Posts tagged with #value-creation demonstrate how to realise the full potential of technology initiatives through comprehensive value management.
Organisations are demanding disciplined, comprehensive business cases for AI initiatives that balance traditional financial rigour with frameworks capturing AI’s unique value creation patterns. In this fourth article in my series on AI business cases, I provide a step-by-step guide to building AI business cases that secure approval and set the foundation for successful implementation.
Finding high-value AI opportunities requires looking beyond the obvious. While most organisations gravitate toward trendy applications like chatbots, the most impactful AI initiatives often lie in less visible but more strategically significant processes. By applying a structured evaluation approach that examines process characteristics, strategic alignment, and implementation feasibility, boards can identify AI investments that deliver transformative value across multiple business dimensions. This systematic method ensures scarce resources target opportunities with the greatest potential impact rather than those with merely the highest visibility or short-term appeal.
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 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?”
Last night, I had the pleasure of returning to the Chief Wine Officer event series on behalf of AWS, this time addressing leaders from private equity portfolio companies on a topic close to my heart: turning organisational data into strategic value. The evening, hosted at Spring at Somerset House, brought together executives eager to explore how to accelerate value creation through data monetisation within PE timeframes.
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.
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.
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.