Measure readiness. Identify gaps. Advance human-centric AI.

The ten domains

1. 01

   Human-Centric AI Awareness

   Can we demonstrate AI humanistic principles and define human-centric AI and identify ad hoc practices and challenges?

2. 02

   Strategy and Leadership

   Can we demonstrate AI humanistic principles and implemented governance, policies and performance indicators to measure progress and impact?

3. 03

   Governance, Ethics and Accountability

   Is it clear who may approve, oversee, question, pause or stop an AI system — and who answers for its outcomes?

4. 04

   Human Needs and Lived Experiences

   Can we clearly define people’s needs and explain the problem this AI is intended to solve, who is affected, and what success should look like?

5. 05

   Participatory Design and Co-creation

   Have the people affected by AI helped shape tool selection, how it will change workflows, decisions, roles, and interactions?

6. 06

   Safety, Trust, Inclusion and Equity

   Would we be comfortable explaining to a patient how the organization’s AI handles fairness, transparency, privacy, safety and security?

7. 07

   Workforce Readiness and Change Capacity

   Do clinicians and staff have the training, time, support and authority to question, override and report AI output without fear?

8. 08

   Human Impact, Control and Oversight

   Have we considered who may benefit, and who may be overlooked, burdened or harmed?

9. 09

   Adoption, Value and ROI

   Do we know the expected impact before we decide, whether outcomes improve after launch, and who monitors errors, overrides, safety, privacy and equity?

10. 10

    Continuous Monitoring and Improvement

    Do we have a plan to gather feedback, improve the process, reassess readiness, and pause, reverse course, or adjust before scaling?