Tagged with: #ai-implementation

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?

Every AI Centre of Excellence (AI CoE) needs a clear operational mandate. Through my experience designing and building Cloud Centres of Excellence for AWS customers, extensive research, and practical implementation, I’ve identified the essential functions that provide comprehensive AI governance without creating bureaucratic overload. These functions, organised around the Five Pillars mechanism, ensure your AI CoE can effectively govern multi-speed adoption while building the capabilities needed for sustainable AI transformation. Understanding these functions, and how they interconnect is crucial for boards establishing effective AI governance.

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.