An AI use-case intake should answer one question before a team gets a pilot, a vendor meeting, or a budget: can this workflow produce an output that somebody can check cheaply enough to run again?
Most requests arrive dressed as a solution. “We need an agent for account research.” “Can we use AI to review contracts?” “Could this automate the weekly report?” The person making the request may be right that there is useful work here. The request still has not named the work, the check, or the person who will live with the result. That is how an AI backlog fills with impressive ideas and empty results.
A useful intake makes the hard decision early. It asks for one recurring output, its owner, the verifier, and the cost of a bad release. If the answer is vague, the request is not ready for a pilot. It may be a good idea. It is not a funded workflow yet.
The request is not the use case
“Use AI for sales” is a department wish. “Prepare a Monday account brief from the CRM, recent calls, and public news for each account executive” is a use case. It has a starting point, a result, a recipient, and a cadence.
That distinction keeps the intake from becoming a collection of tool preferences. A model can draft a contract summary, classify a support request, or prepare a research brief. None of those capabilities tells a director where to spend the next dollar. The decision belongs to the recurring work around the capability.
Ask the requester to describe the work as if a new colleague had to take it over next week. What starts it? What sources does it use? What arrives at the end? Who receives it? If they cannot answer those questions in plain language, the right response is to keep learning about the work before bringing AI into it.
The first workflow in an AI adoption plan needs exactly this level of clarity. It gives a leadership team something they can inspect in thirty days, instead of another attendance count or vendor demo.
The verifier belongs at the top of the form
Most intake forms begin with the department, expected value, and preferred vendor. Those are later questions. The first question is how the organization will know a particular output is acceptable.
The verifier turns a plausible use case into a candidate for real work. A finance reconciliation can tie to source records. A support triage can check against policy rules and reopen rates. A daily research brief can require named sources and a recipient who can spot a bad recommendation in minutes. A strategy memo may still be worth drafting with AI, but a senior leader’s judgment remains the expensive part.
The verification test explains why this matters. Generation is cheap enough that a convincing demo tells you very little. The workflow that survives needs a quick way to reject a bad output before it travels.
An intake can fit on one page.
| Field | What it needs to settle |
|---|---|
| Recurring output | The thing that arrives differently, its recipient, and its cadence |
| Workflow owner | The person who can change the method and answer for its upkeep |
| Verifier | The check, who performs it, and how long it takes |
| Release boundary | What can happen automatically and what still needs approval |
| Decision signal | What will justify expansion, redesign, or a clean stop after thirty days |
The table is deliberately incomplete. It does not ask for a long benefits estimate or a technology architecture. Those details acquire meaning after the team knows that an output can be trusted and owned.
A sponsor is not an owner
An executive sponsor can clear obstacles. They cannot maintain a workflow by being enthusiastic about it in a steering meeting.
The workflow owner is the person who can say when the inputs changed, why the output was wrong, and whether the result still saves enough time to keep. They are also the person who receives the escalation when the run fails or a source disappears. If the only named person is the executive who approved the budget, the request has not found an owner.
This is where a steering committee has a narrow but useful job. It can decide who has authority to approve a release boundary, which systems a workflow may use, and who takes responsibility when the work crosses teams. Decision rights belong there. The committee should not become the owner of every request. A group cannot notice that Tuesday’s report stopped arriving.
Ownership also protects the person who built the first version. Work that lives under one employee’s account and serves a department becomes an invisible obligation. The AI software renewal record needs to show the workflow, its cost, and its owner well before someone asks whether the tools are worth renewing.
The intake should produce a decision
The output of an intake is not a recommendation to study the idea. It is one of three decisions.
A workflow with a named output, owner, fast verifier, and contained release risk earns a small production cut. Give it real volume, a short review date, and a result that can either expand or end. A workflow with useful potential but an unclear check goes back for process work. The team may need source records, exception rules, or a human review point before it is safe to automate. A workflow that depends on slow expert judgment remains a drafting aid. That can still save time, but it should not be sold as unattended automation.
This is a better filter than a polished pilot. Most pilots answer whether a model can produce something impressive. The intake asks whether the company can operate the result after the novelty wears off.
The decision signal matters because it prevents a fourth outcome: permanent experimentation. A short brief can say that the team will expand if the reviewer accepts most outputs without redoing the work, redesign if a specific failure repeats, and stop if the check costs as much as the original process. Those are management decisions, not model scores.
The backlog gets smaller and more valuable
An intake with a real verifier will reject many requests. That is good news. It keeps a director from funding a portfolio of demos whose owners cannot describe what they would run next month.
The requests that remain are easier to fund. They have a person, a recurring output, a check, and a decision date. They also create a record that can support the next budget conversation: here is the work we changed, here is how we checked it, and here is what we decided to do with it. Measuring AI ROI starts from that kind of evidence, because it is the only evidence that survives the slide deck.
The intake is the first test of whether the work has a place to land.