Framework
Verifiability-First AI Use Cases
AI succeeds where humans can quickly and independently verify its output. Build first for use cases where the result is easy to confirm or reject - these create compounding value. Avoid use cases where verification is slow, expensive, or requires specialized expertise to assess.
How it works
- 1The verifiability test: after the AI produces an output, can the human using it determine within 60 seconds whether it is correct? If yes, the use case will succeed. If no, it will stall.
- 2High-verifiability successes: writing and summarization (you read it), code generation (experienced developers can scan it), document extraction (does the field value match what was in the document?)
- 3Low-verifiability challenges: autonomous agents making sequences of decisions, medical diagnoses requiring specialist review, legal precedent discovery across large case databases
- 4Verifiability determines iteration speed: when humans can quickly correct AI outputs, the feedback loop is tight and the model improves rapidly - low verifiability produces slow, expensive improvement cycles
- 5Self-driving car analogy: it took 15 years from first Waymo prototypes to commercial deployment in select cities because verifying safe autonomous driving required that no one die - the verification cost was extraordinarily high
- 6Startup implication: your first AI feature should be in the category where a non-expert user can tell immediately if it worked - this creates the fast feedback loop that makes the product improvable
Hear it on the podcast
Your Company Is Sitting on a Data Gold MineListen to the episodeMore from this episode
Invisible AI IntegrationThe highest-value enterprise AI runs in the background of existing workflows without requiring users to change their behavior. AI that demands new interfaces, new habits, or new training faces adoption friction that kills deployment. AI that simply makes existing workflows better spreads without resistance.The Last-15% Quality ProblemGetting an AI system to 80% accuracy is easy. Getting to 95-99% is where all the real work lives - and in enterprise contexts, it is the only accuracy that creates deployable value. Founders building for enterprise must invest in the last 15% or their product will not survive the pilot stage.
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