Competency Framework for AI Change Leader
- This framework is structured around five dimensions, each with key competencies and behavioural indicators
- It draws from an anthro-complexity approach, Cynefin-informed leadership, and ethical AI practice
1. Complexity-Informed Judgment
| Competency | Description | Behavioural Indicators |
|---|---|---|
| Contextual Sense-Making | Uses frameworks like Cynefin and Estuarine Mapping to assess situational complexity | Distinguishes between ordered and complex domains; applies WRAS strategy accordingly |
| Abductive Reasoning | Generates plausible hypotheses from incomplete data | Interprets weak signals; avoids premature convergence |
| Cynefin and Estuarine Mapping Boundary Awareness | Identifies natural, governance, and token boundaries (counterfactual, vulnerable, liminal) in AI adoption | Creates E/T grid to map constraints and affordances; sequences interventions |
2. Ethical & Strategic AI Stewardship
| Competency | Description | Behavioural Indicators |
|---|---|---|
| Explainability Advocacy | Promotes transparent, interpretable AI systems | Communicates full-stack explainable AI; engages stakeholders in ethical review |
| Risk Framing & Mitigation | Anticipates unintended consequences of AI deployment | Uses Estuarine Mapping to surface liminal risks; create precautionary principles that emerge from high-level convo |
| Value Alignment | Ensures AI systems reflect organizational and societal values | Facilitates cross-functional dialogue on fairness, bias, and trustworthiness |
3. Human-Machine Collaboration Design
| Competency | Description | Behavioural Indicators |
|---|---|---|
| AI Tool Selection & Hybrid Reasoning | Finds appropriate balance of human and machine intelligence | Facilitates discussion on AI task & purpose with human replacement/augmentation; Validates each specific installation with AI principles |
| Learning Cultivation | Adjusts substrate for intuitive, apprentice-style learning | Encourages safe-to-fail experiments to learn & reflect; enables team and individual skill development |
| Cognitive Resilience building | Supports mental well-being and adaptive capacity | Applies neuroscience-informed practices; fosters psychological safety in change contexts |
4. Participatory Facilitation & Engagement
| Competency | Description | BehavioUral Indicators |
|---|---|---|
| Distributed Sense-Making | Articulates the strategic thinking behind AI implementation; engages diverse voices in interpreting AI impacts | Uses tools like SenseMaker® MassSense; facilitates narrative capture and pattern analysis; reframes resistance as a signal of pending danger and opportunity |
| Complex facilitation | Applies Hexi kit methods & tools for collective collaboration and providing a sense of travel | Helps learning teams to discover emerging patterns and launch change interventions Uses Cynefin to identify domain and thus appropriate methods |
| Token Boundary Navigation | Builds coalitions like entangled trios to cross symbolic thresholds (silos) | Humbly respects existing informal networks, cliques, and internal communities of practice to leverage their warm data relationships and power on the prevailing culture |
5. Adaptive Leadership & Change Navigation
| Competency | Description | Behavioural Indicators |
|---|---|---|
| Role of Decision-makers | Influences decision-makers to switch to Team of Teams paradigm | Engages decision-makers on a “faster, better, cheaper” shift from Command & Control to a Coordinate Information & Allocate Resources role |
| Iterative Implementation | Coaches assembled AI teams through Vector Theory of Change method | Use MassSense data to launch AI micro-experiments; adjusts next right thing to do based on positive and negative emergent outcomes |
| AI Change portfolio | Monitors AI progress using a real-time dashboard | Maps micro-projects into a portfolio; establishes the meeting cadence to inform all teams who is doing well and learn where others can offer time/energy support |