The AI Smart Pathway supports organisations ready to mature their AI governance.
It builds on foundational capabilities by embedding governance practices into core systems, roles, and development processes.
This stage equips organisations to manage increasingly complex AI risks while scaling AI use with confidence.
Organisations can tailor these practices based on their AI maturity, the volume of AI use, and risk exposure. You don’t need to be developing AI in-house to benefit from some of these practices, many apply just as strongly to teams deploying or managing third-party AI systems.
Key practices
Implement Risk-Based AI Governance Processes
- Ensures governance efforts are proportionate to the risk profile of each system.
- Improves efficiency by reducing friction on low-risk tools or use.
- Enhances focus and accountability.
Formalise AI Lifecycle Controls, Roles & Approval Gates
- Creates clear process for sign-offs, reducing ambiguity and delay
- Promotes discipline and traceability through the lifecycle
- Strengthens alignment between governance and delivery teams.
Embed AI Governance in Procurement and Vendor Management
- Improves visibility and control over vendor-supplied AI
- Reduces the risk of governance gaps across third-party systems
- Improves internal governance alignment
Introduce Standardised Model and Decision Documentation
- Improves internal understanding of how AI systems work
- Enables clearer oversight and validation of AI decisions
- Builds confidence in AI use through traceable system records
Develop Transparency and Communication Tools
- Builds public and stakeholder trust
- Increases preparedness for transparency obligations
- Helps anticipate and address reputational concerns
Monitor AI Systems Performance
- Ensures AI systems remain accurate, relevant, and safe
- Enables timely detection and response to emerging issues
- Supports compliance, audit, and lifecycle management
Outputs
A tiered AI risk framework or matrix with defined criteria and governance actions mapped to each risk level (e.g., low, medium, high).
A documented AI lifecycle framework that defines stages (e.g., planning, development, testing, deployment, retirement), with clear role descriptions for decision-making and oversight at each stage.
Procurement and contract templates updated with AI-specific expectations, due diligence checks, and governance role guidance.
Standard documentation template for AI systems, data use, and decision logic and a reference location (e.g. internal registry or shared drive) for storing and accessing records.
Public transparency statements or AI use notices for key systems and internal communication guidelines on AI use.
AI system monitoring plan alongside documented triggers and response pathways (e.g. retraining or rollback criteria).
