{"id":82124,"date":"2026-03-30T14:02:11","date_gmt":"2026-03-30T12:02:11","guid":{"rendered":"https:\/\/blog.richardvanhooijdonk.com\/?p=82124"},"modified":"2026-03-30T15:41:10","modified_gmt":"2026-03-30T13:41:10","slug":"why-becoming-an-ai-first-organisation-is-key-to-business-success","status":"publish","type":"post","link":"https:\/\/blog.richardvanhooijdonk.com\/en\/why-becoming-an-ai-first-organisation-is-key-to-business-success\/","title":{"rendered":"Why becoming an AI-first organisation is key to business success"},"content":{"rendered":"\n
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Executive summary<\/h2>\n\n\n\n

The race to become AI-first is intensifying across every industry. Early movers are already reaping measurable rewards in productivity, speed, and market performance, while those still treating AI as a bolt-on risk falling permanently behind. Yet for most organisations, the real challenge isn\u2019t the technology itself; it\u2019s transforming the structures, cultures, and mindsets around it fast enough to matter.<\/p>\n\n\n\n

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  • AI agent capabilities are doubling every seven months – three times the rate of Moore\u2019s Law – and the pace may still be accelerating.<\/li>\n\n\n\n
  • AI-first companies deliver 1.7x higher revenue growth and 3.6x greater total shareholder return than slower adopters.<\/li>\n\n\n\n
  • Legacy structures, rigid hierarchies, and siloed teams remain the biggest obstacles to meaningful AI transformation.<\/li>\n\n\n\n
  • Only 26% of companies have developed the capabilities to move beyond proofs of concept, and 95% of generative AI pilots are failing.<\/li>\n\n\n\n
  • The average half-life of professional skills has shrunk from ten years to roughly four, with digital fields closer to two.<\/li>\n\n\n\n
  • Embedding sustained organisational change typically takes five to seven years, a timeline at odds with the speed of AI advancement.<\/li>\n<\/ul>\n\n\n\n

    The gap between leaders and laggards is widening quickly, and closing it will demand more than increased spending. Companies that want to compete in an AI-driven economy will need to fundamentally rethink how they operate, make decisions, and develop talent, and they\u2019ll need to do it on a timeline that leaves little room for hesitation.<\/p>\n<\/div>\n<\/div><\/div>\n\n\n\n

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    AI\u2019s rapid rise has moved organisations beyond the experimentation stage and into a year of real transformation. The first signs are already here: AI-driven reorganisations, smaller teams, leaner back offices, and the steady disappearance of junior roles as entry-level tasks are absorbed by AI models and agents. What\u2019s emerging is not simply a more efficient version of the same company, but a different kind of organisation altogether – an AI-first organisation – and many leaders and employees are still working out what that means for them in practice.<\/p>\n\n\n\n

    Early adopters are already seeing tangible benefits: faster execution, lower costs, and measurable improvements in output quality. But this moment is about more than just marginal productivity gains. AI is changing how work is designed, how decisions get made, and how teams are staffed – reshaping operating models in ways that reward speed, adaptability, and lean structures. Organisations treating AI as a bolt-on tool may capture quick wins, but they risk falling behind those rebuilding from the ground up with AI as the foundation.<\/p>\n\n\n\n

    Becoming an AI-first organisation demands a willingness to rethink workflows, challenge legacy structures, and build a culture that can absorb rapid change without stalling. Deploying the technology is rarely the hardest part; redesigning roles, reworking collaboration, and restructuring fast enough to capitalise on the shift is. The window for meaningful advantage is narrowing, and the cost of moving slowly is becoming clearer by the day. In this article, we take a closer look at AI\u2019s impact and show you how to future-proof your organisation for what comes next.<\/p>\n\n\n\n

    What is an AI-first organisation?<\/h2>\n\n\n\n

    An AI-first organisation redesigns workflows around AI, embedding the technology directly into every function.<\/strong><\/em><\/p>\n\n\n\n

    Digital-first. Mobile-first. Cloud-first. The business lexicon is crowded with \u201cfirsts\u201d, and it\u2019s tempting to dismiss AI-first as yet another corporate jargon. But underneath the buzzword lies something more substantial – a genuinely different approach to how organisations structure themselves. Those that get it right don\u2019t just move faster. They tend to make better decisions, learn more quickly, and execute with less internal drag. So, what is an AI-first company, exactly?<\/p>\n\n\n\n

    Many organisations signal their interest in AI through pilots, chatbot experiments, or executive memos encouraging teams to explore new tools. While these can be useful steps, they are often limited in impact. That\u2019s because such efforts are usually layered onto existing structures, processes, and incentives that were designed long before AI entered the picture. The result is incremental improvement at best. An AI-first organisation, on the other hand, takes a fundamentally different approach. Instead of attaching AI to an established model, it places AI at the centre and builds outward.<\/p>\n\n\n\n

    AI at the centre<\/h3>\n\n\n\n

    In practice, that means rethinking workflows from the ground up. In an AI-first organisation, AI handles substantial portions of the work, including analysing data, generating recommendations, and even triggering execution. People focus their energy on the things humans do well – exercising judgment in ambiguous situations, building relationships, and handling exceptions that fall outside established parameters. The division of labour between humans and AI becomes intentional rather than accidental.<\/p>\n\n\n\n

    \u201cAI-first transformation doesn\u2019t live in labs. It lives in the business,\u201d says Amanda Luther, Managing Director and Senior Partner at BCG. \u201cYou only get exponential value when AI is directly embedded in how teams operate, every day, across every function.\u201d Following this principle, AI-first companies distribute AI capabilities throughout the organisation rather than concentrating them in a single team walled off from the rest of the company. AI specialists work alongside marketing, operations, product, and finance teams – building tools at the point where they\u2019ll be used and refining them based on real feedback. This results in faster innovation, fewer handoffs, and AI systems that actually work in the real world rather than just in controlled experiments.<\/p>\n\n\n\n

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    \u201cThe pace of AI change is overwhelming to everybody, and there\u2019s a mismatch between how quickly organisational change can keep up with it.\u201d<\/p>\nJoe Atkinson, PwC\u2019s global head of AI<\/em><\/cite><\/blockquote>\n\n\n\n

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    The velocity gap<\/h2>\n\n\n\n

    We plan in straight lines; the pace of change is exponential.<\/strong><\/em><\/p>\n\n\n\n

    Now that we have a better idea of what an AI-first organisation is, let\u2019s take a closer look at some of the factors that have facilitated its rise. For decades, Moore\u2019s Law served as a guiding light for the tech industry. First articulated by Intel co-founder Gordon Moore, it described an observed pattern rather than a physical law: the number of transistors on a microchip would roughly double every two years. That steady, exponential increase in computing power quietly shaped almost every technological revolution that followed, from personal computers and smartphones to the internet and cloud infrastructure.<\/p>\n\n\n\n

    A similar exponential curve can now be observed in AI\u2019s capabilities, although the tempo is far more intense. Only this time, instead of the number of transistors, progress is measured in terms of what AI systems can actually do. A 2025 report by the nonprofit research institute METR found that the length of tasks AI agents can successfully complete has doubled roughly every seven months over the past six years. Whereas early systems struggled even with short, relatively simple tasks, today\u2019s agents can handle complex tasks that would take a human around 24 hours to finish. To put that into perspective, those figures indicate that AI agents are improving at a rate three times that of Moore\u2019s Law. And the pace may be picking up further. According to AI Digest, this doubling time compressed to just four months between 2024 and 2025. If that trajectory holds, AI systems capable of managing complex projects spanning months could arrive by the early 2030s, if not even sooner.<\/p>\n\n\n\n

    Overwhelming pace of change<\/h3>\n\n\n\n

    Keeping up with such a frantic pace of change is proving extraordinarily difficult for companies. Changing how an organisation thinks and behaves is a painstakingly slow process. People need time to update their mental models, unlearn habits, and build trust in new ways of working. This becomes even more difficult to achieve with groups of people, which carry additional weight in the form of established processes, incentives, reporting lines, and cultural norms. The bigger the organisation, the more momentum it has, and the more energy it takes to turn course. \u201cIt\u2019s a complex time in the world of AI implementation,\u201d says Joe Atkinson, PwC\u2019s global head of AI. \u201cThe pace of AI change is overwhelming to everybody, and there\u2019s a mismatch between how quickly organisational change can keep up with it.\u201d<\/p>\n\n\n\n

    That mismatch can have measurable consequences. PwC\u2019s data shows that organisations adopting AI more quickly generate roughly three times more revenue per employee than those moving at a slower pace. \u201cThat opportunity means that it\u2019s not only a big upside for organisations,\u201d adds Atkinson. \u201cIt is a big upside for employees who lean in and use these tools to help create greater productivity, outputs, and creativity.\u201d The evidence from the market supports this notion. Stripe\u2019s 2024 analysis of companies on its platform found that the top 100 AI companies reached US$1m in annualised revenue in a median of 11.5 months, around four months faster than the fastest-growing SaaS companies. What\u2019s more, AI companies founded after 2020 hit major revenue milestones about three times faster than their older cohorts.<\/p>\n\n\n\n

    Building around AI<\/h3>\n\n\n\n

    Much of that performance advantage comes down to how these organisations are built. AI-first companies place AI capabilities at the core from the outset, rather than layering them onto existing structures. Product development tends to move faster because AI automates tasks that traditionally consume significant human time, from coding assistance to testing, documentation, and customer feedback analysis. Shorter development cycles allow teams to experiment more frequently and respond to market signals with less delay. AI also changes the speed and quality of decision-making thanks to its ability to process large volumes of data quickly and uncover hidden patterns, which helps teams spot trends earlier, anticipate risks, and respond with more confidence. Leaders still make the final call, but they do so with fresher, more comprehensive information. Over time, that speed and accuracy accumulate into a tangible advantage over organisations that rely primarily on human intuition.<\/p>\n\n\n\n

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    \u201cThe companies that are capturing real value from AI aren\u2019t just automating – they\u2019re reshaping and reinventing how their businesses work. And they\u2019re pulling away.\u201d<\/p>\nNicolas de Bellefonds, global leader of BCG\u2019s AI efforts<\/em><\/cite><\/blockquote>\n\n\n\n

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    The economic shift<\/h2>\n\n\n\n

    The fundamentals of work and value are changing.<\/strong><\/em><\/p>\n\n\n\n

    AI-first companies are rewriting the playbook for how organisations are built, scaled, and run. As they expand into new markets and industries, they set new expectations around speed, cost structures, and how value gets created. Traditional companies increasingly find themselves having to revisit fundamental assumptions about strategy, operating models, and the way they approach both talent and technology. Advantages that once came from operational scale, large teams, or heavy marketing spend are starting to erode. Organisational charts are flattening as AI agents absorb back-office work that previously required layers of coordination and oversight.<\/p>\n\n\n\n

    Recent data reveals that the performance gap between leaders and laggards has already grown substantially. According to BCG\u2019s 2025 report, AI-first companies deliver 1.7 times higher revenue growth, achieve 2.7 times greater return on investment, and generate 3.6 times higher total shareholder return than slower adopters. \u201cAI is reshaping the business landscape far faster than previous technology waves,\u201d explains Nicolas de Bellefonds, global leader of BCG\u2019s AI efforts. \u201cThe companies that are capturing real value from AI aren\u2019t just automating – they\u2019re reshaping and reinventing how their businesses work. And they\u2019re pulling away.\u201d<\/p>\n\n\n\n

    The widening gap<\/h3>\n\n\n\n

    This gap is only set to widen in the coming years. AI-first organisations tend to reinvest early gains back into their capabilities, strengthening their teams, upgrading infrastructure, and experimenting with new tools at a pace others struggle to match. According to BCG, these firms plan to spend more than twice as much on AI as laggards in 2025, and they expect that investment to translate into roughly double the revenue growth and 40% greater cost reductions in areas where AI is applied. This puts enormous pressure on trailing firms to act fast and take concrete steps to close the gap before it\u2019s too late. \u201cThe technology is advancing weekly, and leading companies are accelerating,\u201d says Michael Grebe, a managing director and senior partner at BCG. \u201cFor the majority of firms, catching up will require more than investment – it will take reinvention.\u201d<\/p>\n\n\n\n

    The workforce implications will be equally profound, as the accelerated adoption of digital tools, remote work solutions, and technologies like generative AI change how people work and what skills they need. According to the World Economic Forum\u2019s Future of Jobs 2025 report, as many as 39% of workers\u2019 core skills are expected to change by 2030. Henk Volberda, professor at the University of Amsterdam who contributed to the report, predicts that humans will carry out only a third of all work by 2030. The remaining two-thirds will either be performed in collaboration with technology or fully automated. Goldman Sachs goes even further, suggesting that up to 50% of jobs could be fully automated by 2045, with as many as 300 million roles worldwide potentially affected.<\/p>\n\n\n\n

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    \u201cWe are seeing the half-life of skills compress, which means that we constantly need to reinvent ourselves, to learn new skills if we want to innovate.\u201d<\/p>\nStanford lecturer Kian Katanforoosh<\/em><\/cite><\/blockquote>\n\n\n\n

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    The internal barrier<\/h2>\n\n\n\n

    Most organisations are held back by their own internal structure.<\/strong><\/em><\/p>\n\n\n\n

    Relentless pressure to innovate and grow in a competitive, fast-moving environment has pushed digital transformation to the top of the agenda for organisations across industries. However, despite investing a significant amount of time, effort, and money into their digital transformations, most organisations fail to achieve digital maturity. Reworked\u2019s 2024 State of the Digital Workplace Report found that only 24% of organisations consider their digital workplace fully mature, while just 13% say that their core digital workplace is fully implemented.<\/p>\n\n\n\n

    Of course, investment alone doesn\u2019t guarantee results. Bain & Company estimates that more than a third of large organisations are undergoing some form of business transformation at any given moment, yet only around 12% manage to achieve their original ambition. AI initiatives face similar headwinds. While AI programmes have become pretty widespread across industries, BCG research shows that only 26% of companies have developed the capabilities required to move beyond proofs of concept and generate real value. Perhaps most concerning, The GenAI Divide: State of AI in Business 2025, a report published by MIT\u2019s NANDA initiative, reveals that 95% of generative AI pilots at companies are failing.<\/p>\n\n\n\n

    What is holding companies back?<\/h3>\n\n\n\n

    So, why such poor results? The truth is that most legacy companies are held back by their own internal structures. They are typically built around static workflows and rigid processes, frameworks that were not designed to accommodate systems that learn, adapt, and improve continuously. AI-native firms take a slightly different approach. Data sits at the centre of the organisation, with processes, products, and decisions built around it. The contrast also extends to how teams are organised. While legacy firms typically employ teams of specialists organised in deeply siloed departments, AI-native firms prefer smaller, more flexible teams where people can act as generalists, moving quickly across functions as needs change. \u201cAI-native companies scale so quickly because their teams can adapt fast, pivot across functions and seize opportunities without being slowed by rigid roles,\u201d explains Mo Ezderman, Director of AI at Mindgrub Technologies.<\/p>\n\n\n\n

    Outdated organisational hierarchies and bureaucratic decision-making represent some of the biggest obstacles to AI transformation for traditional companies. Complex approval chains, departmental silos, and risk-averse cultures frequently stand in the way of AI progress. However, it\u2019s important to point out that AI does not slot neatly into existing structures. To succeed, companies need to undergo a foundational change, one that begins at the leadership level. That includes flattening hierarchies, empowering decision-makers closer to the front lines, and championing continuous learning throughout the organisation.<\/p>\n\n\n\n

    The shrinking half-life of skills<\/h3>\n\n\n\n

    The structural challenge is compounded by the workforce one, as technological advances accelerate the rate at which skills become obsolete. New technologies now handle not just repetitive and manual tasks but increasingly sophisticated knowledge work that many assumed would remain safely in human hands, such as research, writing, and coding. As a result, the average half-life of skills (the amount of time a skill remains relevant and valuable) has shrunk from around ten years four decades ago to roughly four years today, according to Stanford lecturer Kian Katanforoosh. In digital fields such as AI, it may be closer to two. \u201cWe are seeing the half-life of skills compress, which means that we constantly need to reinvent ourselves, to learn new skills if we want to innovate,\u201d explains Katanforoosh. In a recent IBM survey, executives estimated that 40% of their employees will need to reskill as a result of AI and automation within the next three years. At a global level, that equates to roughly 1.4 billion people.<\/p>\n\n\n\n

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    \u201cThe level of change has dramatically increased over the last few years, and it requires a structural change in how businesses operate.\u201d<\/p>\nJack Azagury, Group Chief Executive of Strategy & Consulting at Accenture<\/em><\/cite><\/blockquote>\n\n\n\n

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    The time trap<\/h2>\n\n\n\n

    The mathematics of change no longer add up.<\/strong><\/em><\/p>\n\n\n\n

    For most of human history, technological change unfolded at a glacial pace. The tools people learned to use in childhood often occupied the central role throughout their entire lives. For example, our ancestors needed 2.4 million years to learn how to control fire. Thankfully, the pace has picked up considerably since then. In just 66 years, humanity went from the first flight in 1903 to the Moon landing in 1969. It\u2019s not uncommon for recent generations to become completely dependent on technologies that were unimaginable in their youth.<\/p>\n\n\n\n

    AI has only accelerated this already fast-paced technological change even further. Development cycles that once stretched across years are now measured in months, and in some cases, weeks. New capabilities emerge, scale globally, and reshape expectations before organisations have fully absorbed the previous wave. The tempo of change itself has become a defining feature of the environment in which businesses operate. Accenture\u2019s Index indicator analysis found that the pace of technological change has risen a staggering 183% since 2019. \u201cThe level of change has dramatically increased over the last few years, and it requires a structural change in how businesses operate,\u201d says Jack Azagury, Group Chief Executive of Strategy & Consulting at Accenture. \u201cIncremental changes in ways of working and performance are no longer sufficient to compete.\u201d<\/p>\n\n\n\n

    Change takes time<\/h3>\n\n\n\n

    Yet while technology moves quickly, organisations and people do not. Change remains a slow and often fragile process, particularly when it touches culture and behaviour. \u201cChange takes time, especially when a culture change is involved,\u201d explains Isabella Brusati, Change Management Director at Prosci Europe. \u201cResearch shows that, on average, it takes 5 to 7 years to embed sustained change in an organisation. Most companies are trying to speed this process up, but the human brain is not geared to deal with multiple and continuous changes. This leads to stress and ultimately, burnout.\u201d<\/p>\n\n\n\n

    The strain is becoming visible. A 2024 BT survey found that nearly 90% of businesses were investing in new technologies to improve productivity and strengthen competitiveness. At the same time, 58% of business leaders expressed concern about their ability to keep up with current technology trends. Almost nine in ten reported that the pressure to adapt is increasingly contributing to work-related stress. As the pace of technological change continues to climb, organisations face a growing tension between what technology enables and what people can realistically absorb.<\/p>\n\n\n\n

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    Closing thoughts<\/h2>\n\n\n\n

    There\u2019s a certain irony in laying out the urgency of AI transformation. The velocity gap, the economic shift, the structural barriers, the time trap – presented together, they risk making the whole thing feel overwhelming, like a problem too large and too fast-moving for any single organisation to meaningfully address. But that framing misses the point. The companies pulling ahead right now aren\u2019t the ones that had everything figured out before they started. They\u2019re the ones that started anyway, accepted a degree of discomfort, made imperfect bets, and built the organisational reflexes to course-correct quickly. Becoming AI-first was never about flipping a switch. It was about developing the capacity to keep pace with a world that no longer waits for five-year plans to finish.<\/p>\n\n\n\n

    What makes this moment different from previous technology shifts is the sheer breadth of what\u2019s changing simultaneously. It\u2019s not just one function or one industry being disrupted; it\u2019s the underlying economics of how work gets done and how value gets created. That breadth is precisely what makes the structural challenge so acute, but it\u2019s also what makes this one of the most genuinely exciting periods in the history of business. The barriers to building something meaningful have never been lower, and the organisations that get this right won\u2019t just be more efficient, they\u2019ll be fundamentally more creative, more responsive, and more capable of solving problems that previously seemed intractable.<\/p>\n<\/div>\n<\/div>\n<\/div><\/div>\n\n\n\n

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    <\/p>\n","protected":false},"excerpt":{"rendered":"

    Becoming an AI-first organisation is no longer optional; it\u2019s a matter of survival. So why are so many companies still struggling to make the shift?<\/p>\n","protected":false},"author":10,"featured_media":82128,"parent":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_coblocks_attr":"","_coblocks_dimensions":"","_coblocks_responsive_height":"","_coblocks_accordion_ie_support":"","footnotes":""},"categories":[5374],"tags":[],"article-type":[],"trends":[5485,5660],"class_list":["post-82124","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-work","trends-artificial-intelligence-en","trends-gen-ai-chatgpt"],"yoast_head":"\nWhy becoming an AI-first organisation is key to business success<\/title>\n<meta name=\"description\" content=\"Becoming an AI-first organisation is no longer optional; it\u2019s a matter of survival. 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