The working documents behind my adoption practice — the templates that turn pilots, rollouts, and enablement from good intentions into repeatable operating moves.
Pilots
AI Pilot Success Criteria Template
Defines what a pilot is testing before it starts, so the end date produces a decision instead of a debate.
What it covers
—Decision the pilot exists to enable (continue / stop / change)
—Adoption signals to watch (repeat usage, workflow change, question quality)
—Quality and business signals, with baseline noted before launch
—Time box, checkpoint dates, and who reviews at each one
—Explicit non-goals — what this pilot is not evaluating
Adoption
AI Use Case Intake Form
A structured way for teams to propose AI use cases that forces workflow thinking over tool enthusiasm.
What it covers
—The workflow step this would change, described without naming a tool
—Frequency: how often this task happens and who does it
—Effort today vs. judgment required — is this high-effort, low-judgment?
—Data and knowledge the AI would need access to
—What 'working' would look like 90 days after launch
Workflow
AI Workflow Review Checklist
A walkthrough guide for mapping how work actually gets done before deciding where AI fits.
What it covers
—Trace one real unit of work end to end, including workarounds
—Count the systems touched and where information actually lives
—Mark every handoff and what context gets lost at each
—Identify repeated questions, lookups, and reformatting steps
—Ask what people dread — high-effort, low-judgment tasks first
Evaluation
AI Vendor Evaluation Rubric
Practical implementation criteria for comparing AI platforms beyond the demo.
What it covers
—Business fit: the workflows this actually improves, named specifically
—Data handling: what leaves the building, and under what terms
—Security and governance requirements, reviewed with the right teams
—Integration readiness with existing systems and identity
—Support model, admin tooling, and rollout lift
—Measurable value: what number or behavior should change, by when
Measurement
AI Adoption Dashboard Outline
The reporting structure I use to make adoption legible to leadership — built to drive decisions, not to decorate a slide.
What it covers
—Access vs. active usage vs. repeat usage, by team
—Engagement depth: novelty patterns vs. habit patterns
—Cost visibility connected to usage, not reported in isolation
—Emerging use cases and practices worth scaling
—Barriers surfaced this period, and the enablement response to each
Enablement
AI Enablement Workshop Outline
A working-session format where participants leave having done a real task a new way.
What it covers
—Pre-work: each participant brings one real, recurring task
—15 minutes of concept — the minimum viable framing, no tool tour
—Guided work block: apply AI to the task brought, with live help
—Share-out: what worked, in each person's own words
—Exit commitment: the one task each person will repeat this week
—Follow-up path: where to ask questions after the session
Measurement
Executive Readout Template
A one-page structure for reporting AI adoption to leadership in decision-ready form.
What it covers
—The question this period's data answers
—Adoption picture: where behavior changed, where it stalled, and why
—What we learned that changes the plan
—Decisions needed, with a recommendation for each
—What we're scaling next, and what we're stopping
Enablement
AI Support Path Model
The support structure that catches people at the moment they struggle — the highest-leverage moment in adoption.
What it covers
—First-line: self-serve examples and SOPs, findable in under a minute
—Second-line: a visible channel where questions get fast, unembarrassing answers
—Third-line: office hours or a named person for workflow-level help
—Escalation: where policy, security, and data questions go
—Feedback loop: recurring questions become new enablement content
Rollout
AI Rollout Checklist
The sequence for taking an AI workflow change from plan to durable practice.
What it covers
—Readiness: workflow mapped, success criteria written, owner named
—Access, policy, and security questions answered before launch day
—Enablement in place: role-specific examples, SOP, support path
—Launch communication that explains the why, not just the what
—30/60/90-day checkpoints against the success criteria
—Post-launch ownership: who tends this after the novelty fades
Want a walkthrough of how any of these work in practice? Reach out — I'm glad to talk through the thinking behind them.
Working on enterprise AI adoption?
I'm glad to talk about deployment, enablement, pilots, and what it takes to make AI stick inside real teams.