AI commercialization use-case selection guide
Our positionA viable scenario is not one AI can do — it is one someone will pay for once it is done.
Screen for AI opportunities that are valuable, buyable, and repeatable using value density, data availability, and delivery boundaries.
You are probably stuck here
- 01Thirty scenarios on the list and not one of them signed off
- 02Technically feasible, but no customer will pay for it
- 03Everything is urgent, so everything ships half-done
If one of these is yours, the rest of this page is worth your time.
After reading, you should be able to answer
- 1What filter removes most scenarios
- 2How to estimate value density and data readiness
- 3Where the delivery boundary belongs
Who this is for
Leaders choosing an AI product direction, service wedge, or high-value internal use case.
What you'll take away
- 01Prioritize frequent, expensive, testable problems with an existing budget.
- 02Sell a clear outcome before deciding which models and automations produce it.
- 03Delivery boundaries and ongoing operating cost determine commercial viability.
Method breakdown
- 01
Start where customers already spend
The strongest signal is not interest in AI; it is money, headcount, or management attention already spent on a problem. Compare the current alternative with what AI could change in cycle time, marginal cost, or consistency.
What this step asks for01- Quantify frequency and current cost
- Find the role with budget and authority
- Separate curiosity from an urgent operating problem
- 02
Control delivery risk with three boundaries
A viable use case has a clear input, an assessable output, and an acceptable failure path. Tasks with chronically incomplete inputs, purely subjective outputs, or irreversible consequences rarely make a good first productized offer.
What this step asks for02- Define the minimum required input
- Agree on samples and quality thresholds
- Route low-confidence results to human review
- 03
Build a narrow but complete loop
Choose one workflow that can run from trigger to outcome instead of promising to remake a department. Once the loop is stable, expand through adjacent steps, more data sources, and higher automation. This creates evidence and repeatable delivery assets.
What this step asks for03- Ship the first usable loop in two to four weeks
- Track time saved, adoption, and exception rate
- Turn repeated delivery work into a reusable pack
- Back to step 01 — run it again
Three steps are a cycle, not a checklist: after one pass, what step 01 should measure has changed.
Action checklist
Tick what you already have. Whatever is left unticked is your next move.
5 still open. Do not fix them all at once; start with the one that hurts.
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AI commercialization use-case selection guide
- Our position
- A viable scenario is not one AI can do — it is one someone will pay for once it is done.
- You are probably stuck here
- Thirty scenarios on the list and not one of them signed off; Technically feasible, but no customer will pay for it; Everything is urgent, so everything ships half-done
- After reading, you should be able to answer
- What filter removes most scenarios; How to estimate value density and data readiness; Where the delivery boundary belongs
The copied text carries the source link — safe to drop into an internal doc, mail, or meeting note.
Use these delivery packs next
Reference cases
Bay Area Automotive GroupManufacturing CompanyTake this page to your AI
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