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How this program works
The program is four live sessions over eight weeks. Each session is live instruction with a chance to share what you're learning with your peers. New concepts are introduced in each one; after each session, you work on your capstone. By the end you have a fully functioning AI project that you and your team can use.
The capstone is the frame the course is built around. Every assignment in the program is a stage of that one build — there is nothing else to submit. Between each session is a field period where the building happens; plan for roughly three to five hours a week. A scored AI Calibration, taken before and after, measures how far you've come.
Your Goal
The Capstone
What you'll deliver
A working AI prototype: one working solution to support a real workflow on your team, with the value measured and the risks handled.
You build it in stages. The first three define and approve the work (your Capstone Charter). Stage four is the working version. Then you prove it works (evaluation), handle its risks, and sign off data governance. The last stage is your deployment plan — how you roll it out to your team, including communication and training.
1
Problem Definition
The one workflow you're changing — who it serves and why now.
Charter
2
Value Case
A baseline number, a target number, and a date to measure.
Charter
3
Model or Tool
The approved enterprise AI tool you'll build with.
Charter
4
Inputs → Prompts → Expected Outputs
The working first version of the solution.
Build
5
Evaluation
Does it produce the right output? Tested against a golden source before go-live and measured against your baseline.
Finish
6–7
Risks & Governance
Risks named and handled; data governance signed off.
Finish
8
Deployment Plan
How you roll it out to your team — communication and training.
Deploy
How your capstone is judged
The Capstone Rubric
The seven criteria your capstone is reviewed against. There is no separate exam — the capstone, against this rubric, is the measure of success. Read it now and build toward it.
Open PDF →
Before Session 1
Before you begin
Complete these on the platform before your cohort starts — about three hours in total. They frame your role and set your baseline before any skill-building begins.
1. Pre-Assessment
One guided pre-work assessment, done in a single sitting. It captures your role and context and scores your learning baseline — the opening input to Session 1.
Role & Context
Function, team size, current team AI use, and what you're walking in concerned about.
AI Calibration
A scored learning baseline, measured again at the end of the program to evidence your growth.
Available when your cohort opens. Allow about 30 minutes.
Watch before Session 1
Assigned a minimum of two weeks before your cohort starts. These are the foundation the program builds on — the only required viewing. It's important that you engage with them before the course so you have the prerequisite knowledge to get the most value out of it. About two hours in total, and it's recommended you do them in small chunks. It's also highly encouraged to engage with the exercises — and note there is an AI coach to assist you along the way if you have questions.
M0
The Role of the Manager in AI Transformation
The four responsibilities the whole program is built around.
~9 min
R10
Working in the Age of AI
The doing-to-directing shift, function by function, with a self-audit framework.
~15 min
7
The Fundamentals Podcast Spine
The seven Fundamentals episodes — experienced as a preview of what your team will receive.
7 episodes
3 Pre-Work Reflection — watch with your team in mind ▼
As you go through the seven Fundamentals episodes, watch with your own team in mind: where is this likely to land easily, and where will people have questions? Nothing to submit — this is your own preparation — but it's worth doing. The clearer your read going in, the more you'll get from Session 1.
During the program, Jeff may point you to additional modules and exercises as they're useful to your capstone. Beyond the foundations above, none are required viewing.
Deliverables
Everything you'll submit
The full list, in order — and every item is a stage of your capstone, bracketed by the before/after calibration. The team debriefs aren't here on purpose: they're practice alongside the build, with nothing to submit. Each item is explained in the session where it's due.
Before Session 1
Complete the Pre-Assessment & AI Calibration baseline, and the pre-work video modules watched.
Field Period A
Capstone Charter — Stages 1–3. Approved by your line manager and reviewed by Jeff before you build.
Field Period B
Working first version (Stage 4) with a “what's broken” note, and your team work analysis — the wide angle on the capstone.
Field Period C
Finished, risk-checked build (Stages 5–7), your Capstone Dossier, and your Deployment Plan — communication and training.
Session 4
Capstone presented against the rubric, three 90-day commitments, and your post-program AI Calibration. Credential awarded.
The Program
The eight weeks, session by session
Each session is one hour with Jeff, about two weeks apart. After each comes a field period where you build. Every assignment below is a stage of your capstone — the gold edge marks the spine. Where the fourth responsibility comes in, a Leading your team panel sits alongside: that's practice, with nothing to submit. Open any item for the detail.
1
Session 1
Leading the Shift
You leave knowing you're not completing a course — you're leading your team through a change — with your capstone framed and the next eight weeks clear.
In the session
- Why workforce training changes little if managers don't change how the team works day to day.
- The doing-to-directing shift, made personal — applied to your own work first.
- The four responsibilities as the spine: define, baseline, build, bring people through.
- The capstone introduced and framed now — so it has eight weeks to develop, not two.
- The seven-stage process introduced as the method you'll use to build.
Field Period A · Frame your capstone
Define what you'll build and get it approved before building starts. Stages 1–3 of the capstone.
Your capstone this field period. Everything here is a stage of the one build. Assemble Stages 1–3 into your Charter and get it approved.
A-1Stage 1 — Problem Definition▼
Pick one concrete workflow — not a category. Using the capstone worksheet, submit one sentence: the workflow, who it serves, and why it's being changed now.
A-2Stage 2 — Value Case▼
Submit a baseline number, a target number, and a date to measure. Estimated is acceptable; absent is not.
A-3Stage 3 — Model or Tool▼
Confirm and document the enterprise AI tool: named tool, approval status, and a one-line fit statement.
A-4Assemble the Capstone Charter▼
Stages 1–3 together form your Capstone Charter. It goes to two reviewers in parallel: your line manager approves against three questions — is this real work that matters, is it small enough to finish, is AI the right tool — and Jeff gives direct written design feedback before building begins. The build clock starts only after both clear.
Submit by Session 2: your Capstone Charter (Stages 1–3), approved by your line manager.
2
Session 2
Facilitating the Debrief
You can confidently lead an honest team conversation about AI — drawing out real reactions and handling the difficult ones — rather than forwarding links.
In the session
- What a good debrief does: surfaces honest first impressions before a false consensus sets in.
- The hard situations, rehearsed live: the skeptic, the over-enthusiast, the “my job has no use cases” person, the silent team.
- Training people for transformation — the human side of change, not training on a tool.
- Reading the room: what team sentiment looks like and how to move it.
Field Period B · Build the first version
Build a rough, working first version — ugly and working beats polished and imaginary. Stage 4.
Your capstone this field period. Build the working first version, and map the wider team work it sits inside.
B-1Stage 4 — Inputs → Prompts → Expected Outputs▼
Work the three sub-parts of Stage 4: name inputs and their owners; write the prompt using the 6-step framework; define outputs and the human handoff. Submit the working v1 plus a three-line “what's broken” note.
B-2Team work analysis — the wide angle on your capstone▼
Still capstone work, one level up. Apply the job-analysis framework to your whole team's work, so you can see where this build sits and what comes after it — the portfolio your capstone is the first piece of. Broader than the one workflow, but in service of it. Submit the completed template before Session 3.
Submit by Session 3: your working first version (Stage 4) with a “what's broken” note, and your team work analysis.
3
Session 3
Job Analysis & Use Case Judgment
You can judge where AI belongs in your team's work and where human judgment must stay — and can pressure-test your own capstone with that judgment.
In the session
- The job-analysis framework: separating work AI can take on from work that requires human judgment and accountability.
- Use-case evaluation — which tasks are genuine AI candidates.
- Reviewing your own team job-analysis (submitted as B-2) live.
- Setting a baseline: a rough, defensible estimate is enough; an exact number is not required.
Field Period C · Finish the build, mitigate risks, plan deployment
Get the build to done, work through risks and mitigation, then plan how it reaches your team. Stages 5–7, the Capstone Dossier, and your Deployment Plan.
Your capstone this field period. Finish and test the build, name and handle its risks, bind everything into your Dossier, and plan the rollout to your team.
C-1Stage 5 — Evaluation▼
Prove the build works before go-live: build a small “golden source” of known test cases; run the build on at least three real instances; log failures; record early impact against the A-2 baseline. Identify where the golden source isn't enough (edge cases, unusual or adversarial inputs) and say what further testing they need.
C-2Stage 6 — Risks & Mitigants▼
Name the top three to five failure modes specific to this workflow, each with a mitigant and a named owner.
C-3Stage 7 — Data Governance▼
Write the one-paragraph data-handling statement for the workflow and obtain data-owner sign-off.
C-4Assemble the Capstone Dossier▼
Bind the completed stage-artifacts together into one package — your finished capstone, documented end to end. The Dossier must also include your observability process: the three changes that trigger re-evaluation (data, model, prompt) and the owner responsible. It's a required part of the Dossier, evidenced at the 60-day review rather than scored at the presentation. Brought to Session 4.
C-5Stage 8 — Deployment Plan▼
Plan how the capstone reaches your team — this is where you bring your people through the change. Cover two things: communication (who needs to use this, what you'll tell them, and how you'll set expectations) and training (how you'll get the team using it well, including the team debriefs and hands-on support). The plan is the deliverable; you carry it out over the 90 days that follow.
Submit by Session 4: your finished, risk-checked build (Stages 5–7), your assembled Capstone Dossier, and your Deployment Plan.
4
Session 4
Capstone Review & 90-Day Commitments
You present a working AI capstone for your team, leave with a clear deployment path, and complete the program credential.
In the session
- Structured capstone review against the Capstone Rubric — not a pitch contest.
- What “done” means: a working prototype, fully documented, with a path to production.
- The 90-day commitment: deployment is your owned next step; data-governance sign-off and production rollout live here.
- Sustaining the change after the program ends.
Closing Work
Present the finished capstone and commit to deploying it. The capstone, against the rubric, is the credential.
Your capstone, finished. Present it and commit to putting it to work — these two are the last of the build.
D-1Present the capstone against the rubric▼
Present your working capstone to the cohort, reviewed against the published Capstone Rubric.
D-2Three 90-day commitments▼
Submit three 90-day commitments for deploying the capstone on your team. Together with the capstone, these are the program credential.
Measurement · the closing bookend
AI Calibration (post)
Not an assignment — the second half of a measurement. Retake the AI Calibration to close the pre/post comparison against the baseline you set before Session 1, evidencing how far you've come.
After the Program
Credential & Beyond
Learning Measured
Your post-program AI Calibration is benchmarked against your pre-program baseline — evidence of the learning gain.
Credential Awarded
McMillanAI Certified AI Manager — awarded against the Capstone Rubric: a working capstone, the dossier, and your 90-day commitments.
60-Day Review
A sponsor-facing session at about day 60: the cohort's capstones reviewed against their 90-day commitments — what deployed, what stalled, what is next.