The Great Remaking: AI and the Race to Transform the Very Essence of Work

Five major technology revolutions have reshaped how organisations operate across the past half-century. The desktop computer transformed individual productivity, the internet transformed connectivity, the web transformed distribution and commerce, mobile transformed access and ubiquity, and the cloud transformed how organisations provision, build, and scale. Having lived through all five and worked professionally at the heart of the last three, I can say that each felt transformative at the time — each was significant, and each genuinely changed how businesses operated.

Yet AI feels qualitatively different from all of them — not merely larger in scale of impact, but different in kind. The reason is precise: none of those five technologies restructured the essence of how organisations actually work — how they think, how they decide, what they create, how they deliver. AI does. That is what makes this not the sixth wave in a familiar sequence, but something fundamentally new.

Most organisations recognise this instinctively, yet the prevailing response remains incremental — bolting AI onto existing processes rather than redesigning how work is structured. What defines this moment is not simply that AI enables system-level transformation — it is that AI restructures every dimension of what organisations do. Not just how they analyse, but how they commit. Not just how they design, but how they build and deliver. The title of this article makes a specific claim: AI transforms the very essence of work. What follows is the evidence that this is happening, a definition of what that essence actually is, and an argument for why it constitutes a race with compounding consequences. This is The Great Remaking.

The evidence — both sides of the remaking

The Great Remaking is not hyperbole. It is supported by converging evidence across both the cognitive and the physical domains of how organisations operate.

On the cognitive and intellectual side, the transformation is already at scale. McKinsey’s State of AI 2025 report found that 88% of organisations now use AI in at least one business function, up from 78% just one year earlier, with every industry sector showing meaningful increases. Their earlier analysis estimated that generative AI alone could add $2.6–4.4 trillion annually across 63 use cases spanning 16 business functions. Goldman Sachs projects a 7% GDP increase over ten years from AI adoption. The scale of committed capital confirms this is not speculative — AI hyperscaler capital expenditure is projected to reach $527 billion in 2026, continuing upward revisions that have exceeded analyst expectations for two consecutive years. BCG’s “AI at Work 2025” research found that when organisations redesign workflows around AI rather than simply augmenting existing processes, 67% of employees save more than an hour daily — compared with only 49% who experience similar gains from basic augmentation. The pattern is clear: the real productivity gains come not from adding AI to existing ways of working but from redesigning how the work itself is structured.

Simultaneously, the transformation is moving beyond screens and into the physical world. The International Federation of Robotics reports that global industrial robot installations reached 542,000 units in 2024 — more than double the figure from a decade ago — and that the global market value of industrial robot installations has reached an all-time high of $16.7 billion. At CES 2026, Nvidia CEO Jensen Huang declared that "the ChatGPT moment for physical AI is here." Bank of America projects humanoid robot unit costs falling from $35,000 in 2025 to $13,000–17,000 within the decade. Deloitte’s Tech Trends 2026 report captures the broader shift: intelligence is no longer confined to screens — it is embodied, autonomous, and solving real problems in the physical world.

At the level of nations, the White House Council of Economic Advisers published a report in January 2026 explicitly titled Artificial Intelligence and the Great Divergence, drawing direct parallels between AI’s potential economic impact and the Industrial Revolution’s role in causing industrialising nations to accelerate their growth relative to the rest of the world. The divergence the White House describes between nations is already visible between organisations.

The evidence confirms that both the cognitive and the physical are being restructured. But that observation, by itself, is not yet actionable. The critical question for any leadership team is: what, specifically, is being remade?

The essence of work

Every organisation, regardless of sector, size, or geography, exists to perform four fundamental types of work. These are not functions or departments — they are the irreducible dimensions of what every organisation does — the very essence of work.

Thinking — the work of making sense of the world. Analysis, research, synthesis, pattern recognition, sense-making. This is what consultancies, research teams, strategy functions, and intelligence units do as their primary activity — but every organisation thinks. AI’s impact on thinking work is already profound: it can analyse faster, across more data, at greater scale than any human team. But the moat for organisations whose primary value lies in thinking work is their proprietary knowledge — the closed, institutional data and context that AI cannot access from the outside. The human residual is judgement: knowing which questions to ask, what matters, and what the data does not contain.

Deciding — the work of committing to a course of action and bearing the consequences. Allocating capital, approving a project, choosing a supplier, approving a hire, signing off on a strategy. This is distinct from thinking — the output is not insight but the act of commitment and the willingness to bear consequences. AI already outperforms humans in many bounded decision domains and is increasingly capable in complex ones. But decision work exposes a tension that no other dimension shares: AI can increasingly assume the agency — the capacity to assess, recommend, and act — but accountability does not transfer with it. An underwriter’s value is not the analysis; it is the signature. A Board’s value is not the insight; it is the commitment. AI can inform every decision an organisation makes. The question is which decisions it will increasingly make — and who bears the consequences when it does.

Creating — the work of designing and building something new. Products, content, code, strategies, campaigns, financial instruments, drug formulations. Generative AI has made this the most visibly disrupted dimension of work — producing artefacts at speed and scale that would have been unimaginable three years ago. This is where Elon Musk’s “Macrohard” project within xAI is most directly aimed. As Musk stated at an internal all-hands meeting: “When you look at the most valuable companies in the world, their output is digital. They don’t actually make hardware. So it should be possible to completely emulate any company where the output is digital.” Whether or not Macrohard succeeds, the intent reveals the strategic logic: for organisations whose primary work is creation, AI-driven substitution is not a distant risk but an active programme at one of the world’s most valuable technology companies. His broader argument — that augmenting human productivity is merely a transitional step — suggests the trajectory runs not from manual to assisted, but from assisted to replaced. The moat in creative work is originality, cultural resonance, taste, and meaning — the things that make a human-created piece resonate rather than merely function.

Delivering — the work of producing outcomes in the physical world. Manufacturing, logistics, construction, healthcare delivery, energy generation and distribution, maintenance, fulfilment. This is where work crosses from the conceptual into the material — where atoms move, not just bits, and where the real world imposes constraints of physics, geography, time, and safety. AI’s impact on delivery work is both restructuring — through predictive maintenance, autonomous quality control, and real-time supply chain optimisation — and increasingly substitution, through robotics, embodied AI, and autonomous vehicles. Musk projects that humanoid robotics will begin transforming physical work within two years and have massive impact by the end of the decade. With unit costs falling sharply and embodied AI advancing rapidly, the economics of substituting physical work are approaching viability at scale. The moat in delivery work is the sheer complexity of the physical systems themselves — but this moat is shrinking faster than most leadership teams recognise.

Across all four types of work, one element resists AI replication most stubbornly: relationships. Selling, negotiating, building trust, managing stakeholders, earning confidence, caring. This human dimension runs through thinking, deciding, creating, and delivering alike. It is arguably the most durable source of human value in the age of AI — because trust is relational, not functional, and AI cannot yet be a trusted counterparty in the way a human can.

The diagnostic question for every leadership team is this: across these four dimensions of work, where is AI’s impact in your organisation — is it augmenting (making people more effective), restructuring (changing how the work is organised), or substituting (performing the work itself)? And how fast is it moving along that trajectory? Because these are not static states. They are a direction of travel. Work that is being augmented today may be restructured within two years and substituted within five. The pace varies by dimension — creative work is further along the curve than delivery work today, but embodied AI is accelerating the timeline rapidly. The strategic response differs profoundly depending on where each dimension sits and how fast it is moving.

Why this is a race — and why late movers cannot catch up

The Great Remaking is not a transition that organisations can join at their convenience. It is a race with compounding consequences, and the window for meaningful participation is narrowing.

The argument rests on a core principle of systems thinking: optimising individual components of a system in isolation often produces worse outcomes than redesigning the system as a whole. This is precisely the difference between organisations that bolt AI onto existing processes and those that redesign how work is structured around AI — and it is why the gap between early movers and late movers compounds rather than remains static.

When organisations move beyond isolated AI pilots and redesign how they think, decide, create, and deliver, they create self-reinforcing advantages across multiple dimensions. Their data assets become richer because AI-integrated workflows generate higher-quality, better-structured data — which in turn makes their AI systems more effective. Their talent becomes more capable because people working alongside well-designed AI systems develop skills and judgement that cannot be replicated in organisations still using AI as a bolt-on tool. Their operational processes compound efficiency gains because redesign creates feedback loops where each improvement enables the next. These are the dynamics that systems thinking predicts: interconnected improvements that reinforce one another, creating emergent capabilities that no single component can produce alone.

BCG’s February 2026 research underscores the scale of this divergence. Of the organisations they studied, only around 5% have achieved substantial financial gains from AI — defined as meaningful increases to revenue or cash flow alongside significant process and workflow improvements. But that small group shows three-year total shareholder returns roughly four times higher than AI laggards. The mechanism is telling: BCG estimates that approximately 10% of AI value comes from algorithms, 20% from the technology required to implement them, and 70% from rethinking the people component — redesigning workflows, upskilling talent, and restructuring how human and machine capabilities integrate. This is not a technology gap. It is a systems gap.

These are not linear advantages. They compound. An organisation that redesigned its core workflows around AI eighteen months ago is not merely eighteen months ahead of a competitor starting today — it is structurally ahead, because the compounding effects create a gap that widens over time rather than remaining static. The moat operates across all four dimensions of work — proprietary knowledge in thinking, institutional speed in deciding, human-AI integration in creating, physical-digital depth in delivering — and in every dimension, the advantage compounds. The organisations that are furthest along in The Great Remaking are building moats that are deep, wide, and increasingly difficult to cross.

In the internet era, fast followers could often catch up because the advantages of digital distribution were relatively easy to replicate — build a website, establish an e-commerce channel, develop a mobile app. The Great Remaking is different because the advantages compound through an organisation’s unique combination of data, talent, processes, and operational knowledge across all four dimensions. These advantages cannot be purchased off the shelf or closed through a single transformation programme.

The earlier BCG workflow redesign finding reinforces this — 67% versus 49% saving significant time tells us that redesign produces better results than augmentation. But the organisations that redesign also learn more — about what works, where AI creates most value, and how to integrate human judgement with machine capability. That learning itself becomes a competitive asset that organisations still treating AI as a bolt-on tool never acquire.

The window is narrowing

We are in the early phase of The Great Remaking — the period where the evidence is overwhelming but most organisations are still treating AI as an enhancement to existing ways of working rather than a fundamental restructuring of the essence of work itself. The gap between those two approaches is already measurable — and it is compounding. Unlike the five technology waves that preceded it — each of which changed how organisations accessed, connected, distributed, scaled, or consumed — this transformation restructures how organisations think, decide, create, and deliver. No function, no business unit, and no sector is exempt.

The implication is stark: there is a window during which organisations can meaningfully participate in The Great Remaking. The organisations that wait for certainty before committing to redesigning how they work may find that by the time they are ready to act, the race may already be over.