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Why AI Workflows Fail When You Start With the Prompt
Teams that lead with prompt engineering ship novelty. Teams that lead with process ship throughput. Here is the difference, and why it matters before the first model call.
Blog
Field notes on designing AI workflows that produce reliable output.
Practical writing on workflow discovery, review gates, department-specific systems, and the operating discipline behind AI rollouts that survive first contact with real work.
Latest writing
04 posts
Nº 02
Mapping the Real Work — Discovery Before Design
Most AI rollouts skip the part where you watch the work happen. That is where every meaningful improvement lives. A practical guide to running a workflow discovery that actually changes the design.
Nº 03
The Review Gate — Where AI Workflows Earn Trust
An AI workflow without a review gate is a liability. With the right one, you get throughput without giving up the quality bar. Here is how to design the gate.
Nº 04
Department-Specific AI Beats One Assistant For Everyone
Generic AI assistants flatten the work and lose the leverage. Department-specific workflows compound it. Why we design one system per team, and what that looks like in practice.
Field notes
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One useful note when there is something worth saying — concrete patterns from real engagements, not tool hype.
01Review gates— designing the moment of trust
02Workflow maps— what the work really looks like
03Rollout lessons— what survives first contact
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