The AI Trusted Pathway supports organisations with advanced AI maturity looking to embed governance across their full ecosystem.
This stage focuses on institutionalising trust, accountability, and transparency at scale.
Organisations at this level are ready to lead in responsible innovation, meet growing assurance demands, and help shape the future direction of AI through public and industry engagement.
Key practices
Drive a Culture of Responsible Innovation
- Empowers staff to raise concerns and propose improvements
- Sustains governance behaviours through periods of change or challenge
- Attracts and retains talent who value ethical innovation
Engage with External Standards and Benchmarking
- Improves alignment with external expectations and legal requirements
- Identifies areas of strength and gaps against peers
- Builds credibility and transparency across stakeholders
Measure Impact and Continuously Improve
- Highlights emerging issues and areas of improvement
- Demonstrates alignment with strategic outcomes
- Supports continuous learning and adaptive AI governance
Prepare for Assurance and Accountability Demands
- Reduces risk of penalties and reputational damage
- Builds trusts with regulators, customers and the public
- Demonstrates leadership in responsible innovation
Strengthen Human Oversight and Enable Contestability
- Enables timely human intervention when AI gets it wrong
- Builds trust through clear challenge and review processes
- Reduces risk from over-reliance on automation
Anticipate and Govern Emerging AI Use Cases
- Improves visibility over fast-evolving AI applications
- Supports safe experimentation without losing control
- Adapts governance as AI becomes more autonomous
Outputs
Internal communications and onboarding materials that reflect responsible AI values along with role-specific guidance or prompts integrated into delivery processes and team rituals, and recognition artefacts that capture how behaviours are incentivised.
Internal mapping of AI governance practices to selected external frameworks.
A defined set of agreed AI governance performance indicators or signals and a schedule or mechanism for periodic AI governance reviews.
An audit-ready record of AI governance decisions, roles, and artefacts.
User-facing explanations and appeal options visible and oversight activity tracked, documented and reviewed.
Register of emerging AI applications and documented governance approach for high-autonomy systems.
