Responsible AI Governance
Put responsible AI into everyday decisions, with clear owners, proportionate controls and a workable route from idea to use.
Responsible AI Governance
A policy alone does not tell a team how to submit a use case, who decides, which evidence is needed or what happens when the system changes. Too little governance creates unmanaged exposure. Too much sends useful work around the process.
An AI governance operating model people can use: a clear intake route, accountable roles, proportionate assessment and ongoing oversight connected to existing governance.
AI accountable officials, executives, risk and governance leaders, architecture teams and organisations moving beyond informal experimentation.
When use cases are growing faster than oversight; when staff are unclear about acceptable use; or when current committees and policies do not address AI decisions in practice.
Map current decision rights and obligations. Design a proportionate route for different kinds of use case. Test the process against realistic scenarios. Refine it with the people who will own, apply and oversee it.
Review existing governance; define use-case ownership; shape intake and risk classification; connect privacy, security, architecture and procurement reviews; define escalation and exceptions; plan monitoring, incidents and reassessment.
A governance operating model; roles and accountability map; intake workflow; AI use-case register structure; assessment and escalation pathways; responsible-use guidance; monitoring and reassessment approach; and an implementation roadmap.
Give teams a clear route to useful AI adoption and leaders visibility of the decisions being made. Reuse existing forums where they work, and add only what the organisation needs.
Support agencies applying the Policy for the responsible use of AI in government and relevant DTA guidance. Map agency accountability, registers and assessment processes into day-to-day working practices. Existing agency authority and mandated tools remain in place.
Connect AI decisions to investment, architecture, risk, vendor management and operational ownership. Address both centrally sponsored initiatives and business-led adoption.
Use a practical model that fits available capacity. Make privacy, safeguarding, accessibility, staff guidance and mission impact visible without creating a committee for every decision.
Begin with a governance gap review or a working session using one representative use case. Agree the operating model and artefacts that need to follow.
Apply the model to a live use case through AI Assurance. Add role-based education and adoption support through AI Readiness & Adoption.
Choose the outputs that the agreed scope needs. Each should make a decision, responsibility or next step clearer.
A governance operating model
Roles and accountability map
Intake workflow
AI use-case register structure
Assessment and escalation pathways
Responsible-use guidance
Monitoring and reassessment approach
And an implementation roadmap
Your next decision
You do not need a finished brief.
