The core lesson: people value what they help build — but only when it actually works The IKEA effect captures a simple but powerful truth: we tend to value things more when we’ve had a hand in making them. In the original research by Norton, Mochon, and Ariely, people valued self-assembled products more highly than identical pre-built ones. But there was an important catch: the effect weakened—or even disappeared—when the work was unsuccessful, incomplete, or undone. Put simp

If data is the new oil, why are so many leadership teams still running on fumes? Across boardrooms, a familiar frustration is surfacing. Organisations are awash with data—rich customer insights, operational metrics, and market signals—yet strategic decisions still lag behind fast‑moving competitors. Growth feels harder than it should. Agility remains elusive. This is not, as many assume, a data or technology problem. It is an operating model problem. As AI accelerates and dat

# Navigating the Complexities of Modern Digital Infrastructure ## Understanding the Fragility Beneath the Surface Modern digital infrastructure looks impressive from a distance. It is layered, connected, automated, and dressed up as progress. Yet this graphic makes a blunt and uncomfortable point: much of it is built like a tower of blocks rather than a fortress. For CxOs leading business transformation, that is a warning worth taking seriously. The message is not that digita

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

Boards are approving AI budgets. Vendors are promising step-change productivity. And yet—quietly—many organisations are discovering the hard truth: the technology isn’t the constraint. The constraint is the data underneath it. Gartner puts a number on what many leaders feel but struggle to quantify: poor data quality costs organisations around US$12.9 million per year on average. IBM research is equally sobering—more than a quarter of organisations estimate they lose over US$

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

At a high level, transformation may appear well managed: a new ERP roadmap, connected factories, automation, data platforms, and several AI pilots suggest that we are making progress. However, production performance still often relies on increasing exceptions, integrations, and manual workarounds between IT systems and the shop floor. The challenge is not digital change itself, but the increasing complexity that accompanies it. Each temporary workaround, unique routing rule

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

Over the past ten plus years, digital transformation has become essential for businesses. Companies across every industry are investing heavily in cloud computing, AI, data analytics, and automation to stay competitive. Still, despite the need and the money spent, a surprising number of digital transformation initiatives fall short of expectations—or fail entirely. Many people in the business world estimate that up to 70% of digital transformation projects fail to achieve the

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

Business leaders are hearing a lot about AI—usually through polished decks, confident roadmaps, and big promises. But before you scale anything, there’s one question that matters most: Is your data foundation strong enough for AI to be trusted in the real world? AI isn’t a strategy. It’s a capability. The strategic decision is simpler—and harder—than most roadmaps admit: Will this reliably improve a decision you care about (pricing, risk, service, fraud, forecasting) at scale

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

How to Align People, Data & Agents We’re moving from the era of “digital transformation” into a new phase: digital autonomy. It’s not happening overnight—and that’s normal. We tend to rush into new trends and only later realise they aren’t magic solutions. Recent stories about AI "agents" make that clear. Klarna, for example, put major effort into building an AI-driven workforce culture, but a year later, the promised gains in efficiency and customer satisfaction reportedly d

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

Most of us who have worked for organisations have lived through at least one "big transformation", which promised everything but quietly faded away. New platforms launched, consultants turned up, ran workshops, and disappeared. Slide decks multiplied, and a year or two later, people were still emailing spreadsheets around and wondering what, if anything, had actually changed. That usually isn’t because the tech was bad. The deeper issue is that transformation isn’t really a

A data strategy is a practical roadmap that explains how an organisation will use and manage its data to achieve its business goals. It sets out a clear approach for how data is collected, stored, analysed, and governed. This white paper walks through how to design and implement a data strategy, including the key steps involved, common challenges, and best practices. 1) Start with business goals Building an effective data strategy begins with understanding what the organisati

Enterprise data management (EDM): The processes, policies, and technologies used to effectively manage an organisation’s data assets. Core goals Ensure data quality Improve data accessibility Reduce data management risk Data management lifecycle (typical stages) Collection Storage Processing Integration Analysis Dissemination Archiving Governance and key disciplines Data governance: Overall management of data availability, usability, integrity, and security. Data quality: The

For data to be a key enabler of business success, it needs to meet four requirements: Findable — People should be able to quickly find the data they need to make decisions. Accessible — Access should be provided to the right people, at the right levels, based on clear rules and governance. Interoperable — Data should integrate across systems so it stays relevant and usable across business functions. Reusable — Data assets should be reliable and repeatable so that the orga