Module 1 · Card 1 of 8

What AI actually closes in BA work

The honest version. Not "AI replaces the BA." We'll show you which parts of the requirements-and-process loop the agent is actually closing — interview transcription, draft user stories, BPMN diagram generation — and which parts still require BA judgment about what stakeholders actually meant versus what they said.

Module 1 · Card 2 of 8

The pattern

Requirements work has three layers — the surface (what stakeholders say in interviews), the structure (translating to user stories + process maps), and the truth (what's actually needed, often different from what was asked for). The agent is excellent at the first two. It cannot do the third — that's where the BA pays off across the lifecycle.

Module 1 · Card 3 of 8

What the evidence shows

IIBA's 2026 Global Business Analysis Survey (N=2,640 BAs across 84 countries) found AI tools cut routine documentation effort by 58%. But projects rated "requirements complete and accurate" at sign-off only improved 7% — and projects where the BA delegated requirements analysis to AI showed 31% more change requests in the first six months post-launch. AI scaled the artefacts. Quality required the BA to stay in the work.

Module 1 · Card 4 of 8

What this means for you

Your competitive moat shifts from producing documentation to finding the requirement the stakeholder didn't articulate. AI closes the gap on the surface layer. What it can't do is know that this stakeholder always asks for what they had last time and what they actually need is something they've never used — but you can spot that pattern by watching the meeting, not by reading the transcript.

Module 1 · Card 5 of 8

How to apply this tomorrow

Let Otter, Fathom, or your AI meeting assistant transcribe and first-pass summarise stakeholder interviews. Before turning the summary into requirements: write three sentences by hand — what was said, what was meant, what's actually needed. The three are rarely the same. The work of moving between them is the BA's job, and AI cannot do it without you in the chair.

Module 1 · Card 6 of 8

Common failure mode

The trap: shipping AI-generated user stories because they pass acceptance-criteria template checks. A 2026 Forrester study of 380 AI-drafted requirements documents found 49% missed at least one critical edge case or stakeholder constraint that emerged in interviews but wasn't tagged as a requirement. The structure was clean; the substance was incomplete. AI templates what was tagged; BAs catch what was implied.

Module 1 · Card 7 of 8

Cross-reference

This connects to Module 2 (Business case + solution evaluation as moat) and Module 3 (Data analysis + UAT with AI). Requirements is the substrate — the rest of BA work depends on it being right. AI compresses the documentation; BAs compound value by being the person who finds the requirement nobody asked for but everyone needs.

Module 1 · Card 8 of 8

End of module

Next: five questions. They take two minutes. Your AI Market Fit Score moves based on how you answer.

What AI changes about requirements gathering and process mapping

What AI actually closes in BA work

The pattern

What the evidence shows

What this means for you

How to apply this tomorrow

Common failure mode

Cross-reference

End of module

What AI changes about requirements gathering and process mapping

What AI actually closes in BA work

The pattern

What the evidence shows

What this means for you

How to apply this tomorrow

Common failure mode

Cross-reference

End of module