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In Production
What it takes to make AI work in production — the failure modes, misdiagnoses, and the questions worth asking.
4 posts
The Tool Wasn't the Point
A sales team deployed AI to personalize outbound emails. Response rates climbed. Closed deals didn't. The tool created over 120 hours of new work per month that produced zero qualified leads — because the value was never in the tool.
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The Difference Between Relevant and Reliable
Most production failures get diagnosed as relevance failures. But many enterprise AI systems fail for a different reason: the model had access to the relevant information, and the surrounding system still produced the wrong outcome.
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What Actually Makes AI Work in Production
A model can interpret a request, draft a response, and still fail in production. Reliable AI systems need interpretation, boundaries, context, measurement, and human judgment working together.
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Who Authorized That Decision?
Organizations point their AI at the policy document and assume that counts as enforcement. It doesn't. The gap between pointing at rules and delegating authority to enforce them is where hidden governance exposure lives.
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Subscribe to The Algorithm
Notes on building AI systems that actually work.