From AI access to measured AI adoption

Your teams already have the tools. Claude, ChatGPT, Gemini, an internal assistant, AI surfaces in Slack. The rollout shipped, and usage is still uneven: a few people run half their job through these tools, most opened a chat window twice and went back to the old way.

The gap lands hardest on non-engineering teams. Engineers get the tooling, the training time, and permission to experiment. People, Finance, Ops, Support, and Legal get a license and a launch email.

And the AI engineers who could close that gap can't. They're building the product. Pulling them inside to run instruction is how roadmaps slip.

For companies that already bought the tools and want the usage to follow. That's the AI-native shop where engineering sprinted ahead of everyone else, and the established company partway through a rollout that stalled at "we have licenses." The room is your business teams:

• People / HR
• Finance
• Ops
• Support
• Legal / Compliance
• Strategy / PM

Mixed comfort levels in one room is by design. The skeptics ask the questions everyone needs answered, and your quiet power users surface fast.

Not inspiration. Six habits, drilled on their own work:

Every block runs 10 to 15 minutes of concept, 30 to 45 minutes of lab on work participants actually own, and 10 minutes of readout. Nobody spends the afternoon watching demos of someone else's job.

Your advanced users become table captains instead of bored observers. And there are no slide marathons: if a concept can't survive contact with real work, it doesn't make the agenda.

Every rung shares the same DNA: labs on real work, a measurement card, and a recommended next step. Start where your team is, not where a sales deck says you should be.

The working session starts at $5,000 and credits toward a larger engagement. Onsite engagements start at $39k. Exact scope and pricing on the scoping call. Travel billed at cost.

Day formats run onsite-first. Remote delivery is available for distributed or non-US teams.

Every team climbs the same ladder. The workshops move people up it deliberately, and nobody gets pushed past the rung their work supports.

1. One-off prompt. Useful once, gone tomorrow.
2. Reusable template. The prompt that worked, written down so the whole team can run it.
3. Loop. A repeating workflow with inputs, steps, and a review point.
4. Skill request. A spec your AI team can build from, not a vague ask in a Slack thread.
5. Supervised agent request. The same spec discipline, plus the checkpoints a human signs off on.
6. Managed multi-agent. Literacy only: your teams learn to read and request this work, not run it.

Take-home artifacts along the way:

• tool-choice map
• Loop Engineering Canvas
• structured request pack
• skill-request cards
• agent-request cards
• business quality-check card
• measurement worksheet

Each workflow built in a lab gets a measurement card, filled in before anyone declares victory:

• baseline
• new workflow
• time signal
• quality signal
• speed signal
• impact signal
• safety gate

Where volume exists, you get observed lift. Where it doesn't yet, you get directional estimates and a named list of blockers. Either way, the readout runs on numbers, not vibes.

I did the job your teams do. Enterprise sales at Cision, with a $1.6M ARR book across 125+ accounts. While I carried that number, I built the team's 50+ entry Skills library and ran AI workflow sessions at the company summit, before any of this was my job. I know what adoption looks like when the learner has a quota.

Now I build with these tools daily: 25 shipped projects, 5 hackathon wins, and the From Chatbot to Builder cohort I teach. The workshop material isn't theory I collected. It's the operating habit I use.

Closest public case study: Prompt Runner, a sales Skills library turned into a web app for an enterprise team with strict data-security requirements.