AI commercialization use-case selection guide
Screen for AI opportunities that are valuable, buyable, and repeatable using value density, data availability, and delivery boundaries.
Our position
A viable scenario is not one AI can do — it is one someone will pay for once it is done.
What to take away
- Prioritize frequent, expensive, testable problems with an existing budget.
- Sell a clear outcome before deciding which models and automations produce it.
- Delivery boundaries and ongoing operating cost determine commercial viability.
Method breakdown
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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.
- Quantify frequency and current cost
- Find the role with budget and authority
- Separate curiosity from an urgent operating problem
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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.
- Define the minimum required input
- Agree on samples and quality thresholds
- Route low-confidence results to human review
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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.
- 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
Action checklist
- Is the customer already paying a measurable cost for the problem?
- Can inputs, outputs, and acceptance criteria be standardized?
- Can a complete small loop ship in two to four weeks?
- Is there a low-cost, traceable fallback for bad results?
- Could the solution repeat across three similar customers or teams?
Who this is for
Leaders choosing an AI product direction, service wedge, or high-value internal use case.