Article Highlights
- POPIT — People, Organisation, Process, and Information Technology — gives BAs a single mental model for where AI can add value across the discipline.
- Natural Language Processing transforms the People layer: capturing themes, surfacing requirements, and analysing stakeholder sentiment across multiple sessions.
- Generative AI accelerates the Organisation and Process layers — turning long strategy documents, raw notes, and workshop transcripts into structured first drafts.
- In the IT layer, Generative AI bridges business and technical language, helping BAs translate requirements without losing intent.
This is Part 2 of a three-part series, The Journey of a Business Analyst using AI, by guest author Peter Agoro of The BA Mentor. In Part 1 we set the scene and named the three core BA challenges. Part 3, on Wednesday 27 May 2026, covers how to embed AI into your BA practice sustainably.
Live webinar with the author: Join Peter Agoro for AI + Business Analysis: A Powerful Combination on Wednesday 10 June 2026 at 12 PM EDT. Walk through how to apply AI across POPIT, live.
AI and the POPIT Model: Putting it into practice
Welcome back.
In Part 1, I asked you a question before I left you. The question was:
"If AI could take one thing off your plate as a BA today, what would it be?"
I hope you sat with that. Whatever your answer was, I want you to hold onto it as you read this post — because at the end there is a very good chance you will see exactly how AI can help you with it.
In Part 1 we covered the challenges, which involved:
- Capturing key bits of stakeholder information in meetings
- Producing our Business Analysis outputs with speed
- Producing outputs that reduce variation and are consistent
We also talked about AI being the support system around the BA, not a replacement for the BA. If you have not read Part 1 yet, go back to it first — it sets the foundation for everything we are about to get into now.
Right. Let us get into it.
A quick reminder about POPIT
Remember the POPIT model? Every company — whether for profit or not for profit — has four things in common:
- People
- Organisation
- Process
- Information Technology
As a BA, your job is to understand how all four of these areas connect and work together. When a business wants to move from where it is today to where it wants to be, the change will almost always touch at least one of these four areas. Usually more than one. Ideally, all of them.
This is where AI becomes genuinely interesting for us as BAs, because AI does not just help with one part of the job. It has the potential to support us across all four areas of POPIT. And that is what this part of the series is all about. If you want a level-set on the AI types we are about to discuss, the Introduction to Artificial Intelligence (AI) course is a one-day foundation built for exactly this kind of role-relevant overview.
People: Understanding what your stakeholders are really telling you
Let us start with People, because this is where I see the most immediate and exciting value for BAs.
Think about how much of what we do as BAs relies on conversations. Interviews. Workshops. Walkthroughs. Every one of those interactions contains information that matters. But here is the reality. A lot of that information never makes it cleanly into a document.
Natural Language Processing (NLP) is a type of AI that can read, interpret, and make sense of human language. For us as BAs, this is very powerful.
Here is an example: you run a stakeholder interview and you record it. NLP can then process that recording and identify the key themes from the conversation, produce the originally elicited requirements that were mentioned, and flag moments where there was uncertainty or contradiction in what the stakeholder said. These genuinely could be things you might have caught in the room but would have spent hours pulling back out of your notes afterwards.
That is not a small thing. That is a genuine shift in how efficiently you can move from conversation to structured output.
It gets better, though.
NLP can also help you analyse sentiment. So if you are running multiple stakeholder sessions across a programme, you can start to build a picture of how different groups of people are feeling about a change. This definitely helps in fine-tuning your personas and customer segments. NLP can answer questions such as:
- Who is on board with the potential change?
- Who is resisting the change?
This really helps when you have walked into a new project and want to understand what is currently happening now. That kind of insight used to come from experience and gut feel. Now it can be supported by data.
Organisation: Making sense of complexity
The Organisation layer is where things can get complicated very quickly. Structures, hierarchies, governance, strategic goals, the way decisions get made. As a BA you need to understand all of it, and you need to understand how a proposed change fits within it.
This is where Generative AI starts to show its value in a different way.
Generative AI is a type of AI that takes information you provide and generates structured, coherent content from it. Think about what that means for a BA working at the organisation level.
For example:
- You have meeting notes from a discovery session with a senior leadership team member.
- You have a strategy document that runs to forty pages.
- You need to produce a stakeholder map.
- You need business objectives written in plain language.
- You need a summary of the organisational constraints that will affect the change programme.
Generative AI can take your raw inputs and help you produce a first draft of all of that in a fraction of the time it would have taken you to do it manually. It is not doing the thinking for you. You still need to sense-check it, apply your contextual knowledge, and refine the output. But it gets you to a solid starting point much faster.
Speed at this stage of the work really does matter. The earlier you can put something structured in front of your stakeholders, the earlier you can get feedback. Earlier feedback means fewer expensive course corrections later down the line. This is the same principle the AI-Driven User Requirements course applies end-to-end — moving from raw need to structured artefact without losing fidelity, with IIBA CDU credits attached.
Processes: From messy to mapped
Process mapping.
If you have been a BA for any length of time, you will know exactly what I mean when I say process mapping.
It is one of the most valuable things we do, and also one of the most time-consuming.
You spend hours in workshops getting a process out of people's heads and onto a whiteboard. Then you spend more hours turning that into a proper process model using various diagramming tools. Then you go back to the stakeholders and they spot something that you have missed.
Now here is where AI, and specifically a combination of NLP and Generative AI, can make a real difference.
If you have a transcript or a set of notes from a process walkthrough session, Generative AI can analyse the content and produce a first draft process model in a structured format. It can identify the steps, the decision points, the handovers between teams, and the exceptions that were mentioned. You are not starting from a blank page anymore. You are starting from a draft that provides you with good headway to form a complete process map.
Now, I want to be clear on something important here. AI will not always get it right first time. It will make assumptions. It is possible that AI can misinterpret something from the transcript. Your job as the BA is to review it, challenge it, and refine it. The AI gets you to a draft faster. Your expertise turns that draft into something genuinely fit for purpose — please do not forget this.
Information Technology: Bridging the gap between business and tech
The IT area of POPIT is where BAs often sit right in the middle of two very different worlds. Business stakeholders who speak in outcomes and problems. Technical teams who speak in systems and solutions. Your job as a BA is to translate. I always like to say that BAs are the glue that joins the business and tech together.
AI can support you here in a way that I think a lot of BAs have not fully explored yet.
Generative AI can help you take a set of business requirements written in plain language and restructure them in a way that is more technically precise, without losing the original intent. It can also work the other way: take a technical specification and help you produce a version of it that a non-technical stakeholder can actually engage with.
You are still the BA. You are still the one building the relationship, facilitating the conversation, and making the judgement calls. But you have a much better-informed perspective to bring to the table. For practitioners pursuing senior recognition of this skill set, the CBAP Training and Certification case-studies course is one of the most direct paths.
So where does this leave us?
Think back to the question from Part 1: "If AI could take one thing off your plate as a BA today, what would it be?"
Whether your answer was about capturing workshop output, speeding up analysis, producing more consistent documentation, or bridging the gap between business and technology, I hope you can now see how AI can help.
What I want you to take away from Part 2 is this. AI is not one thing. It is a collection of different types of capability, and each maps onto a different part of what we do as BAs.
NLP helps us make sense of language and conversations. Generative AI helps us turn raw inputs into structured outputs. Apply these across the POPIT model and the impact becomes genuinely significant. If you want a structured way to build this capability into your daily BA work, Learning Tree's AI-Driven Business Analysis course is the direct training path for exactly the POPIT-shaped thinking covered here.
The BAs who get ahead in this new landscape will not be the ones who resist these tools. They will be the ones who learn how to use them well, who understand where AI adds value and where human judgement is irreplaceable, and who bring both to the table every day.
Coming Up Next in This Series
In Part 3 we will go further. We will look at how to build AI into your BA practice in a sustainable way, how to make the case for AI adoption within your organisation, and how to stay ahead as the tools continue to evolve.
Before we get there, I have got another question for you:
Which area of the POPIT model do you think would benefit the most from AI in your current role?
Drop your answer in the comments. I read every single one. You can also find more from me at The BA Mentor.
Recommended Learning Tree Training
To put POPIT-based AI thinking into practice, pair this article with structured training across foundations, requirements, and certification:
Frequently Asked Questions (FAQs)
What is the POPIT model and why does it matter for AI adoption?
POPIT stands for People, Organisation, Process, and Information Technology — the four areas every business has in common. BAs use POPIT to make sure a change is considered holistically rather than in isolation. POPIT matters for AI adoption because AI does not just help with one slice of BA work; it has the potential to support practitioners across all four areas, which means a single mental model can guide where and how to introduce AI.
How does Natural Language Processing help BAs work with stakeholders?
Natural Language Processing reads, interprets, and makes sense of human language. For BAs that means taking the recording of a stakeholder interview, identifying themes, extracting requirements that were elicited, and flagging moments of uncertainty or contradiction. NLP can also analyse sentiment across multiple sessions so you can see which stakeholder groups are aligned with a change and which are resisting it. Insights that used to depend on gut feel can now be supported by data.
Where does Generative AI fit into a BA's day-to-day work?
Generative AI shines when you need to turn raw inputs into structured first drafts — stakeholder maps, business objectives, summaries of a forty-page strategy document, or a first-cut process model from a workshop transcript. It is not doing the thinking for you. You still sense-check, apply contextual knowledge, and refine. But it gets you to a solid starting point in a fraction of the time it would take manually, which means stakeholders see something tangible sooner and corrections happen earlier.
Can AI help translate between business stakeholders and technical teams?
Yes. BAs often sit between business stakeholders who speak in outcomes and technical teams who speak in systems. Generative AI can take a set of business requirements written in plain language and restructure them in a more technically precise way without losing the original intent — and it can do the reverse: take a technical specification and produce a version a non-technical stakeholder can engage with. The BA still owns the relationship and the judgement calls; AI just makes the translation step faster.
