AI for Change Management

Name: AI for Change Management
Rating: 4.9 (9 reviews)

Change management after generative AI & automation: ChatGPT, Claude, Copilot, AI agents, adoption & governance

Role Play

New

Created byGrant Gamble

Last updated 8/2026

English

English [Auto],

What you'll learn

Lead AI-enabled change programmes using an AI-native approach that adapts Kotter, Prosci and traditional frameworks to fast, continuous AI adoption cycles.
Explain generative AI, predictive AI, agents and integrations in plain language so you can challenge decisions and lead AI change with practical fluency.
Write structured, reusable prompts that produce usable change artefacts: leadership briefings, stakeholder emails, FAQs, plans, risk logs and training content.
Apply agent mode and automated AI workflows to change tasks safely, with the right human checkpoints, oversight and escalation built in.
Use ChatGPT, Claude and Microsoft 365 Copilot deliberately, choosing the right tool for each change task instead of defaulting to a single platform.
Apply AI across the full change lifecycle: discovery and readiness, change strategy and design, communications, training, adoption and measurement.
Design human-centred adoption that tackles fear, fatigue and deskilling, and builds trust through transparency, explainability and clear accountability.
Build governance for responsible AI change, managing risk in agentic workflows and standing up a practical AI governance operating model.
Create an AI adoption roadmap and capability plan that upskills your change team and future-proofs your role as a change professional.

Course content

6 sections • 31 lectures • 2h 49m total length

0.1-Welcome to AI for Change Management1:32
0.2 Downloading the Courseware PDF0:56
0.3 Downloading the Files for the Practical Exercises0:55
Each section ends with a practical exercise. Here, you an download the work files necessary to complete each exercise.
1.1 The New Context for Change Leaders5:41
How AI is compressing change cycles and breaking the assumptions behind Kotter, Prosci and their peers. Covers the shift from pilots to embedded AI, the difference between assistance, automation and agency, and how the change leader's role expands into workflow design and orchestration.
1.2 Core AI Concepts That Matter in Practice10:15
A practical, non-technical mental model of AI in four parts: generative, predictive and analytical, agents and tool use, and API integration. Ends with how the four layers combine, and the principle to hold on to — every AI output is a draft or a signal, never a final answer.
1.3 Today’s AI Product Landscape for Change Work4:46
What ChatGPT, Claude and Microsoft 365 Copilot are each genuinely good at, and why. Covers agent mode and custom GPTs, Claude's long-document reasoning, Copilot inside Word, Excel and PowerPoint, and how to choose a deliberate multi-tool mix rather than defaulting to one.
1.4 Human-Centred Change in an AI Environment10:12
The human side of AI adoption: why resistance differs by role, how to map fear and fatigue, and what works at scale. Covers trust and explainability, which decisions must never be delegated, avoiding deskilling, and treating adoption as a behavioural system of cues, habits and incentives.
Practical Exercise2:00
Segmenting Resistance Instead of Launching One Campaign

2.1 Prompting for Change Management Work4:40
The five-part prompt structure — context, task, audience, format, constraints — applied to the artefacts change managers actually produce, from leadership briefings to FAQs and plans. Includes critique loops for improving output in passes, and how to build a prompt library that stays useful.
2.2 Working with Agents and Agent Mode7:05
What changes when you move from chat to agent mode, and why your job shifts from writing prompts to supervising work. Covers designing safe agent tasks with clear goals and permissions, supervising agents effectively, and a practical rule for choosing between chat, Copilot and an agent.
2.3 Working with Documents, Data, and Presentations12:33
AI across the three workflows that consume most of a change team's time: change narratives and policy revision in Word, readiness analysis, sentiment trends and benefits tracking in Excel, and executive and training decks in PowerPoint — then how to orchestrate all three into one deliverable.
2.4 Using APIs and Automation in Change Programmes5:43
When an API-based workflow is worth building and when it is not. Covers simple, repeatable automation patterns, connecting AI to enterprise tools through connectors and platforms like Power Automate, and what it takes to move a working prototype into something production-ready.
Practical Exercise2:20
Scoping an Agent Task That Was Handed a Vague Goal

3.1 Discovery and Readiness Assessment5:10
AI-assisted discovery: mapping stakeholder groups, interests and influence patterns, reading surveys, comments and meeting outputs at scale, scoring readiness so friction surfaces before rollout rather than after, and baselining current skills and AI maturity.
3.2 Strategy and Change Design9:25
Designing the change itself with AI support — framing objectives, mapping impacts across the organisation, defining success measures that measure change rather than activity, scenario and option testing, audience segmentation, and redesigning the future workflow rather than just supporting it.
3.3 Communications, Engagement, and Training9:44
Role-specific messaging frameworks and practical prompting for change communications, manager talk tracks and toolkits that stay current, AI-generated learning content with the quality control it requires, and moving from static FAQs to adaptive conversational support.
3.4 Adoption, Reinforcement, and Measurement4:14
Measuring behaviour change rather than attendance: adoption dashboards paired with narrative reporting, continuous improvement loops that act on what the data shows, and connecting adoption through to value realisation and benefits tracking.
Practical Exercise2:14
Quality Control on Forty AI-Generated Learning Modules

4.1 Enterprise Change in Microsoft 365 Environments5:20
Copilot where people already work — Word for policy, comms and leadership messaging, Excel for analysis and reporting, PowerPoint for mobilisation and storytelling, and Copilot agents that business users can build inside familiar tools without a developer.
4.2 Cross-Platform Knowledge Work with ChatGPT and Claude3:45
ChatGPT for rapid synthesis and action-oriented workflows, Claude for long documents and structured reasoning, how to compare outputs across tools on the same task, and how to build a multi-tool operating model your team can actually follow.
4.3 Sector-Specific Change Scenarios8:49
How AI-enabled change differs by context, with the patterns that recur in each: financial services and regulated transformation, healthcare and public services, manufacturing and operations, and professional services and knowledge firms.
4.4 Role-Based Adoption Pathways6:03
Four audiences, four different pathways — executives and sponsors, change managers and PMO teams, people managers and team leaders, and end users and specialists. What each group genuinely needs, and why a single pathway for everyone reliably fails.
Practical Exercises2:28
Adapting a Corporate Adoption Pathway to a Clinical Setting

5.1 Responsible AI for Change Leaders4:42
Bias, fairness and representational risk, transparency and explainability in practice, privacy, confidentiality and data handling, and where human accountability and escalation sit once AI is inside the workflow.
5.2 Risk Management for Agentic Workflows7:14
The risks specific to agentic workflows: prompt injection and workflow manipulation, permissions, boundaries and approval gates, hallucination, overconfidence and hidden failure modes, and auditability — then how to build a risk-aware workflow from the start.
5.3 Governance Operating Models for AI Change6:10
Policy frameworks for enterprise AI use, tool selection and approval processes, measuring risk, value and compliance together, and standing up an AI change governance forum that speeds decisions up rather than becoming something teams route around.
Practical Exercise2:30
Auditing an Agentic Workflow That Has Never Visibly Failed

6.1 Capability Building for AI-Enabled Change Teams6:07
The four capabilities an AI-enabled change team needs — prompting and review judgement, agent supervision and workflow design, data literacy and interpretation, and a real experimentation culture — and how they combine into team capability rather than individual skill.
6.2 Designing an AI Adoption Roadmap5:38
Sequencing adoption properly: quick wins, lighthouse use cases and scaling paths, the capability maturity stages, which interventions fit which stage, and how to embed AI into business-as-usual instead of running it indefinitely as a programme.
6.3 The Future Role of the Change Professional8:53
How the role itself is changing — from communicator to workflow architect, from training owner to capability strategist, from adoption tracker to value partner — and what it takes to lead in an organisation where change never really stops.
Practical Exercise2:26
From Communicator to Workflow Architect

Requirements

No AI or technical background needed — every concept is explained in plain language for change, transformation and HR professionals.
Some experience of organisational change, projects or transformation is helpful, but not essential if you are moving into a change role.
Access to at least one AI tool — the free tier of ChatGPT or Claude is enough to complete every exercise in the course.
Microsoft 365 Copilot access is useful for the Microsoft-focused lessons, but all exercises have alternatives if you do not have it.
No coding, data science or prompt engineering experience required — you will build these skills from scratch as you work through the course.
A current or recent change initiative to use as a working example will help you apply the templates and exercises to real work.

Description

This course contains the use of artificial intelligence. AI tools are used in the production of this course, including AI-assisted speech delivery based on the instructor's own voice. All content, demonstrations, workbooks, and prompts have been personally designed, reviewed, and verified by the instructor to ensure accuracy and practical value.

Change management is being rewritten. Generative AI and automation have compressed change cycles from quarters into weeks, put AI-generated analysis into everyday decisions, and pushed automation into knowledge work itself. The frameworks most of us trained on — Kotter, Prosci and their peers — were designed for slower, more linear change, and several of their assumptions no longer hold. This course is a practical, hands-on guide to leading change in an AI-native organisation. You will not just learn about AI — you will use it, on real change work, in every section.

No technical background is needed, and no coding is involved. You will build a working mental model of the four AI layers that matter — generative AI, predictive and analytical AI, agents and tool use, and enterprise integration — then apply them across the tools your organisation actually uses: ChatGPT, Claude and Microsoft 365 Copilot in Word, Excel, Teams and PowerPoint. You will master a five-part prompt structure that turns AI into a reliable drafting partner for leadership briefings, stakeholder emails, FAQs, change plans, risk logs and training content, and build a reusable prompt library for your team. You will work with agent mode and automated workflows, learning where to place human checkpoints and how to supervise delegated work. Each of these sections closes with a practical exercise and a role play, so you leave with both a finished artefact and the rehearsed experience of the conversation that goes with it.

From there, the course walks the full change lifecycle: AI-supported discovery and readiness assessment, change strategy and design, communications, engagement and training, then adoption, reinforcement and measurement. You will look at functional and sector use cases, role-based adoption pathways, and the human side that decides whether change sticks — fear, fatigue, deskilling, trust, transparency and clear human accountability. Again, every section ends with a hands-on exercise and a role play — briefing a sceptical stakeholder, handling a difficult adoption conversation, defending a plan under pushback — so you are practising the human side of change, not just reading about it.

Finally, you will build the governance to do this responsibly: risk management for agentic workflows, a workable AI governance operating model, and an AI adoption roadmap and capability plan for your own team. As with every section, you finish with a practical exercise you can run on a live initiative and a role play you can rehearse before you take it into the room. By the end you will have a repeatable AI-enabled change method, a prompt and template library, and the confidence to lead AI adoption — rather than be led by it. Enrol now and start applying it to your next change programme this week.

Who this course is for:

Change managers and change leads who want to apply AI across the change lifecycle rather than just read about it.
Transformation and programme managers leading AI, digital or operating model change in large organisations.
HR, People and OD professionals responsible for adoption, capability building and workforce readiness in an AI-enabled workplace.
Project managers, business analysts and PMO leads who are being asked to add AI capability to their change and delivery work.
Consultants and internal advisors who want practical AI methods, prompts and templates they can use with clients from day one.
Senior leaders and sponsors who need to understand AI adoption, governance and risk well enough to make confident decisions.
Prosci, ACMP or APMG-certified practitioners updating traditional frameworks for fast-moving, AI-native change.
Anyone moving into a change role who wants to build AI fluency early and stay relevant as the profession evolves.

AI for Change Management

What you'll learn

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Course content

Section 1: AI-Native Change Management Foundations9 lectures • 36min

Section 2: AI Tools and Techniques for Change Leaders6 lectures • 32min

Section 3: Applying AI Across the Change Lifecycle6 lectures • 31min

Section 4: Functional and Sector Use Cases6 lectures • 26min

Section 5: Governance, Risk, and Responsible Adoption5 lectures • 21min

Section 6: Building Organisational Capability for the Future5 lectures • 23min

Requirements

Description

Who this course is for: