Use-case inventory
Candidates captured in a consistent format, including ideas already in flight.
AI Services
Use Case Identification and Prioritization
Find the AI use cases hiding in your operations, score them consistently, and pick the few worth starting with.
What it is
Good use cases rarely come from a brainstorm. They come from looking at where people spend time on repetitive judgment, where queues build up, and where the same question gets answered a hundred times a week.
We run structured discovery with your operational teams, then score every candidate the same way — business value, feasibility, data readiness, risk and change effort. Consistent scoring is what makes the prioritization defensible when two executives disagree.
Who it is for
- Organizations with a long, unranked list of AI ideas
- Teams who need a first wave chosen on evidence rather than seniority
- Companies where each business unit is pushing its own pilot
- Leaders who need to decline some requests with a reason
What is included
The deliverables below are the default scope. We adjust them to your situation during scoping and confirm the final list in writing.
Scoring model
The criteria and weights, agreed with you so the result is not our opinion.
Scored and ranked portfolio
Every candidate scored, with the reasoning visible.
First-wave recommendation
Two to four use cases to start, with what each needs to succeed.
Decline list
Candidates we recommend against, each with a stated reason — useful when someone asks later.
Process and timeline
Indicative timings for a typical engagement of this type. Your scope, environment and availability of stakeholders will move them.
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Discovery sessions
Workshops with operational teams, plus review of volume and effort data.
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Define scoring
Agree criteria and weights before any candidate is scored.
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Score and rank
Score with input from the teams who would own each use case.
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Recommend
First wave, declines and the evidence for both.
Outcomes we aim for
These are the goals we design the engagement around. They are objectives, not guarantees, and the result depends on decisions and effort on your side as much as ours.
- A ranked portfolio with transparent reasoning
- A first wave chosen for value and feasibility together
- Fewer pet projects consuming delivery capacity
- A reusable scoring model for the next round of ideas
Questions
About use case identification and prioritization
How many use cases should we start with?
Two to four. Enough to learn from variety, few enough that your delivery and change capacity can actually absorb them.
What makes a bad AI use case?
Low volume, unclear decision rules, data that does not exist, no owner willing to change their process, or a consequence of being wrong that nobody has thought through. Any one of those is usually enough to defer it.
Is use case identification and prioritization the right next step?
Tell us the symptom rather than the solution. If a different service fits better — or none of ours does — we will say so.