Posts tagged with #enterprise-ai show how to scale AI from departmental experiments to enterprise capabilities through approaches that balance innovation with responsible oversight.
New research from MIT provides compelling validation for the AI adoption challenges I’ve been highlighting since 2024: whilst organisations are investing billions of dollars in generative AI, only 5% successfully move from pilot to production. The study confirms what I’ve observed first-hand — the difference between transformation and experimentation lies in coherent governance, not technology capability.
The EU’s General-Purpose AI (GPAI) Code of Practice, effective August 2025, signals a new era of regulatory divergence. While the EU sets transparency and systemic risk guardrails, the U.S. accelerates through deregulation. For Boards, the challenge isn’t choosing sides but mastering dual-track governance — turning regulatory complexity into strategic advantage.
Traditional ROI calculations capture the obvious: cost savings, faster processes, fewer errors. Yet AI’s most powerful returns often emerge much later, as cascading second and third-order effects transform capabilities, business models, and competitive position. In this article I explore how Boards can identify and measure these hidden gains using leading, lagging, and predictive indicators, while ensuring governance frameworks balance opportunity with risk.
In the race to deploy agentic AI, organisations face a fundamental paradox: they’re building tomorrow’s autonomous systems on yesterday’s infrastructure. Drawing from the cloud transformation journey, this article explores how the same legacy architectures that constrain agentic AI also present an unprecedented opportunity. By retiring technical debt, organisations can clear the path for technological change that will define the next era of business competition. For Boards, the choice is clear: deploy agents within existing constraints, or use them to architect the foundation for future competitive advantage.
You’ve built your AI Centre of Excellence. It’s governing multi-speed adoption, delivering value, and - as we explored in the previous article - scaling beyond pilots to enterprise transformation. But here’s the uncomfortable truth: the AI landscape will look radically different in eighteen months. Multi-agent systems, decentralised agent ecosystems, embodied AI, neurosymbolic reasoning, quantum-AI hybrids, cross-modal intelligence, federated AI networks, and artificial superintelligence will challenge every governance framework you’ve carefully constructed. Having achieved scale, this final article tackles the strategic imperative of continuous evolution: how to future-proof your AI CoE to govern these disruptive technologies whilst building the adaptive capacity to thrive on change rather than being disrupted by it.
The successful completion of your AI Centre of Excellence’s first 90 days marks an important milestone, but it also brings into sharp focus the next critical challenge. Whilst the AI Initiative Rubric has proven effective for pilot selection and early wins have demonstrated value, the transition from isolated successes to enterprise-wide transformation requires fundamentally different approaches. This progression from pilot to scale represents one of the most significant hurdles in AI adoption, demanding new structures, governance models, and ways of thinking that go well beyond what initial success required.
This sixth article in my AI Centre of Excellence (AI CoE) series transforms theory into practice with a comprehensive 90-day implementation roadmap. Moving from capability building to value delivery, it introduces the AI Initiative Rubric - a systematic pilot selection tool that ensures your first initiatives deliver Well-Advised value whilst strengthening Five Pillars capabilities. Complete with sprint portfolios, stakeholder engagement strategies, and common pitfall avoidance, this article provides the practical guidance needed to demonstrate tangible AI CoE value from day one.
The fifth article in my AI Centre of Excellence series provides a comprehensive guide to building essential capabilities across the Five Pillars. Moving from governance frameworks to practical implementation, it details how to develop capabilities that match your multi-speed AI reality - from transforming shadow AI into governed innovation, to creating comprehensive literacy programmes. Complete with a 90-day implementation sprint, maturity assessment tools, and practical templates, this article transforms theoretical understanding into actionable capability development.
In the first three articles of this series, we’ve established why Boards need an AI Centre of Excellence (AI CoE), explored the eighteen essential functions that drive AI success, and used the AI CoE Simulator to reveal the multi-speed reality of AI adoption. Now comes the critical question: how do you structure an AI CoE that can effectively govern this complex, multi-speed landscape?
In the first two articles of this series, I’ve explored why boards need an AI Centre of Excellence (AI CoE) and detailed the eighteen functions that determine AI success. But before you can build effective governance, you need to understand where you actually are today - not where you think you are, or where you’d like to be. To help boards navigate this challenge, I’m sharing the AI CoE Simulator - a practical assessment tool taken from my AI governance toolkit that operationalises the AISA framework for real-world use.