When AI Becomes a Coworker: The Workplace Category We Haven’t Invented Yet

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For most of the artificial intelligence era, the public conversation about work has revolved around a deceptively simple question: Will AI replace human workers? It is an understandable question, because automation has historically been discussed in terms of substitution. A machine performs a task that a person once performed, productivity rises, and some jobs disappear while others emerge. But the newest generation of AI systems is beginning to make that framework feel inadequate. Artificial intelligence is no longer confined to answering questions, generating text, or performing isolated tasks. AI agents are increasingly able to research information, analyze files, work across connected applications, create documents and spreadsheets, monitor changing conditions, and carry out multi-step workflows with varying degrees of independence. OpenAI now describes ChatGPT Work as capable of researching and analyzing information, working across connected apps and files, and creating finished documents, spreadsheets, presentations, reports, and websites, while workspace agents can be configured to own entire workflows. The important change is not simply that AI can perform more work. It is that AI is beginning to occupy a new position inside the structure of work itself.

That position does not fit comfortably into any category we currently have. An AI agent is not traditional software because traditional software normally waits for a human to operate it step by step. It is not an employee because it has no legal personhood, employment contract, wages, benefits, career ambitions, or independent rights. It is not management because it possesses no legitimate organizational authority of its own. Yet an agent can increasingly exhibit characteristics associated with all three. It can use tools, coordinate tasks, make recommendations, monitor outcomes, initiate follow-up work, and in some cases execute actions without a human directing each individual step. The distinction sounds semantic until organizations actually begin relying on these systems. Then the question becomes practical: What exactly have we inserted into the workplace?

Some organizations are already reaching for the language of employment to explain the relationship. In an interview published this week, RPG Group CIO Rajkumar Ayyella suggested companies should treat AI agents more like junior colleagues than ordinary software, systems that require onboarding, context, supervision, clear processes, and correction when they make mistakes. The U.S. Army is reportedly experimenting with something even more literal, training AI agents for defined cyber “work roles” such as developers, analysts, and data engineers while requiring human oversight for decisions involving significant operational risk. These examples do not mean AI agents have somehow become employees. They reveal something more interesting: organizations are already discovering that the old vocabulary of “software tool” does not fully describe how these systems behave inside complex workflows.

This matters because organizational categories carry responsibility with them. When a spreadsheet formula produces a wrong number, we generally understand who is responsible for checking it. When an employee makes a consequential decision, companies have systems of supervision, authority, documentation, liability, and accountability built around that person’s role. AI agents muddy those boundaries. Imagine an agent reviewing a contract, making a scheduling decision, approving an expense, screening a job applicant, communicating with a customer, changing a database, or identifying a cybersecurity threat. If the system gets something wrong, responsibility does not disappear merely because the immediate action was performed by software. Was the employee supervising the agent responsible? The manager who approved its deployment? The organization that configured it? The developer that built the underlying model? The company whose system supplied faulty data? Most organizations do not yet have mature answers to these questions. And as agents move from generating recommendations to taking actions, accountability stops being a philosophical concern and becomes an operating requirement.

There is another reason the coworker metaphor matters: it changes the economics of the individual worker. For decades, most knowledge workers have been constrained by a fairly stubborn resource: time. A talented employee can only research so many markets, analyze so many documents, write so many reports, attend so many meetings, and monitor so many developments in a day. AI agents begin to loosen that constraint. A person who learns to coordinate several capable AI systems may be able to operate more like the leader of a small team than a traditional individual contributor. One agent might monitor competitors, another analyze customer data, another draft communications, and another track a project while the human remains responsible for judgment, prioritization, strategy, and final decisions. The worker has not disappeared. The effective size of the worker has changed.

That possibility creates enormous opportunity, particularly for individuals and small organizations. Sam Altman recently argued that one of AI’s underappreciated possibilities is its ability to increase individual empowerment and enable the creation of many more small businesses, while also warning about the danger of advanced AI becoming concentrated among a handful of powerful actors. Those ideas may sound contradictory, but they may actually describe the same transition. AI can dramatically expand what an individual can accomplish while simultaneously increasing the power of the corporations controlling the most capable systems, infrastructure, data, and distribution networks. The same technology could allow a two-person company to perform work once requiring twenty people and allow a giant corporation to operate with fewer workers across thousands of roles. Capability can become more distributed while economic control becomes more concentrated. Both things can be true at once.

That tension is likely to become one of the defining labor questions of the agentic era. If one employee equipped with AI can accomplish the work previously performed by several people, there are at least two very different ways organizations can respond. They can allow workers to become more capable, creative, autonomous, and productive, sharing some of the economic gains produced by that amplification. Or they can treat the productivity increase primarily as an opportunity to reduce headcount. We are already seeing hints of the cultural battle surrounding that choice. Corporate messaging about AI and layoffs has become noticeably more cautious as workers grow increasingly anxious about employment disruption. At the same time, workers themselves are rapidly signaling AI competence in the labor market. A recent analysis of 29.4 million LinkedIn profiles found a dramatic increase in workers retroactively emphasizing AI-related skills in their professional histories. Before the economics of AI labor have fully settled, workers appear to understand that knowing how to collaborate with these systems is becoming part of employability itself.

The resulting workplace may therefore be less about humans competing directly against artificial intelligence and more about humans competing inside systems increasingly organized around artificial intelligence. The valuable employee may not necessarily be the person who can personally perform the largest number of tasks. It may be the person who understands the problem well enough to direct machines toward it, recognize when their outputs are wrong, integrate their work with human goals, and decide what should happen next. Domain expertise may become more important rather than less important because expertise is what allows someone to distinguish a plausible AI answer from a correct one. Judgment becomes the scarce resource. Context becomes the scarce resource. Responsibility becomes the scarce resource.

That is also why the familiar phrase “human in the loop” deserves closer scrutiny. It sounds reassuring because it implies that human oversight automatically provides a safety mechanism. But putting a person somewhere inside an automated workflow does not guarantee meaningful oversight. If one worker supervises ten agents operating at machine speed, the human may technically remain in the loop while having little realistic ability to understand or review everything those agents are doing. The organization may retain the appearance of human control while gradually transferring practical decision-making authority to automated systems. The design of the human role matters enormously: what the person can see, what actions require approval, how mistakes are surfaced, whether decisions can be reversed, and whether someone actually has the time and authority to intervene.

This is where the language we choose becomes important. Calling AI a “tool” can encourage organizations to underestimate the governance problems created when the tool begins acting independently. Calling it an “employee” anthropomorphizes a system that does not possess the responsibilities, rights, motivations, or social existence of a human worker. Calling it a “coworker” is useful because it captures the collaborative nature of the relationship, but even that word may eventually prove inadequate. We may need an entirely new organizational category for artificial agents: systems that can participate in economic activity, carry out delegated authority, interact with humans and machines, and produce consequential outcomes without themselves possessing legal or moral responsibility.

History offers plenty of examples of technology transforming work, but this transition contains an unusual twist. Previous machines primarily extended human muscle, memory, communication, transportation, or calculation. AI increasingly extends something closer to agency itself. We are beginning to delegate not only execution but portions of planning, interpretation, coordination, and decision-making. That does not make AI human, conscious, or equivalent to a person. It does mean that our institutions are beginning to accommodate nonhuman systems capable of participating in activities that until recently required human cognition.

And perhaps that is the more important question hiding underneath the endless debate about whether artificial intelligence will “take our jobs.” Jobs are bundles of tasks created by institutions. They change constantly. The deeper transformation may be the emergence of organizations in which humans and artificial agents operate together, dividing cognitive labor between them in ways we are only beginning to understand.

The outcome is not predetermined.

AI agents could become mechanisms for concentrating economic power, reducing employment, intensifying worker surveillance, and extracting more value from fewer people. They could also become extraordinary instruments of individual capability, allowing small teams, entrepreneurs, nonprofits, researchers, artists, and ordinary workers to accomplish things that once required enormous organizations and budgets.

Most likely, they will do both.

Which means the central question of the agentic workplace is not simply what can AI do?

It is who gains power when AI can do it?

At Conjugo, we keep returning to the idea that the future of artificial intelligence will be shaped not only by increasingly capable machines, but by the relationships humans construct with them. The arrival of AI agents in the workplace may be one of the first places where that relationship becomes visible at societal scale.

AI is becoming something more than software before we have decided what that something should be.

We should probably decide before the org chart does it for us.