Why Organisations Fail with AI-and Others Don't. This item is unavailable.Why Organisations Fail with AI-and Others Don't

AI is everywhere. The value is not.

Organisations buy licences, launch pilots, build applications and train employees. Yet the hardest question often goes unanswered: what has demonstrably improved?

Why Organisations Fail with AI-and Others Don't explains why technology is rarely the real bottleneck. The real difference lies in an organisation's ability to build AI, govern it and realise value. A successful pilot is not yet a robust capability. Governance only matters when someone can actually intervene. And productivity only counts when work, capacity, customer value or earning power genuinely changes.

Michiel Krol connects strategy, technology, organisation design, leadership and governance with practical experience. He shows how seemingly sensible AI initiatives stall when technology advances faster than the organisation around it-and what leaders must organise differently to move from experimentation to sustainable results.

At the heart of the book are three board-level questions: Can we build it? Can we govern it? Can we realise value from it? These questions are developed through the AI Capability Framework, seven interconnected capability groups and ten recognisable capability paradoxes. Together they provide a practical lens for decisions about strategy, investment, people, risk, operating models and performance.

The book also examines the deeper organisational consequences of AI. What happens to professional development when AI takes over the tasks through which juniors traditionally learn? How do organisations preserve meaningful human oversight? When does outsourcing accelerate progress but weaken internal learning capability? How should leaders think about cybersecurity, supplier dependency, digital sovereignty and the EU AI Act? And what evidence should cause an earlier AI decision to be reconsidered?

Written for board members, executives and senior managers who must make decisions about AI without being AI specialists themselves-and for the professionals who advise them-this is not a tool guide, trend book or hype story. It is an accessible but intellectually substantial management book for leaders who want to connect technology, people, processes, governance and value as one coherent system.

The technology is already here. The difference will be made by the organisation that learns best what it needs to become.

Michiel Krol connects technology, strategy execution and organisation design. He taught himself to programme at a young age, founded a software company in his early twenties and then spent around ten years as a strategy-execution consultant. He subsequently held a senior leadership role at a bank for seven years at the intersection of data, analytics, AI, transformation and professional services.His experience spans both building technology and organising the environment in which it must work. He has worked on a.o. international ERP programs, end-to-end process management and shared definitions, data-driven assurance, analytics and data products, AI applications, cybersecurity experiments, operating models, and the development of professionals and communities.Within a global Internal Audit organisation, he helped scale data-driven assurance from seven pilots into a broader organisational capability. Around three hundred auditors received data-literacy training, a community of roughly eighty active users emerged, and later an AI Accelerator was created for twenty auditors. This experience forms an important practical basis for his thinking about capability: a successful pilot becomes organisational capability only when platform, skills, process, governance, performance management and learning reinforce one another.In other cases, he saw why technically strong solutions do not automatically win. One model could assess a much larger share of a population than an existing sample-based approach, but it did not automatically change the professional and governance system around it. An international technology programme proved functional potential but lost momentum at scale because of differences in governance, operational requirements and support. A successful BI team had to broaden its role from dashboards to reusable data products and meaningful information provision for future AI.That combination gives the author a credible position between different worlds. He does not write as an outsider about governance, as a technologist about organisations, or as an adviser without delivery responsibility. His perspective is that of a builder, leader and systems thinker who has experienced both success and failure at close range.Through FibonetIQ, he is developing this thinking further into the AI Capability Framework, an executive decision simulator, leadership and assurance programmes, and a Human-led, AI-enabled Living Lab. The book forms the intellectual foundation and public reference work for that broader practice.