The foundational ability to communicate with, prompt, and collaborate alongside AI systems. Includes prompt architecture, output interpretation, tool selection, and adaptive interaction across agentic workflows.

Key question: Can I reliably get useful results from AI tools across different task types?

The cognitive discipline to evaluate AI outputs, detect confabulation and bias, calibrate trust dynamically, and challenge machine-generated recommendations when the stakes demand it.

Key question: Do I systematically verify AI outputs before acting on them?

The capacity to determine when AI delegation is safe, when human intervention is necessary, and when exceptions must be escalated. Includes pre-mortem thinking and the Exception Triage Framework (Green / Amber / Red / Grey).

Key question: Do I have clear criteria for when to trust, intervene in, or escalate AI-driven processes?

The breadth of knowledge and cognitive flexibility to supervise AI systems operating across multiple business functions. Combines deep domain expertise with cross-functional fluency and translation capability (T-shaped orchestrator).

Key question: Can I evaluate AI outputs across functions beyond my core expertise?

The interpersonal capabilities that become more critical as technical execution shifts to machines. Includes empathy, trust-building, psychological safety, and maintaining human connection in automated workflows.

Key question: Am I supporting the emotional and relational dimensions of AI adoption in my team?

The responsibility to ensure AI systems remain accountable, unbiased, and aligned with organisational values. Encompasses fairness, transparency, privacy, EU AI Act compliance, and the Ethical Pre-Flight Checklist.

Key question: Is there a named human accountable for every AI-augmented decision in my area?