Future of Work Credit:thebestblogever
Introduction
Four major technology revolutions have reshaped how organisations operated during the past half-century. The desktop computer transformed individual productivity, the internet transformed connectivity, the web transformed distribution and commerce, mobiles transformed access and ubiquity. Each was significant, and each genuinely changed how businesses solved problems. However, the introduction of AI has added a new quality of transformation. 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 as better-structured data makes their AI systems more productive. Individuals working with well-designed AI systems develop skills and judgement that cannot be replicated in organisations still using AI as a tool with no major improvements to their processes. A new approach is required which redesigns how work is performed both on an organisational as well as an individual level.
The Remaking of Work
According to an article written by Mario Thomas, Head of Applied AI & Emerging Technology at Amazon Web Services (AWS), titled The Great Remaking: AI and the Race to Transform the Very Essence of Work, most organisations recognise this requirement instinctively, yet the prevailing response remains marginal, bolting AI onto existing processes rather than redesigning how work is performed. In contrast, AI has the potential to restructure every activity of what organisations do. Not just how they analyse, but how they commit, not just how they design, but how they build and deliver. Consequently, AI transforms the very essence of work. Regardless of sector, size or geography every organisation performs four fundamental types of work:
Thinking: The activity of making sense of the world. AI’s impact on thinking work is profound. It can analyse faster, across more data, at greater scale than any human team. But the key for organisations whose primary value lies in thinking, working represents their proprietary knowledge. The human contribution is judgement, knowing which questions to ask and what really matters.
Deciding: The work of committing to a course of action and bearing the consequences. Allocating capital, approving a project, choosing a supplier, approving a new hire or signing off on a strategy. This is distinct from thinking as the output is not insight, because the act of commitment includes the willingness to bear consequences.
Creating: The work of designing and building something new such as products, content, code, strategies, campaigns, financial instruments or drug formulations. When you look at the most valuable companies in the world, the result of their work is digital. They do not make hardware. Hence it should be possible to emulate any company where the output is digital. For organisations whose primary work is creation, AI-driven substitution is not a distant risk but an active programme at the world’s most valuable technology companies.
Delivering: The work of production delivered to the physical world. Manufacturing, logistics, construction, healthcare delivery, energy generation and distribution as well as maintenance are part of the physical world. This is where work crosses from the conceptual into the material as the real world imposes constraints of physics, geography, time and safety. Elon Musk predicts that humanoid robotics will begin transforming physical work within two years and have a 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. This AI supported activity is advancing faster than most leadership teams recognise.
Across all four types of work, relationships cannot be substituted by AI. Selling, negotiating, building trust, managing stakeholders, earning confidence and caring is the most durable source of human value in the age of AI, as trust is relational, not functional. Hence the question that needs to be answered 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 as work that is being augmented today may be restructured within two years and substituted within five. The remaking of work 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 Case of Humanoid Robots
Organisations originally built for sequential improvement cannot compete with those operating in continuous learning loops. The traditional playbook assumed you had time to get it right. That assumption no longer holds. The organizations that succeed will probably be engaged with the most sophisticated technology. AI 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 USD 16.7 billion. At the CES 2026 consumer electronics conference, Nvidia CEO Jensen Huang declared that the ChatGPT moment for physical AI is here. Bank of America projects humanoid robot unit costs falling from USD 35’000.- in 2025 to between USD 13’000.- and 17’000.- within the decade. Deloitte’s Tech Trends 2026 report captures the broader part of this shift as their research suggests that intelligence is no longer confined to AI applications supported by computer screens. It is embodied, autonomous and solving real problems in the physical world. Amazon deployed its millionth robot, and its DeepFleet AI software coordinates the entire robot fleet, improving travel efficiency within warehouses by 10%. Another example of robot efficiency comes from BMW where its factories have cars driving themselves through kilometer-long production routes without human intervention.
Human Assets
Across all four types of work, one element that resists the application of AI most stubbornly is the forging and maintenance of relationships such as selling, negotiating, building trust, managing stakeholders, earning confidence or caring. This is certainly the most durable source of human value in the age of AI, because trust is relational, not functional and AI cannot yet be trusted in the way a human can. Comparing AI to individual intelligence misses something essential about what human intelligence is all about. Our intelligence does not work at the level of isolated individuals. It is social, embodied and collective. Once this is taken seriously, the claim that AI is set to surpass human intelligence becomes far less convincing. The rise of AI should therefore lead to more thought about what we mean by intelligence, pushing us to move beyond narrow cognitive metrics, towards richer, more contextual definitions. Human cognitive achievements are often attributed to exceptional individuals, but this is misleading. Research in cognitive science and anthropology shows that even our most advanced ideas emerge from collective processes such as shared language, cultural transmission, cooperation and cumulative learning across generations. This collective capacity is not an optional add-on to human intelligence; it represents its foundation. Human intelligence is also embodied as our thinking is shaped by physical experience, emotion and social interaction. Developmental psychology shows that learning begins in infancy through touch, movement, imitation and shared attention with others. These embodied experiences represent the foundation of abstract reasoning later in life. So far AI lacks this capability. Language models learn statistical patterns from text and not from lived experience. They do not understand concepts in the way humans do and they approximate linguistic responses based on correlations in data. This limitation becomes clear in social and ethical contexts as well as shared cultural understandings we are socialised into.
Conclusion
We are in the early phase of the remaking of work, 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 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.