AI GUIDE FOR BOARDS IN SINGAPORE
Section II: Strategy and Value Creation
5.3 Defining Success Criteria
5.3.2 Evaluating the AI innovation pipeline
Boards should require that AI initiatives be assessed against clearly-de ned criteria before progression to broader deployment. Setting out expectations through well-de ned criteria reduces ambiguity and improves resource allocation decisions. ese criteria may include: • Performance and reliability thresholds. • Operational feasibility. • User adoption levels. • Compliance with governance and risk requirements. • Commercial viability.
Boards should oversee the discipline and eectiveness of the organisation’s AI innovation pipeline. Eective governance helps ensure that resources are directed towards initiatives with the highest strategic and operational value. A well-functioning AI innovation pipeline typically demonstrates: • Alignment with strategic priorities. • Structured progression across de ned stages. • Evidence-based decision-making. • Appropriate risk management. • Timely termination of underperforming initiatives. An excessive volume of experimentation without scaling could be a key warning signal that innovation activity is not translating to business impact. Boards should be cautious where organisations maintain large numbers of pilots without meaningful progression into operational deployment.
5.3.1 Emphasising evidence over activity
Boards should focus on measurable outcomes rather than innovation activity metrics. Indicators such as the number of pilots or experiments do not demonstrate value. Instead, boards should monitor: • Conversion rates from pilot to production. • Measurable improvements in operations.
• Financial outcomes delivered. • Strategic objectives supported.
Boards should require management to clearly articulate what de nes minimum acceptable performance standards, and what risks are accepted. Boards should recognise that AI systems may not require perfect accuracy before deployment. Instead, organisations should de ne acceptable operational thresholds based on business context, risk exposure, regulatory obligations and human oversight requirements.
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