A pilot stalls when the spend has no owner and no number. I run enablement as a small P&L instead: find the expensive repetitive work, gate every project on a number, tie every dollar to the initiative that owns it, and build standards instead of buying seats. Hands-on, from the engineering to the adoption.
The whole thing follows from one decision: the team that puts AI to work carries a budget and has to show a return against it. Run right, it is a loop, where each shipped win pays for the next one.
Find the expensive, repetitive work the way a founder does discovery, and prove the value with the cheapest experiment before building a platform.
Read the write-up → 02A project does not start until someone can name what it will move: hours saved, cost removed, pipeline added, or risk reduced. The gate kills toys early.
Read the write-up → 03Each initiative gets its own metered credential up front, so the provider rolls spend up by initiative and every dollar carries the label of the thing that spent it.
Read the write-up → 04Encode how the team builds so each project reuses the last, and control model cost through routing and caching instead of cancelling licenses.
Read the write-up →Details changed enough to protect the client, true to the shape. Each one names a number and ties the engineering to it.
A marketing team with 200+ ungoverned email templates across two tools, dreading a manual migration. Run as an AI-driven pipeline it goes 80 to 90% automated, with a human keeping the judgment. The cleanup is the first win, before the migration even starts.
A vendor support agent for a customer-facing workload in a regulated industry. The work was the evals, a calibrated judge, and adversarial tests before turning it on, then measuring it after. That is where the value sat.
A code-knowledge graph so an agent stops rediscovering the codebase, proven with a one-day test under $5 before a $63-a-month build, plus model routing and prompt caching. The bill comes down through engineering.
Every project I take on has to name its number. Move the inputs and the P&L reads out live: what a repetitive task costs when done manually, and what enablement returns.
A back-of-the-envelope, not a quote. How I attribute the spend →
Buying the setup and the first measured wins, so the P&L is real before anyone talks about a permanent hire.
How a 200-person org adopts AI: baseline the work, pilot on real code, measure outcomes, guardrail the agents, and route every model call through one gateway. Written three weeks into a live rollout.
The full operating model: why the committee-plus-licenses setup cannot pay off, and the four moves that do.
Find the pain, gate on a number, and prove value with a one-day experiment before you spend on a platform.
Give each initiative its own metered credential up front, and the spend attributes itself.
Rationing seats caps your upside to save a fraction of your downside. Control cost through standards and engineering.
The marketing ops cleanup and migration, stage by stage, with an interactive walkthrough of the pipeline.
If you are standing up an AI enablement function, or you have one and it feels like a backlog of ideas rather than a set of shipped, measured wins, I am happy to get into the specifics of your setup.
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