AI
Ask me before you add AI to your product.
Adding AI to a product sounds like progress. Sometimes it is, when it solves a real problem. More often it is an expensive way to discover that the real problem lives somewhere else. A short check looks at where AI actually adds value, and where it adds complexity, maintenance and supplier dependency.
Check this before you commit
What I check first.
- 01
Which specific problem does the AI feature solve?
- 02
What does your product do without AI, is it already working?
- 03
How do you measure success, accuracy, speed, cost reduction?
- 04
What is the cost per user per month at realistic usage?
- 05
Which data goes to the supplier, and is that acceptable to your customers?
Common patterns
What I see happen most often.
- AI gets added because it has to be on the pitch deck.
- A process problem is solved with technology instead of a process change.
- The per-user cost curve becomes a surprise as usage grows.
- A model update unexpectedly breaks production behaviour.
What the conversation produces
What you leave with.
- 01
Clarity on whether AI adds value here or complexity.
- 02
Visibility into cost, maintenance and reliability.
- 03
A workable next step: build, delay, or approach the problem differently.
Further reading
Articles on this challenge.
- 6 min read
AI Is Not a Strategy
AI can accelerate, but it also accelerates wrong decisions. When does it add value, and which questions to answer before you add it?
- 7 min read
AI Can Make You Faster at Making Mistakes
AI reduces the cost of producing software. It does not reduce the cost of choosing the wrong software.
- 8 min read
What Should Be in Your MVP and What Should Wait
A decision framework for keeping the first release complete, credible and much smaller.
Ready to talk?
Will AI actually solve this problem?
AI sounds attractive. But users pay for solutions, not technology.