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Draft briefing: awaiting a named author and editorial review before publication.
Ask a leadership team for their AI use cases and you will usually receive a long list: ideas from workshops, vendor suggestions, pilots already underway and requests from individual functions. The list is not the problem. The problem is that lists get funded the way they were collected: opportunistically, one sponsor at a time. The result is a spread of small bets with no shared logic for what happens after each one.
A portfolio treats the same ideas as competing investments. Each candidate is scored on three axes, and the scoring conversation matters more than the scores themselves.
Value: name the operating metric
A use case has value when it moves a metric someone already manages: cycle time, cost per transaction, conversion, service level, decision latency. If the sponsor cannot name the metric and its current baseline, the idea is not ready to score. It is ready for discovery. Vague value ('productivity', 'efficiency') is the most common reason pilots later stall: nobody can say whether they worked.
Feasibility: data and workflow, not model capability
Model capability is rarely the constraint. Feasibility is dominated by two questions: does the data the use case needs exist in a usable, governed form, and can the output be embedded into a real workflow with a human owner? A use case that requires three systems to be integrated and a new team habit is expensive regardless of how good the model is.
Risk: proportionate, not uniform
Risk scoring should classify consequence, meaning what happens when the system is wrong, rather than applying one governance process to everything. A drafting assistant with human review carries different obligations than an automated decision affecting customers. Proportionate classification is what lets low-risk use cases move quickly while high-consequence ones get the controls they genuinely need.
Sequence with decision gates
A scored portfolio still fails if everything is funded to completion up front. Sequence the top candidates through explicit gates (after discovery, after value proof, before production) where continuing is a decision, not a default. The discipline sounds slow; in practice it is what prevents the two-year pilot that nobody can stop because nobody agreed on what stopping looks like.
The output worth aiming for: a one-page portfolio the executive team re-scores quarterly, with owners, baselines and the next gate for each investment. That page, not the model, is where AI strategy lives.