AI Guide For Boards in Singapore

AI GUIDE FOR BOARDS IN SINGAPORE

Section I: The Board Mandate

Chapter 2: The Case For (and Against) AI

AI is becoming a core driver of enterprise value, and moving beyond theoretical potential to real-world applications.

By examining both industry-specic and enterprise-wide use cases, directors can better evaluate how AI might redene the competitive landscape of their own organisations.

2.1 Defining Strategic Ambition

Boards should understand how AI can be leveraged to solve business problems: where value creation is already occurring, and how future waves of AI adoption will transform industries and business models.

Management may naturally gravitate toward safer productivity plays. Boards play an important role in ensuring the organisation does not focus solely on doing the same things faster.

2.1.1 Incremental to transformational change

Board directors must provide clear guidance and support for transformational change and doing things dierently.

Boards must help management distinguish between two fundamentally dierent approaches to AI adoption: Incremental Productivity vs Transformational Change .

Incremental Productivity vs Transformational Change

e “best-in-class” approach focuses on improving existing workows and operating models. AI is deployed to make current processes faster, cheaper, more scalable or more accurate. ese incremental gains are oen the easiest to implement and measure, and oen generate the most immediate return on investment. e “only-in-class” approach represents a fundamental shi in the operating model or value proposition. is is where AI transforms from an assistant to a system or an agent that executes repeatable, multi-step work. e focus could be on new business models, entering new markets, or “productising” internal intelligence.

Transformational change requires budget, resources and a healthy risk appetite. ere are also potential risks of distractions from the core business.

Ultimately, success may not necessarily belong to the rst mover. In many cases, the greatest value may come from being a disciplined and fast follower: learning from early market experimentation while scaling proven use cases eciently.

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