Artificial Intelligence (AI)

In 1998, I walked into the headquarters of our newly acquired newspaper group in Leeds, a result of a private equity-backed management buyout of United News & Media’s regional newspaper and magazine titles. Tasked with ‘doing the internet’, I found a small semblance of online activity in the form of a basic advertising website for the Yorkshire Post. A couple of weeks of getting my feet under my desk yielded a digital goldmine - 271 years of content waiting to be transformed into digital assets.

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.

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.

As artificial intelligence (AI) advances into the workplace rapidly, one of the most pressing questions on everyone’s mind is, “If machines can do our jobs faster, more accurately, and at a lower cost, what happens to us?” It’s true that AI is beginning to redefine the very essence of work, raising concerns about the potential displacement of knowledge workers. However, I’d like to propose an alternative perspective—one where those same workers earn the same or more whilst working far fewer hours.

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.