A feature can work exactly as specified and still make the business worse. We have seen this for decades! What does using AI do with this dynamic?
Picture a change that saves one team two minutes on every request. Everyone on that team is happy. Their cycle time drops, their dashboard turns green, and the sprint review is a celebration. Meanwhile, the team downstream is now spending ten extra minutes on every one of those same requests, fixing data that arrives in a new format, chasing details that used to be captured upstream, or working around a step that quietly disappeared.
The feature works. The local metric improves. The overall process gets worse.
I have watched this happen more times than I can count. And, I bet you have too!
Now imagine that same pattern running at AI speed.
Why This Is Getting Worse
Teams are organized around backlogs, and each team optimizes what it can see: its tickets, its features, its metrics. That was always a limitation. It was manageable when change moved slowly enough for people to notice the side effects and compensate.
AI changes the math. When a team can build and ship in days, local optimizations stack up faster than anyone can trace their consequences. Ten small changes across five teams, each one a win on its own terms, can add up to a process that is slower, more fragile, and more expensive end to end. Nobody made a bad decision. Nobody had the view that would have revealed the problem.
This is the gap business analysis exists to fill. A backlog shows what a team plans to change. It rarely shows what that change will do to the wider process, the data, the architecture, the controls, the customers, and the people doing the work.
To BAs: This Is Your Expanded Territory
If you have been feeling squeezed as AI speeds up delivery, this is where your role gets bigger. The future BA helps teams see the consequences their individual backlogs cannot reveal.
That requires stepping past the feature, past the ticket, and past the team boundary. It is harder and more visible work. It is also the work organizations will need most as the pace keeps rising.
Tip 1: Trace Every Meaningful Change One Step Upstream and One Step Downstream
Start small. For any change that touches a process, ask two questions before it ships:
- Who hands work to this step, and what changes for them?
- Who receives work from this step, and what changes for them?
Then go ask those people. A fifteen minute conversation with the downstream team will surface more risk than any amount of internal review. You will hear things like “we rely on that field for reconciliation” or “if that email stops coming, nobody on our side knows to start.”
Once this habit is in place, widen it. Look at the data the change touches and where else that data is used. Look at the controls and audit points that depend on the current behavior. Look at what the customer experiences across the whole journey. Every layer you add catches problems a feature-level view will miss.
Tip 2: Measure the Process, Alongside the Feature
Local metrics are where this problem hides. If the only measure of success is the team’s own cycle time or ticket throughput, a change that shifts work onto someone else will always look like a win.
Help teams define at least one end-to-end measure for any change that touches a shared process. Total time from request to resolution. Rework rate downstream. Customer effort across the full journey. Error rates at handoff points.
Then check it after release. When a change improves the local metric and degrades the end-to-end one, that is the signal. Catching it in week two costs far less than discovering it in a quarterly review, after three more changes have been built on top of it.
Tip 3: Bring the Right Perspectives In While Decisions Are Still Shapeable
Wider analysis does not mean another approval gate. Nobody needs a new committee that slows everything down and still catches problems too late.
It means getting the right people into the conversation early, while there is still time to shape the decision. A downstream team lead in a prototype review. A data owner in the first design conversation. Someone from controls or compliance looking at the flow before it is built.
The BA is well positioned to do this because the BA already knows who those people are. Your job is to connect them to the decision at the moment their input can change it. Five minutes of the right perspective at the right time is worth more than a formal review at the end.
Bigger Picture, Wider Influence
As AI accelerates what each team can build, the most valuable skill in the room is seeing how the pieces fit together. Teams will keep getting faster at their own work. Someone needs to make sure all that speed adds up to a better business.
That is where business analysis is heading. Wider analysis. Wider influence. A seat in the conversations where consequences get decided.
If you lead a BA team, this is the shift to invest in now. If you are a BA, this is the muscle to start building this week.
Build the Skills to See the Whole System
My Maven course series covers how BAs analyze end-to-end impact in AI-accelerated delivery, including process analysis, downstream risk, and how to bring the right perspectives into fast-moving decisions.
Visit www.maven.com/angela-wick to explore current courses and upcoming cohorts.
Your team’s backlog shows what is changing. You can show what it means.