The Journey of a Business Analyst using AI Series: Part 3 — Making It Stick

Article Highlights

• Sustainable AI adoption starts with one task, not the whole job — build a new habit deliberately rather than transforming your practice overnight.
• Protect your judgement: AI gives you a starting point, but your knowledge of stakeholders, organisation, and politics is what turns a draft into a deliverable.
• Make the business case in delivery language — quantify hours spent on workshop notes, time to first draft, and rework caused by inconsistency — then prove value with a small pilot.
• Stay ahead by staying curious about new tools, connected to the BA community, and grounded in what Business Analysis actually is: a discipline that helps businesses run better.

This is Part 3 — the final instalment — of The Journey of a Business Analyst using AI, by guest author Peter Agoro of The BA Mentor. In Part 1 we set the scene; in Part 2 we mapped AI onto POPIT. This post is about making it all stick.

Live webinar with the author: Join Peter Agoro for AI + Business Analysis: A Powerful Combination on Wednesday 10 June 2026 at 12 PM EDT. Bring your sustainable-adoption questions to Peter live.

Building AI into your BA practice: Making it stick

Welcome back.

At the end of Part 2, I asked you a question:

"Which area of the POPIT model do you think would benefit the most from AI in your current role?"

I hope you thought about it.

If you had an answer, even just a gut feeling, that tells me something important. You already know where the friction is in your day-to-day work. You already know where time is being lost and where the quality of your outputs could be sharper. AI can help you with this.

In Part 1 we covered the challenges BAs face. In Part 2 we mapped those challenges across the POPIT model and looked at the types of AI that can support each one. If you have not read Parts 1 and 2 yet, go back to them first. Part 3 will land much better with those foundations in place.

Now we get into the part that I think a lot of BAs skip over. Not because they are not interested, but because it feels like the harder conversation.

How do you actually build the use of AI into the way you work? How do you make the case for it in your organisation? And how do you keep up as things keep changing?

Let us get into it.

How to build AI into your BA practice in a sustainable way

Here is the mistake I see most often. Someone gets excited about AI, tries to change everything at once, hits a wall, and then decides it is not for them. Does this sound familiar?

Sustainable adoption does not work like that. It works the same way good Business Analysis works. You start with the current state, identify where the pain is, and make targeted, deliberate changes that build over time.

Here is a practical way to think about it.

Start with one task, not the whole job.

Pick one thing in your current role that takes more time than it should. Maybe it is writing up notes after a workshop. Maybe it is producing a first draft requirements document from a set of bullet points. Maybe it is summarising a long strategy document before a meeting. Whatever it is, that is your starting point.

Find the type of AI that can help with that specific task. Think about it:

1. Transcription and language understanding points you towards Natural Language Processing.
2. Generating structured content from raw inputs points you towards Generative AI. Try it. Learn from it. Refine how you use it. Then find the next task.

This is not about transforming your entire practice overnight. It is about building a new habit, one task at a time, until AI becomes a normal part of how you work rather than an extra thing on your list. As BAs we have to stay committed to building the AI capability. If you have never formally explored what AI is and where it fits, the Introduction to Artificial Intelligence (AI) course is a one-day, foundation-level starting point. From there, Learning Tree's AI-Driven Business Analysis course is the BA-shaped follow-on that builds the habits this post describes — one task, one prompt, one pilot at a time.

Protect your judgement.

As you bring AI into your practice, there is one thing you must never let go of. You must never let go of the ability to think critically about the output. AI will give you a starting point. It will surface themes, draft content, and structure information. But it does not know your stakeholders, your organisation, or the politics of the project you are on. You do. That is where your value as a BA sits.

Remember to:

1. Review everything AI produces.
2. Question it.
3. Refine it.
4. Treat it the same way you would treat work from a junior team member who is talented but still learning the context. Good first draft.

Ultimately, it is your final judgement that makes your outputs great.

Build a prompt library.

One of the most practical things you can do is start collecting the AI prompts that work well for you. A prompt is simply the instruction you give to an AI tool. The better your prompt, the better the output. Over time you will build a set of prompts that consistently deliver useful results for common BA tasks. Share them with your team. That is how you start solving the consistency challenge at a team level, not just for yourself. If you want a structured course that builds this discipline into your elicitation and requirements practice, the AI-Driven User Requirements course is built around it.

How to make the case for AI adoption within your organisation

Hopefully, at this stage, you are convinced that AI can make the BA function better. Now you need to convince someone else. Whether that is your line manager, your delivery lead, or a senior stakeholder, the conversation needs to be convincing so that there can be adoption.

Here is the thing.

The people you need to convince may not necessarily be that interested in the AI technology. They are interested in outcomes. So that is where you need to start.

Go back to the three challenges from Part 1. Capture. Speed of analysis. Consistency. Now put a number against each of them to measure how long something takes or how much of something is produced. For example:

1. How many hours a week does your team spend writing up workshop notes?
2. How long does it take to produce a first draft of requirements after discovery?
3. How much rework happens because two BAs produced documents in different formats and a stakeholder flagged the inconsistency?

When you put actual time and resource against those problems, the case for AI stops being a technology conversation and becomes a delivery conversation. Delivery conversations are ones your organisation already knows how to have.

Start small and demonstrate value before you try to scale. Run a pilot on one project with one BA on one task. Track the difference it makes and bring that back as evidence. A concrete example of time saved and quality improved is far more persuasive than any amount of theory. The data will tell the true story. If you are positioning yourself or your team for senior recognition while doing this, the CBAP Training and Certification case-studies course is one of the most direct paths.

Be honest about the limitations too. AI is not perfect. It makes mistakes and requires oversight. Knowing and saying this upfront actually builds more confidence, because it shows you are thinking with your head as well as your enthusiasm.

How to stay ahead as the tools continue to evolve

I am going to be straight with you. The AI landscape is moving fast. Tools that are new today will feel standard in twelve months. Capabilities that seem impressive now will become the baseline expectation. And new types of AI will emerge that we are not even talking about yet.

That can feel overwhelming. But here is how I think about it as a BA.

The tools will keep changing. The underlying principles will not. This is very important.

The reason AI is useful for BAs is not because of any specific tool. It is because AI can process language, generate structure, and identify patterns at a pace and scale that a single person cannot match. Those capabilities are not going anywhere. So if you understand the types of AI and what they are good at, you will always be able to figure out how a new tool fits into your practice, even if you have never seen it before. Learning Tree's broader Artificial Intelligence training topic page is a good way to keep tabs on where the discipline is going.

Here are three things that will keep you ahead regardless of what changes:

1. Stay curious. Set aside a small amount of time each week to explore what is new. Read about it. Try things. Talk to other BAs about what they are experimenting with. The BAs who stay ahead are not necessarily the most technical. They are the most curious.
2. Stay connected. The BA community is one of the most generous professional communities I have come across. A particular one called The BA Mentor would be worth joining (https://www.skool.com/the-ba-community-7492/about). People share what they are learning, what is working, and what is not. Being part of that conversation means you are never starting from scratch.
3. Stay grounded. In the middle of all the noise about AI, remember what Business Analysis actually is: a discipline that helps businesses run better. AI is a tool in service of that. It does not change the mission. It changes some of the methods. This is VERY important.

So where does that leave us?

We have covered a lot of ground across this series. Let us bring it back to where we started.

Business Analysis is about helping businesses get from where they are to where they need to be, across People, Organisation, Process, and Information Technology. That has not changed. What has changed is the toolkit available to us, and how fast we can deliver value when we use it well.

AI is not something to fear. It is not something to ignore. It is something to get genuinely good at, the same way you got good at stakeholder management, requirements gathering, and process modelling. It takes time, practice, and a willingness to keep learning.

The BAs who will thrive in this new landscape are the ones who approach AI with the same mindset they bring to everything else. Curious. Structured. Focused on outcomes.

Thank you for reading through this series.

I want to leave you with one final question:

"What is the one thing you are going to try differently in your BA practice as a result of reading this series?"

Drop it in the comments. I read every single one. You can find more from me at The BA Mentor.

Recommended Learning Tree Training

To embed the AI-driven BA mindset across your practice, pair this series with structured training across foundations, requirements, and certification:

Explore AI Training at Learning Tree

Frequently Asked Questions (FAQs)

What is the most sustainable way to bring AI into a BA practice?

Start with one task, not the whole job. Pick one thing in your current role that takes longer than it should — writing up workshop notes, producing a first-draft requirements document, summarising a long strategy paper — and find the AI capability that fits. Try it, learn from it, refine how you use it, then move on to the next task. Sustainable adoption looks like building a new habit one task at a time, not transforming your whole practice overnight.

How do I make the business case for AI in my organisation?

Go back to the three BA challenges — capture, speed of analysis, and consistency — and put a number against each one. How many hours per week does your team spend writing up workshop notes? How long does it take to produce a first draft of requirements after discovery? How much rework happens because two BAs produced documents in different formats? When you put real time and resource against those problems, the case for AI stops being a technology conversation and becomes a delivery conversation. Then run a small pilot to prove the saving.

How do I protect my judgement when using AI?

Treat AI output the way you would treat work from a talented junior team member who is still learning the context: review it, question it, refine it. AI does not know your stakeholders, your organisation, or the politics of your project. You do. Review every output, challenge what looks wrong, and never let AI become the final word on something that depends on context. That critical step is where your value as a BA sits.

How do I stay ahead as AI tools keep changing?

Tools will keep changing; the underlying principles will not. AI is useful for BAs because it processes language, generates structure, and identifies patterns at a scale a single person cannot match. Those capabilities are not going away. Stay curious by setting aside time each week to explore what is new. Stay connected to the BA community. And stay grounded in what Business Analysis actually is — a discipline that helps businesses run better. AI is a tool in service of that mission.