Understanding
Can people explain what the system is doing and what they should do next?
- Comprehension testing
- Explanation usefulness
- Support questions
Evidence & measurement
No single number can capture human readiness. Combine experience, behaviour, workforce evidence, outcomes and governance quality.
What success looks like
People understand what is happening, can recover when it goes wrong, and leave the interaction with their confidence and control intact.
01 / Signal library
Use multiple signals together. A high completion rate can still hide confusion, over-trust or assisted workarounds.
Can people explain what the system is doing and what they should do next?
Does the journey increase confidence—or transfer it uncritically to automation?
Can people complete, pause, recover and return under real conditions?
Where is the service creating friction, uncertainty or repeat effort?
Does the service recognise heightened pressure and respond proportionately?
Can staff use, explain and challenge AI responsibly?
Is oversight meaningful, accountable and evidence-led?
02 / Maturity model
Maturity is the ability to sense, interpret and respond to widening gaps—not simply the amount of AI deployed.
Human adaptation is considered after issues appear.
Basic governance exists, but behavioural insight is uneven.
Support, accessibility and explanations are designed in.
The framework shapes design, risk and operations.
Customer and workforce evidence continuously improves AI.
03 / Behavioural signals
Observed behaviour is a prompt to investigate the service and context—not a diagnosis of the person.
Move from evidence to action
Use the interactive HARMONIC checklist to surface gaps and leave with a focused action plan.
Open the toolkit