Data Culture E-Book

Alex Vail

• Herausgeber: Icon Books
• Kategorie: Fachliteratur
• Sprache: Englisch

Seitenzahl: 276

Veröffentlichungsjahr: 2025

Beschreibung

Capturing the views of over 300 business leaders on the common causes of digital transformation failure, this book sets out an actionable framework to help organisations of all sizes to build successful data-driven cultures. Alex Vail took a sabbatical from his job on the board of one of the country's leading manufacturers to conduct several research projects, including the largest ever study into the UK's corporate AI capabilities. In total, he surveyed 234 senior leaders and interviewed 92 executives from FTSE350 companies to identify why digital transformations succeed or fail; the data dependency of organisations; and their levels of data literacy at senior levels. What emerged from the research was a clear set of success factors, grounded in mindset and behaviour elements, which have been used to create a framework that any company can follow, regardless of their size or complexity, that will guarantee successful data transformations. This book captures all of the research in an easy-to-follow guide packed with relatable scenarios of real-world technology deployment and valuable opinions from people at the coal-face of digital transformation.

Leseprobe

 

 

Published in the UK in 2025 by

Icon Books Ltd, Omnibus Business Centre,

39–41 North Road, London N7 9DP

email: [email protected]

www.iconbooks.com

ISBN: 978-183773-231-9

ebook: 978-183773-230-2

Text copyright © 2025 Alex Vail

The author has asserted his moral rights.

No part of this book may be reproduced in any form, or by any means, without prior permission in writing from the publisher.

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Contents

Introduction

PART ONE: Why transformations fail

1.A short history of digital transformation

2.Transformation vs. the 100-year business plan

3.Culture: why humans reject transformation

PART TWO: Why industrial AI is destined to fail

4.AI: the next oligopoly

5.Capitalism vs. AI policymakers

6.Bridging the AI capability gap

7.Unlocking industrial value from AI and machine learning

PART THREE: How to succeed with Data Culture

8.The Periodic Table of Data Strategy Elements

9.The Data Success Framework

Conclusions

Research notes & methodology

Acknowledgements

References

Introduction

Like so many great ideas, it started with cocktails...

Picture the scene: two friends, who have worked together on and off for the last twelve years, are sitting in a fancy cocktail bar in Greenwich Village, New York City. It’s early May, an unseasonably warm New York evening and local residents stroll past the window in T-shirts and shorts, lost in their own worlds as their designer dogs stop to sniff the last of the cherry blossom on the sidewalk.

I had been made redundant the previous week along with the rest of my team, when Series B funding for the AI innovation startup I was working in fell through. My friend Michella, a very smart woman, did the responsible thing and booked us both cheap flights from Heathrow to the Big Apple for a few days, so I could regroup and plan next steps.

Perched on high stools in the cocktail bar, as our feet throbbed from the 15 miles we’d walked that day around Manhattan and Central Park, she asked me a series of leading questions, as if interviewing me for my future. I talked about the anguish I felt about having had to lay off such a talented team. I explained how grateful I was that I’d had a chance to work with such a visionary CEO and how disappointed our clients had been that some of our really exciting transformation projects would never come to fruition. But most of all, I spoke about how much I had learned in the role. After 25 years of working across several industries which were struggling with the onslaught of new technologies – where it had been clear that no one seemed able to succeed with digital transformation – I had finally been able to understand, and help companies solve, the root causes of transformation failure.

For the last year and a half, I had been in the eye of the storm, spending time with Chief Technology Officers (CTOs), Chief Information Officers (CIOs), Heads of Data Strategy and data innovation experts, talking about the problems their old-world industrial and services businesses experienced in trying to keep pace with technological change.

Automotive, manufacturing, law, banking, pharmaceutical, precision engineering, healthcare, aerospace, defence... regardless of which sector they worked in, every single one of them had faced the same struggles as they tried to reverse-engineer artificial intelligence (AI), machine learning (ML) and data science into well-established, complex, process-driven environments, to secure the future of their businesses.

They were all using emerging technologies in different ways, for different purposes, but their challenges were precisely the same, because it wasn’t the technology that was the problem. They were all operating in cultures which rejected AI transformations for the same reasons that digital transformations the world-over have consistently failed in companies for more than thirty years.

They needed a data-driven culture. And they didn’t know how to create one. The human problem felt insurmountable.

‘Are you telling me,’ Michella asked incredulously, ‘that you finally managed to solve digital transformation?’

‘I think so,’ I replied. ‘We were dangerously close to proving it. If we’d just had another couple of months …’

‘Then when we get back to London, it seems like you have two choices about what to do next,’ she said, matter-of-factly. ‘Either you draw a line under it, chalk it up to experience and find another job …’

I took another sip of my margarita and considered this while we watched the dog walkers outside the window. ‘Or? What’s the alternative?’

‘Keep on digging in. Prove it,’ she shrugged, with a glint in her eye.

That was how I found myself, back in London a week later, launching the first pieces of research that would eventually lead to this book.

I made a few calls and asked for help from a couple of friends who worked in different research agencies and knew each other socially. They challenged me to be clear on what question I was trying to answer. I told them that previous clients had clear struggles with data and with digital culture. We came up with the following challenge statement:

Can we pull together enough data points and expert opinions to prove my earlier anecdotal evidence, that barriers to digital transformation are common, pervasive, complex and cultural?

Once we’d established the research ‘mission’, we segmented my professional network into ‘technology people’ and ‘sustainability people’ and they helped me build two concurrent research projects:

1.A qualitative survey with board members and senior executives in businesses which had made Net Zero pledges, to identify the complexity of their management information (MI) and the importance for them of being able to understand and interpret sustainability data.

2.Qualitative interviews with senior innovation, change and transformation leaders about implementing digital culture change in large businesses.

Over the next five weeks, I carried out deep-dive interviews with 22 leaders on the culture question and the online survey received 105 responses. The data told a really interesting story, but halfway through the project, I realised we’d missed a trick by not targeting CTOs and CIOs, and really ‘zeroing in’ on the broader challenges around digital transformation and implementing new technology.

I needed to speak to the problem owners. So in June 2023, two new studies were launched:

1.Qualitative interviews with CTOs, CIOs and Chief Data or Digital Officers in major global corporates about challenges implementing Software-as-a-Service (SaaS) solutions at-scale.

2.A survey with CIOs, CTOs and AI directors in enterprise-scale businesses, exploring the business risks and barriers around digital transformation.

The respondent pool was a little more niche, but the 17 interviews I carried out and 38 survey respondents completely fit the profile we were looking for: these were very senior people in really big companies and their feedback was invaluable. Once a pattern emerged in the responses around a set of challenges with digital talent, skills and culture, I held the first round table, kindly hosted by one of the earliest participants and involving several of the earlier interviewees, to dig a little deeper into the problem. A structured, but free-flowing discussion provided a huge amount of context and insight into one of the biggest problems they all expressed frustration about: that their chief executives, boards and executive teams didn’t have a great level of understanding when it came to data strategy. That round table also started to build a community which would be the foundation stone of what came next.

Moving into July, I asked our previous interviewees and participants to introduce me to the people they most wanted me to talk to:

1.I carried out 16 interviews with non-executive directors (NEDs), Company Secretaries, Chairs, board advisors and management consultants, about the current state of play with executive data literacy.

2.I held 11 interviews with senior governance professionals and management consultants, to dig deeper into the earlier study about trends in MI and board reporting.

3.Finally, I held a round table in London with some of the interviewees, to discuss their perspectives on data risks and reporting.

Context is critical and it is worth noting that all of these research projects were taking place against a media backdrop in which AI was in the news on a regular basis. With the launch of ChatGPT in November 2022 and the widespread coverage capturing the public discourse around the rise of artificial intelligence – and generative AI in particular – many of the discussions touched on its real or perceived impacts. Certainly, the view from many board members I spoke to was that AI would be the biggest technological change their businesses had faced in decades.

One thing that was consistent with the interviewees was the sense that AI development was an arms race that everyone suspected they might be losing, often to their direct market competitors. For competitive reasons, companies tend not to reveal levels of investment or tech development publicly, so this burgeoning community told me it would be helpful if there was a benchmarking study, to identify which sectors were leading the pack, so that at least they could begin to understand what ‘good’ looked like.

So, in August 2023, I launched a new project, to better understand this issue:

1.I conducted 26 interviews with senior executives (heads of, VPs, directors & C-suite) about their corporate AI capabilities, talent, skills and investment.

2.I polled 91 senior leaders (CEOs, chairs, NEDs, C-suite, directors, heads of) about corporate AI, machine learning and data science capabilities in the large businesses they worked in.

Several months into the research, featuring 92 in-person and virtual interviews, three round tables and 234 survey responses from across every market segment, capturing the views of hundreds of market-leading organisations, I could now comfortably say that I had met the original challenge statement by gathering ‘enough data points and expert opinions to prove my earlier anecdotal evidence, that barriers to digital transformation are common, pervasive, complex and cultural’.

Not only that, but the depth and quality of those conversations helped me to identify some very clear common executive-level barriers, and what is needed for organisations to overcome them.

1.Most business leaders don’t understand data, or how to unlock value from it.

•Just 23% of CEOs, executives and board members understand and trust the data they make strategic decisions with;

•Only one in five CTOs believes their Executive Committee (Exco) and board has the skills and culture to deliver their digital strategy;

•Fewer than 4% of board members and 9% of executive leaders in UK PLCs are perceived to have any technology background or experience;

•58% of large firms do not have an AI expert in the top three tiers of management.

2.Poor communication between experts and executives leads to data strategy failure.

•Lack of executive data literacy and poor data strategy account for nearly a third of transformation risks;

•87% of senior executives and board members feel that MI has become more complex over the previous three years;

•78% of leaders say they expect to be ‘largely’ or ‘completely’ reliant on data to make decisions in the next two years.

This book tells the story of how, with a willing band of volunteers, we began to create a new kind of global peer-learning and research network for senior technology executives, to help them address these problems: a truly collaborative community of transformational changemakers, who are learning to help themselves, their companies and each other, to adopt new ways of working and communicating. By recognising common barriers and supporting each other, these pioneers are actively developing workplace cultures where humans understand how data can help them to do more, faster, better.

As companies become more reliant on interpreting and understanding data, it has never been more essential to break down communications barriers between technology professionals and senior managers, to influence change in their organisations. In my role as a board advisor and professional career coach, I work with data experts and senior executives, to help them find ways to communicate more effectively, navigate conflict, manage upwards and ask the right questions.

With collaborative peer-to-peer communities, providing opportunities for professionals to share knowledge and best practice, leaders have a chance to work together, communicate more effectively, recruit and retain technical talent, build more inclusive and responsible AI and embed resilient data cultures in their organisations.

Over the next few years, data will continue to rise in prominence in corporate risk registers. Drawing on the extensive body of research in this book, and working with our community of domain experts and problem owners, I have developed a set of cultural principles, laid out as a comprehensive list of 44 ‘Elements’, and distilled those into a practical action plan for leaders to follow, all in Part Three. Collectively, these identify the most consistent behaviours and attributes that experts say are the most likely indicators of digital transformation success.

Spoiler alert: this book isn’t really about technology. It’s about people. More specifically it’s about how you can help humans in your organisation adapt to transformation programmes, minimising the risk of ‘corporate tissue rejection’.

I hope you enjoy Part One, which explores thirty years’ worth of research into why most digital transformations fail. Part Two, which I hope you find enlightening, explores the biggest current transformation challenge facing most businesses: how to unlock value from artificial intelligence... and why most businesses will fail to unlock value return from their AI investments, for the same reasons they tend to fail with all digital transformation.

However, most of the actionable value of the book is in Part Three, where you will find the Periodic Table of Data Strategy Elements and the Data Success Framework: two resources designed from a great deal of work and research, which should provide you with an easy-to-follow set of principles and actions to turn transformation into a positive process of necessary change.

The Framework helps business leaders to: understand the cultural, communications and development risks they have around data; identify and engage high-potential talent; benchmark their cultural and strategic data capabilities; build measurable action plans for success; and embed a culture where AI and other data-driven technologies thrive.

This book is also an attempt to collate all of the incredible insights from across different industries, bringing together voices from some of the world’s most impressive experts and companies, to identify the root causes of digital transformation failure, to paint a clear picture of where we are right now, where we are headed, what the challenges and risks are, and to make some clear recommendations for anyone designing, building or just living through a business change programme.

I hope you find it transformative.

PART ONE

Why transformations fail

1

A short history of digital transformation

If you don’t include the fruit and veg shop where I had my Saturday job as a teenager, or the river piers I worked on in my summer holidays, I have spent 25 years in the workplace. The positions I have held and the companies I’ve worked in have varied but they were all office jobs. From my first full time role as the office junior, to C-suite and board positions, all these jobs would these days be referred to as part of the ‘knowledge economy’.

All of these roles would now be subject to disruption from the rapid rise of artificial intelligence, some of them with the same devastating effect that my mother’s profession was, following the launch of Microsoft’s Windows 95. You see, my mum was a typesetter, which involved her taking other people’s words and laying them out beautifully in book form. This was a very specialised skill set which, thanks to Bill Gates’s pledge to put ‘a PC on every desk and in every home’, has become more a vocation for the few than a profession for the many in the intervening years. As a child in the 1980s, I remember helping my mum change fonts, an elaborate process which involved ejecting the Helvetica cartridge from the hard drive and replacing it with a Garamond cartridge. The enormous orange Quadritek 1200 computer she worked on in the spare bedroom was the pinnacle of word processing technology in 1979, because you could plug in up to four fonts at a time! Mum moved with the times and upgraded by investing in an astronomically expensive Apple Macintosh computer in 1994, but her entire business model was almost extinct ten years later.

The timing of my working life also means that I’ve had a front row seat at the theatre of digital transformation for 25 years. This has taken many forms, and they haven’t all been grand enough to call them ‘change programmes’, but from launching companies’ first websites and apps, to migrating conflicting CRM systems, to ‘digitising’ tens of thousands of paper records (we now all realise that scanning and PDFing was really not digitising anything!), to bringing together the disparate systems and processes of all entities in a six-way merger... managing change has formed a significant chunk of, and persistent drumbeat to, my career.

Most companies are doomed to failure when it comes to unlocking value from artificial intelligence, for the same reasons they tend to fail at all types of digital transformation. So in this chapter, we will explore what those reasons are, and what can be done to reduce the failure rate of change programmes.

During June 2023, while carrying out an industry survey of CTOs, CIOs and technology leaders about their own digital transformation experiences, I also conducted a meta-review of hundreds of surveys, thought leadership reports and research papers from academics, consulting firms and trade bodies, to establish the current state of play, levels of technology investment and common problems of businesses facing into data-driven business change programmes.

Given the seismic impact the pandemic had (and is still having) on ways of working in just about every business in the world, I read with interest – but largely discounted – any research pre-dating Covid-19 and focused the review instead on research and content published since 2020.

The most-often cited statistic on this issue in the last few years (which almost everyone I spoke to took as gospel) is the Boston Consulting Group finding from 2020 that 70% of digital transformations fail, often because of a lack of cultural readiness.1

Since then, many thousands of business leaders in every industry sector have been quizzed on how their businesses operate, and how their past or present data transformation programmes have fared. Here are some of the key statistics from that review, grouped loosely into three categories: culture and skills, leadership, and the pace of technological change...

Culture and skills

29% of CEOs rate ‘scarcity and cost of talent with the right skills to accelerate growth’ in their top three greatest business risks – EY, 2023

24% of global technology leaders rank risk-averse cultures in their top five digital-transformation challenges – KPMG, 2022

73% of employers believe their current workforce does not have the necessary skill set to deliver on their digitalisation strategy – Eversheds Sutherland, 2022

Two-thirds (67%) of employees say opportunities to learn new skills are a key factor in any decision to job-switch – PwC, 2024

‘Lack of capable digital talent’ was cited as the most common obstacle to the adoption of new technologies – KPMG, 2022

72% of business leaders say improving organisational agility is a strategic priority – PA Consulting, 2022

71% of major corporates have low levels of data literacy and competency – Capgemini Research Institute, 2022

67% of senior leaders have experienced at least one underperforming transformation since 2017 – EY, 2022

‘Finding enough employees with critical skills’ was cited as the most common workforce issue for leaders – Alix Partners, 2022

47% of business leaders struggle to attract and reskill tech-savvy executive talent – McKinsey & Company, 2022

Leadership

83% of CEOs say their board of directors impedes the process of adopting essential new technology solutions – Alix Partners, 2022

80% of business leaders complain that their senior leadership’s risk aversion means their organisation is slower than competitors to embrace new technology – KPMG, 2024

80% of business executives do not trust the data they receive to make decisions with – Capgemini Research Institute, 2022

45% of CEOs believe their company won’t be viable in 10 years if it stays on its current path – PwC, 2024

Just 20% of businesses have corporate digital responsibility ownership and oversight at board-level – Eversheds Sutherland, 2020

‘Executives were too scared or simply unwilling to learn the digital skills they needed or to embrace their evolving role within the organisation’ – EY, 2020

72% of CEOs say their executive team lacks the agility to deal with impending disruption – Alix Partners, 2022

52% of executives feel that in the next five years their biggest competitor will be a startup or ‘digital native’ company – Deloitte, 2021

53% of senior executives have identified data and analytics as their top investment priority in the next two years – EY, 2022

85% of CEOs say it has become increasingly difficult to know what to prioritise – Alix Partners, 2022

57% of C-suite respondents cite lack of buy-in from senior leadership as holding them back from improving interoperability – Accenture, 2022

9 in 10 digital leaders say they still need to get better at helping the board understand the potential of new technologies – KPMG, 2023

The pace of technological change

78% of business leaders worry they are struggling to keep up with the pace of change – KPMG, 2024

52% of executives feel the fast pace of technology change is not good for their company or their customers – Deloitte, 2021

69% of operations and supply chain leaders say tech investments haven’t fully delivered expected results – PwC, 2024

95% of global executives believe next-generation computing will be a major driver of breakthroughs in their industry over the next decade – Accenture, 2023

63% of CEOs say their company cannot keep up with the pace of technology advancements – Alix Partners, 2024

Just 14% of UK business leaders feel confident in their ability to pivot, augment or retrofit their digital technologies quickly to comply with changes in applicable law – Eversheds Sutherland, 2022

79% of leaders are concerned about the proliferation of ‘dark data’ but are unsure how to approach the issue – Accenture, 2022

96% of executives agree that the convergence of digital and physical worlds over the next decade will transform their industry – Accenture 2023

85% of tech leaders feel major change is happening faster than ever and are worried about economic disruption – CIONET, 2022

And, interestingly, in a new statistic to challenge the BCG gospel that 70% of digital transformations fail, McKinsey & Company found in 2023 that only 12% of transformation programmes achieve their performance goals and sustain them for more than three years.

Many of these statistics proved to be useful talking points in discussion groups and interviews with chief technology and information officers during the Barriers to Digital Transformation research study, in which I carried out:

(a)qualitative interviews with CTOs, CIOs and CDOs in major global corporates about the challenges implementing SaaS solutions and new technologies at-scale; and

(b)an online survey with CIOs, CTOs & AI Directors in enterprise-scale businesses, exploring the business risks and barriers around digital transformation.

While this research sample was relatively small, with 38 survey respondents, 17 in-person or virtual interviews and one round table discussion group, the calibre of participants (who were not anonymous) was incredibly high, with 92% of them self-identifying as ‘problem-owners’, in the top three tiers of management. This sample was from large organisations: enterprise firms which had an average of 26,800 full-time employees in their UK entities.

Have you experienced a failed digital transformation in the last five years?

In the survey, I asked respondents whether they had experienced a failed digital transformation in the last five years. Over three quarters (76%) said they had, while only 16% said they had not, with nearly one in ten saying they didn’t know or would prefer not to say.

Technology leaders told me they lacked confidence that their workforce had the necessary culture and skills to deliver on their company’s digital strategy, with 86% expressing low confidence levels.

Some CTOs, CIOs and AI Directors expressed greater confidence in their executive team and board, but one in five also expressed no confidence at all.

With 0 representing no confidence and 5 representing full confidence, how confident are you that your workforce / executive team and Board members has the necessary culture and skills to deliver on your company’s digital strategy?

One of the consistent issues that came up in our discussions was a growing recognition among leaders that new technology deployment feels disruptive – sometimes threatening – and can only succeed when people at all levels are willing to adopt new ways of working with confidence and enthusiasm.

Given that most of the interviewees had a great deal of personal and professional experience of corporate change programmes, I asked them to identify the most likely and least likely indicators they look for in a successful data transformation strategy. The questions I asked this group – and the answers they provided – formed the bedrock of months of discussion and collaboration, which would eventually lead to the Periodic Table of Data Strategy Elements and the Data Success Framework, which are explained in Part Three.

The majority of CTOs, CIOs and technology leaders agreed that ‘aligning the transformation with the overall business strategy’ was the most important strategic priority, while ‘leading with empathy and kindness’ was the most important people priority. Taken in aggregate, the most successful indicators of a data transformation programme can be mapped as follows:

Where 1 is least likely and 5 is most likely, please rank the following strategy / people priorities as the most likely indicators of a successful digital transformation

What does the digital transformation market look like?

The need for businesses to adapt to constant technological change is near universal. While my desk research focused on the 400 biggest businesses in the UK, most of which tend to skew older, during the course of writing this book I have spoken to organisations of all sizes, in all industries and all ages. The smallest and youngest company – where I interviewed the CEO – had fewer than 30 members of staff, was less than a decade old and actively works in the emerging technology space. And she told me even they have invested over £200,000 in the last year on a digital transformation programme which is proving to be incredibly difficult to deliver.

The studies carried out over many months focused on the biggest firms with the most complex requirements, on the premise that it is better to develop solutions that work across all challenge areas and scale them down, as required, to meet fewer levels of complexity, than it is to design them for fewer challenge areas before attempting to scale up accordingly.

Digital transformation is an incredibly lucrative industry on its own. Almost every professional services, IT, technology or consulting firm seems to offer services to help identify and solve business change problems.

I began the research from a baseline of anecdotal evidence which pointed to a correlation between digital transformation failure and two variables, which tend to be inextricably linked: complexity of data sources and the number of humans in a workforce. The greater the data complexity or the number of workers, it appeared, the harder the challenge was likely to be.

As defined by the Department for Business, Energy & Industrial Strategy (before it was phased out in 2023),2 there are over 7,700 large organisations currently operating in the UK, employing more than 250 members of staff, with an estimated combined total of 10.64 million people. Without the contacts or resources to conduct a comprehensive national review of all ‘large’ businesses, I began by targeting the largest ‘enterprise-scale’ companies, by reviewing FTSE and other UK stock market listings, annual reports, investor websites and publicly available data to identify the 437 businesses in the UK with the highest revenues from 2022 (or in some cases, 2021). I excluded from the research a small number of hedge funds and other (mostly financial services) firms with far fewer than 250 staff, so that the sample consisted of large employers.

The remaining 400 business I focussed on - mostly enterprise-scale, employing more than 1,000 people - are headquartered in, or have a major presence in, the UK. These companies employ 8.11m people in the UK (26% of the workforce) and are most likely to be dealing with extensive legacy infrastructure, the result of decades of growth, mergers and acquisitions, which provides inherent data complexity issues.

On average, these firms are 107 years old, employ 28,613 people each and spend an estimated £13.5m per year on technology R&D. The top 250 UK-headquartered companies (by revenue) have combined revenues of $1.927.5tn, approximately 55% of the UK’s GDP.

Of the 400 largest employers in the UK, 89% are older than the internet. More of them pre-date the Industrial Revolution than have been established since the birth of the web. These are multi-generational companies, with shareholders, tens of thousands of employees and – culturally at least – they are looking further ahead than the standard five-year business strategy. Many of these businesses are investing in new technologies with the same enthusiasm their predecessors brought to electrification. They plan to be around at the turn of the next century. And the one after that.

Date of establishment of the UK’s largest 400 companies

These companies have not survived without change. They have adapted, merged, acquired and evolved. Many processes have had to be updated but inevitably, some have stayed the same for generations, with ways of working passed from manager to apprentice. For some of their workers through the years, the introduction of the computer may have seemed like a passing fad.

Each of these organisations holds personal, business, sensitive and behavioural data on thousands or millions of customers worldwide. Each one is the custodian of billions or trillions of proprietary data points, hidden in silos, from complex schematics and patented technology, to ground-breaking IP and detailed intelligence on international supply systems.

The 358 companies in the market research sample which were founded before the 21st century are the most likely customers for such services, as they don’t have digital technologies baked into their DNA. There can be found a corresponding range of solution providers for any business, regardless of what stage they are at on their journey, to help reverse-engineer existing business models into successful data-driven organisations.

Digital transformation is such a big marketplace that there is even a thriving cottage industry devoted to estimating its growing size and forecasting its growth. Averaging out the estimates from six different research firms, the global market size of digital transformation as a sector is probably somewhere in the region of $625bn per year.

When failure is so pervasive, across industries, specialisms and geographies, the fear of it increases. In 2022, technologist Caroline Gorski was the first to compare digital transformation to what author Timothy Morton called ‘hyper-objects’: entities of such vast spatial and temporal disruption that they defy traditional ideas about what a thing is in the first place.

So when I started talking to business leaders, collecting the views of over 300 organisations, I found they were beginning to see transformation not as a problem that could be solved, but as a set of symptoms which at best could be managed. This limiting of aspiration from business leaders may in itself be one of the symptoms: EY found in 2022 that 67% of senior leaders have experienced at least one underperforming transformation since 2017.

Because so many of them already bear the scars of failure, the expectation most senior executives and board members now have of consulting firms working in this space has shifted accordingly. One interviewee, a Chief Data Officer in a high street retailer, compared his company’s evolving relationship with a Big Four consulting firm to preparing his village’s flood defences: ‘We used to ask them to build a better river wall, but the water kept rising, so now they make us the best sandbags and waterproof our furniture instead.’

This growing sense of failure being inevitable eventually corrodes morale at every level: as EY found, 50% of workers who experienced an underperforming transformation agreed that ‘transformation’ was just another word for ‘layoffs’. Speaking with leaders across finance, healthcare, engineering, pharmaceutical, energy and other sectors, I have discovered that no industry is immune and everyone is facing the same challenges:

1.Most business leaders don’t understand data, or how to unlock value from it.AI seems to be in the news every day, but business-relevant insight is rare, so the vast majority of decision-makers don’t really understand what data-driven technologies might mean for them, their organisations, their people or their bottom lines. Limited educational support means boards and executive teams struggle to make well-informed decisions around AI, machine learning and data strategy. Consequently, the lack of executive support and understanding hinders most transformation programmes.

2.Poor communication between experts and executives leads to data strategy failure.