an inaccurate count to unauthorized access or theft. Perhaps the tool tracks inventory as soon as products arrive at the warehouse, but the discrepancy suggests that there is an error somewhere. After identifying the source of the problem, it becomes a matter of correcting it accordingly. Inventory systems are excellent at generating records, but human input usually facilitates reconciliation processes. Cycle counts are also an efficient way to minimize inventory discrepancies, although this depends on the staff members being trained to identify errors.

All in all, inventory tools typically reflect what the business buys or sells in numbers, but accuracy is crucial to ensuring that the staff members do not authorize new stock purchases or attempt to find products that do not exist. There is no specific inventory tool or software that fits all organizations’ needs, which makes it necessary to contemplate the options provided by different software. This tool simplifies the reconciliation process by eliminating complicated spreadsheets and streamlines the inventory management process, ensuring that everyone has access to up-to-date figures. Inventory management software ensures that the staff members can work with accurate numbers, which is a fundamental image of financial records.

Ultimately, inventory management tools provide companies with the reassurance that their book inventory reflects the actual stock levels. There are numerous free inventory tools and software, but an organization should ensure that they can utilize such solutions efficiently before selecting them. There are always certain challenges in any inventory management process, which is why some problems to think about include inventory accuracy, multi warehouse management, restocking procedures, integration tools, and similar issues. Which inventory challenge is the largest for a particular business?

Why Building Intelligent Factories Starts With Your Data

Memona — Wed, 21 Oct 2026 20:03:38 +0000

Imagine an intelligent factory. As a production order comes up, the system checks that the needed parts are in inventory. It confirms, then, the part is not there. Inventory shows it as available; however, the warehouse lacks the current item.

An AI driven system may not help in this case. You need to establish good data practices before trying to implement AI solutions

The problem is indicative of the issues that arise when putting together an intelligent factory.

This article will discuss these difficulties and how to deal with them

1. Why manufacturing data is fragmented

Modern factories often use a mix of technologies and approaches in managing production:

Enterprise Resource Planning (ERP): Tracks business processes, procurement, and inventory management functions at the enterprise level

Manufacturing Execution System (MES): Focuses on production planning and execution, material management, and quality control

SCADA: Supervisory Control and Data Acquisition systems monitor and control industrial equipment

Industrial internet of things (IoT) sensors: Enable operational visibility and control

Radio Frequency Identification (RFID) tags and readers, location tracking devices: Provide information concerning the location and status of tagged equipment, materials, and people

Each of these technologies serves a particular purpose in production management. They often do not interoperate seamlessly within a single framework. This lack of uniformity has consequences. For instance, an ERP system may indicate that a certain part has come into inventory, but it may take hours or days for an MES to recognize it as available for production.

This problem becomes an issue when trying to establish a clear picture of the shop floor status. Each of these technologies is likely providing valuable data, but data quality is difficult to maintain if sources do not share a common framework.

2. Why data quality takes priority over AI

Artificial intelligence applications depend on data collected from operational technologies. When those data are incomplete or wrong, the results are misleading

Take predictive maintenance models, for instance. They require accurate data concerning the timing and nature of changes in equipment condition. These models may fail if timestamps within a data source are not consistent. As such, predictive maintenance does little to help with equipment failures when timestamps are uncertain.

Manufacturers may consider taking one of the following three actions to improve data quality.

Consistent identifiers

Designate a set of identifiers that apply consistently throughout the production lifecycle. Consistency helps align the information from different systems and determines if a particular set of data corresponds to the same item in two different databases.

Reliable data exchange

Standardize the manner in which data are shared between different systems. Specify procedures concerning the frequency with which data updates occur and how the integrity of data exchange is maintained in the event of an interruption. Depending on requirements, systems may communicate via application programming interfaces (API), industrial field bus technologies, events, or middleware applications.

Data validation

Develop procedures that validate the integrity of data. Where appropriate, define rules and procedures for addressing missing data, events that occurred inconsistently, unexpected data values, and other irregularities. In essence, it is critical to ensure that no data make it into the AI models that do not meet specific criteria.

Such actions help ensure that data integrity is consistent and that there is a baseline of quality data with the required degree of completeness and consistency.

3. How to establish real-time operational visibility

Location tracking technologies can help manufacturers obtain an up-to-date view of production operations

Systems such as radio frequency identification (RFID), ultra-wideband (UWB), and others support real-time or near–real-time tracking of containers, tools, equipment, and people. The information helps manufacturers understand the location and status of items and optimize production processes. However, there are limitations to such technologies.

RFID information, for instance, is of limited use unless it can be correlated with production orders. As such, one may consider how this technology can supplement other data to improve overall insight. The combination of RFID and production order information, for instance, informs the materials manager when a particular tag has arrived at the production line and if its contents match a given production order.

The extent to which location technologies contribute depends on the application and environment. Issues such as the need for tagging, interference with other devices, update speed, and tagging power may limit the value of a given technology. As such, manufacturers considering location tracking technologies should address the following questions:

In which context is location tracking applicable?

What level of precision and how frequently does the technology need to update the position of a given item?

How will obstacles and interference affect the performance of a given technology?

What is the technology’s power consumption and lifecycle?

How much does the technology cost to implement and operate?

These considerations help ensure that the chosen technology is appropriate for the intended application.

4. A phased approach to implementing AI solutions

A phased approach may help manufacturers adopt AI applications with limited risks and costs.

The process generally involves the following steps:

Define the problem.

Focus on a specific issue that the proposed solution will address.

Avoid identifying an AI-related solution before determining what the problem is.

Determine the data landscape

Identify what data are needed to address the identified issue and which systems house this data.

At times, this phase reveals roadblocks that necessitate additional preparation before proceeding.

Establish a baseline.

Depending on the identified problem, this step requires establishing a threshold or a point of reference against which improvements will be measured. For instance, if one focuses on reducing the search time involved in finding materials, one would establish the rate at which workers currently search for materials.

This phase is important because it helps establish concrete evidence of improvements rather than assumptions.

Test the solution in a controlled environment

This phase generally involves identifying a small application area or procedure within operations and subjecting it to the proposed solution.

It is important to verify that the data landscape supports the projected results and that the solution delivers on expectations under natural experimental conditions. The target audiences should also test and use the technology under ordinary working conditions.

Scale based on results, costs, and other considerations

Compare the results, implementation costs, and other relevant factors against established expectations. Apply the changes fully if the results are positive and sustainable within the given context. Adopt a wait-and-see attitude if the results are inconclusive.

5. Determining if the solution works

The proposed solutions should ideally contribute towards achieving the initially identified goals.

If an organization focuses on reducing the time it takes to identify materials, for instance, it might measure the average time it takes to manually locate parts or supplies after an order comes in. The organization might also count the proportion of time inventory information is inaccurate.

In either case, the manufacturing organization should measure the extent to which the application has improved productivity in the targeted area. It should further consider other relevant factors, such as the rate of change in production levels to ensure that it measures progress toward overall production goals.

Putting it All Together

Smart manufacturing entails much more than simply adding more technology to the production floor. Even as manufacturers invest in AI applications, they need to identify problems that AI can meaningfully address and ensure that appropriate data exist to support AI applications.

Solving some production-related issues may be as simple as applying good materials management practices. Addressing the issues that affect production requires an understanding of how different data sources relate. For instance, the ability to locate a particular production container can be helpful but insufficient without correlating this information to production orders.

For engineers and manufacturing technology specialists—what has been the biggest challenge you have faced while bringing together these disparate systems and technologies; was it the data quality, technology interoperability, ensuring real-time information exchange, or training and change management on the shop floor?

Building Industrial AIoT: From Sensors to Actions

Memona — Wed, 21 Oct 2026 14:04:57 +0000

Say we have a motor in an industrial facility that runs and runs and runs until one evening we notice that it runs hotter than usual, vibrating differently than we expect.

A simple rule based monitoring system could alert us when any one metric exceeds an acceptable range; however, what if our changes of interest are more complex, take time to develop, or require contextual information to decide if an event has occurred in order to alert human operators?

These are some of the problems at which the combination of artificial intelligence (A.I) and the internet of things (IoT) can be productively applied.

IoT can help collect the information that AI needs to find patterns, while also defining what constitutes an actionable insight.

Let's explore how we might build monitoring and alerting functionality around such a system, beginning with basic rules and moving toward AI-assisted operations.

1. Basic Architecture Layers

We can think about an industrial monitoring system as having four broad layers:

Sensors gather data about the monitored system, such as temperature, pressure, humidity, equipment status, and vibration
Connectivity: Each monitored device communicates its current sensor values through appropriate means to a gateway, local service, or cloud application
Preprocessing: Our application layer validates, timestamps, and stores sensor values for later analysis
Decision making: Rules or an AI model analyze sensor values and take appropriate action, such as raising an alert

Ideally, a system like this is architected to allow for separate development of these different components: the data collection and preprocessing layers should not make assumptions about which particular AI model they are supporting, while the alerting rules should ideally also be comprehensible and modifiable by domain experts.

2. Begin with Simple Rules

AI assisted monitoring is not a given for a business critical system.

We can productively begin by asking: What monitoring rules would be most useful, given the data we intend to collect?

Let's begin with a very simple example involving temperature and vibration:

def check_motor_status(temperature, vibration):

if temperature is None or vibration is None:

return "Invalid or incomplete sensor data"

if temperature > 80 or vibration > 7:

return " Inspection recommended"

return "No threshold exceeded"

Again, we must keep in mind that the values shown here are examples.

In practice we would determine the relevant metrics' values, units, ranges, and acceptable operating windows by equipment specifications sheet, the type and accuracy of sensors being used, and applicable engineering standards and practices.

The above example shows us how simple alerts based on monitored data can be constructed, but it does not account for outlying readings, changing baseline conditions, or incorporate any machine-learning capabilities in its analysis.

That said, rules and threshold-based monitoring do bring certain strengths and limitations:

We know what criteria our alerts are based on when they are triggered
If our rules are simple enough, they may be modified with little additional analysis or equipment
Rules can fire based on any combination of criteria, including changing, aggregating, or nonlinear functions of our input variables
Thresholds may not account for slowly drifting baselines or anomalous sensor readings
A single rule often cannot easily account for a variety of different abnormal conditions
Rules require relatively careful specification in order to be effective; they are not self-improving

We therefore ask ourselves more practical questions, such as the following, before even beginning to draft alert rules:

How do we want the system to behave when a sensor becomes unresponsive or reports values incorrectly?
Do we want to consider one anomalous reading to be sufficient for raising an alert, or wait for consistency across readings?
Who is supposed alert take priority, and who do we expect to receive and process such an alert? What should they do in response?
How do we, as developers, evaluate whether a rule-based system has been helpful or wasted time and resources?

To implement a working rule-based system we may find ourselves considering these same questions, whether or not we eventually incorporate additional methods.

3. Introducing AI Assisted Monitoring

We may find that rules are appropriate for some alerts and entirely inadequate for others.

A good example of the latter is equipment behavior whose normal variations are difficult to separate from abnormal patterns.

A particular motor vibration signature might, for instance, be largely dependent on the equipment's load, whereas a simple range check would raise too many false positives or fail to account for changes in the system's "voice" over time.

In such a case, we might train an anomaly detection model based on known normal behaviors.

A possible implementation path may involve the following steps:

Collect training samples from the monitored equipment
Label normal operating periods or equipment states based on sensor data
Clean training data to identify and remove outliers, null values, etc.
Identify and train an appropriate model (see below) based on cleaned data
Test and iteratively refine based on held-out test data and developer judgment
Evaluate potential false positives and negatives with maintenance staff to establish operational value

Even in this context, a detected anomaly does not necessarily indicate a developing fault - it may represent an abnormal operating condition or sensor error.

This kind of modeling task, however, does illustrate how rules and basic statistical analysis might be combined with a trained machine learning model to achieve monitoring goals.

4. Evaluation: Not Just Accuracy

An operational monitoring system can generate accurate predictions without being genuinely useful, simply by overwhelming operators with unactionable insights and false positives.

We therefore have standards beyond mere model accuracy when it comes to evaluating our systems.

Among the most basic are the following:

- Precision : Among predicted positive examples, what percentage of them were actually positive?

- Recall : Among true positive examples, what percentage of them did the model correctly predict as positive?

- False alarm rate : Among predicted negative examples, what percentage of them were in fact positive?

- Operational impact : Does our system really make a practical difference, or is it largely ignored once deployed?

- Detection latency : How quickly does our system react to an oncoming true positive?

We identify metrics most relevant to our particular situation in order to evaluate how best to proceed.

A false negative may be much more concerning than a false positive, depending on context, while other situations may have different cost ratios or operational requirements.

5. Considerations and Constraints

Creating a working prototype is one thing; getting it reliably deployed is another.

Data quality

Sensor data is often noisy, with missing, duplicated, or out-of-order values.

Such data must be validated and filtered before further processing in most cases.

Timestamps, calibration, and clear null value conventions help when creating reliable preprocessing pipelines.

Edge cases and latency

Some systems require low-latency responses or operate in environments where communication is not guaranteed.

Applications requiring such capabilities should consider using distributed computing or edge processing methods, while cloud-based technologies remain appropriate for other contexts.

This decision is not made in isolation but rather based on each application's operational realities.

Security

The more interconnected a system is, the more points of attack it has for malicious actors.

Authentication, encryption, auditing, and compartmentalization become more important as more networked devices are added to an AIoT system.

Security concerns must be considered for every aspect of system design.

Drifting baselines

Equipment, workload patterns, and environmental conditions change, which may make models outdated or ineffective over time.

We should continuously monitor drift in either our input data or the results of our models during operation.

Operations integration

Without concrete action to follow up on an alert, such a feature is not likely to be genuinely useful.

In practice, an effective operational alert must be integrated directly into relevant human operators' workstreams and followed up afterwards.

We return to this topic shortly.

6. A Development Roadmap

When building such a system, we may productively follow a phased development plan that allows us to make meaningful progress while avoiding premature generalization - in other words, we begin with specific objectives rather than attempting to accomplish everything at once.

1. Identify one specific goal : Determine which particular objective we wish to accomplish, e.g.: finding anomalous vibration patterns

2. Identify equipment : Understand the system we are working with; determine which kinds of sensor values we need

3. Develop a trustworthy data pipeline : Make sure the system reliably records, timestamps, and categorizes sensor values as appropriate

4. Establish baseline behavior : Develop simple, transparent rules to detect abnormalities and establish their limitations

5. Determine whether AI provides benefits : Based on baseline rules, explore what value a trained AI might provide to this particular use case

6. Account for failure scenarios during test runs : Introduce null values, communication issues, and rapidly shifting operating conditions to test preparedness

7. Consider post deployment monitoring : Based on results, determine relevant evaluation metrics and track ongoing performance

We examine these practices more closely below, but this overall plan avoids getting bogged down on any one particular aspect of the system before sufficiently understanding the domain as a whole.

For general information about Industrial AI and related operations concepts, we encourage you to visit (https://apertureventurestudio.com/) and explore additional resources.

Conclusion

Industrial AIoT systems involve pairing physical monitoring equipment with software that helps humans understand changing system states.

The best practices in this space tend to favor systems that collect useful information on a consistent basis, provide actionable insight with minimal disruptions, and integrate productively with relevant operational considerations rather than focusing exclusively on model performance.

Begin with a clear understanding of what we want to accomplish, develop a strong baseline, determine whether AI provides real benefits, and only then move ahead and implement more complex rules or neural network models.

Question for developers: When developing an industrial system, would you prioritize raw sensor data quality, low-latency responses, or reducing false alarms first? Why?

AIoT in Commercial Construction: From Data to Better Decision Making

Memona — Tue, 20 Oct 2026 15:16:27 +0000

Many commercial construction sites naturally produce data.

Equipment is moved from one worksite to another, materials arrive in shipments, and project timelines dictate the rhythm of work.

However, collecting data does not always contribute meaningful information to a commercial construction project.

Workers and managers need accessible and interpretable data that can guide the operations on site, and artificial intelligence can assist in that process.

What Is AIoT?

In general, artificial intelligence of things (AIoT) refers to the connection between the Internet of Things (IoT) and artificial intelligence (AI).

IoT devices generally refer to physical objects that contain sensors or other data collection mechanisms.

These collect and relay information through software systems and connected services such as RFID tags and beacons, GPS positioning, and camera surveillance.

AI typically involves algorithms that analyze information, detect anomalies, recognize patterns, and provide other insights.

In the context of commercial construction, IoT and AI can work in tandem to track equipment, manage and organize materials, observe general progress, and potentially detect issues or inconsistencies in the workflow.

Essentially, IoT enables an interconnected ecosystem while AI assists in analyzing this information to generate actionable insights.

How Does AIoT Work in General?

For the purposes of discussion, consider a general scenario in which a construction company invests in several pieces of heavy commercial equipment.

An AIoT system would allow for the collection and analysis of information pertaining to their general whereabouts and utilization.

The process would usually involve the following steps.

First, data is collected from sensors, beacons, cameras, or other IoT devices.

This information is then transmitted, usually through an available network, to an edge computing system. Edge computing generally refers to data processing that occurs closer to the source rather than a distant server.

After that, the software system identifies discrepancies, analyzes the information, flags issues, and generates alerts.

Finally, the identified problems are brought to the attention of human operators who can confirm and address them.

Using the example of commercial construction equipment, an alert would be issued if a particular machine was not utilized on a certain day or during specific hours.

This might be because the equipment was waiting for repairs, or it was determined to be unneeded for the day’s work.

This information, combined with work schedules and utilization data, could help to determine whether this is an issue and what it might need.

With that in mind, here are a few of the key applications for AIoT in commercial construction work.

4 Applications for AIoT in the Office and Jobsite

Equipment Usage

Commercial construction equipment can be expensive and difficult to acquire.

Moreover, there are often various tools and machines, and they may be utilized at a different rate.

In certain cases, equipment tracking might show that a particular tool sees excessive idle time or is regularly tied up, precluding its utilization at other parts of the worksite.

Analytics and AI can help recognize patterns in equipment usage and enable informed management decisions.

Visibility into Materials and Inventory

Materials are often at the center of commercial construction work and are required to complete a project.

A shortage of certain parts or supplies can delay an entire operation, or materials might not be appropriately organized or stored.

In addition, inventory might fluctuate throughout the course of a project depending on the stage it is in.

Therefore, AIoT’s potential to assist in organizing and navigating inventory needs is very attractive.

This likely goes hand-in-hand with maintaining good material records and practices across the different aspects of a project.

Tracking General Progress and Possible Delays

There are several interrelated threads to commercial construction in that progress at all stages needs to be considered.

For instance, deliveries of materials can enable certain aspects of the work, the preparation or allocation of equipment can be affected by delays, or an installation might require permits.

In theory, one of the applications for AIoT in commercial construction is that it might help tie these operations together by assisting in anticipating and detecting delays.

If a particular stage requires another element to be completed, AIoT can flag when or if that condition can occur.

In another instance, an analytic system might be able to recognize that certain equipment has not arrived yet to make a specific part of construction possible.

This system will likely not completely eliminate delays; however, it provides information that can guide further decisions at a manager’s discretion.

Assist in Workforce Organization and Access

In many cases, commercial construction involves multiple companies or contractors working at the same time.

These are generally contracted for specific tasks, areas, or parts of a larger project.

Digital access systems and records might be able to help coordinate and organize workers and enable them to recognize problems, such as when authorization or the necessary resources are not given.

The use of AIoT technologies in this instance could optimize general organization at the jobsite.

However, such a system would necessarily provide privacy and the ability to restrict or modify access and would need to be sensitive to both rules and regulations as well as general sensibilities regarding personal information.

Difference Between AIoT and “Regular” IoT

At its simplest, IoT refers to interconnected devices, while AIoT refers to IoT with the added aspect of analytics.

Therefore, regular IoT is primarily about collecting and transmitting data, while AIoT also analyzes it to recognize patterns and identify problems.

This can be likened to IoT indicating that something went wrong, while AIoT has the ability to recognize that something did go wrong.

However, there are also situations in which a basic analytics system or even one based on specific rules might be more cost-effective, straightforward, and accessible.

The choice of which system to utilize ultimately depends on the situation, scope of work, and other factors.

Issues to Consider Prior to Implementation

Beyond the question of whether IoT in construction will provide a benefit, it is necessary to carefully consider what options are available.

A few of the possible issues to evaluate include general compatibility, connectivity, and data quality, security, and privacy, adoption and interpretation, costs and measurement, and related concerns.

IoT and AIoT both require hardware that can collect or convey information, and not all options may be compatible with or useful for an application.

Likewise, IoT applications are generally only viable on sites in which there is some degree of connectivity, and data quality can be an issue if there are missing or questionable entries.

Security and privacy should be of particular concern in the context of construction projects; the use of such systems should entail appropriate safeguards.

Beyond that, employees and operations will need to understand how this technology affects their work, and an appropriate business plan and system should be in place.

The benefits should definitely justify the expenditure and any other commitments.

How Can Construction Tech Experts Get Started?

One viable method is to choose a problem space to test at an appropriate scale.

While it might be ideal to optimize all possible applications simultaneously or at least at the same general time, there is more practical starting points.

Select one area of concern or interest and determine the current state of operations.

Decide what technology is most appropriate for the task and establish general expectations.

Choose a course of action, and evaluate results by comparing a test period with the prior performance.

Then, based on observations, consider if and how this might be applicable on a wider scale.

For example, a company may be concerned with excessive amounts of underutilized equipment.

It can set up tracking and analytics systems, assess overall utilization per specific tool, and determine whether certain items should be restricted, limited, or replaced based on the findings.

Summary: AIoT for Construction Projects

Commercial construction projects have a variety of data-driven applications in the fields of equipment tracking, material or inventory usage, and other areas of organization and planning.

The use of AIoT enables organizations to connect aspects of construction and optimize them.

However, AIoT is not necessarily definitive; in other instances, a simpler analytics system or a specific rule-based system might be more appropriate.

Finally, while artificial intelligence can potentially benefit a broad variety of applications, the most compelling areas of interest should be selected first.

For this question, readers are invited to share their thoughts and identify the area of more concern: equipment tracking, materials, project delays, or other factors, or the integration of these elements in combination.

How AI, IoT, & Digital Threads Improve Product Lifecycle Management

Memona — Tue, 20 Oct 2026 14:36:26 +0000

Product teams have to work with data that spans their engineering design tools, manufacturing software, connected equipment, and maintenance records. The lack of a unified process creates islands of information about a product that can be challenging to govern and apply effectively.

How can companies benefit from using product lifecycle management (PLM) to connect artificial intelligence (AI), the IoT, and digital-thread capabilities?

AI and IoT Enable Better Connections and Decisions in PLM

AI can help engineering and manufacturing teams make better decisions about a product by analyzing relevant information, looking for patterns, and automating processes. When used with PLM, AI tools can help manage design reviews, requirements, quality investigations, maintenance procedures, and more.

For instance, engineering teams could use AI to examine historical quality reports and identify common issues and their root causes. Then, they may want to investigate the cause and whether any changes are required to improve product quality or performance.

On the other hand, recommendations resulting from AI processing should always be evaluated by trained personnel. This is especially important for quality investigations, regulatory approvals, and changes that impact product design, operation, safety, and other essential factors. Similarly, reliable results and effective governance depend on accurate information and proper change control.

Internet of Things (IoT) Devices Allow Companies to Add Real-World Data About How Their Products Are Used

IoT sensors and devices create an opportunity to relay additional information about how products function in the real world. This data can augment design reviews, quality investigations, and other PLM processes. It is vital to assess what information is necessary for a given use case and ensure the connected devices are reliable.

For example, machinery manufacturers may want to receive vibration data about how their industrial equipment is operating. If the IoT sensors identify unusual vibrations, maintenance personnel may want to investigate the problem and take corrective actions. At the same time, engineering professionals might use the same information to find design or performance issues that may need to be resolved during the next revision of the equipment.

This is an illustration of a broader concept of “closing the loop” between a product’s design, manufacturing, and maintenance. One of the main challenges that companies face when trying to analyze this information is having the right tools that connect these data sets to product design and PLM records. Similarly, organizations must ensure that the data coming from IoT devices is reliable and secure.

Digital Threads Help Make Connections Between Various Pieces of Information About a Product Easier to Govern and Apply

A digital thread is a unified record that contains necessary information about a product’s design, manufacturing, service, performance, and other parameters in a connected and accessible format. By analyzing this information, product teams can identify patterns, trace problems, and make better design or maintenance decisions.

In practice, this means that designers and engineers can look at a given aspect of the manufactured product (e.g., a particular component or its failure mode) and see all relevant information that may impact it. For instance, if there is a question about a certain aspect of a product design, all connected records, including drawings, manufacturing instructions, test results, and service records, should be available for review. This helps different departments (e.g., design, manufacturing, and maintenance) collaborate and make decisions based on the same information. In practice, this is about making sure that the proper people can see the required information and that the data retrieved from it is accurate.

It is essential to note that a digital thread is not necessarily a single software platform. In practice, companies adopt a collaborative approach that involves integrating disparate systems and tools, assigning unique identifiers to connected records, and ensuring that all participants can see and update relevant information.

Why Product Lifecycle Management Customization Is So Important

It is important for companies to realize that different industries have varying requirements for PLM software, and customized solutions are vital for ensuring that individual organizations can maximize benefits. At the same time, the most useful customizations are those designed around business requirements and are not dependent on a given company’s ability to implement complex and costly software solutions.

Before initiating PLM customization, businesses must also consider these factors:

• Can different software platforms (including engineering design, PLM, and product lifecycle intelligence tools) exchange information?

• Does the company have high-quality records of manufactured products?

• Are there appropriate security measures to ensure that product design and operational data are protected?

• Can the customized solution scale and support the company’s growing needs?

• Will the proposed customizations allow the company to adapt its processes and tools when new regulations, software versions, or other factors are introduced?

• Are there adequate governance practices and change control mechanisms in place?

• Does the proposed solution adequately address business needs?

By understanding how a given organization can benefit from connected PLM tools and processes, it becomes easier to customize these solutions to meet industry specific, individual requirements.

Bring Everything Together to See How Connected Product Information Improves Your Business

When implementing connected product lifecycle management solutions, companies have to remember that the combined application of AI, IoT, and digital threads is much more powerful than each tool individually.

In practice, IoT devices generate additional data about a product’s performance, which, when combined with other information, becomes instrumental in identifying patterns and potential problems. Meanwhile, AI helps analyze this data and find trends that require further review. Finally, the digital thread concept ensures that all this information is connected and can be accessed and utilized by different departments.

For example, manufacturers can ensure that they fully consider equipment performance by combining data from IoT devices and maintenance records, analyzing this information together with other data about a product, and reviewing the findings in context. Notably, such a review is always done by human analysts who investigate the trends identified by AI and make decisions about necessary changes to connected product records.

At the same time, companies must ensure that they only collect relevant data, have the appropriate tools to analyze it, and have the proper governance mechanisms in place. Only by taking such steps, businesses will be able to maximize the advantages of connected PLM tools.

Companies that are considering how to leverage connected technologies in industrial settings can learn more about industrial AI, IoT, and digital threads at Aperture Venture Studio .

AI Literacy for Workers: Practical Skills That Support Everyday Tasks

Memona — Mon, 19 Oct 2026 15:05:34 +0000

Artificial intelligence has a huge potential to change the way people perform their work. Administrative professionals, customer service representatives, logistics managers, or factory workers everyone can use contextually appropriate tools to change and improve the way they perform their work and manage information flows.

The key is to understand how artificial intelligence works and when it is appropriate to apply it.

Below are several suggestions that can help in learning AI literacy and applying AI tools and systems in the workplace.

1. Begin with Solving Real Examples

Using artificial intelligence often means that people turn to it to solve real problems instead of just adopting it as a “fashionable” thing. Therefore, people need to identify specific tasks for which they can be applied, e.g.,

summarizing long documents and meeting minutes
writing routine letters
structuring disorganized materials
classifying feedback
identifying patterns and relationships in the data

For instance, if an employee regularly has to provide a summary of the key aspects of a meeting, the AI-based tool that is approved for use can help prepare a rough draft.

A critical step here is for the employee to take responsibility by making sure that the main points were not missed, the wording corresponds to the approved standards, and certain steps were not forgotten.

2. Define Precise Instructions

When using AI-powered solutions, one of the essential skills is providing necessary context and precise instructions.

Depending on the phrasing and specificity of the question, the results received can vary significantly. Thus, instead of a general instruction like “summarize this report,” it makes sense to use a list of specific directives,

summarize in five bullet points
identify the main findings and problems not resolved
indicate recommendations and additional steps needed
use only information provided in the report
highlight questions not addressed in the report

In addition, the instructions can be supplemented with the context on who needs to use the summary, what restrictions apply, and what details can be omitted.

3. Always Verify the Final Output

Perhaps, one of the most critical skills when using AI tools in the workplace is verification. This means clarifying whether the solution’s response fully reflects the reality of the situation, does not omit any important aspects, and highlights the most crucial points.

AI systems are quick to make mistakes or “fabricate” information, especially when the question is vague, so even when the user has provided the correct instructions, verifying the response against factual data is essential.

For instance, if the AI tool was directed to summarize the main points of a production report and identify the reason for a delay, the user must double-check facts that might support or refute this claim.

In this case, a checklists for verifying responses would include the following items:

• Facts: can the information be verified?

• Details: Were any important aspects missed? If so, which ones?

• Conclusions: Does it align with reality?

• Consistency: Does this output correspond to the context and can it fit within the business frame? Will providing this information lead to errors, delays, or security issues?

Depending on the importance of the request, employees should devote a sufficient amount of time to verifying information, applying their expertise, and analyzing processes using data from similar tools to ensure there are no inaccuracies.

4. Recognize Specific AI Features in Industrial Facilities

AI tools can do more than just help clerks or executives deal with routine tasks.

Industrial AIoT (Artificial Intelligence of Things) connects AI to operational technology (OT) systems and analyzes sensor data from equipment, inventory, and other sources.

This integration enables teams to optimize operations and identify patterns. In particular, AIoT can be used in predictive maintenance, inventory management, production analysis, and supply chain traceability. Inventory managers can look into data sets about warehouses, while production supervisors can examine causes of production delays.

As in all cases, AI tools only support and supplement human actions without replacing them. In practice, the success of an AI implementation depends on workers’ literacy and understanding of the system’s input and output data.

For those interested in this space, the OEMNex AI website offers several articles concerning the Industrial AI application.

5. Maintain Privacy and Follow the Policies

AI literacy skills are crucial not only in terms of technology but also for compliance requirements, such as protecting personal data. Using AI tools is not limited to asking for information.

Since many documents and data contain strictly confidential information relevant to the company’s operations, employees need to know which tools to use and what information to provide.

Practical guidelines that help use AI tools in the workplace while following the rules and protecting personal data can include

• using approved tools authorized by the facility or department

• not providing sensitive data to such solutions

• observing rules related to personal data

• receiving approval for requests of this type

• applying standard and special reviews for information obtained from such tools

• contacting the helpdesk in case of problems arising

Compliance is one of the most important aspects that must be taken into account when using AI tools. A wide range of individual solutions in various fields can process and store data, so employees must ensure that they do not violate the rules.

6. Focus on Learning AI Applications That Apply to Your Work

Employees should be guided by the principle of gradualness and the possibility of implementing changes in the practice of each day. Instead of trying to learn all the details at once, it is better to start with those aspects of AI that seem to be the most beneficial and relevant.

For instance, to check the practical application, an employee can use a suitable tool to simplify the task assigned and compare the result with the solution without using this tool. As a result, the ability to obtain structured information is critical to ensuring that AI tools are useful.

Once the benefits and limitations of the tools are understood, the person can move on to learning how to use other AI features.

However, language, soft skills, analytical skills, and subject matter expertise should never be replaced by AI-based technology. The ability to communicate with other people, think critically, solve problems, and apply knowledge in practice will always be in demand.

Thus, AI literacy seems to be primarily the ability to select appropriate tools that meet the needs of one’s work and to ensure that work meets high quality, accuracy, and precision criteria.

Discussion: What is the task in your work that AI can simplify and how will you verify the accuracy of the result?

AI, Robotical, IoT and Big Data Joint Functionality for Business

Memona — Mon, 19 Oct 2026 14:13:11 +0000

Imagine a robotic machine notifying about anomalous vibrations, an AI system diagnosing the issue, and the maintenance crew fixing the problem before it interrupts the production.

Such synergies between robotics, artificial intelligence (AI), Internet of things (IoT), and big data analytics are possible due to their combined business potential, addressing diverse needs within business processes and algorithms.

This essay, explaining robotic, AI, IoT, and big data concepts, provides particular examples, jointly discusses these technologies’ application in diverse businesses, and describes the critical success factors of integrated robotic, AI, IoT, and big data solutions.

1. Robotics and Its Role in Business

Robotic technologies enable the realization of forces or motions to automate physical tasks.

Such technological equipment facilitates the achievement of high productivity, quality control, working accuracy, reduces the possibility of errors, and improves performance, which make robotics applicable in manufacturing, logistics, healthcare, and other fields of operations.

The key success factor in robotic systems is the identification of tasks subject to robotization.

The critical limitations of robotic technologies concern the costs of implementation and maintenance, working conditions, safety, and the need for qualified control personnel.

2. Artificial Intelligence and Its Role in Business

AI systems leverage software instructions to perform cognitive functions, train computer vision, speak languages, detect patterns, and perform predictions.

In business operations, AI finds application in inventory management, quality control, demand forecasting, fraud detection, and other activities.

For instance, AI can be applied to build machine learning algorithms to predict demand based on historical and seasonal tendencies, gross product, and current needs, enabling firms to create self-learning inventory systems that anticipate shortages.

However, such forecasts are regarded as reliable and applicable to decision-making in businesses only when proven correct over extended periods.

3. IoT and Its Role in Business

IoT refers to a network of objects that connect through the Internet, collect and share data, and often apply artificial intelligence to enhance their functioning and aid decision-making.

Internet-enabled sensors in industrial settings enable the continuous monitoring of physical conditions, including temperature, vibrations, pressure, electricity consumption, and other variables.

IoT systems can empower informed decisions and uninterrupted operations through remote surveillance, predictive analytics, and instant notifications.

However, interconnected objects and systems are reliant on networks and prone to security breaches that might cause operational disruptions or data distortion.

4. Big Data and Its Role in Business

Modern enterprises operate as interconnected networks of smart devices, machinery, and human capital, which generate vast amounts of structured and unstructured data.

Big data analytics enable the transformation of such information into actionable insights that drive process improvement and cost reduction.

For example, advanced data analytics can uncover inefficiencies in supply chains, leading to optimizations resulting in annual cost savings.

In business operations, big data analytics are leveraged to mine data sets for patterns or laws, train AI systems, detect operation bottlenecks, improve customer relationship management programs, and operate robust enterprise resource planning systems.

However, the primary limitation of big data concerns not the volume but the quality of data.

Firms should ensure that the collected information is relevant and reliable to draw precise conclusions and extrapolate trends identified within one dataset to another.

Illustrative Example

An integrated approach to operational performance analysis can be observed in a firm that seeks to minimize interruptions in industrial equipment functioning.

Such a system involves IoT-enabled sensors, which record vibrations and temperatures, and data analytics, which process and visualize this information.

Then, the AI systems utilize these data to estimate the likelihood that the observed deviations are precursors to malfunction and predict the possible points of failure.

Finally, robots, when possible and safe, can make the necessary adjustments to the equipment within the permissible margins and notify the human maintenance crew about identified operational issues.

Thus, this chain of big data, IoT, robotics, and AI can facilitate informed decision-making about further maintenance and equipment upgrades.

However, the described AI-driven model is complex, involves numerous points of potential software or hardware failure, and requires proper setup and cybersecurity measures.

5. Conclusion

Robotic and AI technologies, IoT, and big data analytics possess distinct application areas that enable businesses to automate repetitive or inaccurate tasks performed by humans, collect and share information, operate high quality analytics, and improve supply chain performance.

Integrated solutions applying IoT and robotics and combining big data and AI technologies can yield superior results.

The key to such synergies lies in the interconnectivity of devices and data, which enables informed business decisions.

For developers, it is essential to utilize robotic technologies and interconnected big data analytics, which have been proven to be viable in actual business conditions.

Moreover, integrated solutions should allow companies to measure success, improve performance, and refine decision-making based on empirical evidence rather than theoretical assumptions.

Finally, while big data analytics provide valuable insights, human judgment should be deployed to make ethical and viable choices.

Big data analytics, in turn, can empower humans in making business decisions by eliminating potential sources of error.

This article presents my thoughts on robotic, AI, IoT, and big data technologies in business applications, addressing their main advantages and critical limitations.

From IoT Data to Industrial Intelligence: Turning Sensor Readings Into Decisions

Memona — Thu, 15 Oct 2026 15:40:53 +0000

A machine's temperature, vibration, and rotation start to drift away from normal levels. Its behavior has shifted somehow, but production continues.

An industrial IoT system could capture these changes in sensor readings. The hard part would be figuring out what they mean, when they require action, and who needs to know.

This is where Artificial Intelligence (AI) and Internet of Things (IoT) can complement each other. The combination, sometimes called AIoT, uses AI to extract value from data collected by IoT devices.

For developers, creating solutions based on AI and IoT extends well beyond plugging sensors into a data lake. Proper data pipelines, simple detection logic, system integration, and the ability to judge results all contribute to an effective AIoT system.

Predictive Maintenance Begins With Reliable Data

One application for industrial AIoT is predictive maintenance .

This domain has traditional approaches, such as scheduled servicing, and condition-based methods, which attempt to recognize anomalous patterns . Either way, the goal is to understand what constitutes normal operation and detect deviations that may indicate problems.

The process for detecting abnormal readings typically includes the following steps:

**1. Collect: **Obtain numeric measurements (vibration, temperature, pressure) and time data for a machine or device.

2. Validate: Verify time stamps, confirm that no readings are missing or erroneous, and that values are in expected ranges.

3. Establish baseline: Determine a reference for normal variation in measurements during specific modes of operation.

4. Detect: Identify data points that deviate from the norm using simple rules or a statistical/machine learning model .

5. Alert: Deliver relevant information to maintenance personnel

6. Evaluate effectiveness: Compare findings with known maintenance and inspection records for the asset to confirm value.

Note that an alert does not necessarily mean that a machine is about to fail. Some assets operate at variable speeds, and variations in temperature and pressure can be normal for different modes of operation.

If the system can support maintenance personnel in their inspection and decision making tasks, it can be considered useful.

2. A Simple Example of Sensor Anomaly Detection

Before building a predictive maintenance solution based on a neural network, one could try simpler approaches.

This example will use a rule-based threshold to identify outliers. In real-world scenarios, the threshold should be established for the specific asset type and conditions of operation.

For illustration purposes, here's a temperature monitoring function written in Python:

def check_temperature(reading, threshold):

if reading is None:

return {

"status": "invalid_reading",

"alert": False

}

if reading > threshold:

return {

"status": "above_threshold",

"alert": True

}

return {

"status": "normal",

"alert": False

}

A complete monitoring application would do the following:

Address calibration and measurement units
Handle missing, late, or duplicated values
Consider persistence: An anomalous temperature should probably be seen in more than one consecutive reading
Take equipment state into account
Implement appropriate auditing and alert escalation
Evaluate the rule using actual readings and maintenance records

Simple logic can work as a baseline, and more complex analytics can be introduced later, if justified by the business case.

3. Inventory & Asset Insights

Industrial facilities have assets in addition to machines, such as goods, tools, raw materials, and supply chains.

IoT devices, barcode scanners, RFID readers, and inventory management software help with asset tracking. Data analysis and anomaly detection can play a supporting role by identifying exceptions, patterns, and opportunities for improvement.

For instance, if one detects that a certain piece of machinery was moved, while available inventory reports do not reflect an update, a helpful action would be to flag the asset for verification. It should be possible to raise an exception, re-read the RFID tag, and update the inventory records.

Inventory insight is an example of a situation where data analytics supports human action. In principle, any system that has been designed to take action can be automated. However, detecting an inventory discrepancy is often not sufficient; the application needs to be capable of inventory correction.

Sometimes, simpler rules can replace a complex model.

4. Industrial Supply Chain Insights

IoT can provide location data and readings for logistical assets, and analytics can help with detecting supply chain disruptions.

However, a signal is only as good as the action it supports. In this case, location and event data can be valuable if the application can interpret them in the context of the supply chain

One of the considerations when designing an alert is the following:

A helpful alert will articulate what changed and why it is relevant. The model should also be able to select the most appropriate audience and provide guidance on what to do next. It is worth considering a few examples.

Suppose that the application detected that a delivery truck is late. It is essential to establish who this affects and what actions should be taken. If the shipment contains components for a machine that is due to be assembled next week, this insight might be worth an alert.

Linking the logistics data to inventory and production planning adds value but requires additional context and processing. There are engineering and system integration considerations: data freshness, identifier mapping between systems, and the ability to handle missing values and errors.

5. Industrial AIoT Engineering Considerations

The industrial AIoT application can work on a small scale, but what does it mean for production? Some design considerations should be reviewed before building a full-scale application.

Data quality

IoT devices can generate a wealth of data, but it is critical to examine the inputs and ensure that the application can handle missing values, erroneous readings, and other issues.

System integration

Large-scale industrial applications typically involve legacy systems, incompatible communication methods, equipment from various manufacturers, and software components that cannot interoperate or scale easily. The design should address these concerns.

Latency & connectivity

The application might require event processing at the edge or a more centralized data warehouse architecture that allows batch processing at regular intervals.

Security

Security is a concern for any application that utilizes IoT devices, including device-level credentials, authentication, and network security.

System reliability

In an ideal scenario, every event is processed at exactly the right time, and the response is delivered immediately. In production, the application should have procedures and expectations in place for situations when the data warehouse is unavailable, edge nodes are offline, values are missing or incorrect, or other incidents occur.

Human in the loop

Operators need action items that they can understand and verify. This consideration covers model explainability, false positive rates, and opportunities for incorporating feedback from workers on the ground.

Engineering considerations are only useful if the system is deployed. This brings us to the question of value: does the pilot project have a reasonable expectation of success and ROI?

6. Success Metrics for Industrial AIoT Applications

In the example above, the goal was to build a predictive maintenance system. However, the engineering considerations only covered aspects of the system before it went live. How can an organization assess whether the time and resources were spent wisely?

Determining the answer requires a return to the original business scenario and defining what constitutes success. In this particular case, one could argue that the value was captured if a maintenance worker opened an alert, was able to identify and repair an issue, and saved enough time and money to justify the investment.

Before building any system, it is critical to establish what constitutes a successful pilot and an acceptable loss scenario. One possible framework could be the following:

- Identify the current system for the identified scenario: In the case of predictive maintenance, it might be a scheduled maintenance checklist.

- Establish a baseline: Document the current performance, KPIs, cost, and other relevant factors.

- Define success criteria : This step should include data requirements, training/familiarization, and the ability to operate the system.

**- Build an MVP : **Create a solution that can be integrated, deployed, and evaluated within reasonable time and costs.

- Test the application : Evaluate the system using data warehouse inventory or a test environment that simulates the production data stream.

**- Assess value and prepare for production launch: **Determine if the application was valuable and what system requirements, if any, might need to be adjusted ahead of full-scale deployment.

A machine learning model does not account for the realities of operational costs. Before deploying any application, it is essential to review these costs alongside the engineering and analytical aspects.

7. From Engineering to Entrepreneurship

Many of these considerations still apply if one decides to build an industrial AI business around these systems.

A new technology venture typically involves a carefully considered technology concept, but it also includes opportunities and threats that are difficult to address without a proper understanding of its intended application.

This is where Aperture Venture Studio comes in: Aperture focuses on building technology startups in industrial AI and IoT domains

For these ventures and applications, the technology concept makes up only a small fraction of the overall idea. A promising new technology is not a silver bullet; rather, its value is determined by a proper understanding of the problems it can solve and the ability to implement it appropriately.

Final Thoughts

Artificial Intelligence and IoT have a wealth of applications in the industrial domain, from predictive maintenance to asset management. However, these applications are often more challenging to implement than their theoretical POC suggests. The application can only realize its potential if the technology, use case, and general operations are taken into account throughout the development and deployment process.

For developers and technical founders, this means that building a system that produces valuable and actionable information is more rewarding than a high-profile deep learning application with questionable accuracy and support.

Discussion: If you were to design an industrial AIoT system, which design consideration would you prioritize first: data quality, legacy-system integration, false alerts, or actionable insights?

Building Inventory Applications That Don’t Lose Track Of Their Inventory

Memona — Thu, 15 Oct 2026 14:24:07 +0000

Let’s imagine we have an inventory system. Two of its customers decide to order the last remaining unit of a given product at roughly the same time. Does our application allow both of these orders to go through?

This is just one example of a complex series of constraints and rules which go into making an inventory system.

Spreadsheets can be useful, but businesses that want centralized visibility, automation, and protection from mistakes need something more powerful.

Let’s look at some fundamentals of the inventory application development process and consider how engineers can build more robust, reliable tools.

1. Build Inventory Records With Audit Trails

Begin by creating or identifying the data model which our application will use. At the simplest possible level, this might consist of a product ID, product name, SKU, and stock quantity.

In practice, however, it will likely be necessary to track more information. Perhaps we will need to track where inventory is stored, or what products have been reserved for specific customers. Auditing and debugging issues later on will become significantly easier if we leave behind a paper trail of what events occurred and when.

Some examples of this information might include:

• What inventory adjustments have occurred? Has inventory been received from a supplier, sold to a customer, or returned? Has it been transferred from one warehouse to another?

• How did the application reach the state that it is currently in?

• What is the reason for this adjustment? Does it involve an order or reservation?

• What quantity of the given product has been physically affected by this transaction?

• What time did this occur, and what other contextual information might be relevant?

We may also want to associate inventory records with other values, such as an order ID or a customer reserving this product. In a multi-warehouse environment, a given inventory record might also have a location field.

Depending on specific business logic, the data model will take shape. It is important to track all of the relevant adjustments as transactions and build an auditable paper trail.

2. Avoid Overselling With Transactional Integrity

Let’s return to the example of two customers attempting to place an order for the last remaining unit of a product. We want to sell one unit of this product to one customer and prevent the other customer from getting the same item, but it is very difficult to do so if both processes read and write information concurrently.

Using a relational or NoSQL database engine, it might be possible to prevent this sort of issue through transactions and concurrency controls.

The above SQL statement, for instance, will reduce a product’s inventory by 1 if the quantity of this product is at least 1 and prevent any such changes from occurring otherwise.

An application making this request should then be able to determine whether one row was affected (in which case the update was successful) or zero rows (in which case the requested update has failed). This is useful, but there are also additional nuances and edge cases which need to be considered. This includes whether this is the right approach in the context of a reserve-inventory system, what happens if a similar request comes in again later, what occurs when transactions need to be applied concurrently, inventory location and availability, or how this will impact order cancellations.

The point is to ensure that the application itself prevents overselling rather than relying solely on application-layer controls.

3. Automate Inventory Updates With Caution

Inventory automation systems can be immensely helpful. By ensuring that inventory changes occur automatically when certain processes are followed, there is less of a need for manual records or adjustments to go wrong. An automated inventory application might automatically update quantity fields when a purchase is made or an order is processed.

There is a good reason to be cautious, however. The same automation that helps can also hurt if there is no appropriate protection in place. For instance, if the application tries to update inventory again after it has already done so successfully, it will lead to double-counting errors.

We can avoid such issues by including appropriate transaction constraints, such as idempotency keys. Inventory record validation is also important, particularly since we will be working with other systems such as payment processors, order management tools, and warehouses.

4. Barcode Reader And RFID Inventory System Software Requirements

Barcode scanners and RFID (radio-frequency identification) readers offer similar advantages. They allow products to be uniquely identified without requiring human intervention. They are valuable tools in warehouses, as they can help ensure that physical inventory changes are reflected in digital inventory records. They can work in tandem with inventory applications or be incorporated as part of an integrated system.

RFID readers operate differently than barcode scanners and can be further categorized into active and passive variants, depending on whether RFID tags are powered by their own batteries.

An RFID reader will generally operate within a working range (a distance of several centimeters to a few meters) and scan for RFID tags within this area. If RFID tags are affixed to products within this range, it will be possible to obtain unique RFID identifiers for these products.

As with barcode scanners, the specific details will vary depending on the specific use case. Some examples might involve scanning barcodes or QR codes, scanning multiple products at once, determining quantity, or performing inventory verification.

From a software standpoint, such systems should be able to translate a barcode or RFID tag into a given product. The application will also want to verify that the request is legitimate and actually allowed and perform the relevant inventory adjustments for the given RFID or barcode.

It is necessary to choose a method based on the specifics of the inventory environment. RFID systems tend to be more valuable at certain points and less valuable at others. They are generally much more expensive than traditional barcode scanners, for instance, but do not always need to be applied to individual products and can be read without being seen.

5. Inventory Application API: Exposing Product Catalog And Inventory

Inventory applications tend to integrate with a variety of other tools, ranging from e-commerce platforms to enterprise resource planning systems. An API will generally want to expose how inventory information is structured, how requests should be processed, and what error conditions can occur. There are also broader architectural decisions which need to be made as well.

Some important considerations include authorization, authentication, validating incoming requests (particularly IDs), rate limiting, transaction retries, error reporting, and product categorization.

Larger applications, in particular, should consider implementing an event-driven or asynchronous architecture, rather than making direct requests and receiving responses. In practice, asynchronous systems can be even more challenging to develop and are often more error-prone, unless there is a very compelling reason to adopt them. A database and transaction engine will likely suffice for the needs of most applications.

6. Build Inventory Management Reports And Insights To Support Decision Making

Inventory records can be analyzed to produce insights on inventory performance. The application can detect patterns and trends relating to inventory records, such as the most popular products, inventory turnover, or inventory shrinkage.

Historical sales and purchase records, when combined with current inventory records, can help in building forecasts and detecting demand fluctuations.

Reports produced by an inventory application should be focused on answering specific questions. What products are approaching their minimum threshold? What records need to be adjusted? Which products are consistently low-selling or high-selling? Where are the main bottlenecks and delays in operation?

Consistent metrics allow managers and executives to analyze the performance of this application and the state of the business in general, but only if the underlying records are accurate and trustworthy.

7. Test Edge Cases And Failure Scenarios In Inventory Applications

Applications should be tested not only against normal operations, but also against abnormal operations. It is important to have processes in place to manage unexpected problems, such as orders going through which should not have, inventory adjustments canceling each other, or inventory records becoming completely unusable. In the context of inventory applications, a few sample test scenarios might include the following:

• Attempt to reserve the same inventory item for two orders simultaneously.

• Submit the same request several times to an API endpoint.

• Cancel an order that had previously reserved some inventory.

• Return a product and increase its inventory.

• Cancel a warehouse transfer.

• Submit a request to an external API that times out.

• Adjust inventory quantity through an unauthorized or unexpected method.

• Ensure that failed transactions do not leave the system in an inconsistent state.

• Ensure that a few important inventory transactions are properly traceable in case of an incident.

It is important to test the integrity of the system under conditions simulating some of these problems. In practice, inventory errors tend to occur not during normal operation but at the interfaces between systems.

8. Inventory Platforms: Selecting The Best Option For Your Business

Inventory control and management systems vary widely in terms of scale, scope, and capabilities. The choice of one option over another will be informed by the specific needs of the business and its suppliers. Businesses considering this decision should examine its existing procedures and plan accordingly.

The Inventory Master web page offers a number of resources for engineers and managers interested in building or selecting an appropriate toolset. Businesses can use this resource to learn more about the available approaches and select a viable option. Documentation and requirements analysis can help set expectations and ensure that a given selection fits the needs of the business rather than the other way around.

Why Do AI Agents Fail Company Policies Although All Tool Calls Are Successful?

Memona — Wed, 14 Oct 2026 17:55:20 +0000

AI agents can violate company policies despite successfully performing all tool calls. Such an agent might update a database or send an email as requested but engage in some other action that should not have been performed.

The root cause of such behavior is that a successful tool call does not imply that an action conforms to the company policies.

Therefore, building a reliable AI system and avoiding similar issues requires understanding how an AI agent performs its actions.

AI Agents Can Cite Success in All Tool Calls as a Precondition for Policy Violations

Typically, an AI agent takes action by interpreting the user’s instruction, selecting an appropriate tool, and calling it. However, a successful response from the tool only verifies that the tool performed the requested action.

It does not necessarily mean that the action was legitimate according to the company’s rules.

For instance, a customer service AI agent may update a client’s details in a database. If the details were incorrect or sensitive, the AI violated the company’s regulations. However, the database now has updated information, and the tool call was successful. Thus, it becomes essential for the system to prevent such an action and ensure that no policy is breached, even if it means refusing a seemingly valid request.

Why AI Agents Violate Company Policies

The Following Are Some Common Causes of Policy Violation

The instructions do not explicitly state business rules.
The AI agent has too many permissions.
The system checks authorization after an action.
The monitoring procedure focuses too much on failures.

The Instructions Lack Specific Business Rules

An AI agent may receive generalized instructions, for instance, “to help customers.” However, the agent should also know which actions should not be performed, such as issuing a refund without managerial approval. Therefore, a reliable system should include explicitly stated and testable rules concerning the necessary aspects of the business.

The AI Agent Has Too Many Permissions

If the instructions allow the AI agent free access to a database, it can update crucial information that affects the business’s smooth functioning. However, providing excessive permissions to an autonomous AI agent can cause more harm than good, so using the principle of least privilege is essential.

When designing such a system, it is helpful to consider who, what, and where the AI agent can access, edit, or view.

The System Checks Authorization After an Action

Sometimes, a system may check whether a request was legitimate after processing it. Thus, an agent can go through with a request that should have been denied and cause some adverse effects. Sensitive actions should be checked for validity before the AI agent processes them. In general, it is advisable to authorize every request and determine the relevant tools’ responses beforehand.

The Monitoring Procedure Only Tracks Failures

Failure monitoring procedures include recording denied requests, unavailable tools, and other unacceptable causes of failure. However, an AI agent can violate company regulations and successfully complete a request that should have been denied. Companies can benefit from designing procedures that track policy violations and abnormal behaviors.

How to Stop AI Agents From Violating Company Policies?

Preventing an AI agent from violating company regulations goes beyond providing it with detailed instructions. The following recommendations can help design a reliable system:

Apply policies that an agent cannot bypass when developing a product.
Ensure the reliability of all sensitive actions performed by an AI agent.
The authorization process should approve acceptable actions.
Whenever possible, a reliable system should require manual confirmation for any sensitive action.
Design logging procedures that record all the necessary information about the action that the AI agent has performed.

A sensitive action performed by an AI agent should be denied whenever it lacks the necessary approvals, tools, or permissions. Although a monitoring procedure is essential to detect any policy violations, it is better to build the system in such a way that such failures rarely occur. This approach will reduce debugging costs and make the AI agent safer.

In addition, the procedures should test the system’s behavior when instructed to violate the company’s regulations. Ideally, an AI agent should refuse to perform a sensitive action when the regulation is not explicitly stated. Developers should also consider cases when a request’s failure mode is not covered by the existing tools and deny all unapproved actions by default.

A real world example of an AI agent that violates company regulations is a customer support agent that updates client information and processes refunds. Typically, a customer would contact the support desk, give their details, and request a refund.

Depending on the size of the refund, the system should either allow or deny the request. Even though such a task seems relatively straightforward, the agent can violate company regulations in several ways. If the instructions only state that it should be able to update customer information and process refunds, the agent may violate data protection laws and process the refund unconditionally. A company policy may stipulate that a financial officer needs to approve all refund requests, but the AI agent lacks this information and follows the instructions correctly by executing the refund.

A more reliable procedure would be to make sure that the system denies requests that exceed the specified size, denies requests lacking an approving signature, or ensures that such requests rerouted to the financial officer for further instructions. Another way to avoid such a violation is to ensure that authorization defaults to an appropriate human operator whenever a request specifies monetary transfer.

The main lesson is that an AI agent’s successfully performed action does not necessarily mean that no company policy has been violated, particularly when designing the procedures concerning the tools the AI agent will use. Therefore, it is essential to ensure that an AI agent cannot freely access and update information that should require a manager’s approval.

AI agents can violate a company’s rules despite effectively executing all tool calls.

How AI and IoT Work in Automotive Manufacturing

Memona — Wed, 14 Oct 2026 15:35:32 +0000

A motor is shaking more than before. Its temperature is rising slowly, but the production process was already running earlier. Will the engineering crew investigate earlier, or will this wait until they are due for a regular equipment check-up?

In a connected, modern auto factory, sensor insights and artificial intelligence can help.

IoT sensors gather physical device data, and AI can help explore the numbers for patterns and insights.

When paired together, the connected car manufacturing ecosystem can help producers make more educated decisions about maintenance, quality inspection, and production process changes.

1. Gathering Necessary Information With IoT

Before making any inferences about a process, an AI engine would require a substantial amount of relevant information.

Industrial IoT platforms can help monitor equipment and the production process by gathering and analyzing relevant insights. Sensors and monitoring services can capture a wide variety of relevant data for automotive manufacturers, including:

• Temperatures and vibrations

• Rotational speed and run-time of a motor

• Cycle durations of manufacturing equipment

• Operational and power status of various devices

• Position and inventory of parts

• Environmental conditions of an area in proximity of manufacturing and testing equipment

For example, a monitoring service could capture temperature and vibrational data of a motor in an automotive assembly plant. These two factors will provide the engineering crew and AI systems with some information about a motor but, typically, not enough. Engineers would have to evaluate how the motor behaves under various conditions before they can predict how the motor will respond at a given moment. For example, engineers would have to build a reference from other similar devices to establish what a “normal” set of vibrations for a specific motor should be.

2. Generating Insights With AI Processing

IoT can enable manufacturers to observe their process. But having raw data about equipment conditions is not enough. To get to meaningful insights, automotive producers would be better off pairing IoT data with artificial intelligence for analysis. There are several ways AI and machine learning can become helpful for manufacturers. A few key use cases include:

Predictive maintenance

By capturing equipment data, a learning model can find behavioral patterns that may signal deterioration in quality. This kind of information can help producers find and address any developing issues.

Automated quality inspection systems

Computer vision enables an ML model to look at images of products and find any problems or irregularities of interest for the manufacturing process. This use case can reduce human effort during the quality inspection phase of production.

Production optimization

By analyzing data from cycle time durations, production equipment status, and interruption frequencies and causes, AI can help optimize a manufacturing process. This data can provide valuable insight into what manufacturing stages and equipment should be prioritized.

Anomaly detection in IoT data

Detecting unusual patterns of behavior can also be achieved by creating a reference of what a “normal” observation should be and flagging any outliers. These irregular patterns can include any information that deviates from established behavioral reference.

The choice of ML technique that should be leveraged for a specific use case will depend on the particular needs, volumes of available information, and acceptable prediction risks.

3. An Illustrative Predictive Maintenance Workflow

Let’s imagine a motor that runs continuously in an automotive manufacturing plant. One of the initial solutions that come to mind in this situation is a predictive maintenance approach.

A monitoring workflow that incorporates AI inference may have the following steps:

Step 1: Sensors

collect vibrational and temperature data at regular intervals

Step 2: Data pipelines

validate this information, verifying that all equipment has proper timestamps and that no values are empty or malformed.

Step 3: Engineers

establish the behavior of this motor at particular conditions, such as higher or lower loads.

**Step 4: **Either a model or an anomaly detection solution will compare new data with the information from step three or the established ranges for this motor.

Based on this data, an engineer would choose either a statistical or a machine learning model for this AI application or another approach (e.g., time series), depending on the volume of available data, the frequency and predictability of anomalous patterns, and other factors.

**Step 5: **Once the engine detects an outlier, it could issue an alert that will prompt human judgment

about the technical condition of the motor. This alert would usually include contextual information about the event, such as timestamps, equipment identifiers, and, possibly, other relevant variables.

In step 6, once there is a human evaluation of the equipment’s current state, engineers will make a conclusion and decide whether it is necessary to halt the production process and schedule maintenance for this equipment.

As you can see, the AI process is not a deterministic one an alert will usually provide valuable information, but not one of a guaranteed equipment or process failure. An anomaly detection model flags values that fall outside of expected ranges.

A predictive maintenance model that flags the likelihood that an equipment may fail at a later point in time involves a few additional steps because it needs to analyze what anomalous patterns may represent equipment wear that could lead to an imminent failure.

However, if the alert system is configured incorrectly, the model could raise false alarms frequently. This makes evaluation of the model’s performance against false positives an equally valuable metric during the solution evaluation phase.

4. Assessing Real-World Performance of an AI Solution

The actual evaluation of any applied model in the physical world is also far from complete. Engineers and analysts will want to measure the following performance indicators specific to a predictive maintenance use case:

1. False alarm rate: How often the maintenance alert system falsely marks equipment as faulty.

2. Missed fault rate: How often such an AI system fails to detect an equipment or process issue.

3. Detection lead time: A crucial time indicator that suggests how much time there will most be be available ahead of a failure for maintenance crews to investigate.

4. Alert response: How soon can the engineering team react upon receiving a model alert.

5. Process interruptions: Will this maintenance platform prevent unplanned system downtimes?

6. Sufficiency of data: Is the volume of incoming data adequate for this model?

Additionally, it will be important to balance how impactful false positives and false negatives will be for the production process. Failing to detect serious equipment issues will likely have negative consequences similar to unplanned downtimes during production, but these consequences will also vary from one setup to another.

Although a model will initially show promising results during training, it will also be crucial to continually monitor the platform and reevaluate its performance after changes, such as new equipment being added or different workload patterns.

5. Expanding Manufacturing Applications to Include Car Safety Features

The same kinds of broad principles that connect IoT devices and AI models apply to modern car safety technology. Automotive manufacturers can install onboard cameras, radar systems, LiDAR, and other sensors to assess their surrounding environment.

AI powered perception could help evaluate the camera feed from these sensors, giving insight into the car’s surroundings and enabling features such as lane-keeping and automatic brake mechanisms. Of course, actual vehicle safety extends beyond model inference to the design and installation of appropriate onboard sensors, systems validation, cyber physical security, and a variety of other factors. Actual automotive safety evaluations will usually require testing the product in relevant, varied, and realistic conditions.

6. Merging AI Components With Existing Factory Infrastructure

Modern auto factories are known to operate multiple systems that control, monitor, and optimize various processes during production. Different solutions serve various purposes for manufacturers:

• Manufacturing execution systems manage and control the production process.

• Enterprise resource planning tools organize and strategize business operations.

• Quality management systems collect and organize quality data and information about manufacturing anomalies.

• Equipment monitoring platforms gather and store data about individual equipment.

When these tools are standalone solutions, it may be difficult for engineers to investigate any production issues. For example, combining quality inspection data about an issue with the relevant machine data and production timeline is essential when looking for the root cause of any manufacturing discrepancy. To optimize the manufacturing process, engineers may want to analyze equipment, part, production scheduling, and maintenance data.

In an AI connected system, these pieces of information will need to be integrated into one unified solution, and engineers may need to consider the following:

• Communication standards: Most factory devices will communicate using standard sets of protocols, but manufacturers may need to build or choose additional communication infrastructure when these standards cannot be met.

• Application programming interfaces (APIs): Where these are available and compatible between systems.

• System availability: Will the necessary systems be up and running at all times to supply the necessary data?

**• Edge computing: **Edge services usually deliver low-latency performance and processing to individual manufacturing devices when necessary. On the other hand, a central cloud platform can be helpful to unite disparate systems, ensure sufficient data storage capacity, and provide the overall flexibility necessary for an AI solution. The particular choice of a design philosophy will depend on specific infrastructure requirements.

7. Additional Manufacturing Challenges Beyond the Core AI Engine

Even the most carefully designed and well implemented AI system will often under-perform if the supporting platform has issues.

Poor data collection and storage: When essential information is missing or inconsistent.

Legacy equipment: Older devices or machinery at a manufacturing site that will require additional infrastructure to support IoT solutions.

Variations in workload: An AI model can become less reliable and accurate when workload requirements change significantly.

**Cybersecurity: **Connecting a factory monitoring platform requires cybersecurity and network segmentation infrastructure to protect devices, data transmission, and processing services from threats.

Human validation: An alert about maintenance may need to be reviewed and corroborated by a human worker or team, depending on the risk level and technical requirements of a given scenario.

System design: Network downtime or unavailable hardware and software can severely impede the ability of an AI-driven factory monitoring solution to deliver on its promised performance. These weaknesses in a broader system should be identified and addressed prior to launch rather than at a later date.

Conclusion

AI and IoT can help manufacturing producers turn equipment and production process data into more valuable and actionable insights. IoT will enable visibility, and AI will help find meaningful patterns that can aid the maintenance process, quality inspection, and production cycle optimization. The best approaches will begin with a well defined objective, collect relevant data, and consider how AI alerts will be routed towards maintenance, inspection, and other operational teams. For readers interested in exploring how connected technology is applied in automotive manufacturing,(https://oemnexai.com/) is a good resource. A reliable AI solution in manufacturing does not always begin with a deep learning model, but rather with a focus on helping the appropriate people get reliable, timely insights to help them take timely and accurate action.