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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. BusinessAnalysis BusinessAnalyst BABOK RequirementsEngineering StakeholderManagement

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.

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. BusinessAnalysis RequirementsEngineering AI ProductDiscovery StakeholderManagement SystemsThinking

Tactical Forking in Practice: An Approach to Microservices Migration Migrating from a monolith to microservices is often seen as expensive, time-consuming, and risky. But what if there’s a way to evolve your system gradually – without rebuilding it from scratch? Join us on August 26 as we explore tactical forking – a strategy that helps you evolve existing architectures, reduce migration risks, and make the transition to microservices more manageable. What we'll cover: 🔹 Why a lack of modularity makes migration so challenging; 🔹 When tactical forking is the right approach; 🔹 How to adopt it without disrupting ongoing development; 🔹 Its impact on architecture, development processes, and teams. 🎙 Speaker: Mohamed Taman, Solutions Architect at Andersen, Java Champion, JCP member, TOGAF-certified architect, and Microsoft Azure-certified professional with 22+ years of experience designing large-scale distributed systems. 💡 This meetup is ideal for Architects, Tech Leads, Java and Backend Developers, and anyone involved in modernizing and evolving complex software systems. 🔗 Register here Event details: ⏰ Time: 17:30 (CEST) 🕒 Duration: 1 hour 🗣 Language: English 💻 Format: the link to the stream will be sent to your email address provided during registration See you there!

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? businessanalysis gagile hashtagbdd aiagents hashtagllmpromptengineering

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. BusinessAnalysis BABOK StakeholderManagement BusinessAnalysisFundamentals

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. SystemAnalysis AIinBusiness ProductDevelopment BA DigitalTransformation

A huge thank you to everyone who joined our Meet up previous week - both in person and online! 🙌 Special thanks to our incredible speaker, Olga Kletskina, for such a deep and honest dive into the topic. We truly appreciated how openly you walked us through the real pains and challenges of working with undocumented products - and shared practical ways to tackle them. We know many of you left with even more questions, and that's exactly what great meetups are about - sparking the right conversations. We'll be sharing key insights and the presentation slides with you soon - stay tuned!

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

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! BusinessAnalysis SalesToIT CareerChange BALaboratory RealTalk ITCareer SoftSkills

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 :)

AI AS A DRIVER OF ANALYST STRATIFICATION: WHO ACCELERATES, WHO FALLS BEHIND AI is not replacing analysts. It is splitting them into two distinct groups. In the same team, under the same conditions, I see radically different trajectories. Group 1 — Accelerators: • Use AI to structure thinking, not replace it • Validate outputs critically • Build faster feedback loops with dev/QA • Focus on decisions, not documents Result: 2–3x throughput, higher impact per task Group 2 — Regressors: • Copy AI outputs without deep understanding • Lose ownership of requirements • Spend more time reviewing than creating • Struggle with edge cases and system thinking Result: illusion of productivity, real drop in quality What’s happening structurally: AI removes the “mechanical advantage” of average analysts. What remains is thinking quality, domain understanding, and decision-making clarity. In other words: AI doesn’t reward experience alone — it rewards how you think under uncertainty. The new differentiation factors: → Ability to validate, not just generate → System thinking over task execution → Ownership of outcomes, not artifacts AI is not leveling the field. It is widening the gap. The question is no longer: “Do you use AI?” But: “Does AI amplify you — or expose your weaknesses?” BusinessAnalysis FutureOfWork ProductManagement DigitalSkills

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?

Safety first. The recent security and regulatory concerns around frontier AI models have already shown that new capabilities may come with slower and more controlled rollouts. @OpenAI GPT-5.6 seems to follow the same logic: limited preview, stronger safeguards, extensive stress testing and red teaming. But what is interesting for Business and System Analysts? Three things caught attention: 1️⃣ Longer, more complex workflows Not just “analyse this requirement”, but work across requirements, meeting notes, API documentation and previous decisions without losing the overall logic. 2️⃣ Better traceability Following the chain from stakeholder input → requirement → business rule → system behaviour → gap or contradiction. This could be particularly useful for large analysis tasks and legacy systems. 3️⃣ More agentic analysis The new max reasoning level and ultra mode with subagents point towards AI coordinating parts of a complex task rather than simply answering one prompt at a time. For analysts, the interesting shift is not that AI writes better requirements. It is that AI is getting better at staying inside the problem long enough to understand the system around them. Worth testing. BusinessAnalyst Traceability ArtificialIntelligence GenerativeAI GPT56 OpenAI

🏛 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!

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

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

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! 💛

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! 🙂