AI projects rarely fail because of technology. It fails when no one takes clear ownership of results. Six months into your AI programme, you see impressive dashboards and the steering committee engages. But when the CFO asks, “What has actually changed in revenue, cost, risk, or customer experience?” and no one can answer, the problem is not AI. It is a lack of ownership. Why AI Programmes Look Successful (Right Up Until the CFO Asks) AI and data programmes often highlight ac

Most organisations do not lose money on AI because of weak models. Losses occur because investors fail to identify the specific decision that the investment is intended to improve. As a result, many pilots stall, quietly pause, or ultimately cancel. This challenge is increasingly pressing for leaders today. Gartner predicts that over 40% of agentic AI projects will be cancelled by the end of 2027 due to escalating costs, unclear business value, or inadequate risk controls. Li

Most organizations don’t have a data problem. They have a decision problem—caused by how data is organized, governed, and delivered to the people who need it. By now, many leadership teams have invested heavily: dashboards everywhere, an expanding data platform, several AI pilots, and specialist teams working hard behind the scenes. Yet decisions are still slow, insights still feel shallow, and the data estate seems to get more expensive every quarter. This isn’t a tooling is

A common scenario is unfolding in boardrooms: a polished AI demonstration is presented, stakeholders express approval, a roadmap is developed, and budgets are allocated. However, challenges quickly emerge: data is inconsistent, processes lack standardisation, and user trust is limited. EASA’s AI Roadmap 2.0 and NIST’s AI Risk Management Framework (AI RMF) address these issues by focusing on the requirements for safe and responsible AI adoption, rather than promoting AI itself

Introduction Today let’s take a look at how digital technology is reshaping aerospace and military manufacturing. This eBook will help you understand the key ideas behind digital transformation and how to apply them in industries where precision, reliability, and compliance matter most. The Beginning of a New Era In the age of Industry 4.0, digital transformation isn’t a “luxury” to have”—it’s becoming essential. It’s changing how organisations design, build, and maintain the

In today’s tech world, many companies are entering the realm of artificial intelligence with great enthusiasm—only to quickly face a disappointing reality: inconsistent results, unreliable models, and performance that fails to meet expectations. What makes these businesses different from those that successfully deploy robust, high-performing AI systems? The quality and readiness of their data. This article explains what it truly means to have data that is suitable for modern

Boards aren’t funding AI because it’s trendy—they’re funding it to drive growth, cut costs, improve cash discipline, and manage risk. Yet many programmes stall after the first dashboard because the business can’t answer basic questions with confidence: What counts as revenue? Who is the customer? Which numbers are actually audit-ready? That isn’t a data science problem. It’s a management system problem: inconsistent definitions, unclear ownership, and end-to-end processes tha

Despite all the excitement—and the clear monetisation opportunities—around IoT and machine/sensor blockchain over the past few years, one major concern keeps coming up. From my experience working on IoT projects as a lead evangelist architect, a key high-risk area—discussed both publicly and behind closed doors—is end-to-end data security throughout the entire data journey, in both states: data at rest and data in motion . While data and communication protocols at the machin

The key to being AI-ready is to reframe the conversation. Skip the jargon and the hype and start, as I always say, with outcomes. Here are four areas to focus on first—so you can bring some reality to your situation: What problems are we solving? AI for AI’s sake is a waste of resources. I’ve said the same thing about plenty of hype cycles before. Define use cases that clearly tie to value—whether that’s reducing operational inefficiencies, improving customer experience, or

Think of your business data landscape as a flexible system that changes as your business does, not as a rigid machine. Interconnected AI/LLM and analytics data products form an ecosystem that lives and evolves, reconfiguring as the business changes. In this (greatly simplified) example, data products perform different steps across a business and analytics process. For example, let's look at a simplified example from a Michelin-starred fine-dining tasting menu, focusing on res

The AI revolution is here—but whether it succeeds depends on one often-overlooked factor: data. While much of the buzz is about powerful models and clever algorithms, the real work for enterprises is making sure the data feeding those systems is solid. The old saying still applies: models are only as good as the data behind them. The challenge isn’t just building AI applications—it’s making sure data is accurate, trusted, and accessible at a new level of speed and scale. For