If you cannot measure it, you cannot justify scaling it. Here is how we define success metrics before a system is even built.
AI budgets get cut when nobody can point to what changed. The antidote is to define success metrics during scoping, not after launch.
We tie every system to a concrete operational metric: hours saved, response time, ticket deflection, or accuracy of a specific output.
Baseline first
You cannot show improvement without a baseline. Before deployment we capture how long the current process takes and where errors occur, so the impact of the system is measurable rather than anecdotal.
Report in business terms
Stakeholders do not care about model benchmarks. They care about the queue getting shorter and the team getting time back. We report in those terms, which is what keeps AI programs funded and expanding.