Structure your organisation’s AI ambitions, business priorities, investment decisions, governance requirements and delivery roadmap in one practical framework.
Compare potential AI opportunities against business value, feasibility, organisational readiness and risk to identify which use cases deserve attention first.
Build a structured case for AI investment covering the business problem, expected value, costs, risks, ownership, dependencies and measures of success.
Establish a practical structure for AI accountability, decision-making, oversight, risk management, lifecycle controls and organisational responsibilities.
Define the mandate, membership, authority, responsibilities, meeting structure and decision rights of an organisation’s AI governance committee.
Create a consistent record of AI systems, tools and use cases across the organisation, including ownership, purpose, data, vendors and governance status.
Identify, evaluate and document the material risks associated with an AI system or use case before deployment and throughout its operational lifecycle.
Maintain a clear record of identified AI risks, existing controls, treatment actions, accountable owners and ongoing monitoring requirements.
Evaluate how an AI system could affect people, decisions, customers, employees and organisational responsibilities before significant impacts emerge.
Define an AI initiative’s purpose, scope, ownership, stakeholders, dependencies, governance requirements, risks and measures of success from the outset.
Assess prospective AI suppliers against business fit, technology, data, security, governance, assurance and long-term supplier dependency considerations.
Plan how an AI capability will move from introduction into sustained organisational use through clear ownership, communication, training and adoption measures.
