Tagged with: #business-strategy

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.

In today’s AI-driven landscape, traditional business case methods fall short when evaluating AI investments. Drawing from my experience developing AWS’s cloud business case tools, I explore why conventional ROI models struggle with AI’s parallel, multi-speed adoption patterns. Unlike cloud’s sequential journey, AI initiatives exist simultaneously across different maturity stages, creating valuation challenges that standard metrics can’t capture. Boards need new evaluation approaches that account for AI’s diverse cost structures, varying timelines for returns, and how investments in one area often enable value in entirely different parts of the business.

In my previous article, Transforming the Board: Using Decision Analytics for Strategic Advantage, I introduced the concept of AI-powered decision analytics as a transformative approach to board decision-making. I explored how these capabilities can help directors move beyond traditional backward-looking metrics to embrace predictive indicators that model potential futures and enhance strategic decision-making.

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 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 night I had the pleasure of speaking at the Chief Wine Officer event hosted by AWS at London’s National Portrait Gallery. The focus was on demystifying generative AI and helping business leaders understand how to harness its potential for real organisational transformation.

In my article about not taking your foot off the gas, published just as the world returned to some degree of normality in July 2021, I discussed that the pandemic was the forcing function that challenged businesses to adapt and engage in broad organisational transformation using the cloud, and that it should not be seen as a reason to slow down.

I recently hosted a fireside chat for the AWS Summit EMEA with Intel’s Global Leader for AI Solutions, Monica Livingston. We discussed how Artificial Intelligence (AI) and Machine Learning (ML) are quickly becoming ubiquitous in business. The conversation prompted me to think about how Boards should be thinking about the use of AI and ML in their businesses, and how they need to ensure they are making the right decisions at the speed of light.

For many Boards, the pandemic has catalysed broad business change by accelerating digital transformation initiatives. The availability of cloud computing has democratised access to technology, and businesses of all shapes and sizes have seized on this opportunity to: increase revenue, margin, and profit; enter new markets and launch new products and services; delight, excite and get closer to their customers; and become more operationally efficient, increasing productivity, cutting waste, and reducing costs.