AI Business Case Series

Traditional business case methods fall short when evaluating AI investments. Unlike cloud’s sequential journey, AI initiatives exist simultaneously across different maturity stages, creating valuation challenges that standard metrics cannot capture. The stakes are high - despite significant investment, more than 80% of organisations haven’t seen tangible enterprise-level impact from generative AI.

This five-part series develops a comprehensive approach to AI business cases that recognises their unique characteristics while maintaining financial discipline. Moving from why traditional approaches fail through to securing Board buy-in, the series provides practical frameworks for identifying, evaluating, constructing, and presenting AI investment proposals.

Articles in This Series

Part 1: What Boards Need to Know

Traditional ROI models struggle with AI’s parallel, multi-speed adoption patterns. This article explores why conventional business case methods fail for AI investments and why Boards need new evaluation approaches that account for AI’s diverse cost structures, varying timelines for returns, and how investments in one area often enable value in entirely different parts of the business.

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Part 2: Creating the Foundation

Building on why traditional business cases fail, this article explores five essential building blocks for more effective AI evaluation: Strategic Purpose using the Well-Advised Framework, Value Spectrum across leading, lagging, and predictive indicators, Investment Profile across AI Stages of Adoption, Readiness Assessment using the Five Pillars, and Scaling Potential for capability synergies.

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Part 3: Finding High-Value Opportunities

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. This article provides a systematic approach to identifying AI investments that deliver transformative value across multiple business dimensions.

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Part 4: Building Your AI Business Case

Having identified promising AI opportunities, the challenge shifts to articulating their value in ways that resonate with decision makers. This article provides a step-by-step guide to building AI business cases that capture AI’s distinctive value creation patterns while providing Boards with the clarity they need for confident decision-making.

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Part 5: Securing Buy-In from the Board

Even the most meticulously crafted AI business case can fail at the final hurdle. 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 addresses the six key Board concerns and builds the stakeholder confidence necessary for successful transformation.

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The Six Board Concerns

Throughout this series, I address the six areas that consistently emerge as central Board concerns during AI business case presentations:

Strategic Alignment — How does this initiative support our broader business strategy?
Ethical and Legal Responsibility — How do we maintain accountability for AI-driven decisions?
Financial and Operational Impact — What are the true costs and returns across time horizons?
Risk Management — What risks does this create and how will they be managed?
Stakeholder Confidence — How will employees, customers, and partners respond?
Safeguarding Innovation — How do we maintain competitive advantage whilst managing risk?

This series integrates several frameworks from my toolkit: