And How to Remain Relevant as the BA Work is Redefined by AI
With all the hype around AI tools capable of not only coding but also drafting process diagrams and acceptance criteria from natural language, you may be wondering: “Will we even need BAs anymore?”
And the answer, of course, is yes–we’ll need business analysis professionals even more.
Regardless of the title held by the BA practitioner, effective business analysis is about helping an organization change so it can achieve its strategic objectives, however they have been defined: enhance customer experience, drive cost reduction, boost productivity, etc. Even in situations where agentic AI removes the need for manual analysis, the business analyst role will still be critical to produce superior solutions to business problems and address the associated IT challenges.
In fact, while we keep hearing that AI will “revolutionize” work, at the moment it seems more likely to unleash chaos, from turning meetings into works of fiction to creating documents that look polished on the surface but are riddled with factual errors and missing nuance.
Still, as AI technology becomes more pervasive in organizations, we shouldn’t be surprised to see paradigm shifts in how business analysts perform their work. In Agentic AI's Blind Spots: A BA’s Guide to GRC for Autonomous Systems, security analyst Adetunji Oludele Adebayo describes how traditional elicitation models, focused on defining known functions through waterfall or agile sprints, are fundamentally inadequate for solutions that exhibit emergent behavior, such as AI agents capable of executing complex tasks independently.
As Adebayo notes,
“That comprehensive, five-page requirements document detailing system functionality? It is now a relic of a pre-AI age. The systems we deploy today are not merely automation tools. They are autonomous, emergent, and self-optimizing machines that make real-time decisions, often without constant human oversight.”
His proposed solution—focus on system behavioral boundaries (what the system MUST NOT do) instead of what it SHALL do—is one I support.
But beyond the specification work to ensure that semi-autonomous systems are designed to behave as intended, BAs will remain instrumental in preventing many of the well-known drivers of software failure, from poor understanding of the business problems to unrealistic project goals and unmanaged risks. In How IT Managers Fail Software Projects (Nov. 2025), Robert Charette highlights why AI can’t help complex software projects succeed:
“For those hoping AI software tools and coding copilots will quickly make large-scale IT software projects successful, forget about it. For the foreseeable future, there are hard limits on what AI can bring to the table in controlling and managing the myriad intersections and trade-offs among systems engineering, project, financial, and business management, and especially the organizational politics involved in any large-scale software project. Few IT projects are displays of rational decision-making from which AI can or should learn. As software practitioners know, IT projects suffer from enough management hallucinations and delusions without AI adding to them.”
Indeed, the more organizations in the private and public sectors embed “intelligent” algorithms into their software systems, the more decision-makers are starting to accept realities such as Copilot cannot function as a complete replacement for human developers. The main reason? AI “struggles with complex problem-solving and understanding nuanced project requirements”—precisely the sweet spot of the business analysis work.
To be clear, it’s quite possible that AI will end up entirely taking over the basic elements of the BA activities.It’s already useful to reduce the manual pain of BA tasks like drafting first versions of functional specs, supporting traceability between requirements and objectives, aligning terminology across documents, creating training outlines that will be later revised by a person.
Furthermore, the same way AI currently supports radiologists by enhancing the speed, accuracy, and volume of their work interpreting X-rays, MRIs, and other diagnostic images, we can expect AI technology to play an increasing role in supporting the quality, consistency, and speed of production of BA artifacts.
Still, even when we talk about IT challenges, a considerable part of business analysis involves understanding and addressing the human components of effective change management. Take for example this question posted to the Ask a Manager’s Open Thread in January 9, 2026:
“I need some advice from people who’ve gone through similar situations. Our company completely switched to an updated version of the industry specific software we use back in early December. It was rolled out over six months with both versions running parallel for three months. Software provider came in and did extensive training on site. Our own in-house experts did multiple trainings. About 20% of the staff in my department can’t cope with the new version. Managers in other departments are reporting the same. There is no one demographic having the trouble. It’s spread out. One woman in my department cries (literally!) about it all day. A few others have just given up. […]”
In one of the replies, another reader describes a similar experience:
“This is kind of what’s happened at my workplace. They rolled out a new version of our software five years ago (!) – the old software was supposed to go away within 6 months, once we worked out all the kinks (like, actual guidance was “use the old software if there’s something you can’t do in the new one, then tell IT so they can fix it”). People would “try” to use the new software but stay logged in to the old program “just in case” they “couldn’t do something” in the new software (and then never tell IT about whatever wasn’t working).
Most of the holdouts who just straight-up refused to learn the new software (things are different COLORS! and the menu is on the RIGHT now, instead of on the LEFT! Jesus wept!) have moved on, but there are a few people somehow still using it. I think the excuse from the upper echelons of management was that certain reports don’t work right in the new software, which sounds like an eminently solvable problem, but what do I know.”
Skilled business analysis professionals will look at these examples and know exactly what went wrong. They understand that change enablement requires preparing individuals, teams, and organizations for change to create readiness and minimize resistance. And while one could prompt an AI chatbot to summarize the root cause of the problem, the solution itself is not going to be produced without people interacting in real life to identify the critical need to change, recognize and communicate the potential benefits, and take various other human-led steps to create momentum to support change success.
One of my favorite illustrations of the strengths of humans vs. machines comes this post from the subreddit r/OpenAI:
No sane person would ever ask us if we wanted them to change their answer to make it "spoiler-free" when we showed disappointment after being told the ending of a movie we hadn't seen yet. Yet, "Do you want me to redo X so it better fits what you were asking for?" is a useful pattern in many interactions, so it’s understandable that a large language model that relies on patterns to produce its outputs would apply this one even when in context it is useless to resolve the issue.
As AI use expands, the real opportunity for business analysts is to find new ways to use their human effort to create more value. Now that we have tireless AI agents ready to do the tasks BAs were trained to be good at, there will be huge opportunities for those brave enough to invent a different future for business analysis–one where the BA process to help an organization and its people transition from a current state to a sustainable target state isn't simply adjusted, but rather reimagined.
Author: Adriana Beal
Adriana Beal spent the past two decades helping innovation companies leverage AI/ML solutions and decision science to improve business outcomes. She recently left her job as a principal data scientist with a global AI consulting group to return to her roots as an independent consultant. You can find out more about her work visiting bealproject.com.