The business analyst is not disappearing. The work around the role is changing at a pace that is forcing analysts, companies and entire departments to rethink where business analysis begins and ends.
AI can generate a use-case outline in under a minute, turn a simple idea into a basic prototype within an hour and produce requirements documentation in a fraction of the time it once took. Practitioners are already using AI to prepare elicitation sessions, draft requirements and identify gaps before validation, giving them more room to focus on the questions that machines cannot answer alone.
The Fastest Work Is Moving to AI
Routine artifacts once consumed a large share of an analyst’s time. AI reduces that effort, making the boundaries between responsibilities more fluid and pushing analysts toward broader roles that can span product ownership, business analysis and delivery coordination.
That shift does not remove the need for business analysis. It changes the value of the work. The boundaries of business analysis have never been fixed, and AI is accelerating the movement of analysts into product ownership, project management and more technical roles.
More than half of organizations had redesigned or redefined roles because of AI. At the same time, 78% of HR leaders agreed that workflows and roles would need to change to realize the value of their AI investments. Those figures point to a workplace where job titles matter less than the ability to connect customer needs, technology and delivery.
The analyst’s role is not disappearing, but its periphery is. As AI handles more of the preparation and documentation, the analyst moves closer to the uncertainty at the center of every project: what does the customer need but fail to say?
The Questions AI Cannot Ask for You
When asked what they need, customers tell what they want, but not always what they need. Routine activities often go unmentioned because customers perform them automatically, every day, without stopping to describe each step.
Edge cases create another challenge because people remember them only when they occur. Connections between departments are understood from a personal vantage point, not necessarily from the perspective of the whole. A customer may describe one part of a process while missing the dependencies that shape what happens elsewhere.
Changing one system almost always changes a process as well. That is why the analyst’s job is to find out what the customer did not say, and to keep asking questions until everything surfaces.
AI can help prepare the conversation, expose gaps in draft requirements and organize the material that follows. It cannot replace the responsibility to trace connections, dependencies and consequences across systems, data and business processes. The strongest analysts will use AI to widen the investigation, not to stop asking questions.
This distinction creates a new advantage. An analyst who can move from a customer’s stated request to the hidden process behind it can guide work across departments, systems and delivery teams. The technology produces speed; the analyst supplies context.
Big Banks Are Turning AI Into Operating Strategy
JPMorgan Chase’s asset management unit announced plans to discontinue using external proxy advisors for shareholder voting in the US and launched an in-house AI platform called Proxy IQ. The platform will be able to aggregate and analyze proprietary data from more than 3,000 annual company meetings.
That move sits inside a much larger technology push. JPMorgan has a technology budget of $18 billion and has rolled out its proprietary genAI platform to over 200,000 employees. The bank is seeking to reengineer workflows for everyone from coders to portfolio managers, while employees can use its in-house AI tools to assist in writing year-end performance reviews.
JPMorgan CEO Jamie Dimon is a “tremendous” user of the bank’s generative AI suite. He said his bank doesn’t “uniquely benefit from AI” since everyone is now using it, and he has previously said he is out to win the AI arms race. JPMorgan’s analytics boss also revealed how the bank is training 300,000 workers on AI.
Dimon thinks JPMorgan’s $2 billion AI investment has already matched its cost in savings. That claim captures the pressure facing every large organization: AI is no longer only a tool for isolated experiments. It is becoming part of how companies redesign work, assign responsibility and measure savings.
Wells Fargo offers another clear signal. The bank has shrunk its headcount by nearly a quarter since Charles Scharf joined in 2019, and Scharf expects the trend of headcount reduction to continue. From 2018 to June of the year, Wells Fargo had a $1.95 trillion asset cap, hindering its ability to grow.
Scharf stated, “The opportunities that exist in AI are very significant, and anyone who sits here today and says that they don’t think they’ll have less head count because of AI either doesn’t know what they’re talking about or is just not being totally honest about it.”
The New Analyst Must Connect Technology and Judgment
The shift reaches beyond banking. BCG’s North America regional chair Mel Wolfgang wants consultants who can combine technical proficiency with critical-thinking skills. BCG increasingly sees its consultants developing technical capabilities once concentrated among specialists in areas such as IT architecture and software development.
That combination points toward the next version of business analysis. Technical ability helps professionals work with AI, systems and data, while critical thinking reveals the consequences hidden behind a request. Neither side is enough by itself.
AI will keep shrinking the time required to produce routine work, but the difficult work will remain: uncovering unstated needs, spotting edge cases, tracing dependencies and understanding how one change moves through an entire business process. Analysts who master that work can move into product ownership, project management, delivery coordination and technical roles without leaving business analysis behind.
The next chapter belongs to professionals who let AI handle the artifacts while they own the questions. As organizations redesign workflows and roles, the analyst who sees the whole system will become the person guiding where the work goes next.
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