More on AI Maturity
Mapping Readiness to AI Maturity Stages
|
Dominant Pattern |
Likely Stage |
Change Priority |
|
1. Low trust, high fear |
Awareness |
Narrative & safety |
|
2. High pilots, low coherence |
Experimentation |
Guardrails & learning |
|
3. Workflow integration |
Operationalisation |
Role redesign |
|
4. Strong trust & ethics |
Integration |
Decision clarity |
|
5. Adaptive culture |
Transformation |
Continuous renewal |
Common Change Failures Mapped to the AI Maturity Curve
|
Failure |
Root Cause |
|
1. AI resistance |
Jumped to Stage 3 without Stage 1–2 sense-making |
|
2. Low adoption |
Tools deployed without role redesign |
|
3. Ethical backlash |
Governance lagged maturity |
|
4. Burnout |
Continuous pilots without integration |
|
5. Talent loss |
Fear unmanaged, skills unsupported |
How Change Management Must Evolve Across the Curve
|
Early Stages |
Later Stages |
|
1. Communication |
Co-creation |
|
2. Training |
Capability ecosystems |
|
3. Change plans |
Continuous adaptation |
|
4. Resistance management |
Identity and purpose work |
|
5. Adoption metrics |
Value and learning metrics |
Key Insight for Change Leaders
AI maturity is really “human maturity with advanced tools.”
Organisations don’t fail at AI because of technology.
They fail because they:
- Ignore emotions
- Underestimate identity loss
- Over-engineer tools and under-invest in people
Practical Use in Your Change Work
You can apply the AI Maturity Curve to:
- AI readiness assessments
- Leadership workshops
- Workforce transition planning
- Ethics and governance design
- Culture change assessments
(main source: Accenture, 2022)