Series
Why AI work stalls somewhere between the demo and daily operation — and the sequence that gets it across: start bounded, say no early, sort the bets, and let evidence buy autonomy.
The pilot worked. That is usually where the trouble starts. A proof of concept answers one question — can the model do this — and none of the questions that decide whether a workflow survives a Tuesday in production: who owns it, what happens when it is wrong, and why anyone should keep paying for it.
This series follows that path in order: why PoCs die on the way to production, why the agents that survive are the boring ones, how to tell the use cases worth operating from the ones that only sound good in a steering committee, how to treat a set of AI ideas as a portfolio rather than a wishlist, and how a workflow earns more autonomy as the evidence behind it grows.
Part 1 of 3 ·
Stop building AI prototypes that never make it to production. Discover how shifting from isolated tech demos to real product ownership turns impressive showcases into reliable workflows that actually drive business value.
Part 2 of 3 ·
The agent demos browse the web, write the code, and seemingly do your job. The agents that actually survive inside an enterprise are far less impressive – and that's exactly why they work. A case for starting boring.
Part 3 of 3 ·
Why a mature enterprise AI strategy asks whether a workflow is worth operating, not just whether it is technically possible, and when to say no.
Some AI ideas are technically possible but not worth operating. Mature AI strategy includes saying no.