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For two years, organisations focused their AI energy on prompt engineering — improving how instructions are written. In production, that's not enough. The question is no longer how well the AI is instructed, but what it knows, what it can access, and whether that information was deliberately prepared or simply accumulated - Context Engineering.
Most organisations never designed for data. Those that did, designed for analytics — not for AI. Either way, the architectural foundations AI requires does not yet exist.
Weekly - Email - One Signal - Overview
From Data-Driven to AI-Driven: Where organisations really are, and what next.
For over a decade, we invested in becoming data-driven. Now boards are demanding AI-driven. This brief explores what it means to be AI-Driven — and argues it was always the same journey.
AI transparency requirements are making data provenance and usage explainability a prerequisite for deploying and scaling AI.
Agentic AI maturity - defined here as degree of autonomy AI systems exercise in the organisation - is not progressing linearly. Most organisations are further along than they realise. Many believe they remain in "early experimentation", yet have already crossed critical maturity thresholds without recognising it
As Data & AI embed in the organisation, the emerging leadership question is: where should we look for meaningful value?
In a landscape where Data and AI are redefining - at pace - how businesses compete and create value, distinguishing signal from noise has never been more critical.
The Executive Data & AI Brief provides a concise, strategic synthesis of the most consequential developments and trends shaping the future business.
September marked a material shift in the AI operating environment, driven Platform Shifts, Escalating Compute Arms Race and Regulatory Action.
Data & AI is complex and constantly evolving, In this blog I explore the making of a winning data & ai strategy and some the pitfalls to avoid, drawing on my experience and literature.
The rise of Artificial Intelligence is reshaping every domain it touches—and data management is no exception. At a recent Public Sector AI Week, I had the opportunity to reflect on how AI is not only disrupting traditional data practices but also opening new doors for value creation, governance, and competitive differentiation. In this piece I share my reflections and lessons from practice.
In today’s data-driven world, scaling insights requires more than data — it demands context and trust. As machines increasingly generate and consume insights, metadata and data quality become essential enablers of sustainable data value. With Generative AI (GenAI), we now have the tools to scale and automate these capabilities like never before.
The Anthropic Fable 5 ban exposed a risk most organisations are carrying: value built on AI capability they access but don't own.