Tagged with: #ai-transformation

AI answer engines like Claude, ChatGPT, and Perplexity are fundamentally reshaping how value flows through information ecosystems. Unlike the web era’s simple traffic exchange, these systems synthesise and enhance proprietary data, creating entirely new possibilities for value creation. Bloomberg and the Financial Times demonstrate how organisations can transform this shift into competitive advantage through innovative AI models and sustainable value exchange frameworks. This article explores how Boards can leverage these lessons to build ecosystems where data owners, AI platforms, and users all benefit from the extraordinary value being created.

Traditional ROI calculations capture the obvious: cost savings, faster processes, fewer errors. Yet AI’s most powerful returns often emerge much later, as cascading second and third-order effects transform capabilities, business models, and competitive position. In this article I explore how Boards can identify and measure these hidden gains using leading, lagging, and predictive indicators, while ensuring governance frameworks balance opportunity with risk.

In the race to deploy agentic AI, organisations face a fundamental paradox: they’re building tomorrow’s autonomous systems on yesterday’s infrastructure. Drawing from the cloud transformation journey, this article explores how the same legacy architectures that constrain agentic AI also present an unprecedented opportunity. By retiring technical debt, organisations can clear the path for technological change that will define the next era of business competition. For Boards, the choice is clear: deploy agents within existing constraints, or use them to architect the foundation for future competitive advantage.

You’ve built your AI Centre of Excellence. It’s governing multi-speed adoption, delivering value, and - as we explored in the previous article - scaling beyond pilots to enterprise transformation. But here’s the uncomfortable truth: the AI landscape will look radically different in eighteen months. Multi-agent systems, decentralised agent ecosystems, embodied AI, neurosymbolic reasoning, quantum-AI hybrids, cross-modal intelligence, federated AI networks, and artificial superintelligence will challenge every governance framework you’ve carefully constructed. Having achieved scale, this final article tackles the strategic imperative of continuous evolution: how to future-proof your AI CoE to govern these disruptive technologies whilst building the adaptive capacity to thrive on change rather than being disrupted by it.

The successful completion of your AI Centre of Excellence’s first 90 days marks an important milestone, but it also brings into sharp focus the next critical challenge. Whilst the AI Initiative Rubric has proven effective for pilot selection and early wins have demonstrated value, the transition from isolated successes to enterprise-wide transformation requires fundamentally different approaches. This progression from pilot to scale represents one of the most significant hurdles in AI adoption, demanding new structures, governance models, and ways of thinking that go well beyond what initial success required.

I’m regularly asked how to use the AI Stages of Adoption, Five Pillars, and Well-Advised together practically. In this article I explain how these three mechanisms integrate to address the unique challenge of AI’s multi-speed adoption across different business functions. I provide a straightforward approach for boards to coordinate AI transformation whilst managing the governance complexities that emerge when different parts of the organisation advance at different speeds.

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.