Responsible AI for Business Impact

S$370

Responsible AI for Business Impact: Executive Workshop on AI Transformation, Governance and Readiness

As AI increasingly influences strategy, operations, customer and stakeholder experience, innovation and workforce development, leaders need a shared understanding of how AI can be adopted responsibly and aligned with organisational priorities. Beyond technology, AI introduces important questions relating to governance, accountability, privacy, security, intellectual property, culture, assurance and workforce capability.

The programme introduces the Rise AI Transformation Methodology, a practical framework that connects business value, readiness, risk, governance, investment decisions, responsible operation and scaling through four phases, seven steps and one Executive Evidence Pack.

Participants will be introduced to widely referenced AI governance approaches, including UNESCO and OECD principles, NIST AI RMF, ISO/IEC 42001 and ASEAN AI governance guidance. The programme uses a customised industry running case to illustrate how governance and risk considerations shape enterprise AI decisions.

By the end of the programme, participants will be better able to explain Trustworthy AI concepts, apply basic governance and risk thinking, interpret AI risks at an enterprise decision level, assess AI initiatives at a high level, and relate AI adoption to organisational structure, culture and assurance.

COURSE OBJECTIVES

By the end of the programme, participants will strengthen their ability to:

• Understand AI and Trustworthy AI: Explain core AI and Trustworthy AI concepts in accessible business language and recognise their implications for organisational leadership.
• Understand the Strategic Role of AI: Describe how AI can create value across strategy, operations, customer and stakeholder experience, innovation and corporate functions.
• Apply Governance and Risk Thinking: Apply a simple governance, risk and accountability lens when discussing AI opportunities and initiatives.
• Interpret Enterprise AI Risks: Interpret key AI risks, impacts and assurance considerations at an enterprise decision level.
• Assess AI Initiatives at a High Level: Use value, feasibility, readiness, risk and accountability considerations to assess whether an AI initiative merits further investigation, a pilot, conditions, redesign, deferment or rejection.
• Align AI Adoption with the Organisation: Relate responsible AI adoption to organisational structure, culture, capability, oversight and assurance arrangements.

LEARNING OUTCOMES

Upon completion of the programme, participants will be able to:

• Explain AI and Trustworthy AI concepts without technical depth.
• Apply basic governance and risk thinking to AI initiatives.
• Interpret AI risks at an enterprise decision level.
• Assess AI initiatives at a high level with confidence and accountability.
• Align AI adoption discussions with organisational structure, culture and assurance.

Understand AI and Trustworthy AI

Explain core AI and Trustworthy AI concepts in accessible business language and recognise their implications for organisational leadership.

Understand the strategic role of AI

Describe how AI creates value across strategy, operations, customer and stakeholder experience, innovation and corporate functions.

Apply governance and risk thinking

Apply a simple governance, risk and accountability lens when discussing AI opportunities and initiatives.

Interpret enterprise AI risks

Interpret key AI risks, impacts and assurance considerations at an enterprise decision level.

Assess AI initiatives at a high level

Use value, feasibility, readiness, risk and accountability to decide whether an initiative merits a pilot, conditions, redesign, deferment or rejection.

Align AI adoption with the organisation

Relate responsible AI adoption to organisational structure, culture, capability, oversight and assurance arrangements.

• • AI as a leadership issue
• • Why organisations need AI governance
• • Introduction to the customised industry running case

• • AI evolution and business application
• • Why AI fails and what it costs — case study analysis
• • Understanding organisational AI capabilities and AI strategy
• • Introduction to the Rise AI Transformation Methodology

• • Identifying AI opportunities aligned to strategic priorities
• • Comparing opportunities on value, feasibility, readiness and risk
• • Building a prioritised AI roadmap and use-case blueprint

• • Global expectations: UNESCO, OECD, EU AI Act concepts
• • Risk management: NIST AI RMF and AI impact assessment
• • AI management systems: ISO/IEC 42001 and the ASEAN Model AI Framework
• • Applying governance to the running case — readiness, risk and impact

• • Evidence leaders should see before approving an AI initiative
• • How value, readiness, risk and governance shape the decision
• • Approve, pilot, approve with conditions, redesign, defer or reject

• • Policies, controls and human oversight in daily operation
• • Monitoring, incident management and continuous improvement
• • Capability, governance maturity and the scaling roadmap

• • Assembling the Executive Evidence Pack
• • Which AI opportunity offers the greatest value?
• • What should leadership sponsor within the next 90 days?