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Evaluating AI Product and Growth Decisions
Choose the problem first. Estimate benefit and operating cost conservatively, run a small test, and record what evidence would change the decision.
By Jake Bauman · Revised
Jake Bauman's decision method. It does not assign universal vulnerability scores, cost percentages, or ROI benchmarks.
Frame the decision
Identify the customer task, the current alternative, the person who owns the outcome, and the risk of a wrong result. Then compare build, buy, and a simpler process change.
Gather decision evidence
- Representative user examples
- Baseline quality and time
- Pilot output and correction rate
- Full operating and review cost
- Customer feedback, including objections
Write the next test
State what you learned, what remains uncertain, and the smallest test that could reverse the recommendation. Survey results provide context; they do not validate an individual product opportunity.
A decision packet
A one-page decision can state the problem, current process, candidate options, test evidence, costs, failure modes, and the next gate. Include a reason to skip or postpone the AI approach. Share it with the person who owns the business outcome and the person who owns the technical risk. Their disagreement is useful evidence about what the pilot still needs to answer.
Sources and context
These sources inform the framework. They do not validate the suggested actions for every business.
- Anthropic, Building effective agents: Provider guidance on agent design and evaluation.
- LangChain, 2026 State of Agent Engineering: Survey of more than 1,300 respondents; findings describe respondents, not all businesses.
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