Programme overview
Technical depth: Conceptual · Practical mode: Case study
Introduction
Organisations are investing heavily in AI platforms, yet many see little change in how work is actually done. Staff use the tools sporadically, distrust their output or fear what they mean for their jobs, while leaders continue to reward the old ways of working. The obstacle is organisational rather than technical. This Core Concept course equips executives to lead the people side of AI adoption with recognised change and technology acceptance models. Participants examine cases from several sectors and leave with an AI Adoption Change Strategy for an initiative in their own organisation.
Course Objectives
- Diagnose why AI initiatives stall after deployment and assess organisational readiness for AI-enabled work
- Apply change and technology acceptance models, including ADKAR, UTAUT and Diffusion of Innovations, to AI adoption decisions
- Direct role redesign, AI literacy and capability programmes based on task-level exposure analysis
- Set the leadership signals, sponsor coalition and employee narrative that build trust in AI
- Oversee adoption metrics, resistance and incentive alignment to sustain AI use after launch
- Produce an AI Adoption Change Strategy ready for executive committee endorsement
Target Audience
- Executives sponsoring enterprise AI programmes and accountable for their return
- Chief human resources and people leaders shaping the workforce response to AI
- Transformation and strategy leaders coordinating AI across business units
- Chief digital, data and technology officers responsible for AI platforms and their use
- Business unit heads whose operating models are being redesigned around AI
- Executive committee members who set AI priorities and approve related investment
Course Outline
Day 1: AI Adoption as an Organisational Change
- The Adoption Gap Between AI Deployment and Everyday Use
- AI Change Typology: Augmentation, Automation and Role Redesign
- ISO/TS 10020 Change Management Process Applied to AI Initiatives
- Organisational AI Adoption Readiness Assessment
- Leadership Signal Diagnosis: Incentives, Role Modelling and Resourcing
Day 2: Change and Technology Acceptance Models
- UTAUT: Performance Expectancy, Effort Expectancy, Social Influence and Facilitating Conditions
- Rogers' Diffusion of Innovations and Adopter Segments
- Prosci ADKAR Applied to Continuous AI Change
- Kotter's Accelerate Model: Dual Operating System for Iterative Change
- ACMP Standard for Change Management Process Groups
Day 3: Leading the Workforce Through AI
- Task-Level Exposure Analysis and Role Redesign Mapping
- AI Literacy Tiering: Awareness, Applied and Advanced Capability Pathways
- Executive Sponsor Coalition and AI Champion Network Design
- Employee Narrative on AI: Job Security, Purpose and Trust Messaging
- Workforce Consultation and Employee Voice Mechanisms
Day 4: Resistance, Trust and Sustained Adoption
- AI Resistance Patterns: Automation Anxiety, Algorithm Aversion and Over-Reliance
- Psychological Safety and Experimentation Norms Using Edmondson's Model
- ISO/IEC 42001 Competence, Awareness and Communication Requirements
- Adoption Metrics Dashboard: Active Use, Depth of Use and Value Realised
- Incentive and Performance Management Realignment for AI-Enabled Work
Day 5: Case Work and the AI Adoption Change Strategy
- Bank Operations Case Study: Reviving a Stalled AI Assistant Rollout
- Public Agency Case Study: Workforce Concerns over Automated Case Processing
- Adoption Readiness Scorecard for an Own AI Initiative
- AI Adoption Change Strategy Drafting
- Executive Challenge Panel and Strategy Defence
Skills You Will Gain
- AI Adoption Leadership
- Workforce Impact Analysis
- Technology Acceptance Diagnosis
- Change Sponsorship
- AI Literacy Strategy
- Trust and Culture Building
- Adoption Performance Measurement
Why Attend This Course
- Leave with an AI Adoption Change Strategy for a live initiative, tested before an executive challenge panel
- Address staff concerns about AI and jobs with a clear, credible narrative rather than reassurance alone
- Recognise whether low adoption stems from trust, skills, incentives or design, and direct the remedy accordingly
- Compare how executives in other sectors and countries are reshaping roles and culture around AI
Conclusion
AI creates value only when people trust it, know how to use it and see their roles reshaped with purpose rather than threat. This course moves from diagnosing the adoption gap, through change and technology acceptance models, to workforce redesign, AI literacy, sponsorship and the metrics that show whether use is sustained. The final day turns that material into an AI Adoption Change Strategy, giving executives a structured basis for leading their organisation through continuing waves of AI-enabled change.