What is this course about?
Enterprise AI pilots succeed and rollouts stall, and the reason is rarely technical. This course covers choosing the first workflow, building a champion network, role-based enablement instead of one generic training, honest adoption measurement, and how to handle resistance — including the resistance that turns out to be correct.
Who is this course for?
- Transformation and change management leads
- Heads of L&D and enablement
- Business unit leaders sponsoring AI adoption
- Internal communications
What do I need before starting?
- Responsibility for an AI adoption program
- No technical background required
What will I be able to do afterwards?
- Explain why pilots succeed and rollouts fail
- Choose a first workflow using frequency, stakes, and visibility
- Build a champion network that scales without central bottleneck
- Deliver role-based enablement instead of one generic training
- Measure adoption depth rather than licence activation
What does each module cover?
Why do pilots succeed and rollouts fail?
40 minThe selection effects that make a pilot unrepresentative of the organization.
Objectives
- Identify the selection effects in pilot populations
- Predict which pilot results will not generalize
- Design pilots that produce transferable evidence
Topics
Activity. Audit a past pilot in your organization for the selection effects that inflated its result.
Which workflow should go first?
45 minHigh frequency, low stakes, visible payoff — and why the most valuable workflow is the wrong start.
Objectives
- Score candidate workflows on frequency, stakes, and visibility
- Explain why highest-value-first usually fails
- Sequence workflows across the program
Topics
Activity. Score ten candidate workflows and defend your first choice.
How do you build a champion network?
45 minDistributed capability that removes the central team as a bottleneck.
Objectives
- Select champions on influence rather than enthusiasm
- Define the champion role and its time commitment
- Support champions so the role is sustainable
Topics
Activity. Design the champion role and identify candidates in three business units.
Why does generic training fail?
50 minRole-based enablement built from what each role actually does all day.
Objectives
- Build enablement from real role workflows
- Differentiate content by role rather than by seniority
- Keep enablement within realistic time budgets
Topics
Activity. Build enablement content for one role derived from a real day's work.
How do you build a prompt and workflow library people use?
45 minA shared library structured around tasks, with contribution and curation that survives.
Objectives
- Structure the library around real tasks
- Design contribution and curation workflows
- Measure actual reuse
Topics
Activity. Build the library structure and seed it with contributions from three roles.
How do you measure adoption honestly?
45 minDepth of use rather than licence activation, and metrics capable of showing failure.
Objectives
- Define adoption depth metrics
- Distinguish activation from meaningful use
- Design measurement that can report failure
Topics
Activity. Design the measurement plan and verify it could detect a stalled rollout.
What do you do about resistance?
45 minDistinguishing resistance that is a change problem from resistance that is correct.
Objectives
- Categorize resistance by underlying cause
- Identify resistance that indicates a real product problem
- Respond without dismissing legitimate objections
Topics
Activity. Take five real objections and classify each as change resistance or a valid signal.
Building the 90-day rollout plan
50 minThe workshop module: a sequenced plan with instrumentation from day one.
Objectives
- Build a sequenced 90-day plan
- Instrument adoption before launch
- Define stop and pivot conditions
Topics
Activity. Build the plan and define the conditions under which you would stop.
What is the capstone project?
90-day AI rollout plan with adoption instrumentation
Produce a complete rollout plan: workflow selection with scoring, champion network design, role-based enablement for three roles, library structure, depth-based measurement instrumented before launch, and explicit stop conditions.
Deliverable: A rollout plan with pre-launch instrumentation and written stop conditions.
How are learners assessed?
- Measurement plan tested for whether it could detect failure
- Workflow selection defended against the frequency/stakes/visibility criteria
- Objection classification reviewed with a genuinely skeptical colleague
What ships with the course?
Facilitator guide
Session-by-session running order, discussion prompts, and the questions that reliably derail a room.
Learner workbook
Exercises, checklists, and the templates each module's activity produces.
Hands-on lab environment
A sandboxed ibl.ai deployment so exercises run against real agents, not screenshots.
Assessment bank
Scenario questions and rubric criteria mapped to each stated learning outcome.
Source bibliography
Every primary regulation and standard cited on this page, linked and dated.
Which AI agents does this course use?
The hands-on modules run against agents already deployable on the ibl.ai platform for enterprise.
Where does the course material come from?
Every module is grounded in primary sources — the regulation, standard, or research itself, not a summary of it. Each was resolved at authoring time.
- Ideas Made to Matter
MIT Sloan
Research on enterprise AI adoption patterns and failure rates.
- AI Index Report
Stanford HAI
Adoption benchmark data across industries.
- Artificial Intelligence research
Brookings Institution
Workforce impact research underpinning the resistance module.
- AI Risk Management Framework
NIST
Governance touchpoints the rollout must satisfy.
Delivery notes
Binding guidance for anyone preparing and delivering this course.
- Module 7 must treat job security concerns as legitimate rather than as resistance to be managed. Programs that dismiss them lose credibility permanently, and participants can tell which framing the course chose.
- Module 6's failure-capable measurement is the discipline that matters. Most adoption dashboards are structurally incapable of reporting a stalled rollout, and the exercise should surface that.
- Avoid transformation-consulting vocabulary. The audience has sat through it and it signals that the course has no specific content.
- Use a real stalled rollout as a case study. Success stories teach less than a specific failure with named causes.
- Coordinate with ENT-3 — adoption depth is the input to the utilization calculation there, and the two should use the same definition.
Why run AI training on a platform you own?
You own the course, not a licence to it
Course content, learner data, and the platform run inside your perimeter — you own all the code and the data.
Model-agnostic delivery
Run the course's AI components on any LLM — Claude, GPT, Llama, Gemini, Command — and switch anytime.
No per-seat training licences
Usage-based or self-hosted, so cost tracks actual use rather than headcount.
Deploy anywhere
Cloud, private VPC, on-premise, or fully air-gapped — including for cohorts that cannot use public AI tools.
Frequently asked questions
What does the Rolling Out AI to 5,000 Employees course cover?
Enterprise AI pilots succeed and rollouts stall, and the reason is rarely technical. This course covers choosing the first workflow, building a champion network, role-based enablement instead of one generic training, honest adoption measurement, and how to handle resistance — including the resistance that turns out to be correct. It runs 5.5 hours across 8 modules across 8 modules, at foundational level, and closes with a capstone: 90-day AI rollout plan with adoption instrumentation.
Who should take Rolling Out AI to 5,000 Employees?
It is written for Transformation and change management leads, Heads of L&D and enablement, Business unit leaders sponsoring AI adoption, Internal communications. Prerequisites: Responsibility for an AI adoption program; No technical background required.
Can we run this course on our own infrastructure?
Yes. ibl.ai is model-agnostic and deploy-anywhere — cloud, private VPC, on-premise, or fully air-gapped — and you own all the code and the data. Cohort data, submissions, and any material learners upload stay inside your perimeter, which matters for enterprise teams that cannot send work to a public AI tool.
How do we get access to Rolling Out AI to 5,000 Employees?
Request access and we will set it up for your cohort — hosted by ibl.ai, or running against your own deployment. Tell us the group size and timing you need, and whether it should run inside your own perimeter.
How much does AI training for enterprise cost on ibl.ai?
There is no per-seat pricing — you pay for usage or self-host and pay only for the infrastructure, so a 5,000-person rollout does not cost 5,000 licences. 1.6M+ users across 400+ organizations run the platform this way, including NVIDIA, MIT, and Syracuse University.