1.1
Do you have a structured, formal AI Strategy formulation and review process and practices in place that are widely practiced across the enterprise and partner ecosystems? No formal AI strategy formulation and review process exists. AI strategy is discussed informally but is not documented. A documented AI strategy formulation process exists and is shared with key stakeholders. Clear, well-documented AI strategy formulation and review practices are fully functional, integrated with overall business strategy, and communicated across the enterprise and its ecosystems.
1.2
Do you have a formal and functional AI financial management process to track and benchmark AI services and use-case total cost of ownership, cost and ROI? Minimal to no visibility into AI financials. Project-level cost visibility only. Input costs, such as API, computing and service costs, are tracked. A full-fledged AI financial management process continuously monitors costs versus benefits and benchmarks AI outcomes and ROI against business KPIs.
1.3
Do you have a formal and fully implemented AI risk management process across the organisation and partner ecosystems? No formal AI risk management process. Ad-hoc, project-by-project AI risk management. Centralised basic risk management for AI experimentation exists. A detailed, formal AI risk management process is practised across the organisation and its ecosystems, covering GRC, security and legal requirements.
1.4
Do you have a formal AI Strategic Outcomes management process to measure, benchmark and discover best practices for AI impact across financial, customer, internal and knowledge dimensions? Success is not formally measured. Only technical metrics, such as model accuracy, are measured. Limited project-level business outcomes, such as cost savings, are measured. A well-practised strategic outcomes process measures business impact, transformation and market outcomes, with peer benchmarking and board-level visibility through value dashboards.
