Tagged with: #governance

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.

The rapid advancement of artificial intelligence is fundamentally changing the velocity of business decision-making and how organisations operate, compete, and create value. With AI, boards are moving from overseeing hundreds of decisions made per day to millions made per second - and they must be confident that each of those decisions is transparent, explainable, and correct.

As organisations adopt artificial intelligence (AI) more widely, a critical challenge emerges: how do you build and manage teams capable of delivering on AI’s promise of increased productivity, enhanced customer experiences, accelerated innovation, and sustainable competitive advantage?

In my early years at Amazon Web Services (AWS), I created a tool for building cloud business cases that went beyond measuring just total cost of ownership and now forms the basis of our approach to costing migrations. I later co-authored the Cloud Value Framework (CVF) which focusses on measuring cloud value across four areas: cost optimisation, risk reduction, increased agility, and resource efficiency. So it should come as no surprise that I often get asked by Boards and the executives I meet “How do we decide if we should make an AI investment and how do we measure its ROI?”

Over the past year, I’ve been asked regularly what the best way of selecting a Large Language Model (LLM) is. With over 146 LLMs listed in Ollama’s model library alone, selecting the right model has become increasingly complex. While ChatGPT dominates headlines, businesses must look beyond hype to match the right model to their specific needs. Choosing the right LLM isn’t just a technical decision—it directly impacts your organisation’s ability to drive innovation and achieve its strategic goals, making collaboration between technical and non-technical teams essential to ensure that both business needs and operational constraints are fully understood.

Last week, I had the privilege of delivering a keynote presentation at Monday.com’s Elevate conference in London. The topic, “Leveraging data and artificial intelligence (AI) for organisational transformation,” allowed me to challenge some common misconceptions about AI adoption and share practical insights on harnessing the power of existing data. In this post, I outline the key themes discussed and provide some additional context.

Earlier this week, I got the opportunity to speak at our Amazon Web Services (AWS) office in London, to an audience of Private Equity firms and their portfolio company executives about driving value creation through AI and data. The presentation focused on how Private Equity firms and their portfolio companies can drive value creation through AI and data. I also took the opportunity to introduce a new concept called the AI Stages of Adoption (AISA), which is designed to help organisations assess their maturity and readiness for AI adoption.

I had an incredible afternoon as a guest lecturer at the The London School of Economics and Political Science (LSE) where I got to discuss harnessing AI for organisational transformation led from the Board, at this summer’s Data Science for Executives class.