Tagged with: #business-transformation

In the Redeployment Dividend I argued that AI’s real prize is releasing intellectual capital from undifferentiated work, not cutting headcount. The evidence has now caught up with the argument, and it is uncomfortable. Teams that deploy AI save the equivalent of five hours per person per week, yet most of that time drains into low-value work, and nine in ten executives report no measurable productivity impact at their own firm. The saving is real; the value is not arriving. In this article, I argue that the dividend leaks because nobody owns it. AI owns execution and managers own the workflow, but unless the Board owns whether freed capacity creates value, the hours AI recovers will simply refill with the work that was already there.

In The Great Remaking I established that businesses are being remade around AI. But “remake with AI” is not “put AI into everything”, and the difference between them is judgement. Asked recently how I decide which AI to use, I said that I do not start there. In this article, I argue for the three questions that come first, and that the one doing the real work is not the question about tools at all, but the one that asks how a piece of work is done today and whether it still needs doing, because a well-judged no is what makes every yes credible.

Every previous technology wave rewarded fast followers. Identify what the leaders built, acquire or replicate it, close the gap. That logic fails for The Great Remaking — not because AI is different technology, but because the source of advantage is not a product that can be studied and replicated. It is operational accumulation: proprietary data shaped by AI-integrated workflows, human capability developed through sustained practice, and institutional knowledge embedded through iterative redesign. None of it can be purchased. All of it compounds with time. This article explains the three self-reinforcing loops that make the gap harder to close with every month an organisation defers the decision to redesign.

Over five decades, five technology revolutions each transformed organisations, but none restructured the essence of work itself. AI does — remaking how organisations think, decide, create, and deliver. The gap between bolting AI onto existing processes and redesigning how work is structured is already producing four times higher total shareholder returns for those who commit. This article defines what the essence of work actually is, why AI is remaking all four dimensions at different speeds, and why The Great Remaking is a race with compounding consequences that late movers cannot close through incremental catch-up.

Stanford’s 2025 AI Index shows 78% of organisations using AI, yet McKinsey finds only 21% have redesigned workflows to integrate it – revealing a governance paradox where widespread adoption yields minimal transformation. In this article, I show how the Complete AI Framework serves as guiding policy that transforms the Six Concerns diagnosis into systematic action, enabling Boards to orchestrate multi-speed adoption through integrated governance rather than hoping disconnected projects somehow cohere into strategy.

Boards are approving AI initiatives at record pace – 92% of companies plan increased investment – yet only 1% have achieved AI maturity: the gap reveals a fundamental misconception about AI strategy. In this article, I expose why accumulating business cases creates fragmentation rather than transformation, and why Boards must shift from project-level approvals to orchestrating systematic AI capability before their disconnected pilots become an expensive collection of failures.

Following your AI amnesty programme, speed matters: employees who disclosed shadow AI usage expect enablement, not restriction - the post-amnesty window is critical. In this article, I provide a roadmap for transforming discoveries into governed capabilities that boost organisational productivity and reduce the risk of AI moving back into the shadows again.

With a 68% surge in shadow AI usage and 54% of employees saying they would use AI tools even if they were not authorised by the company, Boards face a governance challenge traditional compliance cannot solve. This article presents AI amnesty as an important first step to minimum lovable governance - transforming hidden risks into strategic assets whilst capturing employee-validated innovation. When 95% of enterprise AI pilots fail to deliver measurable ROI yet shadow AI thrives everywhere, the path forward isn’t enforcement but structured disclosure programmes that build trust and position early adopters as governance standard-setters.

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.

The EU’s General-Purpose AI (GPAI) Code of Practice, effective August 2025, signals a new era of regulatory divergence. While the EU sets transparency and systemic risk guardrails, the U.S. accelerates through deregulation. For Boards, the challenge isn’t choosing sides but mastering dual-track governance — turning regulatory complexity into strategic advantage.