Coding agents are making software development dramatically faster. Some teams are shipping several times more code than they were a year ago.
10x more code does not mean 10x more problems solved, 10x more opportunities realized, or 10x more customer needs understood.
Yet when leaders ask whether AI is making a difference, the easiest things to point to are pull requests, completed tickets, releases, and shipped features. Those numbers are real and they are rising. They tell us something about delivery activity. On their own, they cannot tell us whether the business is better off.
The processes and analysis that confirm value is actually being delivered have a name. I call it business analysis, and I am not alone.
Why Activity Metrics Are So Tempting
Activity is easy to count. It shows up automatically in tools. It moves quickly, so it makes for good slides. And when an organization has invested heavily in AI, there is real pressure to show a return fast.
Outcomes are harder. They take longer to show up. They require someone to define what success means before the work starts and someone to go look after it ships. They cross team boundaries. That is exactly why they get skipped, and exactly why BAs are needed.
The Questions That Matter
If we want to know whether AI-accelerated delivery is creating value, these are the questions worth asking:
- Did business units, leaders, and customers get what they needed?
- Did the process improve end to end?
- Did we reduce meaningful cost, risk, or rework?
- Did people actually adopt the change?
Business analysis should help define these questions along the way, and help evaluate the answers after release.
Tip 1: Define Value Before the Build Starts
Every meaningful initiative needs a short, clear statement of what value looks like, agreed before anyone starts building. Keep it lightweight:
- Outcome: what changes for the business or the customer.
- Process: what improves in how the work flows end to end.
- Experience: what gets easier for the people involved.
Pick one or two measures for each, with a baseline. This takes an hour with the right people in the room. Without it, nobody can say later whether the work succeeded, and activity metrics fill the gap by default.
Tip 2: Go Back and Look After Release
This is the step almost everyone skips, and the one where BAs can make the biggest difference.
Schedule a value check thirty to ninety days after release. Go back to the measures you defined. Talk to the people using the change. Look at the downstream process. Did adoption happen? Did the outcome move? Did anything get worse somewhere else?
Share what you find openly, including the disappointing results. A team that learns a feature did not deliver value in month two can adjust. A team that never checks keeps building on assumptions.
Over time, these value checks become the most credible evidence an organization has about whether its AI investment is paying off.
Tip 3: Count the Things You Decided Not to Build
This one might be my favorite, and it is the hardest to sell.
Sometimes the most valuable contribution is deciding not to build something. The feature that would have added complexity with little payoff. The request that turned out to be solvable with a process change. The idea that a quick prototype and a conversation revealed nobody actually needed.
Those decisions save real money, time, and future maintenance. They almost never show up on a productivity dashboard. In fact, most dashboards would score them as zero.
BAs can change that by making these decisions visible. Keep a simple log of work that was stopped, reshaped, or never started, and why. Estimate what it saved. Share it alongside delivery metrics.
Then ask your leaders honestly: would your productivity dashboard recognize this as success?
Leaders, This One Is for You
If you lead teams using AI to build faster, you are probably seeing impressive activity numbers. Celebrate them. Then ask what processes and analysis you have in place to confirm those numbers are turning into business value.
If the answer is thin, that gap is where business analysis belongs. Your BAs can help you define value up front, measure it after release, and make the case for the work that should never be built at all.
The organizations that win with AI will be the ones that can prove value delivered. That proof has to be designed, and your BAs are ready to design it.
Learn to Measure What Matters
My Maven course series helps BAs and BA leaders define value, set success criteria, run post-release value checks, and communicate outcomes to leadership in AI-accelerated organizations.
Visit www.maven.com/angela-wick to explore current courses and upcoming cohorts.
More code is easy now. Proving value is the work.