AI Product Innovation: Test Customer Value Before Building
Evaluate an AI feature through customer needs, small prototypes and evidence about the complete experience.
Key takeaways
- Define the customer problem before the feature.
- Prototype limitations and recovery as well as success.
- Include ongoing support and review in the product decision.
Identify a problem customers recognise
Use interviews, support records and observed work to understand an unmet need. Separate what customers actually say from what the team assumes they want.
Write a problem statement with a specific user, situation and desired outcome. Compare an AI feature with improvements to the existing product or service.
Decide what AI should contribute
Define the narrow capability: draft, summarise, classify or suggest. Specify the information required and which decisions remain with the customer or staff.
Google’s People + AI Guidebook offers a design reference. Use our design thinking workflow to turn the problem into a testable concept.
Prototype the whole experience
Show how users provide input, understand the result and recover from a mistake. An illustrative catalogue assistant might suggest products from an approved list and link to the underlying specifications.
Test a request the catalogue cannot satisfy. Do not let a fluent answer conceal that the feature lacks the necessary information.
Evaluate value and operating effort
Measure whether users complete their task accurately and whether support or review effort increases. Include setup, data maintenance and usage costs.
Compare with a simpler search or filtering approach. A feature that impresses in a demo may still be harder to use than an existing process.
Make a bounded investment decision
Document what the prototype established and what remains uncertain. Decide whether to run a controlled pilot, revise the idea or stop.
Check data readiness and use the pilot evaluation guide before expanding access or building integrations.
Sources and further reading
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