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Tagged with: #well-advised
Posts tagged with #well-advised feature my thoughts on the Well-Advised Framework - a mechanism for realising business value through technology investment.
The case for AI in the finance function is no longer in question. Commitment to it now runs well ahead of readiness, but accountability does not wait for that gap to close. The CFO answers for the integrity of the accounts and the stewardship of capital every reporting cycle, ready or not, and AI is already inside the work that produces both. In this article, I argue that AI changes how each of the CFO’s duties is carried out, not who signs for them, and I sort its effect into four groups: where it does the work, where it sharpens the judgement, where it operates out of sight, and where it changes almost nothing.
The strategic value of data is no longer in question. The next frontier is data whose meaning and relationships are explicit enough for machines to reason over rather than merely retrieve. Unstructured information must be interpreted by whoever consumes it, whether that is a person reading a report or an AI system generating an answer. Structured information makes explicit the relationships required for traceability, verification, and defensible reasoning at scale. In this article, I argue that ontologies and the knowledge graphs built upon them have moved from technical infrastructure into Board territory, because they increasingly determine what an organisation officially knows, what its AI systems can work with, and where durable advantage is created.
Llantwit Major |
Published in
Board
| 11 minute read |
For decades, tighter discipline over technology spend has rewarded the finance functions that applied it. AI capital behaves unlike anything they have measured before: it appreciates rather than depreciates through use, accumulates through reinvestment rather than paying back linearly, and surfaces value in functions other than the one that funded it. The project-ROI lens, optimised for predictability and attribution, cannot register these behaviours. CFOs who have scaled AI are seeing returns the rest cannot, not because their execution is better but because their instruments are. This article sets out what those instruments are and how to apply them.
Boards have always governed under incomplete information. What the four indicator types offer is not more information but a progressively higher quality of it. Lagging indicators establish what happened, leading indicators signal direction, predictive indicators model possible futures, and reasoned indicators prove what is certain. Applied in combination to a single decision, they represent maximum fidelity — everything knowable and made available before the judgement is made. This article explains why the distinction between a decision made with maximum fidelity and one made without it matters for every director around the table.
Llantwit Major |
Published in
AI
and
Board
| 12 minute read |
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.
London |
Published in
AI
,
Board
and
Data
| 12 minute read |
AI answer engines like Claude, ChatGPT, and Perplexity are fundamentally reshaping how value flows through information ecosystems. Unlike the web era’s simple traffic exchange, these systems synthesise and enhance proprietary data, creating entirely new possibilities for value creation. Bloomberg and the Financial Times demonstrate how organisations can transform this shift into competitive advantage through innovative AI models and sustainable value exchange frameworks. This article explores how Boards can leverage these lessons to build ecosystems where data owners, AI platforms, and users all benefit from the extraordinary value being created.
London |
Published in
AI
and
Board
| 11 minute read |
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.
Seattle |
Published in
AI
,
Board
and
Emerging
| 11 minute read |
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.
London |
Published in
AI
,
Board
and
Emerging
| 12 minute read |
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.
Llantwit Major |
Published in
AI
and
Board
| 12 minute read |
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.
