Workshop / advisory

Company AI adoption without mandate theater.

Your senior engineers are not wrong to be skeptical. They are waiting for proof that AI-assisted work can survive review, governance, and production pressure.

Discuss a workshop Read about Jay

The real blocker

Resistance is often a quality signal.

Top-down AI adoption fails when the team hears speed and imagines cleanup. The missing object is the contribution perimeter: what is safe, reviewed, measurable, and reversible.

01Engineers do not trust the output because the review contract is vague.
02PMs, designers, and ops can generate code now, but nobody has defined blast radius.
03Leadership wants adoption metrics, but usage counts do not prove durable delivery.

The engagement

A practical harness for teams already under delivery pressure.

The work can be a focused workshop, advisory loop, or fractional operating support. The output is a working standard, not a motivational deck.

01Define the team-specific AI contribution perimeter: allowed surfaces, review depth, rollback, and evidence.
02Turn skepticism into voluntary adoption with receipts instead of mandates.
03Set metrics around reviewed contributions and survival through QA, not vanity tool usage.

Principle

AI adoption is not a tool rollout.

The tool matters less than the harness around it. Without that harness, Cursor, Claude Code, Codex, or any next tool just makes unreviewed change cheaper.

01Study: understand what the new tool actually changed.
02Implement: ship one low-blast-radius slice with the team.
03Govern: write the rulebook so the second slice is easier than the first.

Skeptical has a point until the harness proves otherwise.

Qualified contact

Tell me what adoption is stuck on.

No self-serve checkout. Send enough context for a real conversation: team shape, constraint, and why now.

Resistance is often a quality signal.

Top-down AI adoption fails when the team hears speed and imagines cleanup. The missing object is the contribution perimeter: what is safe, reviewed, measurable, and reversible.

01Engineers do not trust the output because the review contract is vague.

02PMs, designers, and ops can generate code now, but nobody has defined blast radius.

03Leadership wants adoption metrics, but usage counts do not prove durable delivery.

A practical harness for teams already under delivery pressure.

The work can be a focused workshop, advisory loop, or fractional operating support. The output is a working standard, not a motivational deck.

01Define the team-specific AI contribution perimeter: allowed surfaces, review depth, rollback, and evidence.

02Turn skepticism into voluntary adoption with receipts instead of mandates.

03Set metrics around reviewed contributions and survival through QA, not vanity tool usage.

AI adoption is not a tool rollout.

The tool matters less than the harness around it. Without that harness, Cursor, Claude Code, Codex, or any next tool just makes unreviewed change cheaper.

01Study: understand what the new tool actually changed.

02Implement: ship one low-blast-radius slice with the team.

03Govern: write the rulebook so the second slice is easier than the first.