Posts tagged with #ai-stages-of-adoption identify where different parts of your business are in their AI journey and create strategies for multi-speed progress across your organisation. From the Experimenting stage to Scaling, these posts provide insights on building maturity using the five capability pillars that form part of the AI Stages of Adoption.
The EU AI Act, which came into force on August 1, 2024, establishes significant penalties for non-compliance, including fines of up to €35 million or 7% of global annual turnover for serious violations. As regulatory frameworks for artificial intelligence rapidly evolve worldwide, Boards face a new imperative: navigating complex compliance requirements while maintaining the innovation speed necessary to compete.
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.
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?”
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.