Posts tagged with #ai-maturity help you understand where your organisation stands on its AI journey and what capabilities you need to build for successful stage transitions.
Boards frequently overestimate AI maturity by focusing on tool deployments rather than genuine capabilities, mistaking isolated pilot successes for systemic organisational readiness. This article exposes the three patterns that create the illusion—tool-centric thinking, pilot success traps, and hype-driven metrics—and provides a diagnostic framework to reveal true position and enable targeted advancement.
While organisations transfer decision-making agency to AI systems, accountability remains with humans, yet boards approve AI deployment without investing in the verification capability needed to ensure it. In this article, I demonstrate why this creates a strategic choice with measurable consequences: augmentation preserves expertise pipelines whilst achieving efficiency gains, but replacement destroys capabilities that cannot be rebuilt, turning apparent cost reduction into systematic competitive disadvantage.
Stanford’s 2025 AI Index shows 78% of organisations using AI, yet McKinsey finds only 21% have redesigned workflows to integrate it – revealing a governance paradox where widespread adoption yields minimal transformation. In this article, I show how the Complete AI Framework serves as guiding policy that transforms the Six Concerns diagnosis into systematic action, enabling Boards to orchestrate multi-speed adoption through integrated governance rather than hoping disconnected projects somehow cohere into strategy.
The true AI governance challenge isn’t pilot failures – it’s that Boards’ six core concerns demand simultaneous orchestration yet receive sequential attention through project-level adoption. In this article, I show how these interconnected priorities form the proper diagnostic lens for AI governance, revealing why addressing them together as a whole rather than individually determines the difference between transformation and yet another failure.
The fifth article in my AI Centre of Excellence series provides a comprehensive guide to building essential capabilities across the Five Pillars. Moving from governance frameworks to practical implementation, it details how to develop capabilities that match your multi-speed AI reality - from transforming shadow AI into governed innovation, to creating comprehensive literacy programmes. Complete with a 90-day implementation sprint, maturity assessment tools, and practical templates, this article transforms theoretical understanding into actionable capability development.
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.
In my previous articles about the AI Stages of Adoption and the Five Pillars of AI maturity and capability, I briefly touched on the role of the AI Centre of Excellence (AI CoE). Since publishing those pieces, I’ve spoken with numerous Boards and business leaders about AI adoption and the importance of board-level AI governance. A recurring question emerges in almost every conversation: “What are the practical steps to establishing an AI CoE in our business?”
In my previous article on the AI Stages of Adoption (AISA), I outlined how organisations progress through their AI journey—from Experimenting to Adopting, Optimising, Transforming, and ultimately Scaling. Since publishing that piece, many readers have asked the same follow‐up question: “How do we know when we’re truly ready to move from one stage to the next?”
In June of 2024, I introduced the concept of the AI Stages of Adoption (AISA), a framework for understanding where organisations are in their AI journey. Since then, I’ve had countless conversations with business leaders about how this framework helps them navigate their transformation. Today, I want to share a deeper perspective on AISA and how you can use it to accelerate your organisation’s AI adoption.
I'm a Chartered Director and Fellow of the Institute of Directors (IoD) with nearly three decades bridging software engineering, entrepreneurial leadership, and enterprise transformation. As Head of Applied AI & Emerging Technology Strategy at Amazon Web Services (AWS), I define how AWS equips its global field organisation and clients to accelerate AI adoption and prepare for continuous technological disruption.
An alumnus of the London School of Economics and guest lecturer on the LSE Data Science & AI for Executives programme, I partner with Boards and executive teams to build the knowledge, skills, and behaviours needed to scale advanced technologies responsibly. My independently authored frameworks — including the AI Stages of Adoption (AISA), Five Pillars of AI Capability, and Well-Advised — are adopted internationally in enterprise engagements and cited by professional bodies advancing responsible AI adoption, including the IoD.
My work has enabled organisations to move AI from experimentation to enterprise-scale impact, generating measurable business value through systematic governance and strategic adoption of AI, cloud, data, and emerging technologies.
"Antifragile" reveals how uncertainty and disorder can strengthen rather than weaken systems, offering leaders a revolutionary framework for building organisations that thrive on volatility and change. See what else I've been reading.