DEV Community: Improving

Why Most AI Training Fails

Improving — Mon, 11 May 2026 09:38:18 +0000

I have taken more online AI courses than I care to count. And I am going to be honest with you: most of them followed the exact same pattern. A long walk through the history of AI, a glossary of terminology, a bunch of model names and acronyms, maybe some screenshots of someone else using ChatGPT, and then a list of prompts to take home. I would finish a course and realize I could not remember half of what I had just watched. Not because the content was wrong. Because none of it connected to anything I actually do at work.

Sound familiar?

If it does, you are not alone. McKinsey's 2025 Global Survey found that 78% of organizations now use AI in at least one business function. But a WalkMe study from August 2025 reported that only 7.5% of employees have received any extensive AI training, and ManpowerGroup's 2026 Global Talent Barometer found that 56% of workers globally received no AI training of any kind. So the tools are everywhere, but the ability to use them well? That is a completely different story.

For managers and individual contributors, the friction shows up in the same places: meetings that produce unclear outcomes, writing that takes too long to start, and decisions that require pulling together information under time pressure. The courses that are supposed to help with this — don't. They teach information that is easy to absorb during the session and just as easy to forget by the next morning.

Why AI "Doesn't Work" for Most People

AI fails for most professionals not because the technology is broken, but because nobody showed them a different way to approach it.

Someone pastes a meeting transcript into an AI tool and asks for a summary. The output sounds confident, but it includes decisions that were never actually made. The immediate reaction? This thing cannot be trusted.

Or someone asks AI to draft a response to a client. The words are technically fine, but the tone is off. And then for the trivial stuff — someone asks if you are going to be at the meeting on Friday — the answer is just "Yep, I'll be there." You do not need AI for that. The challenge is knowing which messages are worth involving AI in and which ones are not.

These experiences pile up, and pretty soon you start wondering: everybody seems excited about AI, so why am I just continually frustrated with it? I hear that question a lot. And the answer is almost always the same: nobody taught you a different way to interact with the tool.

The Trust Problem No One Addresses Well

When people first sit down to work with AI in any kind of structured way, the number one concern is trust. Not whether AI is useful in theory, but a very practical worry: "I don't know if it's just going to lie to me." I hear some version of that in almost every conversation.

And the concern is grounded. Language models can fabricate. But here is what most training programs miss: they either ignore the trust problem entirely, or they spend an hour explaining the technical reasons behind hallucinations without ever showing you what to do about it.

The practical fix is not asking you to trust AI. It is teaching you how to challenge it. Ask where a claim came from. Ask for direct quotes from the source material. Ask what assumptions were made. Once you learn how to provide the right inputs and ask for the receipts, trust stops being a yes-or-no question and becomes conditional: trust it when you can verify it.

Why Treating AI Like a Search Engine Fails

Most people approach AI the way they approach Google: type something, get a result, move on. But AI works through interaction, and the first response is almost never the one you should keep. A lot of professionals figure this out the first time they push back on an AI response and watch it get better. The realization is uncomfortable, because the problem was not the tool. It was how they were using it.

A single vague prompt almost guarantees disappointment. The more context you give AI, the better the response it will give. Sometimes that context emerges through a back-and-forth conversation as your intent and goals surface through feedback on what you like and don't like. That one reframe changes everything that comes after it.

Why Learning AI Now Matters More Than Later

A lot of professionals are waiting, figuring they will pick up AI skills once the tools stabilize. I understand that instinct. But I think it is a mistake.

Imagine the 100-meter dash at the Summer Olympics. The starting pistol fires and every runner launches off the blocks. But one runner stands up, watches everyone else, studies their techniques, and decides to join once they understand the field. By the time they start running, the race is over.

AI adoption is following that same pattern. Gallup's Q3 2025 workforce survey found that 45% of U.S. employees now use AI at work, nearly doubling from 21% in 2023. And EY's Work Reimagined Survey found that companies are missing out on up to 40% of potential AI productivity gains because of gaps in talent strategy.

People who start earlier build intuition. They recognize when an output is fragile. They know how to recover without starting over. Waiting does not give you a better starting position. It just puts you further behind.

Think about what it would feel like to hire someone today who says, "So am I going to have to use this Internet thing?" Nobody asks that question anymore. But AI is heading in the same direction. Right now, learning AI is still seen as getting ahead. Soon enough, not knowing it will just be falling behind.

Why This Is a Training Problem, Not a Tool Problem

Most AI frustration has nothing to do with missing features. It comes from missing habits.

The numbers back this up. DataCamp's 2026 State of Data and AI Literacy Report found that 82% of enterprise leaders say they provide AI training, yet 59% still report an AI skills gap. The most common format is video-based courses, and 23% of leaders say video training does not translate to real-world application. Organizations are investing in training that is not changing how people work.

When I was designing our AI training, I made a very deliberate decision: I am not just going to tell you about a thing. I am going to have you do the thing. Because the difference between watching someone use AI and actually using it yourself is the difference between a forgettable session and a skill that sticks.

Knowing what a hallucination is does not help you when a meeting summary misrepresents a decision. What helps is learning how to provide context, how to demand evidence, and how to refine output without starting over. Prompt libraries promise shortcuts, but real work rarely fits templates. The durable skill is structured thinking: learning how to frame your requests with enough context and constraints that the system responds appropriately, regardless of which tool you are using.

Three Skills That Change Day-to-Day Work

When I was building the curriculum, I asked AI itself to research what professionals are most frequently asking for help with. The answer kept pointing to three areas.

1. Uncovering Insights from Messy Inputs

Meetings generate noise. Transcripts run long. Reports have more detail than anyone can process quickly. AI can help condense and organize all of that — but only if you stay accountable for verifying what comes back.

Asking for a summary is the easy part. The harder and more valuable skill is asking AI to show its sources. If it claims a decision was made, ask it to point to the exact passage. If a takeaway does not sound right, push back: "I don't remember that from the meeting. Show me where that is." That habit is the difference between speed and error.

2. Generating Ideas Without the Blank Page

The most paralyzing moment in any task is the beginning — staring at a blank page, trying to figure out where to start. AI solves this not by writing the final version but by giving you something to react to. Once you are reacting instead of creating from nothing, you are moving.

Here is a technique I share in every session: ask for multiple options rather than a single answer. Ask for five ideas. Review them. The first two might be terrible. The third might have something worth exploring. Tell AI to go deeper on that one and throw the rest away. That sets up an iteration cycle, and iteration is where the real value lives.

3. Drafting Communication with Accountability

AI can get you 90% of the way on a piece of writing that would have taken significant time to start from scratch. I have never had a situation where AI gave me something I did not have to tweak at all — it never gets it 100% right. But that remaining 10% — the nuance, the tone, the judgment about what to include — that is your job. AI handles the heavy lifting and you focus on the part that requires your expertise.

I draw a clear line here: AI can draft, but it does not send. The human owns tone, intent, and consequences. The discomfort people feel about AI handling communications entirely? That is well-placed. Having AI prepare a draft for your review is a fundamentally different thing from having it respond on your behalf.

Why Hands-On Practice Changes Outcomes

There is a difference between seeing AI used and using it yourself. Demos look clean, but real work does not. When you actually practice with AI, you see where your inputs were too vague, where constraints were missing, and where the first confident-sounding response was wrong.

And then something shifts. You structure a real interaction with clear context and constraints, and the output actually works. The realization is blunt: it did not fail randomly. It failed predictably. That is the moment you stop blaming the tool and start changing how you interact with it.

When you provide context, set boundaries, and iterate, AI produces drafts that hold together. Trust becomes conditional instead of binary, and the rework drops. Someone who spent 45 minutes writing a client update discovers that with clear context and two rounds of iteration, AI produces a usable draft in minutes. The remaining time goes to judgment: refining tone, checking accuracy, deciding what to leave out.

The professionals who make AI stick are the ones who apply it to their own problems early. Someone tackles a proposal outline they have been putting off. Someone else feeds in a meeting transcript and pulls action items. When the stakes feel real, the learning sticks faster.

Does Teaching AI Fundamentals Actually Change Anything?

This is a fair question. Professionals are busy, AI changes fast, and it is reasonable to ask why anyone should invest in learning the basics when the tool will be different in six months. The argument holds up if fundamentals training means memorizing features, watching demos, and leaving with a list of prompts. That kind of training does not change behavior.

But fundamentals defined as interaction discipline — how to structure context, how to iterate, how to verify — are not tied to any particular model or release cycle. They work the same way in ChatGPT as they do in Copilot, and they will work in whatever ships next year. The interface changes. The thinking does not.

The gap most professionals are stuck in is not between basic and advanced knowledge. It is between occasional use and reliable use. You have tried AI, gotten mixed results, and not changed your interaction patterns. That gap closes by practicing a different way of working, not by learning more theory.

Even experienced users pick up useful techniques in fundamentals-focused settings, because knowing a lot about AI and using it effectively are two different things. For the large population using AI occasionally and inconsistently, the bottleneck is almost always interaction habits — not technical depth.

Applying AI to Real Work

Training fails when it stays abstract. Real work has constraints: policies, customers, tone, and risk. Shorter, focused sessions tied to real tasks tend to produce more lasting change than marathon lectures.

A practical starting point? Look at what frustrates you. Tasks that are slow or mentally draining often contain parts AI can compress. Someone who spends two hours each week writing status updates can likely compress that to 20 minutes with the right interaction structure, freeing up time for work that actually requires their judgment.

And the applications go beyond text. AI can generate images, create visual aids for presentations, and produce supporting content. Once you see that you can describe a concept and have AI produce a working version, the range of tasks you consider using AI for expands.

Early use tends to focus on low-risk situations: notes, options, internal drafts. Over time, some uses stick and others disappear. The professionals who make AI part of how they work going forward are the ones who found two or three use cases where it reliably saved them time and built those into their routine.

Key Takeaways

AI does not underdeliver because the technology is broken. It underdelivers because most people were never taught how to interact with it. That is a training problem, and 56% of workers globally have not received any AI training at all.

Three skills make the biggest difference for professionals adopting AI:

Uncovering insights from messy inputs — and staying accountable for verifying what comes back
Generating ideas by pushing past the blank page — through iteration, not one-shot prompts
Drafting communication where AI does the heavy lifting — and you own the final 10%

The skills that actually stick — structured thinking, iteration, verification — are not tied to any specific tool or model. They work regardless of what platform you are using. But if your organization is waiting to build those skills, that wait has a price: EY's research found that companies are leaving up to 40% of their potential AI productivity gains on the table because of gaps in how they develop talent.

AI as a Professional Skill

None of this is complicated. But it does require a different approach than most professionals have been taught. And the longer teams wait to build these habits, the more time gets lost to rework, rechecking, and correcting mistakes that did not have to happen.

If any of this resonated — or if you have your own AI training stories (the good, the bad, and the frustrating) — I would genuinely enjoy hearing about them. You can find me on LinkedIn.

OWASP Top 10 for LLMs: A Practitioner’s Implementation Guide

Improving — Mon, 11 May 2026 09:35:40 +0000

Large Language Models (LLMs) are becoming a core part of modern applications — from copilots and chatbots to AI agents connected to tools and internal systems. As adoption grows, so do the security risks.

The OWASP Top 10 for LLM Applications (2025) highlights the most common security issues teams must address when building AI-powered systems. These risks go beyond traditional application security because LLMs interact with prompts, external data, tools, and autonomous workflows.

In this post, we'll cover a practical overview of each risk and how teams can detect, prevent, and test for them.

LLM01:2025 — Prompt Injection

Prompt injection is when an attacker slips malicious instructions into user input or content the model reads, tricking it into doing something it shouldn't.

Direct injection: A user directly tells the model to ignore its rules.
Indirect injection: The model reads an external document or web page that secretly contains instructions and follows them without realizing it.

Example: An LLM connected to internal tools retrieves a document containing hidden instructions telling it to export database credentials. The model follows the instruction and triggers a data leak.

How to Detect It

Watch for phrases like "ignore previous instructions" or "pretend you are" in user input
Compare inputs against known malicious prompt patterns
Alert on unusual tool calls — especially ones fetching or exporting data unexpectedly
Log all inputs and outputs so you can trace what happened after an incident

How to Prevent It

Make sure system-level rules can't be overridden by user messages
Sanitize and validate any external content before passing it to the model
Use clear separators between instructions and data in your prompts
Apply least-privilege access — the model should only be able to call what it needs
Add output filters to block unsafe responses before they reach users

How to Test It

Run red-team tests that simulate both direct and indirect injection attempts. Use automated prompt fuzzing to probe edge cases. After any prompt changes, run regression tests to confirm your safety rules still hold.

LLM02:2025 — Sensitive Information Disclosure

This happens when an LLM leaks personal data, API keys, credentials, or internal documents in its responses. It can occur through direct questions, indirect prompt injection, or a retrieval system that doesn't properly restrict access to sensitive documents.

Example: An internal HR assistant retrieves employee salary records during a broad query and includes them in its response — even though the user asking had no right to see them.

How to Detect It

Scan model outputs for PII (names, emails, ID numbers) and secrets (API keys, passwords)
Monitor what documents the retrieval system is fetching and whether they match the user's access level
Flag responses with unusual patterns like long random strings, which could be tokens or keys

How to Prevent It

Redact sensitive data before it gets indexed or fed into the model
Only retrieve documents the current user is actually allowed to see
Add an output filter that blocks responses containing classified data
Keep sensitive data stores separate from general knowledge sources

How to Test It

Try prompting the system to extract personal records or credentials through indirect queries. Verify that restricted data can't be retrieved through similarity-based tricks. Check that access controls on your retrieval system are actually working end-to-end.

LLM03:2025 — Supply Chain Vulnerabilities

LLM applications depend on many third-party components — base models, plugins, vector databases, MCP servers, and embedding providers. Any one of these can be a weak link. A malicious or compromised dependency can manipulate outputs, steal data, or take unexpected actions.

Example: An application uses a third-party MCP server for document processing. A malicious update modifies the server's tool responses to inject hidden instructions, causing the app to expose sensitive data.

How to Detect It

Keep a full inventory of every model, plugin, connector, and tool your application uses
Generate and maintain a Software Bill of Materials (SBOM) so you know what's inside
Watch for unexpected changes in model or tool behavior after updates
Correlate version upgrades with any new security anomalies

How to Prevent It

Vet vendors before integrating their tools — check their security practices and update history
Verify model weights and tool packages using checksums and cryptographic signing
Give third-party tools the minimum permissions they need, nothing more
Isolate external services in controlled network segments where possible

How to Test It

Regularly scan dependencies for known vulnerabilities. Test that third-party tools behave exactly as documented with no hidden inputs and no unexpected outputs. Before upgrading a dependency in production, simulate the upgrade in a test environment first.

LLM04:2025 — Data and Model Poisoning

Data poisoning happens when malicious data is introduced into training datasets or the retrieval corpus. In fine-tuning, poisoned samples can embed hidden behaviors that activate on specific triggers. In RAG systems, an attacker can insert crafted documents into the vector store so the model retrieves and trusts corrupted context.

Example: A RAG system indexes public documentation. An attacker adds a document with hidden instructions that changes how the model responds whenever a specific keyword is used.

How to Detect It

Track where every piece of data comes from before it enters your pipeline
Look for documents that appear in retrieval results far more often than you'd expect
Monitor for sudden shifts in model behavior after a dataset update
Check embeddings for outliers that don't fit the rest of your corpus

How to Prevent It

Control who can write to your vector store — don't allow open ingestion
Require human review for any high-impact data before it's added
Version your datasets so you can roll back if something goes wrong
Don't automatically ingest content from untrusted external sources

How to Test It

Use canary data — known triggers — to check whether the model has been altered. Compare model behavior before and after dataset updates. Periodically audit your retrieval corpus for documents that don't belong.

LLM05:2025 — Improper Output Handling

Output risk occurs when LLM responses are used directly — rendered as HTML, inserted into SQL queries, or passed to shell commands — without any validation. Because model output is probabilistic, it can contain unexpected characters or code-like content. Treating it as trusted input is the mistake.

How to Detect It

Scan model outputs for suspicious patterns: script tags, SQL special characters, shell operators
Watch downstream systems for unexpected queries or commands
Enable Content Security Policy (CSP) violation reporting to catch injected scripts

How to Prevent It

Always encode output before rendering it — treat it the same way you'd treat user-submitted content
Never pass model output directly to a shell command, SQL query, or code evaluator
Use parameterized queries instead of string concatenation
Validate outputs against a strict schema — for example, require JSON with defined fields

How to Test It

Deliberately include injection payloads in model responses during testing and verify they are neutralized before rendering. Review all code paths where LLM output flows into execution layers or sensitive APIs.

LLM06:2025 — Excessive Agency

When an LLM agent is given too much autonomy — access to APIs, databases, or infrastructure without proper guardrails — it can chain together actions that were never intended. This can cause real damage: deleted records, unexpected transactions, or service disruptions, often triggered by an ambiguous instruction or injected prompt.

How to Detect It

Log every action the agent takes, including its reasoning steps
Alert when an agent exceeds a set number of actions in a sequence
Track cross-system changes that could indicate the agent acted beyond its scope

How to Prevent It

Require human approval before the agent takes any high-risk or irreversible action
Limit how many steps an agent can chain together
Give agents time-limited credentials with the minimum permissions needed
Keep planning and execution separate — don't let the model decide and act in one step

How to Test It

Test agents against adversarial and ambiguous prompts to identify how they behave under pressure. Verify that kill switches actually stop an agent mid-task. Run stress tests to observe what happens when objectives conflict.

LLM07:2025 — System Prompt Leakage

The system prompt often contains safety rules, tool schemas, internal logic, and operational details that were never meant to be visible. If an attacker can get the model to reveal this content, they learn exactly how to bypass your controls.

Example: A user repeatedly asks the model to repeat its hidden instructions. After several attempts, the model partially reveals the safety rules embedded in its system message.

How to Detect It

Watch for responses that look like internal instructions or policy text
Flag repeated meta-questions like "what are your instructions" or "ignore your rules"
Use automated red-teaming tools to simulate extraction attempts

How to Prevent It

Don't store credentials, API endpoints, or secrets inside the system prompt
Use output filters that block responses referencing hidden instructions
Keep policy logic separate from natural language instructions
Structure prompts so system rules cannot be disclosed in response to user requests

How to Test It

Run structured extraction prompts specifically designed to coerce the model into revealing system content. After every prompt update, re-test to confirm that nothing new has leaked. Rotate system prompts if exposure is confirmed.

LLM08:2025 — Vector and Embedding Weaknesses

RAG systems rely on vector similarity to retrieve relevant documents. Attackers can craft documents with embeddings specifically designed to dominate retrieval results, hijacking the context the model receives. Poorly secured vector stores can also expose source content through embedding inversion — where attackers attempt to reconstruct original content from stored embeddings.

Example: A malicious document inserted into a public knowledge base is embedded to closely match frequent queries, causing it to be consistently retrieved and influence the model's output.

How to Detect It

Monitor for documents appearing far more often than expected across unrelated queries
Check for sudden shifts in the distribution of your embedding space
Audit who can write to your vector store and when changes were made

How to Prevent It

Restrict write access to the vector store — require authentication for all ingestion
Combine semantic similarity with keyword or rule-based filtering as a second check
Encrypt embeddings at rest and isolate vector infrastructure
Periodically re-index and validate your corpus to catch tampered documents

How to Test It

Simulate retrieval hijacking by inserting adversarial documents and checking whether they surface in results. Compare retrieval output from a clean corpus against your live one. Audit ingestion logs to see when and what was added.

LLM09:2025 — Misinformation

LLMs can confidently generate content that is factually wrong — fabricated statistics, non-existent citations, and outdated information. In applications used for decision-making, legal work, or reporting, this can cause serious real-world harm.

How to Detect It

Cross-check claims against trusted knowledge sources or retrieval results
Flag responses that make factual claims without citations in high-stakes domains
Monitor for contradictions across multi-turn conversations

How to Prevent It

Ground responses in retrieved, verifiable sources rather than relying on the model's memory
Require citations for any regulated or high-stakes use case
Add confidence indicators so users know when the model is less certain
Require human review before allowing the model to publish in high-impact contexts — do not permit autonomous publishing

How to Test It

Run benchmark evaluations using fact-sensitive datasets. Test with adversarial prompts designed to produce hallucinated references and measure how often they appear. Put corrections in place and notify affected parties if fabricated content has already been published.

LLM10:2025 — Unbounded Consumption

Without limits, LLM interactions can spiral into excessive token usage, recursive agent loops, or rapid API call chains. The result is infrastructure strain, massive cost overruns, or denial of service — sometimes triggered accidentally, sometimes by a malicious user probing for weaknesses.

How to Detect It

Track token usage per session and per user against expected baselines
Alert on recursive tool calls or unusually deep action chains
Use cost anomaly detection on your API and compute bills

How to Prevent It

Set hard token limits and cap response lengths
Apply rate limiting per user, per tenant, or per session
Limit how deep an agent can chain actions
Require confirmation before the model starts a high-cost operation

How to Test It

Simulate recursive prompts and measure whether your safeguards kick in. Test rate limiting and quota enforcement under high concurrency. After any incident, audit usage logs to understand the financial and operational impact.

Conclusion

LLM security is an engineering discipline, not an afterthought. The OWASP Top 10 for LLM Applications highlights that securing AI systems requires more than traditional application security practices. Teams must also address risks related to prompts, training data, external dependencies, and autonomous agents.

Building secure LLM systems requires layered protections, careful data management, strong observability, and continuous testing. The table below summarizes the key controls across all ten risk categories as a quick-reference checklist for teams designing, deploying, or operating LLM-enabled systems.

Risk	Detect	Prevent	Respond
Prompt Injection	Log inputs, pattern match	Sanitize inputs, least-privilege	Trace and remediate
Sensitive Disclosure	Scan outputs for PII/secrets	Redact data, enforce access controls	Block and audit
Supply Chain	SBOM, behavior monitoring	Vet vendors, verify checksums	Rollback, isolate
Data Poisoning	Track data provenance, monitor embeddings	Control ingestion, version datasets	Roll back corpus
Improper Output Handling	Scan for injection patterns	Encode outputs, parameterized queries	Review execution paths
Excessive Agency	Log agent actions, action limits	Human approval, least-privilege creds	Kill switch, audit
System Prompt Leakage	Watch for meta-questions	No secrets in prompts, output filters	Rotate prompts
Vector/Embedding Weaknesses	Monitor retrieval patterns	Restrict write access, encrypt embeddings	Re-index, audit logs
Misinformation	Cross-check claims, flag unsourced content	Ground in retrieval, require citations	Notify, correct
Unbounded Consumption	Track token usage, cost anomalies	Rate limits, hard token caps	Audit usage, throttle

Understanding these risks is the first step. For edge cases and complex deployments, consider working with security experts who specialise in AI systems.

If you found this post useful or have real-world experiences to share, feel free to connect on LinkedIn.

Everyone Talks About Golden Paths. Nobody Talks About Building Them.

Improving — Mon, 11 May 2026 09:32:33 +0000

Everyone's talking about Platform Engineering lately. Walk into any major technical conference like KubeCon, and you're bombarded with talks on "Golden Paths" and "IDPs." And it's great that the industry is finally focusing on developer experience instead of just more YAML.

But there's a massive gap between the conference talks and your terminal.

You leave these sessions feeling inspired, only to sit back down at your desk and stare at a mess of legacy deployment scripts. Most of the advice out there tells you why you need a platform, but almost nobody shows you how to actually build one without a massive team or a million-dollar budget.

That's exactly what I spoke about at the Platform Engineering Day co-located event at KubeCon Europe 2026. This post is a written version of that talk, with everything you need to go from zero to a working golden path — without needing a big platform team or expensive tooling.

What is a Golden Path?

A golden path is just the "opinionated" route your org sets up to get code into production.

It's about making the right way the easiest way.

I like to think of it as a product, not a mandate. You aren't forcing teams into a cage; you're giving them a well-lit highway with the guardrails already bolted on. If a team really needs to go off-road and hack together something custom, they can — but 99% of the time, they'll choose the highway because it's faster and safer.

A golden path isn't "done" unless it hits these four marks:

Opinionated: You've already made the boring decisions so the developer doesn't have to and can focus on shipping features.
Self-service: If a developer has to ping someone on Slack or open a Jira ticket, it's not a golden path. It's a hurdle.
Safe by default: Security and health checks aren't "extra steps" — they're just part of the plumbing.
Progressive: You don't build the whole highway at once. You start with a single paved mile.

When this works, the ROI is immediate. You stop seeing people copy-pasting crusty YAML from a repo last touched in 2022. New hires actually ship code on day one instead of day ten.

But here's where most organisations get stuck.

They understand what a golden path is. They've seen the talks, read the blog posts, maybe even drawn the diagram on a whiteboard. But when it's time to actually build one, the question is always the same: where do we start?

The problem is that a lot of teams assume they need a full platform in place before they can build a golden path — a proper IDP, a self-service portal, the whole thing. So they wait. And nothing gets built.

You don't need a full platform to start paving a golden path.

The Gap Nobody Talks About

Most teams aren't lacking a Golden Path because they're lazy or "don't get it." They're stuck because they can't find the starting line. Most advice from talks and blog posts assumes you're either part of a 50-person platform team or starting a greenfield project. In the real world? You have neither.

What you do have is a folder with a bunch of deployment scripts. Some are six months old; others were written three years ago by someone who hasn't worked at the company since 2023. Every team is doing their own thing — same task, but a dozen different, messy ways to get it done.

That isn't a platform problem; it's a fragmentation problem. And you don't need to buy a shiny new tool to fix it.

The other myth that kills progress is the "Big Bang" approach — sitting in a room, architecting the perfect platform, getting stakeholder approval, and buying three new SaaS tools before shipping a single thing. That's a recipe for a six-month roadmap that ends in a "deprioritized" project.

Building a Golden Path isn't a project with a deadline. It's an evolution. It matters less where you're starting and more that you're actually moving.

The good news? Your deployment scripts, messy as they are, are already your Phase 0. You are closer than you think.

A Maturity-First Approach to Building Golden Paths

You don't have to reinvent the wheel here. The CNCF Platform Engineering Maturity Model already gives us a roadmap.

This model breaks things down across five pillars — investment, adoption, interfaces, operations, and measurement — to help you figure out exactly where you're standing.

We take that model and map it directly to golden path construction. Instead of asking "how do we build a golden path?", you ask "what does the next maturity level look like for us?" That shift makes the whole thing much less overwhelming.

Each phase builds on the previous one. You can find the complete demo in the Golden Path Construction Demo Git Repo that maps out these phases with actual code — designed as a template you can fork and adapt to your own organisation.

Phase 0: The Chaos

This is where most teams are, even if they won't admit it.

Every team has their own deployment script. Same goal, different approach. One team uses inline kubectl commands; another has a YAML file that's been copied and modified so many times nobody knows what the original looked like. Images are pinned to latest, resource limits are missing, health checks are broken or absent.

kubectl apply -f - <<EOF
apiVersion: apps/v1
kind: Deployment
metadata:
  name: myapp
spec:
  replicas: 1
  selector:
    matchLabels:
      app: myapp
  template:
    metadata:
      labels:
        app: myapp
    spec:
      containers:
      - name: myapp
        image: nginx:latest        # ❌ latest tag
        ports:
        - containerPort: 80
        # ❌ no resource limits
        # ❌ no health checks
        # ❌ no namespace
EOF

Nothing is wrong with any individual script. The problem is there are ten of them, and none of them talk to each other.

Phase 1: Standardize

This is the most important phase — and also the easiest one to ship.

You pick one script. One template. Every team uses it. That's it.

The template enforces the basics by default: resource limits, health checks, proper labels, a namespace. Nobody has to remember to add them. They're just there.

template:
  metadata:
    labels:
      app: ${APP_NAME}
      managed-by: platform-team
  spec:
    containers:
    - name: ${APP_NAME}
      image: ${IMAGE}
      ports:
      - containerPort: 80
      resources:
        requests:
          memory: "64Mi"
          cpu: "50m"
        limits:
          memory: "128Mi"
          cpu: "100m"
      livenessProbe:
        httpGet:
          path: /
          port: 80
        initialDelaySeconds: 10
      readinessProbe:
        httpGet:
          path: /
          port: 80
        initialDelaySeconds: 5

Developers just pass the necessary values; everything else is governed by the template.

What changes from Phase 0: instead of ten different scripts with ten different outcomes, you have one script with one consistent output.

What you gain: predictability. Every deployment looks the same. Debugging becomes faster. Onboarding becomes easier. And you've done this without any new tooling or platform investment.

This is also your first win to show leadership. You haven't built a platform. You've standardised how your teams deploy. That's already valuable.

Phase 2: Validate

Standardization tells teams what to do. Validation makes sure they actually do it.

In this phase, you move from a shell script to a config-driven approach. Teams fill in a YAML file with their app details. A validation layer checks the inputs before anything touches the cluster. Bad configs are rejected early, with a clear error message — not a cryptic Kubernetes failure three minutes later.

def validate(config):
    errors = []

    for field in ["name", "image", "team"]:
        if not config.get(field):
            errors.append(f"❌ '{field}' is required")

    name = config.get("name", "")
    if name and not re.match(r'^[a-z][a-z0-9-]*$', name):
        errors.append(f"❌ 'name' must be lowercase and DNS-compatible (got: '{name}')")

    image = config.get("image", "")
    if image and (":latest" in image or ":" not in image):
        errors.append(f"❌ 'image' must use a specific version tag, not ':latest' (got: '{image}')")

    env = config.get("environment", "dev")
    if env not in ENV_DEFAULTS:
        errors.append(f"❌ 'environment' must be one of {list(ENV_DEFAULTS.keys())} (got: '{env}')")

    return errors

What changes from Phase 1: the interface is now declarative, not imperative. Teams describe what they want, not how to do it.

What you gain: fewer misconfigurations, faster feedback loops, and a foundation that's ready to scale.

Phase 3: GitOps

This phase is a single change with a big impact.

Everything from Phase 2 stays exactly the same — the validation, the manifest generation, the standards. The only thing that changes is the last step. Instead of kubectl apply, you do a git push.

A CD tool like ArgoCD watches the repository and deploys automatically when it sees a change. Every deployment is now a commit. You get a full audit trail for free. Rollback is just a git revert.

What changes from Phase 2: humans are no longer directly touching the cluster.

What you gain: traceability, consistency across environments, and the groundwork for everything that comes next. Git becomes your single source of truth.

Phase 4: IDP and Self-Service

This is where the golden path becomes fully self-service.

Everything underneath is the same as Phase 3. The validation still runs. The manifest is still generated. ArgoCD still deploys. The developer just doesn't see any of it.

Instead, they open a portal, fill in a form with their app name, image tag, team, and environment, and hit deploy. No YAML. No terminal. No kubectl.

The platform carries all the knowledge so the developer doesn't have to.

What changes from Phase 3: the interface. That's it.

What you gain: any developer in your organisation can now deploy safely and correctly, regardless of their Kubernetes experience. The platform enforces everything. The developer just ships.

This is the line from the talk that captures the whole framework best:

The interface changes. The logic doesn't.

From Phase 1 to Phase 4, the core logic is the same. You're just wrapping it in a better interface at each step. That's what makes this approach so practical — you're not rebuilding from scratch at every phase. You're building on what you already have.

Why This Approach Works

Most platform initiatives fail because they try to boil the ocean. They treat a golden path like a destination you reach after eighteen months of development. By the time the platform is "ready," the requirements have changed, the budget is gone, and developers have already moved on to their own shadow IT solutions.

This maturity-first approach flips the script.

You get ROI on Day One

When you start at Phase 1 by just standardizing a single template, you aren't waiting for a portal to be built. You are solving the "copy-paste YAML" problem immediately.

Every hour a developer doesn't spend debugging a bad deployment script is an hour they spend shipping features.

You don't need a UI to prove that value to leadership.

It reduces cognitive load, not just tickets

A common mistake is thinking a golden path is just about automation. It's actually about psychology. When a developer knows there is a "safe" way to deploy, they stop worrying about breaking the cluster. That confidence leads to faster iterations. By building progressively, you lower the barrier to entry for new hires without overwhelming your existing team with a massive new toolset to learn.

It earns developer trust

Developers are naturally skeptical of "mandated" platforms. They've seen too many internal tools that make their lives harder. By evolving your existing scripts into a golden path, you are meeting them where they already live — fixing their current pain points instead of forcing them to adopt a whole new workflow overnight. Trust is built in increments, not in a grand reveal.

Leadership loves predictable growth

For a CTO or VP of Engineering, "we are building a platform" sounds like a high-risk, high-cost gamble. "We are maturing our deployment lifecycle from Phase 1 to Phase 2" sounds like a predictable, measurable improvement. This model gives you a language to speak to leadership that justifies the investment without making impossible promises.

Get Started

Building a golden path isn't about hitting a finish line. It's about building a culture where the right way to work is also the easiest way.

By following a maturity-based approach, you stop treating "The Platform" as a project and start treating it as a product that evolves with your team. You don't need to wait for a massive budget or a greenfield project. You just need to look at your current Phase 0 and decide which piece of logic is worth standardizing today.

The rewards are worth the effort: faster onboarding, fewer outages, and developers who actually enjoy their deployment process.

If you're currently staring at a mess of scripts and wondering how to map out your own Phase 1, I'd love to hear about it — reach out to me on LinkedIn to discuss your platform journey or share what's working for your team.

Enterprise Business Intelligence: A Guide to Strategy, Adoption, and Impact

Improving — Mon, 11 May 2026 09:27:01 +0000

Business leaders are buried in data. Dashboards multiply. Tools are renewed year after year. Yet basic questions still trigger debate instead of answers. Which numbers are accurate? Which reports reflect the true state of the business? Which decisions require analytical evidence rather than opinion? And when should the organization move beyond hindsight reporting to predict what comes next?

Business Intelligence and Advanced Analytics are supposed to fix this. BI provides clarity on performance. Advanced Analytics reveals what's coming. When they work, teams argue less and move faster. When they fail, organizations get reporting graveyards and black-box models that no one trusts, and no one uses.

This guide is for executives who want Business Intelligence and Advanced Analytics to strengthen competitiveness, improve decision quality, and guide strategic priorities — not simply produce more reports and forecasts.

What is Business Intelligence & Advanced Analytics?

Business Intelligence: The Architecture of Trust

BI is an organization's single source of truth — the infrastructure that ensures every leader asking the same question gets the same answer. But framing BI as a reporting function understates its strategic role. What BI actually builds is institutional trust in data: the confidence to act on a number without auditing it first.

That trust is architectural. It lives in governed pipelines, standardized definitions, and models that make historical performance legible at scale. For executives, the real question isn't whether your organization has BI. It's whether your BI is trusted enough to be acted on without footnotes.

Advanced Analytics: The Engine of Foresight

Where BI answers what happened, Advanced Analytics addresses the harder question: what should we do next? Forecasting demand, surfacing attrition risk before it materializes, identifying segments most likely to convert — these are decisions shaped by patterns too complex for manual analysis and too consequential to leave to intuition.

The strategic value isn't the models themselves. It's the compression of uncertainty before a decision is made. Organizations with mature Advanced Analytics capabilities don't just react faster. They compete on a different time horizon, allocating resources before the signal becomes obvious.

BI tells you how well you executed yesterday's strategy. Advanced Analytics informs whether tomorrow's strategy is the right one.

The Semantic Layer: Missing Infrastructure Most Organizations Skip

The semantic layer is a business-friendly abstraction between raw data and every analytics tool. It is the universal translator that ensures "revenue" means exactly the same thing in Power BI, Tableau, your CRM, and your AI agents.

Without it, Finance defines active customers one way, Marketing another, Sales a third. Leadership meetings become reconciliation sessions. AI tools pull data from three different metric definitions to answer one question, making insights meaningless.

Why it matters in 2026: Snowflake's Open Semantic Interchange (OSI) initiative introduces a shared, vendor-neutral semantic standard to keep data definitions consistent across platforms, tools, and AI systems.

Implementation roadmap:

Start with 10–15 metrics in executive dashboards
Document exact business logic — not just calculations, but edge cases and why
Assign business owners (CFO owns revenue, not IT)
Build incrementally by department
Version control every change
Enable self-service within guardrails

Common mistake: Building for your BI tool instead of your business. The semantic layer should be tool-agnostic — or you've just created expensive vendor lock-in.

Why Most BI Strategies Fail: The Numbers

60–70% of BI initiatives fail to deliver business value (Gartner). Not edge cases. The norm.
90% of companies use AI in BI, yet only 39% see any profit impact. They're deploying sophisticated technology on broken foundations.
Poor data quality costs the U.S. economy $3.1 trillion annually (IBM). Bad data creates inventory write-offs, lost revenue, operational waste, and strategic errors from flawed metrics.
By 2027, 80% of data governance initiatives will fail (Gartner).

What Separates Success from Failure

Five factors matter in building a successful BI program:

Executive sponsorship that secures resources and prioritizes initiatives
Data governance ensuring accuracy and accessibility
Analytics aligned to business KPIs, not IT metrics
User-friendly tools matched to organizational maturity
Agile iteration with feedback loops

How Business Intelligence Works: The Critical Layers

BI moves data from operational systems into structured insights that inform decisions across several interconnected layers:

Data Ingestion: Data is extracted from ERP, CRM, marketing platforms, and cloud applications, then consolidated into a central warehouse or lake.
Data Engineering: Raw data is cleaned, standardized, and modeled into structured formats. This is where quality controls and governance rules are enforced. If this layer is weak, every report built on top of it becomes unreliable.
Analytics and Modeling: Structured data is analyzed through dashboards and reports. Advanced analytics extend this layer by predicting outcomes and recommending actions.
Delivery: Insights are delivered through dashboards, automated alerts, embedded analytics, or APIs — placed directly within existing workflows.
Operationalization: Insights must influence real decisions — in planning, pricing, forecasting, and resource allocation. Without this step, BI remains reporting rather than decision support.

Many organizations invest heavily in visualization while underinvesting in data engineering and operational integration. This imbalance is why analytics programs stall.

Data Readiness: The Foundation

Business Intelligence succeeds or fails on one condition: trust in the data.

When leaders do not trust the numbers in their dashboards, adoption declines. Reporting shifts back to spreadsheets. Meetings turn into reconciliation sessions. BI becomes a passive reference system instead of a decision engine.

Data must be:

Accurate and complete across systems, with discrepancies resolved before reports are published
Owned clearly, so accountability exists when issues arise
Standardized by definition, so the same metric produces the same result across departments
Accessible securely without creating unnecessary friction
Available in time to influence decisions rather than explain outcomes after the fact

Analytics Dependency: A 5% error rate in historical sales data might cause minor reporting discrepancies in BI dashboards. The same error amplified through forecasting models can result in inventory planning mistakes costing millions. Organizations implementing predictive analytics without first addressing data quality see 4.2x higher model failure rates and 67% longer time-to-production.

Five Questions Before Expanding Business Intelligence

Do decision makers trust the data enough to act without extra validation?
Is ownership defined for critical datasets and metrics?
Are KPIs consistent across departments and executive reports?
Can data be accessed fast enough to support real decisions?
Do business users understand what the data represents?

If most answers are no, improve data foundations before expanding BI tooling.

The Business Intelligence Adoption Lifecycle

BI initiatives typically don't fail because of technical implementation issues. They fail when behavior, processes, and decisions remain unchanged after launch.

Phase One: Foundations and Metric Alignment

Establish a single, trusted view of the business. Identify the metrics that matter most to executive leadership and align their definitions across departments. Document every KPI — including calculation logic, data sources, and ownership. The priority is alignment, not visualization. Without agreement on definitions, dashboards scale confusion rather than clarity.

Phase Two: Reporting and Dashboard Enablement

Design role-based, outcome-driven dashboards. Executives see high-level performance and trends. Operational teams see actionable metrics tied to their responsibilities. Reports answer specific questions rather than displaying everything available. Performance, reliability, and ease of use matter as much as visual design.

Phase Three: Adoption and Decision Integration

True adoption occurs when insights integrate into regular business processes — used in planning meetings, operational reviews, and performance discussions. Change management is critical. Users must understand why BI exists, how it supports their goals, and how it replaces older ways of working.

Phase Four: Scaling and Continuous Improvement

Automate and monitor data pipelines. Ensure governance keeps new metrics consistent with existing definitions. Measure performance and usage so BI evolves based on how it's actually used. Organizations can extend toward advanced analytics when there's clear business demand — driven by readiness and value, not hype.

AI-Augmented BI: What Actually Works

74% of executives achieve ROI from AI agents within the first year. 39% of organizations have deployed 10+ AI agents (Google Cloud). AI in analytics is no longer experimental — enterprises are operationalizing it at scale.

Natural Language Processing

Modern NLP engines understand business terminology, resolve ambiguous queries, and generate accurate SQL. Instead of learning analytics tools, users can ask "Which product categories underperformed in Q3?" and get instant answers. The NLP market is expected to grow at a 47.1% CAGR from 2026 to 2030. But NLP only works on well-governed data — applied to poorly structured data, it accelerates errors rather than insights.

What AI Delivers

Anomaly Detection: Continuously monitors metrics, learns normal patterns, alerts when things deviate. Detects quality issues and behavior shifts before humans notice.
Pattern Recognition: Identifies correlations across high-dimensional data that manual analysis can't surface — which product combinations predict higher lifetime value, which operational conditions lead to failures, which marketing sequences drive conversion.
Automated Governance: ML systems can fix approximately 60% of data-related BI failures automatically. They learn expected patterns, detect anomalies in real time, and correct issues before they impact analytics.

The Reality Check: AI Amplifies Foundations

Organizations that implement AI before resolving governance and data quality gaps often waste an average of $1.2 million and experience failure within 6 to 18 months. AI amplifies existing capabilities — strong data foundations produce strong AI outcomes; weak foundations produce faster failures.

Timeline to AI-augmented BI:

6–12 months: Establish data foundations if not already mature
3–6 months: Implement initial NLP or automated insights
6–12 months: Achieve broad adoption and measurable ROI

The True Cost of Business Intelligence Failure

When BI fails, the visible cost is wasted technology investment. But direct costs represent only a fraction of total impact:

Opportunity cost: While competitors act on insights, failed BI organizations debate which numbers are correct and delay decisions pending manual analysis.
Trust erosion: When executives burn months and millions on BI that delivers dashboards nobody trusts, they resist future analytics initiatives for years.
Talent drain: Data professionals don't join organizations to reconcile spreadsheets. Replacing them costs 50–200% of annual salary while institutional knowledge disappears.
Strategic misalignment: Different departments optimize different metrics, creating internal conflicts — Sales chases volume, Finance wants margin.
Analytics Opportunity Loss: Organizations stuck in BI failure cycles miss the window to build predictive capabilities. Those 18 months behind in analytics maturity take an average of 4.2 years to catch up, losing an estimated $12–47M in unrealized efficiency gains depending on industry.

Business Intelligence Maturity: Where You Stand and What Comes Next

Progression Strategy

Ad-hoc to Foundational (12–18 months)
Identify 10–15 metrics in executive decisions. Document exact definitions. Assign ownership. Establish single authoritative sources. Build simple, reliable dashboards focused only on these.
Investment: $150K–$500K

Foundational to Scaling (12–18 months)
Implement semantic layer. Deploy self-service for trained users. Embed analytics into workflows. Establish governance for new metrics. Build data literacy programs.
Investment: $300K–$1M

Scaling to Advanced (12–24 months)
Implement predictive analytics for high-value use cases. Deploy AI-augmented analytics. Build real-time streaming for time-sensitive decisions. Integrate BI into automated systems.
Investment: $500K–$2M

Common mistake: Attempting to jump from ad-hoc to AI-powered predictive analytics. Organizations waste millions deploying sophisticated technology on unreliable foundations.

What to Consider Before Choosing a BI Consulting Partner

Choosing how to build Business Intelligence — whether in-house, with a consulting partner, or through a hybrid model — is a strategic choice that directly affects how leaders make decisions. Before engaging a BI consulting firm, evaluate more than technical capability. Assess how the approach supports strategy, governance, enablement, and long-term sustainability.

18 Critical Questions to Ask BI and Analytics Leaders or Consultants

How do you define success for a BI and analytics initiative beyond dashboard delivery?
How do you align, standardize, and govern business metrics across departments?
What is your approach to data quality, governance, and ownership at scale?
How do you drive BI and analytics adoption among executives and operational teams?
How do you integrate BI and analytical insights into day-to-day decision-making workflows?
What experience do you have with organizations at our current BI and analytics maturity level?
How do you balance self-service analytics access with governance and control?
How do you ensure performance and scalability as data volumes and analytical complexity grow?
How do you measure the business value of BI and analytics after go-live?
What role does change management play in analytics-led BI transformations?
How do you enable internal teams to own, extend, and evolve analytics over time?
What does a successful first 90 days look like for both BI and advanced analytics delivery?
How do you prevent metric and model drift as new dashboards, reports, and analytical use cases are introduced?
How do you approach data security, privacy, and access control in analytics environments?
How do you keep BI and advanced analytics aligned with evolving business priorities and strategy?
How do you identify and prioritize advanced analytics use cases that deliver measurable business impact?
How do you validate, monitor, and explain analytical models to ensure trust in insights and recommendations?
How do you transition teams from descriptive BI to predictive and prescriptive analytics over time?

Red Flags to Avoid When Evaluating Partners

Leading with BI tools or platforms before understanding business goals
Downplaying data quality, governance, or metric ownership challenges
Promising rapid BI transformation without an adoption strategy
Treating dashboard delivery as the definition of success
Lacking a clear plan for change management and enablement
Inability to explain how BI impact will be measured post-launch
Over-customized solutions that are difficult to maintain internally
No clear knowledge transfer or long-term ownership model
No experience with statistical model validation or MLOps
Treating all problems as machine learning opportunities
Inability to explain analytical methodology in business terms

10 Common Business Intelligence Mistakes That Waste Millions

Buying BI tools before defining business objectives
Failing to standardize KPIs and metric definitions
Ignoring data quality and governance foundations
Overbuilding dashboards with no clear decision purpose
Treating BI as an IT initiative instead of a business capability
Assuming self-service BI guarantees adoption
Measuring dashboard usage instead of decision impact
Neglecting performance and scalability planning
Lacking clear ownership for data and reports
Letting inconsistent metrics proliferate over time

BI & Advanced Analytics Trends to Watch in 2026

The global BI market will grow from $29.3 billion in 2025 to $54.9 billion by 2029 — a 13.1% CAGR (MarketsandMarkets). Here's what's driving that growth:

AI-Augmented BI Becomes Standard
Over 80% of enterprises are adopting NLP queries and automated insights, cutting analysis time in half. This shift democratizes analytics — but only when data quality supports it.

Semantic Layers Move from Nice-to-Have to Essential
As AI systems pull from multiple platforms simultaneously, consistent metric definitions become critical infrastructure. Without semantic layers, AI-powered analytics produce contradictory insights that destroy trust.

Lakehouse Architectures Unify Data and Analytics
Platforms like Databricks and Confluent combine data warehouse structure with data lake flexibility, enabling real-time analytics 10x faster on unified data.

Embedded Analytics Captures Market Share
The embedded analytics market reaches $77.52 billion in 2026. Sales reps see insights in Salesforce, not separate BI portals. Supply chain managers get recommendations in procurement systems. Context-aware insights drive higher adoption and faster decisions.

Automated Governance Prevents More Failures
ML systems automatically fix approximately 60% of data-related BI failures, detecting anomalies, correcting issues, and maintaining lineage — making scale possible without proportional increases in data engineering headcount.

Conversational BI Goes Mainstream
Voice and text-based queries are growing 40% year-over-year. By year-end, 40% of analytics queries will use natural language rather than traditional interfaces.

Real-Time Streaming Becomes Operationally Critical
Kafka, IoT platforms, and event streaming deliver sub-second alerts for manufacturing quality control and logistics optimization. The infrastructure to support this at scale is now mature and accessible.

Mobile BI Expands Beyond Executives
Mobile BI reached $19.93 billion in 2025 and is growing at 22.8% annually. Field service technicians, sales teams, and operations managers need insights on phones and tablets, not just desktop dashboards.

Data Mesh Gains Interest but Requires Organizational Maturity
Decentralized, domain-oriented data ownership promises to solve central bottlenecks. The concept is compelling, but most enterprises lack the distributed data engineering skills and organizational culture to execute it.

Real-Time Predictive Analytics Merge with Operational Systems
Predictive models embedded in transaction systems enable instant credit decisions, dynamic pricing, and fraud detection. Latency requirements drive architectural changes — models must score predictions in milliseconds, not batch overnight.

Final Thoughts

Business Intelligence delivers value only when it changes how decisions are made. Dashboards, reports, and tools are table stakes. What matters is whether leaders trust the data, teams align around the same metrics, and insights are used consistently to guide action.

Organizations that succeed with BI treat it as a core business capability. They prioritize data readiness before scale, establish clear ownership for metrics, and design BI around real decision workflows rather than static reporting. Adoption is intentional, governance is pragmatic, and success is measured by impact — not output.

As organizations grow more complex and data volumes increase, the role of Business Intelligence becomes even more critical. It is no longer just about visibility. It is about confidence — in the numbers, in alignment across teams, and in the defensibility of every decision.

Frequently Asked Questions

How long does it take to implement Business Intelligence successfully?
Organizations relying on ad-hoc reporting or spreadsheets typically need 12–18 months to establish foundations. Organizations with existing data infrastructure can implement core BI in 6–9 months. Advanced capabilities like AI-driven analytics usually require an additional 12–24 months. Most organizations reach full BI maturity in 3–5 years, but foundational BI can deliver ROI within 12–18 months if implemented correctly.

What's the difference between Business Intelligence and Data Analytics?
BI focuses on descriptive insight — what happened, when, and why — using dashboards, reports, and KPIs to track performance. Data Analytics, particularly Advanced Analytics, is predictive and prescriptive. It identifies what is likely to happen next and recommends actions using statistical models, machine learning, and forecasting.

Can small businesses benefit from BI, or is it only for enterprises?
Small businesses benefit significantly and often see faster returns than large enterprises. Cloud BI tools generally cost $20–$50 per user per month, with foundational implementations starting around $25,000–$75,000. The key is starting with a small set of critical metrics and expanding only when adoption is consistent.

How do you measure BI success beyond dashboard usage?
Dashboard views are not success metrics. Real BI success shows up in:

Decision speed: Faster pricing, planning, or operational decisions
Decision quality: More accurate forecasts and targeting
Operational efficiency: Reduced manual reporting and reconciliation
Revenue impact: Improved conversion rates, ROI, or retention
Cost reduction: Lower inventory, waste, or operational inefficiencies

A simple test: ask leaders whether BI changed a real decision in the past 30 days — and how.

What's the most common reason BI projects fail even with executive sponsorship?
The most common reason is poor data quality and inconsistent metric definitions. Executive sponsorship secures budget and visibility, but it does not resolve conflicts where departments define metrics differently. BI tools surface these inconsistencies rather than hiding them.

Should we build our own BI solution or buy a commercial platform?
In most cases, organizations should buy a commercial BI platform. Custom BI solutions typically require $200,000–$500,000 upfront and $100,000–$200,000 annually for maintenance. Commercial platforms such as Power BI, Tableau, Looker, and Qlik already provide scalability, security, and integrations at $20–$50 per user per month. Custom development only makes sense when requirements cannot be met by existing tools or when analytics must be embedded in a product you sell.

Cost Optimization in Amazon ECS: Leveraging Spot Instances the Right Way

Improving — Wed, 18 Mar 2026 11:03:16 +0000

Cost efficiency is often as critical as performance and scalability. For modern containerized applications, the need to manage infrastructure costs becomes important, as microservices often translate to a large number of continuously running tasks. If not managed properly, these costs can spiral quickly.

We aren't just talking about a few extra dollars — we are talking about the kind of financial disaster where a team chose CloudWatch for a small project because it was "quick to set up," only to find it eating up 40% of their entire budget. Or another instance where a recursive loop in a Lambda Edge function caused their application to essentially DDoS itself through CloudFront.

"Basically, running on default is expensive."

For Amazon Elastic Container Service (ECS), the "default" is often to run every task on On-Demand or FARGATE capacity. While safe, it means you are paying a 70–90% premium for every single microservice, regardless of its priority.

In this post, we'll move past the fear of a surprise bill. We will explore how to build a high-reliability, cost-optimized engine using ECS Capacity Providers. You'll learn how to blend the guaranteed stability of On-Demand with the massive discounts of AWS Spot Instances so you can transform your computing spending from a risk into a strategic advantage.

Understanding ECS Launch Types

Before diving into Spot Instances, it's essential to understand the two fundamental Launch Types available for running tasks in ECS: EC2 and Fargate. These are the distinct compute models that determine how your containers are hosted and managed.

Running Tasks on EC2 Launch Type

With the EC2 launch type, we have full control over the underlying infrastructure. We provision and manage a cluster of EC2 instances that act as container hosts for our ECS tasks.

Running Tasks on Fargate Launch Type

Fargate is the serverless compute engine for containers. It removes the need for us to provision, configure, or scale clusters of virtual machines. We simply specify the CPU and memory required for our task, and Fargate handles the underlying infrastructure management.

Fargate vs. EC2

	EC2	Fargate
Infrastructure Management	You manage it	AWS manages it
Cost Control	Maximum control	Less granular
Spot Availability	EC2 Spot	Fargate Spot
Best For	Cost optimization, specialized instances	Simplicity, rapid deployment

When to choose which:

EC2 instance: When you need maximum cost control, have consistent resource utilization, or require specialized instance types. This is where you can realize the highest savings by aggressive use of Spot Instances.
Fargate instance: When simplicity, security isolation, and a rapid deployment model are priorities. While Fargate is premium-priced, you can still leverage a form of Spot via Fargate Spot.

Why Cost Optimization Matters in ECS

Running containerized workloads on AWS involves paying for the underlying compute resources, whether they are Amazon EC2 instances or AWS Fargate compute units. In an ECS environment, controlling this expenditure is key to maintaining a healthy operational budget.

Leveraging smart cost-saving mechanisms means we can run the same — or even larger — workloads for significantly less money, maximizing our return on investment (ROI).

Where Spot Instances Fit in the Cost Optimization

Cost optimization for containers often begins with choosing the right deployment model. Once we select the underlying compute, the next step is tapping into AWS's surplus capacity — the unused virtual machine capacity within an AWS Region — which is offered at a steep discount.

Spot Instances allow us to utilize this spare compute capacity in the AWS cloud, typically offering savings of up to 90% compared to on-demand prices. Such discounts are game changers for fault-tolerant and flexible ECS workloads.

Optimizing Cost with ECS on Spot

AWS offers two ways to leverage discounted Spot capacity for our ECS workloads.

Fargate Spot

Fargate Spot is a specialized version of Fargate that allows us to run interruptible Fargate tasks at a discount, similar to EC2 Spot Instances.

Pros: Serverless simplicity, instant provisioning, high savings (typically 70% off Fargate On-Demand).
Cons: Less granular control than EC2 Spot; not suitable for tasks that cannot tolerate interruption.

EC2 Spot Capacity Providers

Capacity Providers allow ECS to manage the scaling of the underlying EC2 Auto Scaling Group (ASG), automatically requesting and maintaining the desired capacity. We configure one or more ASGs (for On-Demand and Spot) and define a strategy for how tasks should be distributed across them. This is the most flexible and powerful mechanism for cost optimization in ECS.

Choosing the Right Spot Instance: Manual Data vs. Automated Selection

To successfully integrate EC2 Spot Instances, we must understand their interruptible nature. AWS can reclaim a Spot Instance with a two-minute warning if the capacity is needed elsewhere. The key is to select instance types that are less frequently interrupted and to diversify our fleet.

1. Manual Selection and Diversification using Spot Capacity Advisor

The initial step is to understand the core trade-offs: cost savings versus interruption risk.

The AWS EC2 Spot Instance Advisor is a vital tool for making informed decisions. It provides historical data on an instance type's saving potential and, critically, its Frequency of Interruption.

You might find that an instance type offering a slightly lower discount (e.g., 54% for c6a.2xlarge) is worth the trade-off for its <5% interruption rate, making it a more reliable choice for critical, cost-optimized workloads.

Reducing interruptions by diversifying capacity

For EC2 Spot instances, we must create a dedicated Auto Scaling Group (ASG) for our Spot fleet. Within this ASG, using a Mixed Instance Policy is critical for both cost and reliability.

Select Multiple Instance Types: Instead of relying on a single instance size (e.g., only c6a.4xlarge), the Mixed Instance Policy allows us to specify a mix of suitable instance families and sizes (e.g., c6a.2xlarge, c5.xlarge, c4.xlarge, etc.). This diversification is paramount — the loss of one type won't halt our cluster.
Use Different Availability Zones (AZs): Spread Spot requests across multiple AZs. Capacity availability varies by AZ, ensuring greater capacity stability.

2. Automated Selection with Attribute-Based Selection (ABS)

Manually listing a diverse set of instance types in ASG works, but managing that list becomes complex as AWS constantly releases new generations. Attribute-Based Instance Type Selection (ABS) provides a superior, future-proof approach.

ABS allows you to express your workload requirements (such as minimum/maximum vCPU, memory, networking bandwidth, and instance generation) rather than listing specific instance types.

How it helps Spot: ABS automatically translates your requirements into a vast list of hundreds of potential instance types. The massive diversification ensures your ASG can access the broadest possible pool of Spot capacity, dramatically lowering the risk of interruption.

Maintenance-Free: When AWS releases a new instance type (e.g., a new generation of C7 or M7), ABS automatically considers it for provisioning if it matches your specified attributes — meaning you never have to update your configuration manually.

Understanding Spot Allocation Strategies

When using a Mixed Instance Policy in our ASG, we must choose an allocation strategy that dictates how AWS fulfills our Spot capacity request across the specified instance types.

Strategy	Description	Best For
`lowest-price`	Fills from the cheapest pool(s) first	Maximum cost savings, higher interruption risk
`capacity-optimized`	Fills from the pool with the most available capacity	Lower interruption risk
`price-capacity-optimized`	Balances price and capacity availability	Recommended — best of both worlds

Capacity Provider Strategies

Capacity Provider Strategies are the engine behind flexible task provisioning. They allow us to define a logic for distributing tasks across our available capacity pools (e.g., On-Demand ASG and Spot ASG).

Baseline Reliability Strategy

The main idea for achieving both high reliability and significant cost savings simultaneously is:

Use On-Demand capacity to establish a reliable baseline.
Rely on Spot capacity only for dynamic scale-out.

This means a minimum number of critical ECS tasks are always running on guaranteed On-Demand compute. Only the tasks created as part of horizontal scaling or traffic surges are directed to the highly discounted, but interruptible, Spot Instances.

Base and Weight Explained

The strategy is composed of capacity providers, each with a base and a weight:

base: The minimum number of tasks that must run on a specific capacity provider. Tasks are placed on the base capacity provider before considering any weight distribution.
weight: The relative proportion of the remaining capacity that should be fulfilled by the associated capacity provider after the base is satisfied.

Example: Distributing 100 tasks

Given the following strategy:

Capacity Provider	base	weight
On-Demand	10	1
Spot	0	3

Here's how ECS places the tasks:

Fulfill the base: The first 10 tasks go to the On-Demand provider.
- Remaining tasks: 100 − 10 = 90
Apply weights to remaining tasks: Total weight = 1 + 3 = 4
- On-Demand (weight 1): 1/4 × 90 = ~23 tasks
- Spot (weight 3): 3/4 × 90 = ~67 tasks

Result: ~33 tasks on On-Demand, ~67 tasks on Spot — significant savings with a guaranteed baseline.

Cost vs. Reliability Tradeoff

Strategy	On-Demand %	Spot %	Reliability	Cost Savings
All On-Demand	100%	0%	Highest	None
High base, low weight on Spot	High	Low	High	Moderate
Low base, high weight on Spot	Low	High	Moderate	High
All Spot	0%	100%	Lowest	Maximum

Step-by-Step: Running ECS Workloads on Spot

Here's how to implement a high-reliability, cost-optimized strategy using Capacity Providers:

Create an ECS cluster with capacity providers: Define an ECS Cluster linked to two separate EC2 Auto Scaling Groups — one for On-Demand and one for Spot.
Configure Spot and On-Demand in the strategy: Define the Capacity Provider Strategy when creating an ECS service.
On-Demand Capacity Provider: Set a high base for guaranteed resources.
Spot Capacity Provider: Set a higher weight to ensure most flexible tasks land here.
Deploy the service: Run your ECS service referencing the defined Capacity Provider Strategy.

💡 You can explore a practical Terraform implementation of this setup on GitHub.

Final Words

Cost optimization within Amazon ECS is a continuous process, and mastering AWS Spot Instances is the most powerful lever for maximizing savings without sacrificing critical performance.

By adopting the right approach, we move beyond simply requesting the cheapest compute and embrace a strategic methodology:

Establishing a resilient baseline: Use the On-Demand base in the Capacity Provider Strategy to ensure the most critical ECS tasks are always running on guaranteed capacity.
Optimizing scale: Leverage a high Spot weight to ensure all scale-out tasks are launched on deeply discounted capacity, maximizing cost savings for dynamic workloads.
Enhancing stability: Mitigate interruptions by utilizing the Spot Capacity Advisor and diversifying the EC2 fleet through Mixed Instance Policies and intelligent allocation strategies like price-capacity-optimized.

Ultimately, leveraging ECS Capacity Providers with Spot Instances transforms infrastructure management from a high cost overhead into a strategic advantage — allowing your team to scale faster and smarter while maintaining excellent resilience.

Originally published on improving.com

Backup and Restore Kubernetes Resources Across vCluster using Velero

Improving — Wed, 18 Mar 2026 10:59:04 +0000

In Kubernetes environments, teams are constantly looking for ways to move faster without sacrificing security or efficiency. Managing multiple environments like development, testing, and staging often leads to cluster sprawl, higher costs, and complex maintenance. This is where virtual clusters come in.

Virtual clusters make it possible to create isolated, on-demand Kubernetes environments that share the same underlying infrastructure. They give developers the freedom to spin up their own clusters quickly for testing new features, running experiments, or deploying temporary workloads — all without waiting on cluster admins or consuming extra resources. Each virtual cluster runs its own control plane, offering stronger isolation and flexibility than namespace-based setups. We'll be using vCluster, an implementation of virtual clusters by Loft, to illustrate the concept in practice.

Managing workloads across multiple virtual clusters is a common pattern in multi-tenant environments. However, while virtual clusters make isolation easy, moving workloads across them is not straightforward. That's where Velero comes in — it is a powerful Kubernetes backup tool that migrates workloads from one virtual cluster to another.

In this blog post, we'll understand the importance of backups, how Velero works, and walk you through a practical migration of resources using Velero — from backing up one virtual cluster to restoring it in another.

What is Velero?

Velero is an open source tool to back up and restore your Kubernetes cluster resources and persistent volumes. You can run Velero with a cloud provider or on-premises.

Velero lets you:

Take backups of your cluster and restore in case of loss
Migrate cluster resources to other clusters
Replicate your production cluster to development and testing clusters

Velero consists of:

Velero CLI
- Runs on your local machine.
- Used to create, schedule, and manage backups and restores.
Kubernetes API Server
- Receives backup requests from the Velero CLI.
- Stores Velero custom resources (like Backup) in etcd.
Velero Server (BackupController)
- Runs inside the Kubernetes cluster.
- Watches the Kubernetes API for Velero backup requests.
- Collects Kubernetes resource data and triggers backups.
Cloud Provider / Object Storage
- Stores backup data and metadata.
- Creates volume snapshots using the cloud provider's API (e.g., Azure Disk Snapshots).

How it works:

User runs a Velero backup command using the CLI: velero backup create my-backup
CLI creates a backup request in Kubernetes
The Velero server detects the request and gathers cluster resources
Backup data is uploaded to cloud object storage
Persistent volumes are backed up using cloud snapshots (if enabled)

Velero supports a variety of storage providers for different backup and snapshot operations. In this blog post, we will focus on the Azure provider.

What is vCluster?

vCluster enables building virtual clusters — a certified Kubernetes distribution that runs as isolated, virtual environments within a physical host cluster. They enhance isolation and flexibility in multi-tenant Kubernetes setups. Multiple teams can work independently on shared infrastructure, helping minimize conflicts, increase team autonomy, and reduce infrastructure costs.

A virtual cluster:

Runs inside a namespace of the host cluster
Has an API server, control plane, and syncer
Maintains its own set of Kubernetes resources, operating like a full cluster

Why Backup and Migrate Workloads Using vCluster?

Common reasons to back up or migrate workloads between vClusters:

Promoting apps from dev to staging or prod: Backing up and restoring workloads between vClusters allows smooth promotion of applications across environments, ensuring consistent configurations and deployments without manual rework.
Replicating test environments: It helps recreate identical test setups quickly, enabling developers to reproduce issues, validate fixes, or test new features in isolated environments.
Disaster recovery (DR) setup: Regular backups across vClusters ensure business continuity by allowing workloads to be restored rapidly in another cluster if the primary one fails.
Tenant migration in multi-tenant environments: vClusters make it easier to move tenants between isolated environments without affecting others, maintaining data security and minimizing downtime.
Cluster version upgrades or deprecations: When upgrading or decommissioning a cluster, backing up workloads to another vCluster ensures a seamless transition without losing data or configurations.

Why Use Velero with vCluster?

Virtual clusters built with vCluster are lightweight and isolated, but they don't provide built-in mechanisms for backing up workloads, restoring them, or moving applications between clusters. Without a backup solution, recovery and migration can be risky.

Using Velero with vCluster fills this gap by enabling simple backup, restore, and migration workflows directly inside virtual clusters. It allows you to move applications between clusters with minimal setup and perform migrations with little to no downtime, especially for stateless workloads.

How to Backup and Migrate Workloads Between vClusters

Let's see how to use Velero to back up workloads from one vCluster and restore them into another. Think of it as moving your app from dev to staging across two clusters running on two different Azure clusters.

Prerequisites

Before starting, make sure you have the following:

Two clusters up and running on Azure (any cloud offering works)
Two running vClusters (source and destination)
Velero CLI installed on your machine

Step-by-step Guide

In the source vCluster and destination vCluster, we will install Velero with the same configuration, deploy a sample MySQL Pod, take its backup at source, and restore it in the destination vCluster. We will be using the Azure provider to run Velero.

To set up Velero on Azure, you have to:

Create an Azure storage account and blob container
Get the resource group details
Set permissions for Velero

Velero needs access to your Azure storage account to upload and retrieve backups. You'll need to assign the "Storage Blob Data Contributor" role (or equivalent) to the identity or service principal Velero uses, ensuring it can read, write, and manage backup data in the blob container.

1. Create Azure Resources

Create a resource group:

AZURE_RESOURCE_GROUP=<YOUR_RESOURCE_GROUP>
az group create --name $AZURE_RESOURCE_GROUP --location <YOUR_LOCATION>

Create the storage account:

AZURE_STORAGE_ACCOUNT=<YOUR_STORAGE_ACCOUNT>
az storage account create \
  --name $AZURE_STORAGE_ACCOUNT \
  --resource-group $AZURE_RESOURCE_GROUP \
  --sku Standard_GRS \
  --encryption-services blob \
  --https-only true \
  --kind BlobStorage \
  --access-tier Hot

Create a blob container:

BLOB_CONTAINER=velero
az storage container create \
  --name $BLOB_CONTAINER \
  --public-access off \
  --account-name $AZURE_STORAGE_ACCOUNT

2. Create a Service Principal with Contributor Privileges

AZURE_SUBSCRIPTION_ID=$(az account list --query '[?isDefault].id' -o tsv)
AZURE_TENANT_ID=$(az account list --query '[?isDefault].tenantId' -o tsv)

az ad sp create-for-rbac \
  --name "velero" \
  --role "Contributor" \
  --scopes /subscriptions/$AZURE_SUBSCRIPTION_ID \
  --query '{clientId: appId, clientSecret: password, tenantId: tenant}'

This outputs clientId, clientSecret, subscriptionId, and tenantId. Store these values.

Get the Client ID and store it in a variable:

AZURE_CLIENT_ID=$(az ad sp list --display-name "velero" --query '[0].appId' -o tsv)

Assign additional permissions to the Client ID:

az role assignment create \
  --assignee $AZURE_CLIENT_ID \
  --role "Storage Blob Data Contributor" \
  --scope /subscriptions/$AZURE_SUBSCRIPTION_ID/resourceGroups/$AZURE_RESOURCE_GROUP/providers/Microsoft.Storage/storageAccounts/$AZURE_STORAGE_ACCOUNT

3. Prepare Credentials

With the output received above, create bsl-creds and cloud-creds for the Velero setup.

BSL (Backup Storage Location) — the blob container where Velero stores backups. Velero needs a secret to access this storage location.
cloud-creds — credentials required to access the Azure cluster.

You will need the following values:

AZURE_SUBSCRIPTION_ID=<YOUR_SUBSCRIPTION_ID>
AZURE_TENANT_ID=<YOUR_TENANT_ID>
AZURE_CLIENT_ID=<YOUR_CLIENT_ID>
AZURE_CLIENT_SECRET=<YOUR_CLIENT_SECRET>
AZURE_RESOURCE_GROUP=<YOUR_RESOURCE_GROUP>
AZURE_CLOUD_NAME=AzurePublicCloud
AZURE_ENVIRONMENT=AzurePublicCloud

4. Log in to vCluster and Create Velero Namespace

kubectl create namespace velero

5. Create BSL and Cloud Credentials

bsl-creds.yaml:

apiVersion: v1
kind: Secret
metadata:
  name: bsl-creds
  namespace: velero
type: Opaque
data:
  cloud: <BASE64_ENCODED_VALUE>
  # Encode the following as base64:
  # [default]
  # storageAccount: <YOUR_STORAGE_ACCOUNT>
  # storageAccountKey: <YOUR_STORAGE_ACCOUNT_KEY>
  # subscriptionId: <YOUR_SUBSCRIPTION_ID>
  # resourceGroup: <YOUR_RESOURCE_GROUP>

cloud-creds.yaml:

apiVersion: v1
kind: Secret
metadata:
  name: cloud-creds
  namespace: velero
type: Opaque
data:
  cloud: <BASE64_ENCODED_VALUE>
  # Encode the following as base64:
  # AZURE_SUBSCRIPTION_ID=<YOUR_SUBSCRIPTION_ID>
  # AZURE_TENANT_ID=<YOUR_TENANT_ID>
  # AZURE_CLIENT_ID=<YOUR_CLIENT_ID>
  # AZURE_CLIENT_SECRET=<YOUR_CLIENT_SECRET>
  # AZURE_RESOURCE_GROUP=<YOUR_RESOURCE_GROUP>
  # AZURE_CLOUD_NAME=AzurePublicCloud

Apply the secrets:

kubectl apply -f bsl-creds.yaml -n velero
kubectl apply -f cloud-creds.yaml -n velero

6. Install Velero Using Helm

Use the following values.yaml. Both the source and destination vClusters use the same file:

configuration:
  backupStorageLocation:
    - name: default
      provider: azure
      bucket: velero
      config:
        resourceGroup: <YOUR_RESOURCE_GROUP>
        storageAccount: <YOUR_STORAGE_ACCOUNT>
        subscriptionId: <YOUR_SUBSCRIPTION_ID>
      credential:
        name: bsl-creds
        key: cloud

  volumeSnapshotLocation:
    - name: default
      provider: azure
      config:
        resourceGroup: <YOUR_RESOURCE_GROUP>
        subscriptionId: <YOUR_SUBSCRIPTION_ID>
      credential:
        name: cloud-creds
        key: cloud

credentials:
  useSecret: true
  existingSecret: cloud-creds

deployNodeAgent: true

nodeAgent:
  podVolumePath: /var/lib/kubelet/pods
  privileged: true

Install the Helm chart:

helm install velero vmware-tanzu/velero \
  --namespace velero \
  -f values.yaml

Once installed, you will see velero and node-agent pods running in the velero namespace:

kubectl get pods -n velero

Repeat the same Velero installation steps in the destination vCluster.

Backup and Restore a Sample MySQL Pod

Deploy MySQL in Source vCluster

mysql-pod.yaml:

apiVersion: v1
kind: PersistentVolumeClaim
metadata:
  name: mysql-pvc
  namespace: default
spec:
  accessModes:
    - ReadWriteOnce
  resources:
    requests:
      storage: 1Gi
---
apiVersion: v1
kind: Pod
metadata:
  name: mysql-pod
  namespace: default
  labels:
    app: mysql
spec:
  containers:
    - name: mysql
      image: mysql:8.0
      env:
        - name: MYSQL_ROOT_PASSWORD
          value: rootpassword
        - name: MYSQL_DATABASE
          value: testdb
      volumeMounts:
        - name: mysql-storage
          mountPath: /var/lib/mysql
  volumes:
    - name: mysql-storage
      persistentVolumeClaim:
        claimName: mysql-pvc

Apply the manifest:

kubectl apply -f mysql-pod.yaml

Add Test Data

Exec into the pod:

kubectl exec -it mysql-pod -- /bin/bash

Run the following commands inside the pod to add test files:

echo "test data 1" > /var/lib/mysql/test1.txt
echo "test data 2" > /var/lib/mysql/test2.txt

This creates test1.txt and test2.txt.

Take a Backup

velero backup create mysql-backup \
  --include-namespaces default \
  --default-volumes-to-fs-backup \
  --wait

Check backup status:

velero backup get

The backup status should show Completed.

Restore in Destination vCluster

Update values.yaml for Destination

Make sure the Velero config is the same as the source. Use the same values.yaml, but update these two parameters:

# Change these in values.yaml for destination cluster
configuration:
  backupStorageLocation:
    - name: default
      # Keep all values the same as source — point to the same blob container
      accessMode: ReadOnly   # Destination reads from source's storage

After Velero is installed at the destination vCluster, verify you can see the source backups:

velero backup get

You will see the same backup list as the source vCluster.

Create a Restore

restore.yaml:

apiVersion: velero.io/v1
kind: Restore
metadata:
  name: mysql-restore
  namespace: velero
spec:
  backupName: mysql-backup
  includedNamespaces:
    - default
  restorePVs: true
  itemOperationTimeout: 4h

Apply the restore:

kubectl apply -f restore.yaml -n velero

Check restore status:

velero restore get
velero restore describe mysql-restore --details

To verify the restore, attach the PVC (created after restore completes) to a pod, exec into it, and confirm the data (test1.txt and test2.txt) is present.

Troubleshooting Tips

Issue 1: Backup status is `PartiallyFailed` or `FailedValidation`

Solution: Describe the backup for details:

velero backup describe mysql-backup --details

Check the backup logs:

velero backup logs mysql-backup

If nothing useful appears, check the Velero pod logs:

kubectl logs -n velero deployment/velero | grep mysql-backup

After running the above three commands, you'll likely find the root cause. Common causes include permission issues or incorrect credentials. Sometimes partial failures occur because the node-agent pod isn't running on a node — in that case, manually schedule a pod on that node.

Issue 2: Node Agent Pod is Not Running

node-agent-xxxxx   0/1   Pending   0   5m

Solution: There is a node with no pods running on it, so the node-agent DaemonSet pod is also not scheduled. Manually schedule a sample pod on that node to trigger scheduling. Once a sample pod is running, the node-agent pod will also be scheduled and start running.

Issue 3: Restore Fails Without Specific Errors

Solution: Restart the restore process from scratch:

Delete all resources created by the restore job (pods, statefulsets, deployments, PVCs, etc.)

OR

If restoring a whole namespace, delete the entire restored namespace.
Delete the restore job:

velero restore delete mysql-restore

After the restore job is deleted, ArgoCD (if used) will automatically sync and recreate the restore job, triggering the Velero restoration.

Conclusion

Using Velero to back up and restore workloads across vClusters provides a robust and flexible approach for managing multi-tenant Kubernetes environments. Whether you're migrating applications between development and production, setting up disaster recovery, or replicating environments for testing, Velero simplifies the process significantly.

In this blog post, we explored how to back up and restore Kubernetes clusters using Velero. While the process is straightforward in principle, production environments can introduce added complexity — factors like cluster size, workloads, and configurations often make a difference.

Originally published at improving.com

When MCP Is Not The Right Choice

Improving — Wed, 18 Mar 2026 10:54:59 +0000

Model Context Protocol (MCP) has quickly moved from concept to conversation starter across the AI engineering community. The concept is promising — give your AI models structured access to real tools and watch them transform from chatbots into agents that get work done.

But introducing MCP introduces real complexity, costs, and risks that don't appear in the initial stage. It's powerful when your users need it, and expensive over-engineering when they don't. In this post, we'll cut through the hype to examine the trade-offs that matter in production: when benefits of MCP justify the costs, where simpler approaches work better, and what hidden challenges emerge once you move past the POC phase.

What is MCP and an MCP Server?

MCP is an emerging standard that helps large language models (LLMs) interact with external tools, services, and data in a consistent and predictable way. In simple terms, MCP gives AI models a common language for using tools.

Think of it like a universal plug adapter for AI. Instead of teaching every model how to talk to every API or database separately, MCP defines one standard way to do it. Once a tool is connected through MCP, different AI models can use it without needing custom integrations each time.

An MCP Server runs this protocol and acts as a middle layer between AI models and real-world systems like APIs, databases, or internal apps. Developers define tool connections once on the MCP server and can then reuse them across models from different providers, saving time and reducing duplicated work.

The architecture at a high level: the LLM talks to the MCP server using the MCP protocol, and the MCP server handles communication with the actual tools and data sources behind the scenes.

Benefits of Adding MCP Servers to Your Software

MCP servers provide a durable architectural layer that helps organizations scale AI capabilities without locking into specific models or vendors. They shift AI integrations from short-term hacks to long-term infrastructure.

Standardization and Interoperability

MCP introduces a unified, model-agnostic protocol for accessing tools and resources, allowing AI systems to interact with enterprise data and services through a consistent interface. This abstraction decouples AI applications from individual model providers, allowing organizations to integrate new models or switch providers without rewriting downstream integrations.

Developer Velocity and Resource Efficiency

By separating model reasoning from tool execution, MCP simplifies system design and reduces integration complexity. Tools implemented once on an MCP server can be reused across multiple applications, models, and teams — eliminating duplicated effort and accelerating delivery of new AI capabilities. Over time, this reuse compounds: each new tool becomes shared infrastructure, increasing overall development efficiency and lowering marginal costs for future AI initiatives.

Centralized Control and Governance

An MCP server provides a single point of control for managing tool behavior, permissions, updates, and access policies across all AI clients. This centralization makes it easier to enforce compliance requirements, maintain audit trails, and implement consistent security controls — while supporting multi-client and multi-model architectures.

Architectural Flexibility for Growth

MCP enables organizations to add, modify, or remove tools without redeploying AI applications, reducing operational risk and increasing adaptability. As business needs, workflows, and regulatory environments change, the architecture can evolve without costly rewrites. MCP becomes a durable foundation that grows alongside an organization's AI maturity.

Hidden Costs: What MCP Adoption Really Means

While MCP promises elegant AI-tool integration, the path from proof-of-concept to production adds operational, performance, and organizational complexity that teams must be prepared to absorb.

Operational Burden and Complexity Tax

An MCP server is not a thin abstraction layer — it is a long-lived distributed system. It requires deployment pipelines, configuration management, backward-compatible schema evolution, and capacity planning. Unlike one-off integrations, MCP introduces ongoing responsibilities that scale with usage, appearing gradually during incident handling and dependency changes.

Performance Trade-offs

Introducing MCP adds an extra network hop for each tool invocation, often in the range of tens to hundreds of milliseconds, which can compound noticeably in multi-step or agentic workflows. Under high load, the MCP server can become a bottleneck if not properly scaled, cached, or tuned. Achieving acceptable performance typically requires additional engineering investment in concurrency management, caching strategies, and performance monitoring.

Security Risks if Misconfigured

MCP centralizes access to powerful tools and sensitive data, which increases the blast radius of configuration errors. Overexposed tools or overly permissive schemas can lead to unintended data access, while prompt-driven misuse can cause models to invoke tools in unsafe ways. Without carefully designed permission models, input validation, and guardrails, misconfigurations can be exploited either accidentally or maliciously.

A Nascent Ecosystem

MCP is still an evolving standard, with fewer mature, off-the-shelf tools compared to traditional API ecosystems. Best practices, architectural patterns, and operational playbooks are still emerging — which increases uncertainty and experimentation costs. For simple or single-purpose integrations, MCP may introduce more complexity than value.

Debugging and Observability Challenges

Failures in an MCP-based system often span multiple boundaries: model reasoning, protocol translation, network calls, and downstream services. Non-deterministic LLM behavior makes issues harder to reproduce and diagnose, increasing mean time to resolution. Effective operation requires sophisticated observability infrastructure — logging, tracing, and metrics — adding further tooling and operational investment.

When MCP Is the Wrong Choice: Critical Red Flags

Customer-Facing Latency Sensitivity

MCP introduces per-call overhead that degrades real-time UI experiences, where streaming connections amplify delays in interactive workflows. Transactional paths suffer from routing everything through the protocol, as burst requests from LLMs overwhelm simpler direct APIs. Sidecar integrations or non-blocking patterns deliver better responsiveness here.

Minimal Tool or Static Integrations

Stable, limited tools lead to bloated schemas repeated across interactions, wasting context without delivering dynamic benefits. Direct function calls or basic RAG pipelines handle these more efficiently. Short sessions accumulate unnecessary history, favoring prompt-level optimizations over protocol layers.

Regulated or Enterprise Security Gaps

Absence of built-in SSO, audit trails, and fine-grained authorization leaves regulated setups vulnerable to unmonitored shadow servers and injection risks in containerized deployments. Tool poisoning enables scope overrides, requiring custom gateways beyond the core spec.

Immature Teams or Shadow Deployments

When servers are set up without clear ownership or rules, it leads to inconsistent configurations, poor visibility, and slower troubleshooting. Teams without platform discipline may find that MCP increases complexity instead of improving efficiency. For smaller or early-stage use cases, simple direct LLM API calls are usually enough. You don't need full orchestration until your AI usage becomes more central and complex.

AI as a Peripheral Feature

If AI is just an occasional enhancement — like "adding a chatbot to a settings page" — MCP's architecture is overkill. In these cases, a simple call to your LLM provider's API with some context from your database is enough. You don't need servers, tool schemas, or protocol layers. MCP only makes sense when AI needs to orchestrate multiple tools or capabilities.

Decision Framework for Adopting MCP Servers

Complexity Assessment

Begin by assessing both current and anticipated AI requirements, including the number of models, tools, integrations, and teams involved. The key question is whether complexity is already causing friction or is credibly projected based on the roadmap — rather than being hypothetical. MCP introduces an abstraction layer, so ask yourself: does this layer solve a real coordination, scaling, or governance problem, or does it simply add unnecessary infrastructure?

Team Capability Audit

Evaluate whether your organization has the platform engineering maturity required to implement and operate an MCP server effectively. This includes operational capabilities such as monitoring, incident response, versioning, and access control — as well as a realistic skills gap analysis around distributed systems and API design. MCP can create long-term leverage, but only if the team can properly build, maintain, and evolve the platform without becoming a bottleneck.

Total Cost of Ownership (TCO) Calculation

Look beyond initial implementation costs to understand the full TCO over time. This should include migration effort, infrastructure and operational overhead, training or hiring costs, and opportunity costs. Weigh these against benefits in your specific context: reduced rework, faster delivery, improved governance, and increased vendor optionality.

Strategic Alignment

Assess whether MCP aligns with your broader business and AI strategy. Vendor optionality is most valuable when AI is central to your product or operating model, or when regulatory, cost, or performance considerations may force provider changes. Consider your risk tolerance for adopting an emerging standard and whether MCP supports your long-term AI roadmap rather than short-term experimentation.

Pilot Before Commitment

Before committing broadly, start with a constrained pilot using a non-critical application and a limited set of tools. This allows teams to validate assumptions, uncover operational challenges, and measure real-world benefits in their environment.

Common Pitfalls Organizations Fall Into

Exposing Overly Powerful Tools

A frequent mistake is exposing broad, high-privilege tools to models instead of narrowly scoped capabilities. This increases the risk of unintended actions, data leakage, or destructive operations — especially when models behave unpredictably or are influenced by ambiguous prompts.

Treating MCP As a Security Boundary By Itself

MCP is an integration protocol, not a security control. Relying on it as the sole line of defense — without downstream authorization, validation, and rate limiting — creates a false sense of safety and leaves systems vulnerable to misuse or exploitation.

Skipping Monitoring and Logging

Without comprehensive logging and monitoring, MCP-driven systems become opaque and difficult to debug. Teams often underestimate how essential visibility is for understanding tool usage, diagnosing failures, and responding quickly to incidents in non-deterministic AI workflows.

Allowing Unrestricted Model Access to Production Systems

Giving models direct, unrestricted access to production resources dramatically increases operational risk. Safe architectures enforce environment boundaries, approval gates, and least-privilege access — ensuring that models cannot independently execute high-impact actions without safeguards.

Conclusion

While MCP servers offer powerful capabilities for connecting AI models to tools and data, they also introduce trade-offs in complexity, performance, and operational overhead. Using them indiscriminately adds unnecessary costs and security risks — MCP may not be the right choice for every application. Success depends on careful design, strong security, and platform engineering maturity.

Organizations should evaluate MCP adoption based on their specific use cases, weighing benefits against operational and architectural costs. When in doubt, consult experts before making the decision.

Originally published on Improving.com

End-to-End Observability with Prometheus, Grafana, Loki, OpenTelemetry and Tempo

Improving — Wed, 18 Mar 2026 10:43:47 +0000

Observability provides complete insights into the health, performance, and behavior of your Kubernetes cluster and the applications deployed within it. Companies, whether or not they use Kubernetes, have leveraged open-source observability tools like Prometheus, Grafana, Loki, and OpenTelemetry (OTel) to achieve significant improvements in cost, efficiency, and incident response.

For example, companies that reduced observability costs with OpenTelemetry reported notable savings — 84% of these companies saw at least a 10% decrease in costs. A real-world case study shows how Loki helped Paytm Insider save 75% of logging and monitoring costs. Similarly, a 2025 survey by Apica found that nearly half of organizations (48.5%) are already using OpenTelemetry, with another 25.3% planning implementation soon.

Why Observability is Important

Observability — which uses logs, metrics, and traces to provide deep system insights — is particularly crucial for navigating the complexity of modern cloud-native and microservices-based architectures. It helps organizations reduce downtime, increase efficiency, improve developer productivity, and boost revenue.

The setup combining Prometheus, Grafana, Loki, Tempo, Kube-State-Metrics, Node Exporter, and OpenTelemetry offers an open-source alternative to the ELK stack (Elasticsearch, Logstash, and Kibana), providing seamless integration across metrics, logs, and traces. It scales from local development (Minikube) to enterprise-grade clusters, making it cost-effective and easy to adopt.

In this blog post, we will understand the open source observability setup and deploy it. At the end, we'll deploy a sample Java application to demonstrate how to collect logs, metrics, and traces in action.

Understanding the Observability Setup

Let's dive into the observability setup and clearly understand the role of each component.

Prometheus: A time-series monitoring system used to collect metrics from Kubernetes components and services. It supports powerful querying and alerting.
Kube-State-Metrics: An add-on service that generates detailed metrics about the state of Kubernetes objects like deployments, pods, and nodes. These metrics are consumed by Prometheus.
Node Exporter: A Prometheus exporter that exposes hardware and OS metrics from your Kubernetes nodes.
Grafana: A visualization and analytics tool that connects to Prometheus and other data sources to display real-time dashboards for your metrics.
Loki: A log aggregation system from Grafana Labs that works seamlessly with Prometheus and Grafana. It collects logs from your Kubernetes workloads and enables easy correlation with metrics.
Tempo: A distributed tracing backend used to collect and visualize traces. It helps in tracking requests as they flow through different services, enabling root-cause analysis.
OpenTelemetry (OTel): A collection of tools, APIs, and SDKs for collecting telemetry data (traces, metrics, and logs) from your applications. It standardizes observability data collection.

Prerequisites

Minikube — used to set up a local Kubernetes cluster
Helm — the package manager for Kubernetes
App Repo — the test application we will clone

Step 1: Installing Prometheus

Once you clone the repository, change directory to the observability folder and run the command below. A Prometheus Helm chart with custom config is included to get labels of all the applications to be deployed in Minikube.

Note: The ConfigMap is configured to enable a limited set of metrics, but you can enable any metrics from the Prometheus configuration docs as required.

helm upgrade --install prometheus prometheus-helm

Step 2: Install kube-state-metrics and Node Exporter

helm install kube-state-metrics prometheus-community/kube-state-metrics
helm install node-exporter prometheus-community/prometheus-node-exporter

Once both steps are completed successfully and the pods are up and running, verify that all targets are green in Prometheus by port-forwarding the service:

kubectl port-forward service/prometheus-service -n monitoring 9090:9090

Then, access Prometheus at http://localhost:9090.

To confirm metrics are populating, run the following queries:

kube_pod_info
node_cpu_seconds_total

Step 3: Installing Grafana

helm install grafana grafana/grafana --namespace monitoring

After the Grafana pods are in the Running state, port-forward the Grafana service and retrieve the login credentials from the Grafana secret.

Access the UI at http://localhost:3000, then use the fetched credentials to log in.

Navigate to Connections → Data Sources → Add data source. Set the name to prometheus and the connection URL to:

http://prometheus-service.monitoring.svc.cluster.local:9090

Save and exit.

To verify the metrics, go to the Explore section and run the query below. You will see a time series showing the memory utilisation of all running pods:

avg(container_memory_usage_bytes{pod=~".*"}) by (pod) / (1024 * 1024)

Step 4: Install Loki and Tempo

Run the following commands and wait until all pods are in the Running state:

helm upgrade --install loki -f loki.yaml grafana/loki-stack --namespace monitoring
helm upgrade --install tempo -f tempo.yaml grafana/tempo --namespace monitoring

📄 Note: You can find loki.yaml and tempo.yaml in the Git repository. Promtail in the Loki configuration allows you to parse log lines into labels. Refer to the Promtail stages docs on how to extract labels.

Once the pods are ready, follow the same steps used for Prometheus to add Loki and Tempo as data sources in Grafana:

Loki URL: http://loki.monitoring.svc.cluster.local:3100
Tempo URL: http://tempo.monitoring.svc.cluster.local:3100

To view logs, go to Explore in Grafana, select Loki as the datasource, and run the following query to fetch logs from all namespaces:

{namespace=~".+"} |= ``

Step 5: Install OpenTelemetry and Sample Application

Run the following commands to install OpenTelemetry:

helm repo add open-telemetry https://open-telemetry.github.io/opentelemetry-helm-charts
helm upgrade --install opentelemetry-collector open-telemetry/opentelemetry-collector \
  --namespace monitoring

Once the OpenTelemetry pods are in the Running state, update the sample application's Helm chart to include an init container for trace collection.

To deploy the application, run:

helm upgrade --install calc helm-chart/ --namespace monitoring

In the deployment.yaml file of the Helm chart, you'll find the following init container configuration:

initContainers:
  - name: opentelemetry-auto-instrumentation
    image: ghcr.io/open-telemetry/opentelemetry-operator/autoinstrumentation-java:latest
    command: ["cp", "/javaagent.jar", "/otel-auto-instrumentation/javaagent.jar"]
    volumeMounts:
      - mountPath: /otel-auto-instrumentation
        name: opentelemetry-auto-instrumentation

To generate traces, port-forward the application's service and interact with the app using some inputs to generate trace data. To view traces, navigate to the Explore page in Grafana, select Tempo as the datasource, and run the query:

{}

Why Use This Stack Over ELK?

All these tools together provide a modern, cloud-native, cost-efficient, and tightly integrated observability solution compared to the traditional ELK stack. Key advantages include:

Native support for metrics, logs, and traces: A unified experience and correlation across telemetry types (ELK is primarily log-centric).
Lower resource & storage cost: Loki indexes only metadata (labels), not full log content, making it lighter and cheaper to operate.
Better scalability & resilience in cloud/Kubernetes environments: These tools are built for distributed, elastic infrastructure.
OpenTelemetry compatibility & vendor neutrality: Instrumentation is portable and standards-based.
Operational simplicity & lower overhead: Fewer cluster tuning demands, simpler scaling, and less JVM burden compared to Elasticsearch.

Final Words

You cannot fix what you cannot see. With the sheer amount of data and complexity in modern tech, having a proper observability system in place is critical. The primary aim of this guide was to establish full-stack observability for a Kubernetes cluster by enabling metrics, logs, and traces using Prometheus, Loki, Tempo, and OpenTelemetry — and finally visualizing them with Grafana.

With this setup, you can now monitor, visualize, and troubleshoot applications in real time using metrics, logs, and traces all in one unified observability stack. This not only enhances visibility into the cluster's health and performance but also enables faster root cause analysis and proactive incident response, aligning with modern DevOps and SRE practices.

AI Strategy & Roadmap Assessment: How Enterprises Avoid 88% AI Failure

Improving — Thu, 12 Feb 2026 09:01:54 +0000

Enterprises across industries are investing heavily in AI to improve decision-making, automate complex workflows, and unlock new sources of value. Most organizations today have little difficulty identifying AI use cases or launching initial pilots. The real challenge emerges later, when those experiments need to integrate into core systems, operate under real-world constraints, and deliver measurable business outcomes at scale.

This challenge plays out consistently across sectors:

Healthcare: AI diagnostic tools struggle when privacy, compliance, and audit requirements are not built in from day one.
Financial services: Fraud detection and risk models stall when regulators require transparency and explainability that were never planned for.
Manufacturing: Predictive maintenance pilots often succeed in controlled environments, only to fail when connected to legacy systems and operational realities.

The Scale of the Problem

The data reflects this challenge clearly:

42% of enterprise-scale companies already have AI in production (IBM), and another 40% are actively piloting initiatives.
88% of AI proof-of-concepts never reach production (MIT, IDC).
95% of enterprise AI solutions fail due to data issues (MIT, IDC).
77% of companies are exploring AI, but only 20% achieve significant ROI (McKinsey).

These outcomes stem from repeatable mistakes:

Starting with technology instead of business problems
Underestimating data quality and governance requirements
Treating AI as an isolated IT initiative
Deferring MLOps and production planning
Relying on strategy that lacks execution depth

The difference between success and failure is not ambition or budget, but how AI strategy is approached from the beginning.

What Is an AI Strategy & Roadmap Assessment?

An AI Strategy & Roadmap Assessment is a structured engagement that helps organizations understand where AI can deliver real business value and how to implement AI responsibly at scale.

Rather than jumping straight into tools or models, the assessment evaluates:

Business goals
Data readiness
Technology foundations
Governance requirements
Organizational maturity

Outcome: A clear AI strategy aligned to business priorities, paired with a phased roadmap outlining what to build, when to build it, and what capabilities are required at each stage.

AI Strategy Assessment Engagement Models

Organizations have different needs depending on their AI maturity.

AI/ML Discovery Engagement (2–4 Weeks)

Best for: Organizations exploring AI potential or validating initial use cases

Investment: $25,000+

What's Included

Structured workshops to identify high-ROI AI opportunities
Assessment of data quality, technology readiness, and organizational capabilities
Feasibility analysis for priority use cases with ROI estimates
Phased implementation roadmap with timelines and resource requirements
Skills gap analysis and training recommendations

Deliverables

Prioritized AI use case portfolio
Technology readiness scorecard
Strategic roadmap with success metrics

AI-Driven Organizational Role Assessment (4 Weeks per Department)

Best for: Organizations preparing for AI-driven workforce transformation

AI excels at “collapsible tasks” — work completed in a fraction of the usual time. When tasks taking 8 hours can be completed in 2 hours using AI (75% reduction), organizations must plan for capacity reallocation and role evolution.

Dual-Coach Approach

Process Coach: Evaluates workflows and identifies optimization opportunities
Technology Coach: Assesses AI and automation feasibility

Assessment Focus

Identify tasks where AI achieves ≥75% time savings
Determine whether acceleration creates new demand or reduces resources needed
Design role evolution paths with upskilling requirements
Plan workforce capacity reallocation

Roles most impacted: Payroll processing, quality assurance, administrative coordination, sales operations, software development.

Deliverables

Role-by-role AI impact analysis
Workforce reallocation recommendations
Upskilling roadmap
Change management plan

Why Most AI Strategies Fail

The Hard Numbers Behind AI Failure

88% of AI proof-of-concepts never reach production
56% of organizations remain stuck in “pilot purgatory”
95% of failures stem from data issues
18–24 months wasted on failed pilots
$500,000–$3 million lost per failed initiative

Common Causes

Solving for technology instead of business problems
Spending 60–80% of time on data preparation while budgeting only 20–30%
Treating AI as an IT-only initiative
Skipping MLOps until after models fail
Hiring strategy firms without implementation capability

What Separates Success from Failure

Organizations that scale AI successfully share three traits:

Engineering-backed strategy
Data-first approach
Production mindset from day one

How a Successful AI Strategy & Roadmap Assessment Works

Business & Use-Case Discovery
AI & Data Readiness Assessment
Technology & Architecture Evaluation
Governance & Risk Analysis
Roadmap & Execution Planning

Data Readiness: The Foundation of AI Strategy

Before any AI strategy can succeed, organizations must confront data reality.

Five Data Readiness Questions

Can we access required data in real time or near real time?
What percentage meets AI quality standards?
Do we have documented governance policies?
Can our infrastructure support AI workload volume and velocity?
Have we defined regulatory and compliance standards (GDPR, HIPAA, etc.)?

From Pilot to Production: The AI Validation Journey

Proof-of-Concept Best Practices (4–8 Weeks)

Well-designed PoCs answer:

Technical feasibility
Data sufficiency
Integration viability

The Scale-Up Framework

Infrastructure transition
Data pipeline industrialization
MLOps implementation
Governance activation
Organizational change management

How to Measure AI Strategy Success

Time-to-Value Metrics

30–45 days from strategy to first PoC
30–60 days PoC-to-pilot
6–9 months target for production deployment

Business Impact Metrics

15–20% cost reduction
3–8% revenue increase
26–55% productivity improvement
10–20% improvement in customer satisfaction

Financial Benchmarks

ROI >150% within 18–24 months
Payback <12 months (operational AI)
Payback <18 months (customer-facing AI)

Red Flags to Avoid

Strategy-only firms without engineering capability
One-size-fits-all frameworks
No industry-specific references
Overselling AI as universal solution
Ignoring failure statistics
Proprietary platform lock-in
Unrealistic timelines

11 Common AI Strategy Mistakes

Starting with technology instead of business problems
Underestimating data quality requirements
Ignoring change management
Running too many pilots
Choosing strategy-only consultants
Skipping governance planning
Neglecting MLOps infrastructure
Underinvesting in talent development
Expecting immediate ROI
Treating AI as an IT-only initiative
Overlooking user-centric design

AI Strategy Trends to Watch in 2026

Agentic AI and autonomous systems
AI governance as regulatory requirement
Small language models and edge AI
AI-accelerated software development
Multimodal AI integration
AI cost optimization with FinOps controls
Platform engineering for AI
Operationalized Responsible AI
AI-driven workforce transformation

Final Words

AI adoption is not simply about selecting the right models or tools. It is a strategic transformation in how organizations use data, infrastructure, governance, and operations to create measurable business impact.

Organizations that succeed:

Start with clear business objectives
Assess data and technology readiness early
Plan for production and scale from day one

A structured AI Strategy & Roadmap Assessment reduces risk, accelerates deployment, and increases the probability of measurable ROI.

Offshore Engagement Models: 7 Options Compared for Cost & Risk

Improving — Mon, 09 Feb 2026 08:44:52 +0000

Offshore Engagement Models: Choosing the Right Fit for Scalable Software Delivery

A mismatch between business expectations and the selected IT engagement model often leads to cost overruns, delays, or quality issues. Choosing the right engagement model plays a critical role in offshore software development success.

Engagement models in software development define how teams collaborate, share responsibility, and manage risk. A well-aligned offshore development model bridges geographical distance and ensures offshore partners deliver predictable and scalable outcomes.

In this article, we provide a practical breakdown of when each offshore engagement model works, when it fails, and how to avoid common contract traps.

What Is an Offshore Development Model?

An offshore development model refers to the structured approach used to collaborate with software teams located in another country. The model defines:

Ownership
Pricing
Communication flow
Delivery accountability
Risk distribution

Software development engagement models help organizations align technical execution with business objectives while leveraging global talent. Each offshore business model fits a specific project type, budget pattern, and maturity level. Selecting the right offshore development center model directly impacts productivity, quality, and long-term sustainability.

Offshore Development Models Comparison

Below is a practical comparison of all seven offshore engagement models across cost predictability, flexibility, delivery accountability, and governance effort.

Image: Offshore Engagement Models – 7 Options Compared for Cost & Risk

Let’s take a closer look at each model.

#1 Fixed Price Model

A Fixed Price Model defines scope, deliverables, timeline, and total cost upfront. The vendor commits to delivery within the agreed budget and schedule, regardless of effort. This model assumes stable requirements with minimal change.

Ideal Use Cases

Small to medium-sized projects

Internal tools, microservices, or feature-specific builds with limited complexity.
Clearly defined requirements

Well-documented functional and non-functional requirements, wireframes, and acceptance criteria.
MVPs with minimal expected change

Early-stage validation with controlled experimentation and scope stability.

Advantages

Predictable budget

Easier financial planning and procurement approvals.
Simple contract structure

Clear deliverables and milestones reduce legal and administrative overhead.
Low management overhead

Minimal day-to-day supervision required.

Limitations

Low flexibility

Changes require renegotiation.
Quality risks if scope is underestimated

Vendors may optimize for speed over craftsmanship under margin pressure.

#2 Dedicated Development Team Model

A Dedicated Development Team Model provides a full-time offshore team working exclusively on the client’s product. The team operates as an extension of the internal organization, prioritizing long-term collaboration over transactional delivery.

Ideal Use Cases

Long-term product development

SaaS platforms, internal tools, and developer platforms.
Scaling engineering capacity

Growth without local hiring overhead.
Complex domains

Cloud platforms, AI systems, and distributed architectures.

Advantages

High ownership and accountability
Deep domain and business context
Predictable scalability and team continuity

Limitations

Requires long-term budget commitment
Ongoing client-side involvement needed

#3 Time & Material Model

The Time & Material (T&M) Model charges based on actual engineering effort (hourly or daily). Scope evolves over time, making this model ideal for exploratory or complex work.

Ideal Use Cases

Agile and iterative development
Innovation-driven initiatives
Unclear or evolving requirements
Early-stage product development

Advantages

High adaptability to change
Outcome-focused product thinking
Faster project initiation
Transparent cost visibility

Limitations

Lower budget predictability
Strong governance required
Challenging for procurement-heavy organizations

#4 Staff Augmentation Model

The Staff Augmentation Model embeds offshore engineers into existing teams. The client retains full control over architecture, timelines, and delivery standards.

Ideal Use Cases

Skill gaps and niche expertise
Short-term capacity spikes
Mature internal engineering teams
Parallel execution needs

Advantages

Full control over execution
Fast onboarding
Flexible scaling
Strong cultural alignment

Limitations

Delivery accountability remains with the client
High internal management effort
Depends heavily on internal maturity

#5 Managed Services Model

Managed Services transfer end-to-end responsibility for delivery, operations, and performance to the offshore partner, measured against SLAs and KPIs.

Ideal Use Cases

Application maintenance and support
Cloud and platform operations
Predictable workloads
Cost optimization initiatives

Advantages

Outcome-driven accountability
Reduced internal operational load
Measurable performance standards
Predictable operational costs

Limitations

Limited flexibility
Vendor dependency risk
Not suitable for rapidly evolving systems

#6 SLA / Milestone-Based Model

The SLA/Milestone-Based Model ties delivery success and payments to predefined milestones or performance metrics.

Ideal Use Cases

Regulated and enterprise environments
Performance-critical platforms
Vendor transition scenarios
Programs with fixed delivery commitments

Advantages

Clear, enforceable accountability
Reduced client-side delivery risk
Improved stakeholder confidence
Strong procurement alignment

Limitations

Low execution flexibility
Heavy upfront planning
Risk of compliance over innovation

#7 Hybrid Engagement Model

The Hybrid Engagement Model combines multiple engagement models across workstreams.

Ideal Use Cases

Large enterprises with parallel initiatives
AI platforms and data-intensive systems
Phased digital transformations
Organizations balancing innovation and stability

Advantages

High operational flexibility
Balanced risk distribution
Improved cost efficiency

Limitations

Complex governance
Dependency on vendor maturity
Higher upfront planning effort

Selecting the Right Engagement Model

Key factors to consider:

Project purpose: Innovation vs. stability
Technical expertise: AI, LLMs, or niche domains
Budget constraints
Scope stability
Team size and structure
Product lifecycle stage

Strong alignment between business goals and delivery responsibility leads to successful offshore partnerships.

FAQs

Which engagement model is most cost-effective?

Fixed Price works best for small, well-defined projects. Dedicated teams offer better value for long-term initiatives.

Which model enables the fastest delivery?

Time & Material supports rapid iteration and parallel execution.

Which model minimizes risk?

SLA or milestone-based models reduce delivery risk through measurable commitments.

Which model ensures high quality?

Dedicated Development Teams promote ownership and long-term quality.

Which model suits long-term projects best?

The offshore development center model supports continuity and scalability.

Which model works best for AI projects?

Hybrid models are ideal for LLM and AI initiatives, balancing experimentation with accountability.

Conclusion

Each offshore development model presents trade-offs between cost, control, flexibility, and accountability. Organizations that align their business goals with the right IT engagement model unlock sustainable value and predictable outcomes.

However, engagement models alone don’t guarantee success. The right offshore partner helps reduce risk, adapt to change, and embed proven engineering, security, and operational practices.

Improving consultants help organizations design engagement models built on clarity, accountability, and measurable impact.

What Nobody Tells You About Golden Paths at Scale

Improving — Mon, 09 Feb 2026 08:33:28 +0000

Your platform team just celebrated hitting 85% golden path adoption. Everyone is excited. Onboarding time for new members dropped from three weeks to two days. New services spin up in minutes. Leadership loved the improved metrics.

Six months later, you've got 23 capability requests in your backlog. Your platform team is drowning. ML teams need custom GPU scheduling. The data team wants streaming pipeline patterns. API teams are rolling their own rate limiting because yours doesn’t fit their needs.

You nailed Day 1.

You're dying on Day 50.

This is the hidden scaling problem with golden paths. And it’s not solved by building more golden paths.

Golden Path Promise vs. What Actually Happens

The platform engineering playbook says golden paths reduce cognitive load and bring standardization across teams. They give developers a blessed path from code to production through self-service, accelerating feature development.

This works well for onboarding and early development. But creating new projects and features is maybe 1% of an application’s lifetime. The remaining 99% is operations, debugging, scaling, adding features, and handling edge cases.

Golden paths excel at the first 1%. They struggle with the rest.

Netflix learned this the hard way. They built a polished developer portal with documentation, recommended tools, and curated paths. Developers said it “wasn’t compelling enough” to change habits. Why?

Because it helped them start things, not run things.

The real work happens after deployment. That’s where centralized golden paths become bottlenecks.

Why Your Platform Team Hits a Ceiling

Your platform team can’t scale linearly with the organization. It’s just math.

Imagine:

200 engineers across 20 teams
Each team with distinct needs:
- ML teams need GPU scheduling, Kubeflow, model serving
- Data teams want Kafka, Airflow, stream processing
- API teams need rate limiting, circuit breakers, tracing
- Mobile backend teams need push notification infrastructure
Platform team size: 6 generalists

What Goes Wrong

Queue problem

Every capability funnels through the platform team. Prioritization becomes about who shouts loudest, not what delivers the most value.

Expertise problem

You build “good enough” solutions. ML teams need 12 GPU configurations. They get 3. It checks the box but doesn’t solve the problem.

Maintenance trap

You ship 30 capabilities over two years. Now you maintain all 30.

Kubernetes upgrade? Update 30 configs
Security patch? Test 30 capabilities
Team that requested capability #17 moved on? You still own it

Rigidity issue

Abstractions cover the 80% use case. The remaining 20% fights the platform or bypasses it entirely. This is abstraction debt.

Your platform team becomes the bottleneck for every capability, edge case, and new tool. That’s not sustainable.

Go With a Marketplace Approach

At KubeCon Atlanta, I discussed a different model.

Why should the platform team be the sole provider?

Why not turn the platform into a marketplace?

At a certain point, platform teams should stop being the builders of everything and become marketplace operators.

ML team contributes GPU scheduling
Data team contributes streaming pipelines
API team contributes rate limiting
Security team contributes authorization patterns

The platform provides the infrastructure for contribution, not every capability.

How the IDP Marketplace Model Works

Define clear interfaces

Expose APIs and standards for capability integration. Teams know exactly what to implement.

Build contribution templates

Provide scaffolding so teams don’t guess how to package their capability.

Automate validation

Every contribution must pass automated checks:

Metrics exposure
Security scans
Documentation
Health checks

Create recognition systems

Contribution isn’t charity. Track it. Reward it. Make it count in performance reviews.

Advantages of the IDP Marketplace Model

Parallel capability development instead of queues
Domain expertise embedded where it belongs
Platform team focuses on primitives, not products
Network effects drive adoption and value

Organizations running mature marketplace models see 3–4x faster capability development compared to centralized teams.

But Here’s the Part Nobody Talks About

After KubeCon Atlanta, many teams shared failed attempts at this approach.

Governance Breakdown

No quality standards lead to capability sprawl
Developers don’t trust community contributions
Multiple poorly maintained implementations of the same thing

One organization had three different Postgres operators, none properly maintained. Teams gave up and installed Postgres manually.

Quality Problems

Capabilities work for the original team but fail later:

Security CVEs
Kubernetes upgrades
Hidden network assumptions

Nobody owns the fix. Capabilities become orphaned and unusable.

Contribution Friction

Platform APIs are complex. Contributing requires understanding:

Service meshes
CI/CD pipelines
Monitoring
Security policies

Only senior engineers contribute. Participation dies out.

Maintenance Nightmare

Kubernetes 1.35 drops. Who updates 40 capabilities?
Security patch lands. Who validates everything?
Production breaks at 3am. Who’s on call?

Prerequisites for Making Marketplaces Work

1. Platform Primitives That Enable Contribution

Capabilities must plug in without platform code changes. If every addition requires core modifications, your platform isn’t ready.

2. Enforced Quality Standards

Automated testing
Mandatory metrics and health checks
Security scanning for CVEs and secrets
Documentation requirements:
- Runbooks
- Troubleshooting guides
- Usage examples

No documentation means no shipment.

3. Ownership Beyond Initial Contribution

Define maintenance responsibilities upfront
Clear security patching ownership
Deprecation and migration policies
Explicit handoff mechanisms

“You build it, you own it for 12 months” is a valid rule.

4. Cultural Readiness

Inner-source culture already exists
Contributions count toward goals and reviews
Leadership supports contribution time

If leadership sees contribution as “not real work,” the marketplace fails.

Hybrid Approach

Don’t go all-in immediately.

Golden capabilities for common needs (70–80%)
Marketplace capabilities for specialized domains

Capability Tiers

Platform-blessed

Maintained by platform team, SLAs guaranteed
Community-maintained

Supported by contributors, use at own risk
Experimental

No stability guarantees

Clear expectations prevent surprises.

Next Step for You

If you’re hitting scaling issues:

Audit your backlog for domain-specific requests
Identify teams with deep expertise
Start with a low-risk pilot capability
Build templates and validation, not just docs
Establish governance before scale

If you’re building your first platform:

Start centralized
Design extensibility from day one
Avoid premature marketplace complexity

Real Insight

Platform maturity isn’t “build golden paths and stop.”

It’s:

Build golden paths
Recognize when they become bottlenecks
Evolve your model intentionally

Centralization gives control and consistency.

Marketplaces give scale and expertise.

Neither is perfect.

The right choice depends on your organization’s stage.

I explored platform marketplaces, governance models, and real-world failure modes at KubeCon Atlanta.

Want to discuss platform scaling or share your experience?

Connect with me on LinkedIn. If you’re struggling with platform engineering, contact our consultants—we help teams build platforms that actually scale.

Security: The Thing That Everyone Loves to Hate

Improving — Mon, 09 Feb 2026 08:27:57 +0000

Security often gets pushed to "later" in cloud native development as teams rush to ship features, optimize costs, or scale faster. However, incidents like Log4j (an OSS program behind the 34% increase in vulnerability exploitation between 2020 and 2021) have shown that “later” usually means crisis mode, late-night calls, patching under pressure, and scrambling to contain the damage.

The truth is that cloud native security is as much about how teams think, collaborate, and prioritize it as it is about tools or compliance checklists. And here lies the real challenge: security is still seen as someone else’s problem. Due to this, 50% of organizations now have critical security debt, with high-severity issues left open for more than one year, according to ITPO. Developers focus on shipping, product managers focus on revenue, and platform engineers juggle complexity, while security risks quietly pile up.

At KubeCon + CloudNativeCon India 2025, I, Sonali Srivastava, brought together a panel of cloud native experts. Ram Iyengar, Bhavani Indukuri, Anusha Hegde, and I took this challenge head-on to spread awareness about prioritizing security. Our message was clear: to build truly resilient systems, security must be everyone’s responsibility, baked into the culture from day one, not bolted on at the end.

In this blog post, we explore how security spans differently across roles and why understanding these perspectives is essential for building a security-first organization. From spotting new-age threats like QR phishing to shifting security left in the SDLC and building a culture where accountability replaces blame.

Wake-up Call: New Threats and Everyday Risks

Security threats today evolve faster than awareness. Attack vectors are no longer limited to traditional phishing or endpoint breaches. They are dynamic, social, and increasingly AI-driven.

Emerging Threats Include

Quishing (QR phishing)

Users are tricked into scanning malicious QR codes during daily activities such as payments, restaurant menus, or opening URLs, leading to compromised devices or accounts.
Prompt injection attacks

Attacks targeting LLM-integrated applications that manipulate AI systems into revealing sensitive data.
Jailbreaks

Techniques used to bypass model restrictions or gain elevated access in sandboxed environments.
Dependency confusion attacks

Exploits of package naming conventions to inject malicious code into software supply chains.
Configuration drift exploits

Unsupervised or AI-generated cloud infrastructure changes that introduce unintended vulnerabilities.

The threat landscape is expanding faster than organizational readiness. Security awareness, tooling, and culture must evolve just as quickly, starting with the foundation.

Security Through Different Lenses

Developers’ Lens: Simplicity and Early Detection

Developers are often caught between the pressure to deliver fast and the need to maintain secure practices. Every dependency added, every library imported, and every base image chosen introduces potential risk.

What developers can focus on:

Simplify the stack

Fewer dependencies mean fewer unknowns and a lower vulnerability risk. Question every third-party library.
Use simple base images

Complex images add unnecessary packages that expand the attack surface.
Integrate SBOMs early

Software Bill of Materials (SBOM) generation should be part of the build process, not an afterthought.
Enforce security at the PR stage

Use security linters in IDEs and make vulnerability checks part of standard code reviews.

“You should think of having less dependencies when you are trying to choose your base images. That’s where SBOMs are really important.”

— Bhavani Indukuri

A developer’s role is to make choices that minimize the blast radius of failures.

Security Engineers’ Lens: Discipline Over Band-Aids

Security engineers are often perceived as the people who slow things down, but their focus is on preventing recurring issues instead of applying temporary fixes.

What security engineers can focus on:

Treat governance as discipline, not bureaucracy

Standards like Pod Security Standards (PSS) and regulations such as GDPR act as guardrails, not blockers.
Build resilience through prevention

The goal is not just passing audits, but making insecure configurations difficult to deploy.
Establish security gates

Automated checks that block vulnerable code from reaching production must be mandatory.

“There are governances and compliances in place for a reason; it’s like when you used to go to school, you stood in a straight line.”

— Sonali Srivastava

Security engineers create systems where secure behavior is the default.

Product Managers’ Lens: Security as Strategic Investment

Product managers often face pressure to trade security for speed, treating security as tech debt. This framing is flawed. The average time to fix security flaws has increased 47% in five years, from 171 to 252 days, according to ITPO.

What product managers can focus on:

Reframe security as a product feature

Security directly impacts trust, reliability, and brand reputation.
Prioritize security alongside features

Security requirements must be part of feature specs from day one.
Understand different risk types
- Vulnerabilities: Known CVEs in dependencies
- Misconfigurations: Policy violations and access control issues
Use the right tools for visibility
- VEX for vulnerability management
- Policy engines like Kyverno for misconfigurations

“You have vulnerabilities which are a whole big class of problems. The other class of problems is misconfigurations.”

— Anusha Hegde

When PMs factor security into roadmaps, it becomes a competitive advantage instead of a scramble.

DevOps and Platform Engineers’ Lens: Infrastructure as the Security Boundary

Platform engineers sit between development velocity and operational stability. Their infrastructure decisions directly shape security posture.

What platform engineers can focus on:

Enforce security through automation

Policies should not rely on manual checks.
Maintain least-privilege access

Regularly audit permissions and rotate credentials.
Manage configuration drift

Use infrastructure-as-code and policy enforcement to prevent unsupervised changes.
Build observability into security

Integrate security metrics into daily dashboards and workflows.

Platform engineers either make security scalable or create gaps attackers exploit.

Leadership’s Lens: Culture and Accountability

Leadership determines whether security is a real priority or a checkbox exercise.

What leaders can focus on:

Allocate time for security

Dedicate sprint capacity to security improvements.
Tie security to customer trust

Security incidents impact users, retention, and revenue.
Celebrate proactive security

Reward teams who prevent issues early.
Make security visible

Review security metrics alongside business metrics.
Foster psychological safety

Encourage reporting issues without blame.

Leadership creates the conditions where security can thrive.

Building a Security-first Culture

Understanding individual perspectives is only the beginning. The real work is weaving them into a shared culture.

Educate and empower

Make security training part of onboarding and continuous learning.
Normalize ownership

Encourage every role to think like a security advocate.
Create feedback loops

Use post-incident reviews as learning tools, not blame sessions.
Make security visible

Integrate security metrics into everyday workflows.
Focus on adaptability

Treat security culture as a strategic asset that evolves with new threats.

A multi-layered defense complements this culture, protecting applications, infrastructure, and organizational boundaries.

Next Step: The Cultural Transformation

The threat landscape continues to evolve. AI-driven attacks, supply chain vulnerabilities, and configuration exploits are becoming more sophisticated. Organizations can only keep up through cultural transformation.

Security must be embedded into daily workflows and maintained through transparency. When security becomes a shared conversation rather than a compliance checkbox, true organizational maturity begins.

Each issue becomes an opportunity to strengthen systems and prevent recurrence. This mindset builds stronger systems and more resilient organizations.

At Improving, trust is at the core of everything we do. Keeping software secure is essential to maintaining that trust. Our consistent focus on security and privacy is why enterprises continue to trust us as one of the leading software consulting providers.