Tagged with: #board-advisory

The informational asymmetry between management and the Board has always been the central tension of governance. For AI, it is no longer manageable through existing structural checks; the distance is not merely larger than previous technology waves, it is qualitatively different. A director must be able to interrogate maturity claims, assess whether governance is operational or merely presentational, and identify which AI risks are personal development challenges and which are failures of oversight itself. The IoD has formally named the gap. This article defines what closing it actually requires: not technical fluency, but specific capacities for independent evaluation mapped against the governance obligations every director carries, and a diagnostic framework for identifying exactly where the work needs to start.

An AI agent that can only see the public internet is no more useful to an organisation’s business than a very expensive search engine. The intelligence is not the constraint. The connectivity is. Model Context Protocol — MCP — is the infrastructure standard that connects agents to the proprietary data, systems, and processes that constitute real competitive advantage. This article explains what MCP is, why the major enterprise vendors have already converged on it, and the governance questions Boards should be asking before their technology teams answer them by default.

AI governance is fragmenting into incompatible systems — Europe prioritising trust through transparency, America pursuing speed through scale, China maintaining control through integration — forcing Boards to choose rather than compromise. In this article, I explore the sovereignty trilemma and present three strategic stances for navigating these landscapes without fracturing your strategy.

Even the most meticulously crafted AI business case can fail at the final hurdle - securing Board buy-in. With research showing 88% of AI pilots never reach production, effective presentation isn’t just about gaining initial approval but establishing the path to full implementation. This final article in my series explores how to present AI investment proposals to Boards, addressing their six key areas of concern while building the stakeholder confidence necessary for successful transformation. By understanding Board dynamics, anticipating objections, and structuring presentations that balance strategic vision with implementation rigour, you can navigate the critical journey from business case to production-scale AI.

Organisations are demanding disciplined, comprehensive business cases for AI initiatives that balance traditional financial rigour with frameworks capturing AI’s unique value creation patterns. In this fourth article in my series on AI business cases, I provide a step-by-step guide to building AI business cases that secure approval and set the foundation for successful implementation.

Finding high-value AI opportunities requires looking beyond the obvious. While most organisations gravitate toward trendy applications like chatbots, the most impactful AI initiatives often lie in less visible but more strategically significant processes. By applying a structured evaluation approach that examines process characteristics, strategic alignment, and implementation feasibility, boards can identify AI investments that deliver transformative value across multiple business dimensions. This systematic method ensures scarce resources target opportunities with the greatest potential impact rather than those with merely the highest visibility or short-term appeal.

Building on my previous thoughts about why traditional business cases fail for AI investments, this article explores what I consider to be the essential building blocks for a more effective evaluation approach. This foundation provides boards with the tools to assess AI’s unique value creation patterns while maintaining financial discipline - helping leaders confidently navigate investment decisions that conventional models simply cannot adequately evaluate.

In my previous article, Transforming the Board: Using Decision Analytics for Strategic Advantage, I introduced the concept of AI-powered decision analytics as a transformative approach to board decision-making. I explored how these capabilities can help directors move beyond traditional backward-looking metrics to embrace predictive indicators that model potential futures and enhance strategic decision-making.

In my article The Board in the machine, I argued that “Boards will find that there are no barriers to making the right decisions at the speed of light”. More recently, in AI is transforming governance: Six key Boardroom priorities, I observed that boards “are moving from overseeing hundreds of decisions made per day to millions made per second”. This acceleration of business decision velocity presents both an unprecedented challenge and opportunity for Directors and the Boards they serve.

The EU AI Act, which came into force on August 1, 2024, establishes significant penalties for non-compliance, including fines of up to €35 million or 7% of global annual turnover for serious violations. As regulatory frameworks for artificial intelligence rapidly evolve worldwide, Boards face a new imperative: navigating complex compliance requirements while maintaining the innovation speed necessary to compete.