If the 20th century was the era of industrial machinery and the 21st century the era of digital transformation, the next frontier is stranger: the complete automation of the workforce. Industrial machinery automated the parts of the process of production previously held by humans; the computer and the internet did the same for the century after, but even with the breadth and reach of those inventions, the potential of intelligent agents working in the background could surpass both its predecessors put together in terms of impact.¹ The tasks that characterise office life (reconciling invoices, scheduling meetings, preparing compliance reports, routing approvals from one desk to another) have slowly started to fade into the background. These will no longer be handled by people in cubicles or executives with inboxes full of requests, but by software systems that interconnect, adapt, and act largely on their own. Business is slowly (or in some cases, rather quite quickly) becoming less about something people do and more something that quietly happens, orchestrated by intelligent agents, in the background.
A rather radical expression of this concept was presented in the AI2027 article, which supposed a world where humans no longer have the capacity to supervise the highest form of AI and instead are relegated to monitoring earlier versions, which have been given the supervisory role over their more capable brothers.² In this recursive hierarchy, the daily operations of the enterprise are several levels removed from human intervention. The human role in the workplace has not disappeared, then, but has drastically changed, becoming one of meta-supervision: ensuring that the system-on-system supervision is aligned with strategy, values, and law. What once required multitudes of clerks, accountants, and managers is compressed into invisible processes. Far from science fiction, this could emerge as the operating system of business in the 2030s. It can already be seen today in the ways in which Chat-GPT and Grok are replacing tasks humans for decades did themselves. It can barely be considered a tool anymore; rather it has replaced our desire to think in certain ways. Reports, emails, and communications (the monotony of office life) have been completely replaced, and understandably so, who would choose to write an email for ten minutes or a report for an hour when Chat-GPT can do it in seconds? This is an important window into the human mind; we will always pick the path of least resistance.
This shift is not being driven by a single enterprise or technology but by a convergence. Artificial Intelligence is evolving from being capable of very specific tasks to agentic models that can plan, adapt, and act without continuous prompting.³ At the same time with the digital revolution, two decades of growth have produced a landscape of cloud services, APIs, and modular microservices that make enterprises inherently programmable. If you couple this with the volatility of modern existence (pandemics, climate disruptions, geopolitical shocks) the demand for a system that corrects itself in real-time becomes irresistible.
Consider finance. A vendor submits an invoice from another country at two o’clock in the morning. Within minutes, they have it has been verified against compliance databases, contract terms have been cross-checked, the payment schedule has been reconciled with the treasury forecast, and the system has issued a settlement instruction. One layer above this, a supervisory agent is monitoring patterns across thousands of transactions, noting anomalies or deviations. Another agent examines the broader budgetary implications. At a reasonable time, the following morning, a manager or CFO does they have to consider the approval of a payment; instead, a one-page summary highlighting exceptions, trends, and recommendations is provided. The work of finance, so long the engine of paper trails and human oversight, becomes largely invisible.
This is mirrored in human resources. When an employment offer is accepted, instantaneously invisible systems begin provisioning accounts, dispatching equipment, enrolling benefits, and scheduling onboarding sessions for the new arrival. A supervisory system tracks the employee’s engagement data over time, catching red flags for disengagement and creating a paper trail for improvement plans and incapacity proceedings. This alters the manager’s role, no longer to arrange logistics, but to focus on the culture and long-term growth of the company. What used to be weeks of paperwork and coordination is compressed into background processes that barely register as “work.”
Within the volatility of operations, this is even more powerful. A typhoon closes a shipping route in Southeast Asia. Instead of thousands of people working around the clock to fix the emergency, logistic agents have already rerouted cargo, renegotiated carrier contracts, and updated customer delivery estimates in real time.⁴ A layer above that: revenue forecasts, inventory balances, and production schedules have been updated by a supervisory agent. When the executives wake, it’s not to panic and chaos, but rather to a crisis already resolved. What remains is a conversation over diversifying trade routes and suppliers in the future, and while the disruption was not prevented, its impact was drastically reduced and absorbed by the invisible processes acting faster than its one-thousand-person predecessor ever could.
The potential upside of something like this is obvious. Things that would have taken hours or days can be finished in seconds. Organisations have no need to rely on competent workers to get the administrative aspects of the company completed, making them more resilient and adaptable to disruptions. Even more importantly, scaling no longer relies on the human aspect, but on the invisible processes, which scale elastically with infrastructure rather than with office space. The biggest upside is the liberation of human time. Rather than valuable energy being spent on administrative endeavours, it’s redirected toward creativity, strategy, and innovation. None of the human focus is wasted on aspects of the office that don’t directly grow the company. This is analogous to the transformation of factories by electricity: power no longer depended on a central steam engine, and so production could be distributed, flexible, and continuous. There is a future where enterprises no longer rely on visible, manual work, and business itself would become ambient. The implications for human labour in that situation are something to deal with in another article, but it could be fascinating to contend with.
However, with invisibility comes the inevitable hubris of accountability. When AI supervises AI, accountability diffuses into a grey area. If an autonomous procurement agent violates sanctions law, who is responsible? The developer who built the model? The supervisory AI that failed to catch the breach? The human manager several levels removed from the decision? The very recursion that makes the invisible workforce efficient also makes its lines of responsibility opaque.⁵ In the same way, transparency becomes an issue. The existence of AI supervision makes it difficult to unravel the chains of decisions made by an agent, undermining the effectiveness of audits and regulations, making it drastically more difficult to understand why a particular action occurred. This also amplifies security risks, as an attack on one agent can cascade through supervisory layers like a virus through a nervous system. And for the workforce, the cultural shock may be as profound as the economic disruption. The implications for human labour are something to deal with in another article, but millions of roles would be redefined or lost and not necessarily replaced by another type of employment. Which could mean refining what it means to be a human in and of itself.
This is the supervisory paradox. The more powerful the invisible workforce becomes, the more humans are lifted out of direct involvement. The paradox is that by gaining the leverage afforded by intelligent systems, humans lose intimacy with the very processes they oversee. We have become pilots of systems we cannot fully see and don’t completely understand.⁶ Our role will evolve to set objectives, define rules and ethical boundaries, and intervene in crisis and exceptions, not to micromanage the execution.
The path toward invisibility will not be sudden. It will unfold gradually, through the incremental adoption of these systems slowed down by the obstacle of the old guard, the traditionalists of the corporate environment. The repetitive tasks like invoice processing, report generation, and scheduling will be automated first. Then come agentic models that take on multi-step workflows with access across multiple departments, removing the need for the division of labour. This will be followed by supervisory systems that monitor and adjust those agents, creating recursive chains of oversight. The culmination of this being the enterprise operations running continuously in the background, with humans engaging only in exceptions, strategies, and values. By the time people properly catch on, much of the enterprise will already be invisible.
This is an opportunity for growth. We are sitting at the precipice of an era far more influential to our way of life than the Renaissance, the Industrial Revolution, and the digital expansion of the 21st century put together. The companies afraid of change that still work in obsolete systems will not survive the coming era. The companies that will thrive in this transition are those that have started to lay the foundations for autonomy, that find the balance of invisibility with transparency, and autonomy with accountability. In the end, the invisible workforce is not about eliminating humans but elevating them. The disappearance of visible work is not the disappearance of work itself, but its transformation into something less routine, less transactional, and more strategic.
The future of business is not just digital, and not just intelligent. It is invisible. And in that invisibility lies both extraordinary opportunity and profound responsibility.
— Written and researched by Sean Veldboer, Consultant at CBOS
Footnotes
1.
Brynjolfsson, E., & McAfee, A. (2017). Machine, Platform, Crowd: Harnessing Our Digital Future. New York: W.W. Norton.
2.
AI2027. (2023). AI 2027: What Happens When AI Supervises AI. Available at: https://ai-2027.com (Accessed: 1 October 2025).
Bostrom, N. (2014). Superintelligence: Paths, Dangers, Strategies. Oxford University Press.
3.
PwC. (2025). AI Agents Are the Future of Work. PwC Insights. Available at: https://www.pwc.com/us/en/tech-effect/ai-analytics/ai-agents.html
McKinsey & Company. (2025). Superagency in the Workplace: Empowering People to Unlock AI’s Full Potential. Available at: https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/superagency-in-the-workplace-empowering-people-to-unlock-ais-full-potential-at-work
Workday. (2025). How AI Agents Will Change the Way We Work. Available at: https://blog.workday.com/en-us/a-workforce-reimagined-how-ai-agents-are-reshaping-work-roles-and-strategy.html
4.
McKinsey & Company. (2023).
5.
Floridi, L., & Cowls, J. (2019). A Unified Framework of Five Principles for AI in Society. Harvard Data Science Review, 1(1). https://hdsr.mitpress.mit.edu/pub/l0jsh9f7
6.
Bostrom, N. (2014).
7.
Floridi, L., & Cowls, J. (2019).
Clegg, S., Kornberger, M., & Pitsis, T. (2016). Managing and Organizations. Sage Publications.