Tagged with: #ai-in-business

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.

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.

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.

While here in San Francisco for a business trip, I got the opportunity to spend time with technology innovators and leaders, and what struck me was the contrast in AI adoption approaches, and openness to transformation using AI compared to my experiences in Europe. A new benchmark report from Gallup has confirmed what many of us in technology leadership have long suspected: Europe’s lag in AI adoption isn’t a matter of insufficient capital – it’s a cultural challenge that runs deep.

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.

In the wake of the meteoric rise of generative AI, it’s easy to get swept up in the hype and believe that this single branch of artificial intelligence (AI) is the whole story. Platforms like ChatGPT have undeniably captured the public imagination, marking the first mass consumerisation of AI technology. However, focusing solely on generative AI risks overshadowing the diverse and equally transformative types of AI that have been quietly but powerfully driving business innovation.

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.