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AI Adoption, Change Management: From Pilot to Scale
2,908 students
AI Adoption, Change Management: From Pilot to Scale
AI adoption | change management | AI transformation | automation | prompt engineering | upskilling | Kotter | ROI
Last updated 9/2026
English
English [Auto],
What you'll learn
- Choose which processes to automate first, using an assessment of readiness rather than enthusiasm
- Describe a process in BPMN before automating it, so the tool does not accelerate the confusion
- Structure a prompt from seven blocks and apply chain-of-thought and tree-of-thoughts where they earn their time
- Build context with Projects, RAG and connectors so the assistant answers from your documents rather than the internet
- Design an agentic workflow with MCP and no-code tooling, and decide where a human must still approve
- Train people on a new tool so the knowledge survives the week after the session
- Evaluate adoption honestly: what to measure, what cannot be measured, and how to report it
- Run the rollout through Kotter's eight steps, including the resistance that is really about status
- Learn alongside Mike's 1.6 million students from 185 countries
- Get the author's experience from Preply, Wargaming, iDeals and Alfa-Bank
Explore related topics
Course content
16 sections • 42 lectures • 20h 27m total length
Requirements
- A function or team where AI adoption is being discussed, piloted or postponed
- No coding required. Everything here is prompt, process and no-code automation
- Willingness to describe how work is done today before changing how it is done tomorrow
- English at intermediate level or above, since all lessons are in English
- Access to at least one AI assistant to practise on
Description
This course contains the use of artificial intelligence.
Most AI pilots succeed. That is the problem. A pilot proves the technology works with three enthusiastic volunteers, and proves nothing about whether four hundred people will change how they work.
Why adoption stalls after the pilot
The tool was rolled out to a process nobody had described, so it now accelerates whatever was already confused. Training was a one-hour session and a recording, after which everyone returned to the way they knew. The people who benefit most are the ones with least time to learn. And the resistance is read as technophobia when it is usually about status — a person who was valuable because they knew how to do something quickly, in a world where it is now done instantly.
Then someone asks for the return on the investment, and nobody set a baseline before starting.
How the course is built
Forty lessons in five parts. Where AI actually works first, twelve lessons across a whole function: selection, search, onboarding, evaluation, brand, training, off-the-shelf platforms, rewards, culture, predictive analytics without code, and document and process generation. This is the map of what is realistic today rather than what is demonstrated in keynotes.
Then four lessons on getting usable output: the seven blocks of a prompt, chain-of-thought and tree-of-thoughts, the shift to context engineering with Projects and RAG, and agentic workflows with MCP — including where a human still has to approve.
The process, the people and the change
Then eight lessons on processes, which is the step organisations skip and pay for. Waste, standardisation, maturity, value stream mapping, and the toolkit from 5 Whys to SIPOC. Automating a described process compounds; automating an undescribed one accelerates confusion.
Then ten lessons on teaching people, taken seriously. Adult learning, ADDIE, diagnosing where the gap actually sits, designing the programme, the transfer problem — sixteen factors that decide whether anything is used the following week — post-session support, and honest evaluation.
And finally change management: Kotter's eight steps, the rider-and-elephant model for the emotional half, quick wins, and fixing the change into culture so it survives the person who championed it.
A note on the examples
The twelve adoption lessons run on a single function end to end — people operations — rather than sampling one example from each industry. That is deliberate. Watching AI applied to one function completely, from first pilot to full coverage, teaches the sequencing better than a tour of scattered use cases, and the sequencing is what this course is about.
Who is teaching this
Mike, the number one HR instructor on Udemy. More than 1.6 million course enrolments, over 150,000 professionals trained, PHRi and SHRM-CP certified, HRCI representative in more than 10 countries. I built the people function of the unicorn Preply and worked at Wargaming, Alfa-Bank and iDeals.
Before the rollout
Pick the process you most want to automate and write down how it works today, step by step, including who does what. If that document is hard to produce, automation will not help yet, and knowing this before spending the budget is most of the value here. Enrol now and start the first lesson today.
Who this course is for:
- Managers asked what AI could take over in their area, with no framework for answering
- Transformation and operations leads running an AI programme rather than a pilot
- IT and digital leaders whose tooling was adopted by three people and ignored by everyone else
- L&D and enablement specialists responsible for teaching a workforce new tools
- Process owners deciding what to automate and what to leave alone
- Founders and functional heads with a budget and a lot of conflicting advice
- Anyone whose organisation ran an AI pilot that succeeded and went nowhere