A hands-on enablement clinic for your non-engineering teams. We practice on your work, using your approved tools and review rules. Your AI team can stay focused on the product.
Usually sponsored by a Head of People, Enablement, Operations, or AI transformation lead who needs adoption to move without pulling product engineers off the roadmap.
If the evidence conflicts or the gap isn't clear, send it to the support lead.
The draft stays reviewable: proposed fields, source evidence, and open questions travel together.
Agent confidence isn't the check. Uncertain cases escalate for human review.
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:
Mixed comfort levels in one room are 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:
Chat, a reusable template, a saved workflow, or an agent. Stop forcing everything through one chat window.
Give the model enough to be useful without crossing data boundaries. What's safe to paste, what never is, and how to tell the difference.
Take the task you redo every Tuesday and make it a reusable template, then a loop the whole team can run.
Meaningful outputs get a named reviewer. Your teams learn where the review point goes and what it actually checks.
Some work is faster by hand and some is too sensitive to delegate. Calling that early is part of the habit.
Time, quality, speed, impact. Every workflow gets a measurement card, so "it feels faster" becomes a number.
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.
One recurring workflow, rebuilt live. A 10-minute pre-survey picks it: each participant submits 2 or 3 recurring tasks, one AI win, and one AI failure. Same intake pattern as the day formats, just lighter. You leave with the working workflow, a mini tool-choice map, a measurement card, and a recommended next step.
From $5,000 · credits toward any larger format booked within 60 days
A shared baseline for a bigger group: tool-choice judgment, safe-context habits, and hands-on labs across functions. Discovery runs alongside, so leadership gets a ranked map of which workflows are worth building first.
Leaves behind: ranked workflow opportunity map
Day one builds the baseline. Day two breaks out by function: People, Finance, Ops, Support, Legal, each drafting workflows on their own work, with specs your AI team can actually build from.
Leaves behind: drafted workflows, skill-request specs, measurement cards
The full arc, through agent literacy. Then follow-ups at weeks 2, 4, and 6 while the new workflows meet reality, ending in a leadership readout: what lifted, what stalled, what to request next.
Leaves behind: measured lift, leadership readout
The working session starts at $5,000 and credits toward a larger engagement. Pricing for the day formats is scoped on the call. Travel billed at cost.
The scope depends on how many people are participating, the workflows we'll practice, the preparation involved, and the follow-through you want. We'll agree on those details and the total price before you book.
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.
Take-home artifacts along the way:
The workshop doesn't end with a slide deck. Each lab produces a small operating document that makes the workflow reviewable and reusable.
These numbers are illustrative. The real card uses your workflow and your observed results.
Your AI team gets a buildable request instead of a vague ask in a Slack thread.
Each workflow built in a lab gets a measurement card, filled in before anyone declares victory:
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.
Labs run on real work, not real secrets. No PII, no sensitive data in exercises, and participants drill the boundary as part of the habit.
Meaningful outputs keep a human review point. That's a rule we practice in the labs, not a line on a slide.
New skill and agent requests route through your AI team's governance path. The workshops feed that path; they don't route around it.
Your teams build judgment about surfaces, context, and review points. That judgment survives a tool switch; dependency on one vendor's UI doesn't.
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. 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 personal sales-workflow prototype that turns structured deal context into a visible prompt with copy and provider handoff options.
I taught Build Your First Agent Loop at Vonage and TechWalk's NYC AI Builder Nights at Fabrik DUMBO. Participants used the Loop Lab companion exercise to turn a recurring task into a bounded loop with an independent check and a clear point for human approval.
My article on the Vonage Developer Blog takes that pattern into voice AI: keep the live conversation fast, then use the saved evidence to improve the next run.
Read the workshop case study, including the original exercise worksheet and a participant's account of what she changed afterward.
Start with the workflow measurement card. It includes a blank before/after template and a clearly labeled synthetic example. You can use it without booking a session.
Based in Brooklyn, working with teams worldwide. We can work remotely; the scoping call confirms the format, time zone, and any on-site arrangements.
Yes, that's what the working session is for. One team, one workflow, 2.5 hours, no procurement cycle. If it lands, the fee credits toward any larger format you book within 60 days. You're not betting the quarter on a workshop.
The working session runs remote or onsite in NYC, whichever fits your team. The day formats run onsite-first because the lab energy is the point, with remote delivery available for distributed or non-US teams.
Up to 12 for the working session, one team at a time. The day formats run from about 10 to about 50 people, with the foundations day best up to about 40, and function breakouts once the group is bigger than one room's conversation.
They run in parallel with prep. NDAs, security questionnaires, and vendor onboarding happen while the pre-survey and scoping work are underway, so the session doesn't wait on paperwork.
Three things: tool access for participants on whatever your teams already use, the language of your AI-adoption goals so the labs aim at them, and 1 or 2 internal support people who can unblock access questions on the day.
The 6-week follow-through is built into the 3-day clinic. For the working session and the day formats you can add follow-up sessions, and every format leaves measurement cards behind, so the check-ins have structure either way.
One call, 30 minutes. You bring the adoption goal and the teams that are stuck. I bring the format recommendation and the scope. You get a written proposal within two days. Not sure which format fits? Compare the formats first.
I'll read your notes before the call. If nothing here works, email [email protected] and we'll find a time.
Prefer plain email? Write me at [email protected]. Two or three sentences about the team and the goal is plenty.