🗣 Product + Marketing = Results

Why do some products skyrocket while others go unnoticed?

Quite often, it’s not just about the idea or execution – it’s about how well the product and marketing are aligned throughout the entire user journey 👀

📅 On June 18, we’re holding a meetup where we’ll explore how product and marketing teams can operate as a single system – from market understanding to retention and growth.

Agenda:
– Where collaboration creates the biggest impact;
– What each team truly brings to the process;
– Why a lack of alignment leads to missed opportunities;
– What a practical framework for joint decision-making looks like;
– How to strengthen positioning and drive real impact.

🧠 No theory overload – just real-world scenarios and approaches you can apply right after the meetup.

Speakers:
🎤Natallia Tarasevich – Product Manager/Product Owner/Business Analyst
🎤Aryna Barysionak – Lead Marketing Manager | Digital Marketing Manager

🔗 Register here: https://lnkd.in/dB9AJPD6

Two experts. One user journey. One result!

Meetup details:
⏰ Time: 18:00 (СEST)
🕒 Duration: 60-80 minutes
🗣 Language: English
💻 Online: the link to the stream will be sent to your email specified in the registration form

See you!

Is a software release just a technical procedure? 🤔

For a digital native - I believe yes. But for someone who spent 20 years in Event Management before pivoting to system analysis at 40+, a release is a high-stakes performance.

At first glance, these two universes seem to exist on opposite poles. One is about spotlights, catering, and microphones, the other is about SQL queries, API contracts, and Jira tickets. However, as I navigate my career path, I’ve realized that a software release and a large-scale forum share the same DNA.

Imagine a major international summit. You have months of planning, a diverse group of stakeholders with conflicting interests and ambiguous requirements, and a hard deadline that cannot be moved. That is exactly how a software release feels. In both worlds, your "backlog" is the event program. Your integrations are the external vendors and speakers who must perform in perfect sync. If the sound system fails during a speech, it’s a critical bug in production. If the registration desk is slow, it’s a bottleneck in the system architecture.

As a system analyst, I’ve found that my event brain gives me a massive advantage. In event management, you learn to spot a crisis before it happens. You become a master of requirements gathering because if you misunderstand the client’s vision for a gala dinner, there is no undo button once the guests arrive. In IT, this translates to meticulous analysis. I don't just look at the data fields, I look at the guest journey, user experience.

My transition was never about discarding my past, it was about reformatting it. When I’m analyzing a complex database structure, I use the same logical patterns I used to coordinate a 1000 person event. Both require a high-level view of the system and attention to details. Whether it’s a post-mortem report or a sprint retro, the goal is the same - to learn how to do it better next time.

If you are considering a career shift at any age, stop viewing your previous experience as white elephant. It is your secret weapon. You’ve spent years solving real-world puzzles under pressure. IT is just a different set of tools to solve the same human problems. Experience is the most stable architecture you can build.

BusinessAnalysis SystemAnalysis

GPT-5.5: WHY THIS MODEL UPDATE MATTERS FOR BA/SAs 🔶

OpenAI released GPT-5.5 as a model for complex professional work - and for Business and System Analysts, the key point is not only stronger reasoning.
The real signal is workflow maturity. GPT-5.5 is positioned for coding, online research, information analysis, document and spreadsheet work, and tool-based execution. It also supports long-context work in the API, which makes it more relevant for real analytical environments: specifications, transcripts, legacy documentation, tickets, policies, and system descriptions.

What matters most for BA/SAs:
🔹 Better support for complex tasks
Analytical work is rarely one prompt → one answer. It usually means reading context, comparing versions, finding gaps, checking assumptions, and preparing structured outputs.
🔹 Stronger tool use
GPT-5.5 is designed to work better across tools. For analysts, this matters because BA/SA workflows increasingly connect documents, spreadsheets, tickets, diagrams, and knowledge bases.
🔹 Long-context analysis
Large inputs are normal in analysis: BRDs, API specs, discovery notes, call transcripts, business rules, and old documentation. Better long-context handling means better synthesis and fewer isolated answers.
🔹 More reliable professional outputs
OpenAI also highlights stronger performance in professional tasks and reduced hallucinations in GPT-5.5 Instant. This is important because a polished but wrong AI output can easily become a bad requirement or misleading decision note.

For BA/SAs, the practical use cases are clear:
• requirements review
• acceptance criteria drafting
• gap and contradiction analysis
• stakeholder meeting summaries
• documentation comparison
• impact analysis
• test scenario preparation
• backlog refinement support

My takeaway: GPT-5.5 is another step from AI as a chatbot toward AI as a workflow assistant.
But the analyst’s responsibility does not disappear. The value shifts to context control, validation, traceability, and knowing where AI output is useful — and where it is only a hypothesis.
The best analysts will not be those who simply use GPT-5.5. They will be those who can integrate it into analytical workflows without losing ownership of quality.

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Hey, Community! 🙌
Just one day to go until our meet-up - don’t forget to register, we're waiting for you!

If you'd like to dive deeper into the topic, join our upcoming meetup:
🎤 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗠𝗮𝗻𝗮𝗴𝗲𝗿𝘀 & 𝗠𝗮𝗿𝗸𝗲𝘁𝗶𝗻𝗴 𝗠𝗮𝗻𝗮𝗴𝗲𝗿𝘀: 𝗛𝗼𝘄 𝘁𝗼 𝗢𝗽𝗲𝗿𝗮𝘁𝗲 𝗮𝘀 𝗢𝗻𝗲 𝗧𝗲𝗮𝗺
📅 June 18, 2026
🕓 18:00 CEST / 16:00 UTC / 17.00 Minsk / 18.00 Poland

We'll discuss how Product Managers and Marketing Managers can work together across the entire customer lifecycle—from market research and positioning to onboarding, retention, and growth. I'll share practical frameworks, real examples, common collaboration traps, and the tools that help teams make better decisions together.

👉 Register here: Enter your registration data for the meetup
Looking forward to seeing you there! 🙂

Hey, Community!

Previous week 𝘄𝗲 𝗰𝗮𝗺𝗲 𝘁𝗼𝗴𝗲𝘁𝗵𝗲𝗿 𝘁𝗼 𝗱𝗶𝘀𝗰𝘂𝘀𝘀 𝗵𝗼𝘄 𝗽𝗿𝗼𝗱𝘂𝗰𝘁 𝗮𝗻𝗱 𝗺𝗮𝗿𝗸𝗲𝘁𝗶𝗻𝗴 𝘁𝗲𝗮𝗺𝘀 𝗰𝗮𝗻 𝘀𝘁𝗼𝗽 𝗽𝘂𝗹𝗹𝗶𝗻𝗴 𝗶𝗻 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁 𝗱𝗶𝗿𝗲𝗰𝘁𝗶𝗼𝗻𝘀 𝗮𝗻𝗱 𝘀𝘁𝗮𝗿𝘁 𝗰𝗿𝗲𝗮𝘁𝗶𝗻𝗴 𝘃𝗮𝗹𝘂𝗲 𝗳𝗼𝗿 𝘂𝘀𝗲𝗿𝘀 𝗮𝘀 𝗼𝗻𝗲 𝘁𝗲𝗮𝗺.

I’m pleased to share the key takeaways with you – please study them in the presentation below 🗃

𝗔 𝗵𝘂𝗴𝗲 𝘁𝗵𝗮𝗻𝗸 𝘆𝗼𝘂 𝘁𝗼 𝗲𝘃𝗲𝗿𝘆𝗼𝗻𝗲 𝘄𝗵𝗼 𝗷𝗼𝗶𝗻𝗲𝗱 𝘁𝗵𝗲 𝗺𝗲𝗲𝘁𝘂𝗽, 𝗮𝘀𝗸𝗲𝗱 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀, 𝗮𝗻𝗱 𝗰𝗼𝗻𝘁𝗿𝗶𝗯𝘂𝘁𝗲𝗱 𝘁𝗼 𝘁𝗵𝗲 𝗱𝗶𝘀𝗰𝘂𝘀𝘀𝗶𝗼𝗻! 💛

🎥 𝗠𝗶𝘀𝘀𝗲𝗱 𝘁𝗵𝗲 𝗺𝗲𝗲𝘁𝘂𝗽 𝗼𝗿 𝘄𝗮𝗻𝘁 𝘁𝗼 𝗿𝗲𝘃𝗶𝘀𝗶𝘁 𝘁𝗵𝗲 𝗸𝗲𝘆 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆𝘀?
➕ 𝗟𝗲𝗮𝘃𝗲 𝗮 "+" 𝗶𝗻 𝘁𝗵𝗲 𝗰𝗼𝗺𝗺𝗲𝗻𝘁𝘀, 𝗮𝗻𝗱 𝘄𝗲'𝗹𝗹 𝘀𝗵𝗮𝗿𝗲 𝘁𝗵𝗲 𝗺𝗲𝗲𝘁𝘂𝗽 𝗿𝗲𝗰𝗼𝗿𝗱𝗶𝗻𝗴 𝗮𝗻𝗱 𝗽𝗿𝗲𝘀𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 𝘄𝗶𝘁𝗵 𝘆𝗼𝘂.

𝗦𝗲𝗲 𝘆𝗼𝘂 𝗮𝘁 𝗼𝘂𝗿 𝘂𝗽𝗰𝗼𝗺𝗶𝗻𝗴 𝗺𝗲𝗲𝘁𝘂𝗽𝘀 - 𝘀𝘁𝗮𝘆 𝘁𝘂𝗻𝗲𝗱 𝗳𝗼𝗿 𝗺𝗼𝗿𝗲 𝗲𝘃𝗲𝗻𝘁𝘀 𝗳𝗿𝗼𝗺 us! 💛

Why Clear Requirements Still Lead to Broken Products 🔎

I've seen perfectly written user stories destroy a sprint. Not because they were unclear — but because no one asked what happens around them.

Business Analysts often focus on collecting requirements and turning them into user stories. But without system thinking, even the clearest requirements lead to fragmented solutions and rework.

System thinking shifts the focus from individual features to the whole ecosystem. It’s not just “What does this feature do?” but “How does it affect everything around it?” — including user flows, integrations, data, and edge cases.

This is where things usually go wrong:
• Ignored dependencies between teams or components
• Missing real-world edge cases
• Features that work in isolation but break end-to-end flows

A “simple” change in a login flow can turn into weeks of rework when it impacts authentication, analytics, error handling, and session management. These issues don’t appear later — they were just never considered early.

A system mindset helps catch these connections before they become production problems.

How to apply system thinking:
• Map the full user journey before writing a story
• Check upstream and downstream impacts
• Validate assumptions with dev and QA early
• Think in scenarios: normal, edge, failure

Clear requirements are not enough. Good requirements are context-aware.
System thinking is what turns documentation into real solutions.

PROMPTING FOR BA/SAs: WHY GOOD PROMPTS ARE NOT GOOD ANALYSIS ❓

Good prompting is useful. But it is not the same as good analysis.
A strong prompt can produce a clean user story, a structured summary, or a nice table of acceptance criteria. But it cannot automatically decide whether the requirement is correct, complete, feasible, testable, or aligned with the business goal.

That is still analyst work. For BA/SAs, prompting is becoming a basic skill. But the more important skill is knowing what to check after the answer appears.
- Is the actor clear?
- Is the business value real?
- Are all paths covered?
- Are data dependencies visible?
- Is the source reliable?
- Are we documenting a real rule or just a generated assumption?

The danger is not that AI writes bad requirements. The danger is that it writes confident requirements that look good too early. Good prompts help us draft faster. Good analysis helps us avoid expensive mistakes.

BusinessAnalysis SystemAnalysis RequirementsEngineering AI PromptEngineering

🏛 Architects, Tech Leads, CTOs - this one's for you!

📅 On July 14, we're meeting online to discuss a challenge that almost every growing engineering team eventually faces.
⚖️ BPMN or code-first?
🔄 Camunda or Temporal?
🕸 Orchestration or choreography?
💭 Most importantly, how do you choose an approach that helps your business scale instead of creating new problems a year or two down the road?

During this meetup, we'll explore modern workflow automation and orchestration platforms, compare their strengths and weaknesses, and discuss which solutions actually work in real-world enterprise environments.
🚀 Register here

Agenda:
🧩 When BPMN is the right choice - and when it isn't;
⚔️ Code-first vs. model-first approaches;
📈 Scalability and operational considerations;
🔒 Vendor lock-in and total cost of ownership;
☁️ Cloud-native readiness;
🛠 Developer experience and governance.
🎙 Speaker: Ivan Ishchenko  - Solutions Architect at Andersen with 11+ years of experience designing enterprise systems, cloud-native solutions, and workflow automation platforms for healthcare, fintech, and SaaS companies.
🧠 If you've ever had to choose between "getting it done quickly" and "not regretting it two years later," this session is for you.

Meetup details:
⏰ Time: 17:00 (СEST)
🕒 Duration: 1 hour
🗣 Language: English
💻 Online: The link to the stream will be sent to your email specified in the registration form

See you!

Why Communication Is the Most Important Skill for a Business Analyst❓

A Business Analyst can write perfect requirements—and still fail the project.
I've learned this the hard way.

Because the real problem is rarely in the document. It's in how people understand it.

Two people read the same user story and walk away with different interpretations. A stakeholder assumes one outcome, a developer delivers another, QA tests a third. No one is technically wrong—and yet everything breaks.

Something I keep coming back to in my work as a BA: it's not just about clarity. It's about alignment.

Writing clean, structured requirements is important. But it's not enough. The real value comes from actively closing gaps in understanding—spotting when something sounds “obvious” but isn’t actually agreed on, and turning assumptions into explicit decisions.

Even well-written requirements leave room for interpretation. And that’s where problems begin:
• Different teams make different assumptions
• Edge cases are understood inconsistently
• Decisions are made implicitly instead of explicitly
• Misalignment is discovered only during testing — or worse, after release

In my experience, good communication makes these gaps visible early.

In practice, it often looks like this:
• Rephrasing the same requirement for business and technical audiences
• Asking one more question when everyone else is ready to move on
• Walking through scenarios together instead of relying only on text
• Double-checking that understanding is shared, not assumed

None of this is glamorous. But it's what prevents rework, frustration, and those “but I thought we agreed on…” conversations.

Requirements don’t fail because they’re written badly.
They fail because they’re understood differently.

And closing that gap — one conversation at a time — is what makes this role so interesting.

What’s a misalignment you caught early just by asking the right question?

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.
_________________________________________________

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.

BusinessAnalysis SystemAnalysis AIforBA RequirementsEngineering AIProductivity BusinessAnalyst AIValidation

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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