A Board’s Expectations on AI Strategy and Governance
As artificial intelligence (AI) becomes increasingly embedded in everyday business tools, boards of directors face a subtle but critical governance challenge. AI is no longer confined to bespoke systems or advanced analytics platforms. Today, even commonly used applications, such as productivity software, enterprise systems, and customer platforms, come with AI-enabled capabilities by default.
This reality requires boards to recalibrate their perspective. AI should no longer be viewed solely as a stand-alone technology initiative but as a capability layer increasingly woven into core business processes and decision-making. Consequently, AI governance is no longer about overseeing a few “AI projects,” but about ensuring that AI-enabled decisions across the organization remain aligned with strategy, risk appetite, and ethical standards.
Effective AI Oversight
As with financial stewardship, effective AI oversight demands clarity, accountability, and proportionality. Boards must therefore shape their expectations of management accordingly. A well-governed organization treats AI as both a strategic enabler and a governance concern. Management’s role then is twofold: to leverage AI in pursuit of enterprise objectives while acting as stewards of the risks that accompany automation, data-driven decisions, and algorithmic scale.
Outlined below are key areas where boards should focus their oversight:
1. Clear Definition of AI Systems
Boards should be clear on what management considers an “AI system” for governance purposes. As AI capabilities are increasingly embedded in standard software, not all AI warrants the same level of oversight.
A practical approach distinguishes between embedded or low-risk AI features and material AI systems. Embedded or low-risk AI features, such as AI-assisted spelling or document summarization in productivity software, are typically bundled into commonly used tools and support routine tasks.
In contrast, material AI systems influence consequential decisions or outcomes, such as AI used in credit approval, pricing, fraud detection, hiring, or predictive models affecting financial forecasts. These systems introduce financial, regulatory, ethical, or reputational risk and therefore require stronger controls, clearer accountability, and board-level visibility.
2. AI Strategy as a Guide for Investments
Boards should verify that AI initiatives, whether embedded or stand-alone, are explicitly linked to enterprise strategy. A clear AI strategy serves as a critical guide for technology and capital allocation decisions, ensuring that investments are coherent rather than opportunistic.
Management should be able to explain how AI ambitions translate into concrete requirements for enterprise data platforms, system architecture, and enabling infrastructure.
3. Governance Structures and Accountability
Boards should ensure that AI-related decisions are clearly governed, escalated, and owned. This includes clear accountability for AI strategy, deployment, and ongoing oversight, regardless of formal titles or organizational design.
Unintended consequences often surface through governance gaps. For example, an AI-enabled pricing system may unintentionally disadvantage certain customer groups, triggering reputational or regulatory concerns.
Not all organizations require a dedicated AI governance council; existing structures, such as audit, risk, or technology committees, may suffice.
4. Risk Management and Board Oversight
Boards need to understand how AI risks are identified, assessed, and managed with the same rigor applied to other model-driven systems. This includes governance frameworks covering data quality, assumptions, human oversight, third-party dependencies, and cybersecurity.
Material AI systems should be subject to defined approval processes, ongoing monitoring, and independent review. At the board level, this means having visibility into how AI models are performing and how outcomes are tested against real-world results.
The path forward requires boards to balance innovation with appropriate oversight, asking informed questions and establishing clear expectations. As AI becomes a pervasive feature of modern organizations, governance maturity, not technological sophistication, will distinguish responsible adopters from risky ones.
In an environment where AI is increasingly everywhere, informed and proportionate oversight is no longer optional; it is a core duty of good governance.
