Entry 001
What should we do first?
Risk-Tiered AI Adoption
Sequence AI by risk, not by one ROI gate: start inside, build the muscle, then go customer-facing.
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AI today is alchemy. Let’s turn it into chemistry.
AI should be boring.
Real value comes from AI that’s in production, measured, and trusted exactly as far as it’s reliable. These are the methods we can use to get there, and the work that proves them.
PlaybookSix plays5-minute read
What boring AI looks like
A two-page guide to getting AI out of pilots and into everyday work, safely.
- Sequence by riskBuild skill where mistakes are cheap.
- Fix the work firstDon’t automate the jam.
- Govern in proportionOne front door, two speeds.
- Trust the track recordUse AI where checking is cheaper than doing.
- Keep people in chargeAI does the work. People make the call.
- Prove it with a numberAgree the number before you build.
How I think
The philosophy: AI should be boring. Four methods put it to work at four altitudes: the portfolio, the workflow, the single decision, and the person making it.
Where does AI fit in the work?
Fix First, AI Last
Map the work before you buy the tool: most stuck points need a process fix or a simple rule, not AI.
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How far should we trust it?
Calibrated Trust
Fluency isn’t truth. Use AI where checking is cheaper than doing, and trust it exactly as far as it has earned.
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Who holds the judgment?
Keep It Human
Hand AI the hauling, not the judgment. Let it do the work and sharpen your thinking, but keep the final call yours.
Reviewed · may be out of date
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The proof
Frameworks are claims until something ships. Each project below puts at least one of them into practice.
LiveInteractive essay25 minutes
The Confident Machine
An interactive essay on calibrated trust: why AI sounds sure when it’s wrong, and how to decide when to rely on it, check it, or keep the work.
Puts into practice
Read the essay (opens theconfidentmachine.com in a new tab)Read the case study
LiveWorkflow map2 minutes
What Do I Actually Do?
A two-minute tool that maps your work week into a one-page blueprint: what to fix, what to automate, where AI helps, and what stays human.
Puts into practice
Map your work (opens mapyourworkflow.com in a new tab)Read the case study
About
Today I help lead AI enablement for a large commercial organization: finding where AI fits, guiding it through governance, and reporting what it actually delivers. I got here through analytics, turning marketing and commerce data into decisions teams could act on. The habit underneath it all started with a journalism degree: make complicated things clear to people with no time to spare.
- Where it started · Journalism degreea story
- Data & analyticsa decision
- AI strategy & governancea system people can trust
One job throughout: making complex things clear.