Adaptive Adoption™ — Executive Briefing

Synopsis

Seventy-two percent of organisations score high on AI readiness — the policies, the governance documents, the executive commitments are all in place. Six percent score high on operational maturity, the enacted behaviours and demonstrated capability. That gap is not a communication problem or a training problem. It is a structural failure of the methods being used: the 72 percent did exactly what the change management playbook told them to do, and the 6 percent is what it produced. Written by someone who spent three decades in change management at PwC, IBM and Deloitte before concluding the profession he helped build was not equipped for what was coming, this briefing argues three shifts moved the centre of gravity beyond orthodox change management's reach — the behavioural science revolution that no major change framework ever incorporated, the arrival of continuous emergent change with no go-live date, and AI itself. It then sets out Adaptive Adoption™ as a three-part framework built to treat AI adoption as a durable organisational capability rather than a bounded project: Change Agility™ as the operational method, the AI Leadership Delta™ as the leadership layer, and Behavioural Governance™ as the live control system measuring what people actually do.

Key findings

• Seventy-two percent of organisations score high on AI readiness while six percent score high on operational maturity — the gap between written policy and enacted behaviour is the whole problem.
• Only 10 percent of management training transfers back to the job, and foundational concepts many organisations still rely on — change curves, learning styles, ADKAR, MBTI — have been dismissed by academic psychologists for decades.
• Not a single major change management framework incorporated the findings of behavioural science; a discipline dedicated to changing human behaviour ignored the most significant advances in understanding it.
• Implementation science finds only 25–50 percent of adopted programmes achieve sufficient fidelity — the espoused/enacted gap that governance frameworks measuring structures and policies cannot see.
• Orthodox change management assumes a bounded project with a beginning, middle and end. SAP had a go-live date; AI does not — and when change becomes the permanent condition, the project model collapses.