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Tagged with: #ai-implementation

Transform AI pilots into production-ready systems through practical frameworks that address the 88% failure rate of AI initiatives. These articles provide step-by-step guidance for moving beyond experimentation to strategic deployment, covering technical foundations, organisational readiness, and value realisation. Learn how to build business cases that capture AI’s unique value patterns, implement decision analytics, and create scaling strategies that deliver sustained business impact.

AI Centre of Excellence: The Essential Functions of the Five Pillars

Llantwit Major | Published in AI and Board | 12 minute read |    
A modern control room with 18 illuminated panels arranged in five distinct colour-coded groups, each displaying abstract representations of AI governance functions, with silhouettes of executives observing the unified system (generated by ChatGPT 4o).

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.


Rethinking Business Cases in the Age of AI: Building Your AI Business Case

London | Published in AI and Board | 18 minute read |    
A professional team collaborates around a conference table reviewing an AI business case document. Digital displays show multi-dimensional value metrics, ROI projections across different time horizons, and strategic alignment graphics. The scene conveys analytical rigour combined with strategic vision in building a compelling AI investment case. (Image generated by ChatGPT 4o).

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.


Rethinking Business Cases in the Age of AI: Finding High-Value AI Opportunities

Llantwit Major | Published in AI and Board | 15 minute read |    
A diverse executive team in a modern boardroom reviews a large digital screen displaying a network map of interconnected business processes, with glowing nodes highlighting high-value AI opportunity areas. Surrounding dashboards present analytics on process complexity, strategic alignment, and implementation feasibility. Team members are engaged in discussion, each with laptops showing performance data. (Image generated by ChatGPT 4o).

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.


Rethinking Business Cases in the Age of AI: Creating the Foundation

Seattle | Published in AI and Board | 14 minute read |    
A business team collaborating around a modern table with holographic displays showing five interconnected building blocks that form a complete AI business case evaluation structure. (Image generated by ChatGPT 4o).

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.


Implementing Decision Analytics: A Practical Guide for Boards

London | Published in AI and Board | 11 minute read |    
A diverse business team collaboratively building an AI decision analytics engine in a modern boardroom, with digital data displays and construction tools on a sleek conference table. (Image generated by ChatGPT-4o).

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.


Navigating the AI Regulatory Maze: A Boardroom Survival Guide

Llantwit Major | Published in AI and Board | 14 minute read |    
Illustration of a maze split into two halves: one side representing traditional regulatory complexity with stone walls and paperwork, and the other depicting modern AI innovation with futuristic digital pathways. Board members strategically stand in the centre, navigating between regulation and AI. (Image generated by ChatGPT 4o)

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.


From Shadow AI to Strategic Asset: Building Your AI Centre of Excellence

London | Published in AI and Board | 16 minute read |    
The image shows a modern business setting where AI is seamlessly integrated into operations, enhancing productivity while being governed by ethical guidelines. Executives collaborate with a digital assistant, with subtle guardrails symbolizing responsible AI use in a professional and balanced environment. (Image generated by ChatGPT 4o)

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?”


Increasing AI Maturity: Navigating the AI Stages of Adoption with the Five Pillars

Llantwit Major | Published in AI and Board | 9 minute read |    
A futuristic digital painting depicting the increasing maturity of AI. A glowing blue bridge symbolises progress, supported by five distinct pillars representing different stages of AI development. The left side of the image is darker, illustrating early AI with basic automation, while the right side transitions into an advanced AI-powered city, illuminated with intricate blue light networks, symbolising intelligence and connectivity (Image generated by ChatGPT 4o).

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?”


Understanding the AI Stages of Adoption: A framework for business leaders

Llantwit Major | Published in AI and Board | 16 minute read |    
The Artificial Intelligence Stages of Adoption (AISA).

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.


AI is transforming governance: Six key Boardroom priorities

London | Published in AI and Board | 10 minute read |    
The image shows a futuristic boardroom with diverse professionals engaged in discussion around a central table, surrounded by holographic AI displays showing analytics and decision metrics, set against a bright cityscape and greenery, symbolizing collaboration, innovation, and ethical AI governance. (Image generated by ChatGPT 4o)

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.