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频道帖子
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 and get a couple of outdated files along with a friendly, “Just ask the developers if you need anything.”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 :)
| 2 |  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 | 235 |
| 3 |  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? | 263 |
| 4 |  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 | 269 |
| 5 |  🏛 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! | 459 |
| 6 |  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 | 463 |
| 7 |  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. | 440 |
| 8 | 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 | 461 |
| 9 | 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! 💛 | 530 |
| 10 | 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! 🙂 | 604 |
| 11 |  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.
BusinessAnalysis RequirementsEngineering GPT55 OpenAI | 507 |
| 12 |  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 | 421 |
| 13 | Requirements Engineering is changing fast — and AI is one of the main reasons why.
At IREB exploRE2026 in Wrocław, many discussions focused on the future of requirements work, business analysis, system analysis, and the practical impact of AI on our profession.
As a Business and System Analyst at Andersen, I had the opportunity to speak on the topic: “AI-Aware Business Analysis: New Skills and Practices for IT Analysts in the LLM Era.”
The key problem to address is simple: AI can generate, summarize, compare, and review analytical artifacts much faster than before. But faster wording does not automatically mean better understanding. A polished requirement may still hide weak assumptions, missing context, or unclear business intent.
📝 In my presentation, I focused on several practical ideas:
- AI should support analysts, not replace their responsibility.
- Analysts should move from simple artifact production to analytical orchestration.
- Context engineering becomes as important as prompt engineering.
- AI can help with discovery, elicitation, drafting, review, and system analysis — but every output must be validated.
- Traceability, source awareness, and human approval become even more important in the AI era.
🟢 For me, the future analyst is not an “AI scribe.” The future analyst is a professional who can manage context, challenge outputs, validate decisions, and build reliable, traceable, and responsible AI-supported analysis.
IREB exploRE2026 RequirementsEngineering | 429 |
| 14 |  🗣 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! | 505 |
| 15 |  Major conference of IREB is now behind us 👏
Our community member Alexander Malyarenko recently represented us at IREBexploRE2026 - one of Europe's leading conferences on Requirements Engineering!
As proud partners with IREB, we're thrilled to support this collaboration and gather valuable insights from cutting-edge events like this.
Alexander shared powerful takeaways on Business Analysis in the LLM Era: AI isn't just adding tools - it's reshaping how analysts work. From AI-assisted interviews to orchestrating analytical workflows, the BA role is evolving toward contextual understanding, validation, risk governance, and managing ambiguity. 💡
📌 Stay tuned and Alexander will share more deep insights and practical tips in his upcoming posts!
IREB exploRE2026 BusinessAnalysis RequirementsEngineeringAI | 440 |
| 16 |  Communication is about empathy 🤍
We live in an era of total communication, yet it remains a weak spot for many in IT. There’s a reason why soft skills are a top priority for recruiters. Let’s dive into how we can boost our projects and careers using a few simple, often overlooked tips.
✔️Hearing vs. Listening 👂
What’s really behind your peer’s or manager’s frustration? Have you ever tried to walk in their shoes?
Understanding the roots of the other person's thoughts is the key to success. Try looking at the situation from a different angle. Perhaps, you’ll find out that in the same circumstances you would behave the same way. Make the ability to truly listen to your core trait and it’ll change the way you handle daily communication.
✔️Why your “Awesome” is actually useless?
We can easily write a negative review for a restaurant. But what about our colleagues? Aren’t we afraid to hurt their feelings?
Actionable feedback isn't about offending people — it's about growth. We can’t fix our blind spots if we don’t know they exist.
• Be fair and polite.
• Encourage others to give you feedback, too.
• Be extremely specific! Saying “You did it wrong” or “You’re awesome” is too ambiguous. It gives the other person nothing to work with.
✔️Don’t be a Meeting Ghost
Even if you have a brilliant Scrum Master or PM, don’t hesitate to be their wingman.
Imagine yourself standing in front of a crowd that doesn’t react to your jokes or questions. It’s just as awkward in a Zoom call as it is on a real stage.
Even if it’s not your job to lead, a simple supportive phrase or a proactive answer breaks the ice. It makes the conversation more comfortable for everyone and speeds up the decision-making process.
Communication isn't just about "talking." It's about empathy, clarity, and team support. By mastering these three simple habits, you’re not just being a "nice" person — you're becoming a high-value professional who can navigate any corporate environment.
#SoftSkills #TechCommunication #FeedbackCulture | 496 |
| 17 |  Hey, Community 👋
On June 9, we’ll talk about how to turn chaotic business requests into clear and actually useful dashboards.
At the meetup, we’ll discuss:
— Why many dashboards end up unused;
— How to formalize requirements for metrics and data sources
— How to improve communication between business teams, analysts, and data engineers.
If you’ve ever dealt with endless revisions, last-minute “can we add one more chart?” requests, or discussions like “what exactly should this metric calculate?” – this meetup will definitely be useful for you 👀
🎙 Speaker – Darya Drobova, Principal BI Developer at EffectiveSoft with over 12 years of experience in BI and expertise in Power BI, Tableau, and IBM Cognos Analytics.
🎟 Register here
You’ll get real-world cases, practical approaches, and templates you can start using in your work right after the meetup.
Event 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
This meetup will definitely come in handy 👀
Become a speaker
See you! | 405 |
| 18 |  WHAT BA/SAs SHOULD READ ON AI: APRIL PAPERS AND BOOKS
📕 April was a useful month for BA/SAs who try to understand AI not as hype, but as a working instrument.
My short reading focus would be this:
1️⃣ First, papers on LLMs in requirements engineering. The most interesting part is not that AI can generate user stories. We already know that. The real question is whether it can help with gaps, contradictions, traceability, validation, and domain understanding.
Example: Formal Requirements Engineering and Large Language Models by Alessio Ferrari et al., 2025.
Why it fits: it is not just about generating requirements. It looks at how LLMs can support correctness, reliability and validation in requirements engineering. A good example for the idea: AI can draft, but the analyst must validate.
2️⃣ Second, materials on AI in business process modelling. This is especially relevant for analysts, because the discussion is moving from “AI writes text” to “AI helps structure work”.
Example: BPMN Assistant: An LLM-Based Approach to Business Process Modelling by J. T. Licardo et al., 2026.
Why it fits: this is about moving from text to process logic. The paper shows how LLMs can help create and edit BPMN models, which is directly relevant for BA/SA work.
3️⃣ Third, books about AI and knowledge work. For BA/SAs, the best AI books are not only technical. They should explain how decisions, roles, responsibility and collaboration change when AI becomes part of the workflow.
Example: Ethan Mollick, Co-Intelligence: Living and Working with AI.
Why it fits: this is not a technical book about models. It is about how people work with AI, how responsibility changes, and why human judgement remains central in knowledge work.
My conclusion is simple: reading about AI is useful only if we read with an analyst’s mindset.
Not “what can this tool do?”
But: “what responsibility still stays with me?”
#BusinessAnalysis #SystemAnalysis #RequirementsEngineering #AI #BA #SA | 496 |
| 19 |  Bad requirements or bad users? 🤔
In 1935, Boeing built a plane so advanced it crashed on its first test flight.
Engineers immediately blamed "pilot error". But the reality? The machine was simply "too much airplane for one man to fly".
They did not ask for smarter pilots, they just added a simple checklist.
I’ve been thinking about this from BA perspective.
It is easy for us to design a complex flow and just expect users to "get it". When they get confused, click the wrong button, or abandon the process, our first instinct is often to write it off as user error. We assume they just didn't read the screen carefully.
But honestly, I'm realizing we might be wrong to think like that. Often, what we call "user error" is actually a sign that we designed a system that is just too hard to fly.
When a user makes a mistake, it usually means we built "too much airplane". Look, real users aren't sitting in a quiet room studying our systems. They are:
- distracted
- rushing
- just trying to get a task done
We can't expect perfect users. Instead, we have to accept our own blind spots as the people creating the system, and build safer paths. We need to guide users so naturally that they don't have to struggle.
Great software shouldn't force people to think harder. It should just help them fly safely.
#BusinessAnalysis #SystemDesign #ProductThinking #UXDesign #SoftwareDevelopment | 456 |
| 20 |  “I’ll do it myself 😌” or the story about how hyper-control emerges
Most of the time, the problem in a team is not the team. Sometimes things are broken not by processes. And not by deadlines. But by a person who can’t let go of control.
🪤“Atlas Syndrome: How to Stop Carrying the Project Alone and Start Leading”
🎤 At a meetup on May 20, Julia Karvat (HR expert with over 12 years of experience, Master of Psychology, Corporate Trainer) will break down what is usually left unsaid:
- Why “I’ll do it myself” is not heroism but a trap;
- How hyper-control kills initiative and drains energy;
- Where the “dopamine loop” of micromanagement comes from and how to break it;
- How to delegate without losing quality and finally get some sleep.
🎟 Join the meetup
It’s going to be hot - not because of the weather 🔥
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
Become a speaker
See you soon 👋 | 455 |
