AI governance
AI use-case intake and triage
An intake and triage system that scores AI build requests for return and feasibility and hands back a ranked build queue.
Client not named
Case result
An intake and triage system for AI build requests. Submissions are scored for return and for feasibility, then returned as a ranked build queue, compressing the analysis work that previously gated every prioritization decision.
What changed
Prioritization decisions that used to wait on manual analysis now arrive with a scored estimate attached, and the build queue is ordered by return and feasibility rather than by who asked most recently.
The situation
Once an organization decides AI is a priority, the bottleneck moves almost immediately. It stops being appetite and starts being triage. Requests arrive from every function, each described in the language of the team that submitted it, and each one requiring the same two questions to be answered before anybody can decide anything. What is this actually worth, and can we realistically build it?
Answering those two questions by hand is analyst work, and it is slow. A single request might take days to size properly: understanding the process, estimating volume and current cost, checking whether the data exists in a usable form, and working out which systems would need to be touched. Multiply that across a queue and the analysis becomes the constraint on the entire program.
The failure mode that follows is predictable. Prioritization defaults to whoever escalated most recently, or to whichever executive asked. Good ideas from teams without a loud sponsor sit unexamined. The program looks busy and produces very little worth the spend.
What we did
The first change was the intake itself. Free-text requests cannot be scored, so intake captures the specific things a return and feasibility estimate depends on: the process being considered, its volume, what it costs to run today, the data it relies on, and the systems it touches. That single change removes most of the back-and-forth that used to precede any analysis.
The second change was making the scoring explicit. Return and feasibility are each assessed against defined criteria rather than judgment, so two similar requests from different departments are treated the same way. Feasibility carries as much weight as return, because a high-value use case sitting on data that does not exist in usable form is not a build. It is a data project wearing a build's clothes.
The third change was automating the first pass. A submission comes back scored, with its estimate and its reasoning, instead of joining a queue to wait for an analyst. The analysis that previously gated every decision is compressed into the intake step itself.
The output is deliberately a ranked queue rather than a verdict. The system does not approve builds. It presents a defensible ordering and the reasoning behind it, and a person makes the call. Leadership sequencing a queue is a very different exercise from leadership arbitrating a stream of individual requests.
The steps, in order
- Replaced open-ended requests with a structured intake that captures the process, its volume, its current cost, the data it relies on, and the systems it touches.
- Defined explicit scoring criteria for return and for feasibility, so two similar requests are judged the same way regardless of who submitted them.
- Automated the first-pass analysis, so a submission comes back scored with its reasoning attached instead of waiting in a queue for an analyst.
- Produced a ranked build queue rather than an approve or reject verdict, so leadership sequences work instead of arbitrating individual requests.
- Kept a human decision at the end. The system ranks. It does not authorize a build.
Use-case intake
Systems involved
- Structured intake capture
- Return and feasibility scoring criteria
- Automated first-pass analysis
- Ranked build queue
- Human approval step
What changed
Prioritization stopped being the slow part. Requests arrive scored, which means the conversation starts at sequencing rather than at analysis. Teams without an executive sponsor get read on the same criteria as teams with one, which is the difference between a program that finds its best work and a program that funds its loudest.
It also produces a record. Because the criteria are explicit and applied consistently, the reason a use case ranked where it did is written down. That matters the first time somebody asks why their request is not being built, and it matters again the first time a reviewer asks how the program decides what to fund.
What it proves
This is the same intake and prioritization discipline sold on the strategy and governance side of this practice, applied internally first. The scoring method behind an AI strategy and roadmap engagement, where candidate use cases are ranked on business value against risk and readiness, is the same shape as this system.
The enterprise involved is not named. What transfers is the structure: capture the inputs a score depends on, define the criteria before the requests arrive, automate the first pass, and end on a human decision.
An AI program that cannot explain why it is building what it is building will not survive its first serious budget review. This is the mechanism that lets it explain.
What this case supports
Governance and prioritization discipline applied to an AI program itself, which is the same discipline this practice sells to clients.
See how an AI strategy and roadmap engagement ranks use cases
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