No Documentation. Existing Product. What Now? 👀

On July 30, we'll discuss a situation that many analysts know all too well.
Imagine joining a project that's been running for years. The product is actively evolving, the team is moving fast, and new tasks keep landing on your desk. Then you ask for documentation... and get a couple of outdated files along with a friendly, “Just ask the developers if you need anything.” 😅
Sounds familiar? Then this meetup is for you.

We'll discuss:
⚖️ the difference between developing a new product and improving an existing one
🔍 where to find information when nothing is properly documented
🧩 how to build a complete picture of the product from scattered knowledge
🤝 how to work with a team when key requirements exist only in experts’ heads
⚠️ how to reduce uncertainty and avoid unpleasant surprises
🎯 and most importantly — how to bring order to chaos and develop the adaptability needed to thrive in a constant state of uncertainty.
Speaker: 🔥 Olga Kletskina, Business Analyst, Andersen, Business & System Analyst and Product Owner with 7+ years of experience in IT.
🎟 Registration

Meetup details:
⏰ Time: 19:00 (Minsk time, GMT+3)/18:00 (CEST)
🕒 Duration: 1 hour
🗣 Language: Russian
📍 Offline: Andersen’s office in Minsk
💻 Online: The link to the stream will be sent to your email specified in the registration form
🍦 Don't wait too long to register — spots are disappearing faster than ice cream on a hot summer afternoon!

See you soon :)

Top-3 bad advice life tried to sell me before my IT traineeship in Andersen 🤔

People think moving from any sphere to IT is a career change. Well, be careful, spoilers: that could also be an upgrade. I became an analyst at almost 30, and not despite my background. I became an analyst because of it.

Here’s what selling, hospitality and many other spheres taught me about listening, fearing and speaking. Here’s why that experience is worth more than most certificates. And to express that I’ll share 3 main thoughts people from around tried to sell me, and would sell, if I didn’t know, how sales actually work.
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Tell us in comments about your experience of entering IT. And if you're not in IT yet, tell us about your current step.

Did you come from sales or another "unrelated" field? What lesson from your past unexpectedly helped you in IT? Or — if you’re still on the way — what are you bringing with you that no course can teach?

👇 Share your own story bellow. Let’s find out the diversity of our beautiful and exciting paths!

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How Business Analysts Should Validate AI Outputs: A Practical Framework 🤔

AI tools can generate requirements, user stories, documentation and even diagrams in seconds. But speed does not equal reliability.

For Business and System Analysts, the real skill is no longer just producing artifacts — it’s validating AI-generated outputs before they reach stakeholders or development teams.

A simple validation framework I use:
1️⃣ Context check
Did the AI understand the business domain, constraints, and stakeholders?
2️⃣ Logic consistency
Are assumptions coherent? Do flows contradict each other?
3️⃣ Traceability
Can the output be linked to real requirements, data sources, or regulations?
4️⃣ Completeness
Are edge cases, exceptions, and non-functional requirements missing?
5️⃣ Stakeholder reality test
Would the domain expert actually accept this?

AI accelerates analysis. But analytical responsibility remains human.
For BAs, the competitive advantage is not using AI, but knowing how to challenge it.

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TYPICAL BA MISTAKES WHEN INTRODUCING AI INTO TEAM PROCESSES ⛔️

AI doesn’t fail in teams — implementation does. In multiple projects, I see the same pattern: strong expectations, weak outcomes. Not because AI is immature, but because Business Analysts approach it with the wrong mental model.

Here are the most common mistakes:

• Treating AI as a tool, not a workflow change
Embedding ChatGPT into tasks without redesigning the process → zero real impact.

• Skipping validation layers
AI-generated artifacts (requirements, ACs, mappings) go unchecked → defects shift downstream.

• Over-automation of ambiguity
Using AI where requirements are unclear → amplifies confusion instead of resolving it.

• Ignoring traceability
No link between AI output and source → loss of accountability and trust.

• No feedback loop
Teams don’t track where AI helps vs harms → no learning, no optimization.

The core issue: AI compresses execution, but expands responsibility.
If you don’t redesign how decisions are made — you just accelerate mistakes.

What actually works:
→ AI as a co-analyst, not a generator
→ Explicit validation checkpoints
→ Measurable usage (accuracy, rework, cycle time)

AI adoption is not about prompts. It’s about process architecture.

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The First Foundation of Business Analysis: Start with the Stakeholders 👨‍👩‍👦

Every successful analysis starts with one simple question: Who are we solving this for?

Before discussing requirements, solutions, or business value, identify the people who will influence the change, or who will be affected by it. This is why Stakeholders are the first foundation of Business Analysis. Every other decision depends on getting this right.

A practical framework I use:

1️⃣ Look beyond the sponsor
End users, compliance, operations, support teams, regulators, and data owners often have insights that never appear in formal requirements.

2️⃣ Focus on influence, not job titles
The most important stakeholder isn't always the project sponsor. Sometimes the person who can make or break your solution sits outside the core project team.

3️⃣ Engage the right people early
A missing stakeholder rarely causes problems on day one. The real impact appears later - during UAT, approvals, or even after release, when changes become expensive.

4️⃣ Choose the right level of involvement
Not everyone should attend every workshop. Some stakeholders make decisions, some provide expertise, and others simply need to stay informed. Effective analysis is about managing engagement, not inviting everyone.

5️⃣ Review your stakeholder list continuously
Projects evolve. New systems, teams, and constraints appear along the way. A stakeholder map should evolve too.

Business analysis doesn't begin with writing requirements. It begins with understanding who is in the game. Because if you miss the right stakeholders, you'll likely misunderstand the real business need. And if the need is wrong, the solution will be too.

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AI TOOLS FOR BAs: WHAT ACTUALLY “STUCK” BY 2026 🔗

In 2023–2024 we tried everything. By 2026, a few patterns clearly survived the hype — because they reduced cycle time without degrading analysis quality. Agentic workflows became normal.

Not “chatting with AI”, but delegating:
research → extract → compare → draft → validate.

BAs increasingly run small agents for repetitive work: backlog grooming prep, requirements QA, regression checklist generation, and stakeholder-ready summaries.

Agent browsers for discovery, not for decisions
Browser agents are now the default for:
– scanning competitor flows & docs
– collecting evidence for assumptions
– building a traceable “why” behind requirements
Still: humans own the final judgment. Agents accelerate discovery, not accountability.

Requirements quality gates (“AI as a reviewer”)
The most useful use case isn’t writing user stories—it’s reviewing them:
– missing edge cases & error states
– inconsistent terminology
– unclear acceptance criteria
– weak NFR coverage (security, audit, performance)
Think: AI as a lint tool for analysis artifacts.

Better engines + easier integration
We’re seeing fewer “one tool to rule them all” bets and more composable stacks:
LLM + retrieval + templates + Jira/Confluence + test management.
The winning setups are boring: repeatable prompts, shared checklists, and strong redaction rules.

The BA skill that matters more, not less
By 2026, the differentiator is still: domain modeling, risk framing, negotiation, and building alignment. AI raises the baseline. Seniority still comes from judgment, structure, and accountability.

If you’re using AI in BA work: what’s your most “sticky” use case in 2026?

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AI WON’T MAKE YOU A SENIOR BA — BUT WHAT WILL ❓

AI can write user stories, summarize workshops, generate diagrams, and propose edge cases. That’s useful. But it’s not “seniority”. A Senior BA/SA isn’t the person who has AI doing the work instead of them. It’s the person who can work with AI—and still own the thinking.

Seniority = your ability to use AI as a co-pilot, not a replacement.

What actually makes you senior (and how AI fits):
– You frame the problem. AI drafts artifacts.
Senior BAs define the real problem, constraints, and success metrics. Then AI helps produce faster.

– You validate reality. AI generates hypotheses.
AI can suggest options; you run stakeholder checks, data checks, and “is this true in our domain?” tests.

– You own trade-offs. AI expands the option space.
Seniors decide what to sacrifice (scope/time/risk/UX/compliance) and document why. AI helps compare.

– You think in systems. AI helps with coverage.
Seniors anticipate downstream effects (data, integrations, ops, failure modes). AI helps enumerate and map.

– You manage ambiguity. AI helps structure it.
Seniors don’t “fill gaps” with confident text. They define assumptions, unknowns, and a learning plan.

– You drive alignment. AI helps with communication.
Seniors align incentives across PO/Eng/QA/Legal/Ops. AI helps tailor messages, but you own the negotiation.

A simple rule that changes everything: Use AI to increase throughput, but use your BA skills to increase truth.
If you want a practical habit: Before sending anything AI-generated, add a

“Senior BA layer”:
- What assumptions did we make?
- What can break?
- What decision are we making, and who signs it off?

AI won’t make you senior. Working with AI—while owning judgment, validation, and decisions—will.

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Why Good Requirements Are About Value, Not Just Documentation 📝

In many teams, the quality of requirements is judged by how detailed they are.
Clear structure, full coverage, precise acceptance criteria — all of that matters.
But detailed doesn’t always mean valuable.

I’ve seen a team spend weeks documenting and building a feature with perfect requirements — every edge case covered, every scenario described. After launch, almost no one used it. The problem it solved wasn’t a real problem.

Around the same time, a small change — barely half a page of documentation — reduced friction for users and cut support.

The difference wasn’t in how the requirements were written. It was in how well the value behind them was understood.

Documentation is not the goal. Value is.

As Business Analysts, we are often trained to focus on completeness: cover all scenarios, write precise acceptance criteria, document every detail.
That’s important. But it’s not the end goal.
A good requirement doesn’t just describe what needs to be built. It makes clear why it matters — to the user, the business, or the product.

When that “why” is missing, problems appear: features get delivered but don’t solve real user problems, teams optimize for output instead of outcomes, priorities become unclear or shift, and “done” doesn’t always mean “useful.”

When value is clear, decisions become easier. Trade-offs make sense. Teams align faster. Stakeholders stop treating the backlog as a wish list.

How value changes the way you write requirements?

Focusing on value doesn’t mean writing less. It means writing differently.
Instead of only asking “What should this feature do?” ask: “What problem are we solving?”, “Who benefits and how?”, “How will we know if this is successful?”, “What happens if we don’t do this?”
This shifts requirements from “descriptions of functionality” to “arguments for why this work matters.”

In practice, that means connecting user stories to real user needs, challenging requirements without a clear purpose, keeping them lean when extra detail adds no value, and making trade-offs based on impact, not effort.

Why this makes you a stronger BA?

Teams don’t struggle because they lack documentation. They struggle because they lack clarity about what matters and why.
A Business Analyst who focuses on value helps the team avoid building things nobody needs, makes prioritization conversations more grounded, and becomes a thought partner, not just a requirements writer.

How to start: before writing your next requirement, spend five minutes answering, “What happens if we don’t build this?”
If the answer is “nothing much” — challenge whether it belongs in the sprint at all.
If the answer is clear and painful — let that pain drive the way you frame the story.

Good requirements are not the ones with the most detail. They are the ones that lead to meaningful outcomes.
Because in the end, Business Analysts don’t just document features. They help ensure what gets built actually matters.

The Second Foundation of Business Analysis: Understand the Need, Not the Solution 🤝

After identifying the right stakeholders, the next question is simple: "What problem are we actually trying to solve?"

It sounds obvious. In reality, it's one of the most common reasons projects miss the mark. Stakeholders rarely describe their need. They describe the solution they already have in mind.

A practical way to spot the difference:

1️⃣ Listen beyond the request

"We need a dashboard." "Add a new button." "Build a chatbot." These are proposed solutions, not necessarily the real need.

2️⃣ Ask "Why?" before discussing "How?"

What business problem does this solve? What happens if we don't implement it? The answers often lead somewhere unexpected.

3️⃣ Separate outcomes from features

A feature is something you build. A need is the business outcome you're trying to achieve: saving time, reducing risk, increasing revenue, or improving customer experience.

4️⃣ Challenge assumptions respectfully

Good analysts don't reject ideas. They help stakeholders validate whether the proposed solution is the best way to achieve the desired outcome.
AI can generate requirements.

Teams can deliver features. But if the underlying need wasn't understood, the project may still fail to create business value. Business Analysis isn't about documenting what people ask for. It's about discovering what the business truly needs.

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