Let me name something that a lot of business analysts are feeling right now but not saying out loud.
Developers are getting the tools. They are getting AI-powered coding environments that are transforming how they work. They are getting access to AI systems that write code, create prototypes, draft and analyze requirements, review code, generate tests, and accelerate every part of the build. Their workflows are being reinvented, their productivity is being multiplied, and organizations are actively investing in the tools, training, and infrastructure to make that happen.
And a lot of BAs are watching it happen from the outside.
The BA’s AI story is often much smaller. Maybe you have access to the company’s Co-pilot or ChatGPT license. Maybe you are using Claude for drafting. Maybe you have cobbled together a personal workflow with AI tools you found on your own. But the intentional, resourced, organizational investment in AI for business analysts? In most organizations, it is not happening at the same level. I’m crying for you!
This is not just a frustrating feeling. It is a real pattern, and it matters more than it might seem on the surface. Because while developers are getting tools that are changing what they can build and how fast they can build it, BAs are being left in a posture of catching up to output that is being generated faster than ever, without the tools or the organizational support to evolve their practice at a comparable pace.
This is worth talking about, and it is worth doing something about.
Why This Is Happening
The tools gap is not random. It follows a pattern that is worth understanding, because understanding it is the first step to changing it.
AI investment tends to flow toward the most visible production bottlenecks. For years, software delivery has been bottlenecked on how fast humans could write code. That bottleneck was visible, measurable, and expensive. So when AI tools emerged that could accelerate coding, organizations invested in them. The return on investment was easy to see and easy to argue for.
Business analysis has historically been less visible, not because it is less important, but because the cost of weak analysis tends to show up later and in less obvious ways. Rework, misaligned solutions, failed implementations, and stakeholder misalignment are expensive, but the connection between those outcomes and insufficient upfront analysis is often not made explicitly. So the investment case for BA tools has been harder to make, and organizations have not made it at the same rate.
There is also a perception problem. Many organizational leaders still think of BA work primarily as documentation and communication, tasks that AI tools can assist with at the individual level without requiring organizational investment. They do not yet see BA work as the design and analytical capability that it actually is, the work that determines whether AI-built solutions solve the right problems and work the way the business needs them to. That perception gap is something BAs need to close actively.
None of this means the situation is unchangeable. It means BAs need to understand why it exists and approach changing it strategically.
Tip 1: Get Visible With AI by Getting Involved in What Developers Are Already Doing
The most practical first move is not to wait for organizational investment in BA tools. It is to get visible with the AI work that is already happening in your organization, specifically the AI-assisted development that developers are doing.
Ask to be involved in AI coding tool reviews and rollouts. These conversations are happening in most organizations right now, and the perspectives that are missing from them are almost always the business and user perspectives that BAs are positioned to bring. When a team is evaluating tools, the questions that matter are not just technical: How does AI-generated code get validated against business requirements? What review process ensures the output aligns with what the business actually needs? Who is responsible for catching the gaps? Those are BA questions, and showing up to ask them puts you in the room.
Ask to see what AI tools developers are using and how they are using them. Not to audit or critique, but out of genuine curiosity and a desire to understand how the build environment is changing. What are developers prompting these tools with? What kind of output are they getting? Where are the gaps that require human judgment? The answers to those questions will tell you a lot about where BA capability is needed in the new workflow, and what tools might help you provide it.
Collaborate directly on AI-generated outputs. When AI writes a user story or generates a test case or produces a first-draft architecture, that output needs to be evaluated against business intent. Offer to be part of that evaluation, consistently and visibly. Over time, that visibility changes how the team sees your role in AI-assisted delivery.
Tip 2: Build Your Own AI Practice and Make It Visible
While you are working to get involved in what is already happening, build your own AI practice in parallel and make it visible inside your organization.
This does not require a budget or organizational permission. It requires curiosity and intentionality. Start by identifying the parts of your current BA work where AI tools could genuinely help, not just draft documents faster, but think differently about problems, analyze options more thoroughly, or explore implications you might have missed.
Use AI to help you analyze stakeholder feedback at scale. Use it to stress-test a problem definition by asking it to argue the other side. Use it to generate edge cases and exception scenarios you might have missed. Use it to synthesize research or competitive information that informs a business case. Use it to prototype a requirements approach before you commit to it.
Then share what you are doing and what you are learning. Write it up in a team communication. Bring it to a retrospective. Show it to a manager or a stakeholder. The goal is not to impress anyone. The goal is to make visible that BAs can use AI for high-leverage analytical work, not just administrative tasks, and that organizational support for that work would produce real value.
When you make your AI practice visible, you change the conversation from “BAs are not really part of the AI story” to “here is what BAs are doing with AI, and here is what more investment would unlock.” That is a much more productive conversation, and it is one you can start without waiting for permission.
Tip 3: Ask for the Tools and Make the Business Case
This is the step that feels uncomfortable for a lot of BAs, and it is the one that matters most: ask directly for the tools and investment you need, and come prepared to make the case for why it matters.
Do not wait for someone to notice the gap. The gap will not close on its own, and the organizational leaders who could close it are focused on the most visible priorities in front of them. You need to make the BA tools investment a visible priority by making the business case for it.
The business case is not hard to make. AI-assisted development is accelerating how fast solutions get built. The faster solutions get built, the more important it is that the right problems are being solved, that business rules and edge cases are fully analyzed, that human-AI workflows are thoughtfully designed, and that outcomes are measured and acted on. All of that is BA work. Without investment in BA capability alongside investment in development capability, organizations are building faster in the wrong direction.
When you ask for tools, be specific. Ask for access to AI tools that support analytical work: prototyping, synthesis, scenario analysis, requirements evaluation, business rule checking. Ask for time and support to develop AI fluency, not just tool familiarity but the conceptual understanding of AI capabilities and limitations that makes BA work in this environment effective.
And if the first ask does not land, make it again with more evidence. Run a small experiment that shows what AI-supported BA work produces. Document a specific example where AI-assisted analysis caught something that would have been missed. Build the case incrementally, because that is how organizational investment tends to move.
You Belong in This Story
I want to say this as clearly as I can: the AI transformation happening in software delivery is not a developer story. It is not a technology story. It is an organizational change story, and business analysts are central to it, whether organizations recognize that yet or not.
The work of defining what should be built, ensuring it solves the right problem, designing the human-AI collaboration, governing the systems that get deployed, measuring whether they work, and continuously improving based on evidence; this is BA work. It happens to be the most important work in an environment where building is getting faster and the consequences of building the wrong thing are getting more severe.
BAs who feel left behind by the current wave of AI investment are feeling something real. But the response to that feeling is not resentment or resignation. It is getting visible, getting involved, building a practice, and making the case.
The story of AI and business analysis is still being written. BAs who engage with it actively will be authors of that story.
Get the Knowledge and the Community to Move Forward
My Maven course series is designed for BAs who are ready to engage with AI actively, build real fluency, and position themselves as essential to AI-enabled delivery. Not just the tools, but the analytical frameworks, the organizational strategies, and the community of practitioners working through the same challenges.
Visit www.maven.com/angela-wick to explore current courses and upcoming cohorts.
You belong in this story. Let’s make sure you are in it.