A Quiet Shift in Coordination
When ChatGPT reached 100 million users in just two months, it felt like a consumer milestone. But the more consequential moment happened elsewhere, inside enterprise systems, where AI agents quietly began executing tasks without waiting for instruction.
An invoice reconciled automatically. A marketing campaign adjusted itself mid-flight. A compliance flag triggered a downstream process without human intervention.
Individually, these were small acts of automation. Collectively, they signalled something more profound: the cost of coordination, the invisible glue that holds organisations together, was collapsing.
And when coordination costs fall, employment models shift with them.
The Internet of Jobs: Coordination Is the Real Disruption
The dominant narrative around AI and employment has focused on task automation. Will AI write reports? Replace coders? Draft contracts?
That framing misses the deeper mechanism.
AI agents do not simply automate tasks. They coordinate work across systems, teams and decision layers. They gather information, reason about it, take action and trigger downstream effects. When multiple agents operate together, the organisation itself begins to behave differently.
Research into multi-agent systems has shown that agents can negotiate, allocate tasks and optimise outcomes across distributed environments without central oversight. This isn't just faster execution; it is decentralised coordination.
MIT's concept of the “Iceberg Index” offers a useful lens. The visible layer of work, the outputs, represents only a fraction of the effort. Beneath the surface lies the coordination overhead: emails, meetings, follow-ups, status updates, handoffs. That hidden layer often consumes more time than the task itself.
AI agents target that hidden layer.
When agents can synchronise calendars, route approvals, monitor supply chains and escalate anomalies in real time, the human effort required to coordinate shrinks dramatically.
The internet reduced the cost of communication. Agentic AI reduces the cost of coordination.
And coordination is what most knowledge work actually consists of.
Non-Linear Acceleration
During the internet era, job disruption unfolded gradually. Entire sectors transformed, publishing, retail, travel, but the pace was constrained by infrastructure rollout, device adoption and consumer behaviour change.
Agent-based systems move differently.
They are software native. They integrate directly into existing platforms. They scale at cloud speed. Once embedded, they replicate almost instantly across departments and geographies.
Recent enterprise research underscores how quickly AI expectations are rising. Three-quarters of executives anticipate generative AI will drive substantial transformation within three years. Yet only a minority have built the operational capability to scale it.
This gap between ambition and readiness matters for employment.
When coordination becomes programmable, entire role categories built around orchestration, information routing or intermediary oversight begin to compress. Not because the underlying expertise disappears, but because the friction that justified those roles no longer exists.
Administrative management layers, manual review cycles, process compliance functions, these may shrink faster than anticipated.
At the same time, new forms of work expand.
Systems require supervision. Agents need configuration. Model outputs demand validation. Governance frameworks must be designed and enforced. Orchestration becomes a strategic discipline.
Disruption is not linear replacement. It is structural reconfiguration.
What Changes Inside Organisations
Boards and CEOs often ask whether AI will eliminate jobs. The more strategic question is whether your organisational model still makes sense when coordination costs approach zero.
Traditional employment structures assume:
- Work moves sequentially.
- Information is scarce.
- Decision-making requires manual aggregation.
- Oversight is hierarchical.
Agent-based coordination undermines each of these assumptions.
When AI systems can monitor dozens of variables simultaneously and trigger actions in real time, sequential workflows feel inefficient. When data is continuously analysed and surfaced contextually, manual reporting layers lose relevance.
The rise of “operational AI”, systems embedded directly into workflow execution, means decisions increasingly occur inside software, not in meetings.
This does not remove leadership. It changes its locus.
Leaders shift from approving transactions to designing systems. Managers move from supervising tasks to supervising outcomes and guardrails. Workforce strategy shifts from headcount planning to capability orchestration.
The internet digitised distribution. Agentic AI digitises coordination.
That distinction matters.
The New Labour Architecture
If AI agents compress coordination costs, employment structures will reorganise around three emerging domains:
Oversight and Governance. As automation increases, the need for accountability increases with it. Human roles expand around compliance, ethics, validation and escalation design.
System Design and Orchestration. Organisations will need architects who understand how to compose agent ecosystems, defining boundaries, permissions and integration layers.
Augmented Specialisation. Experts will work with AI as co-pilots, extending reach rather than being replaced outright. Lawyers, engineers, analysts and creatives may handle more volume and complexity, supported by agents handling preparatory coordination.
Early evidence suggests that AI's impact is uneven. Larger firms with modern infrastructure are more likely to scale AI across functions, while others remain in pilot mode. This unevenness means labour shock will also be uneven, concentrated in digitally mature organisations first.
For policy leaders, this signals potential volatility.
For boards, it signals competitive divergence.
The Human Reframing
For individuals, the shift feels less abstract.
If coordination becomes automated, the work that remains visible is judgement.
You will not be valued for forwarding emails or reconciling spreadsheets. You will be valued for defining what matters, setting constraints and interpreting ambiguity.
Your organisation may shrink certain roles not because performance was lacking, but because the system no longer requires human mediation between steps.
Equally, new roles will appear in places previously peripheral: prompt engineers evolving into system architects; compliance officers becoming AI risk strategists; product managers transforming into orchestration designers.
The question for workforce strategy is not “Which jobs disappear?” It is “Which coordination layers become programmable?”
That lens produces clearer answers.
What Happens Next
Over the next 24 to 36 months, enterprises planning workforce strategy must abandon linear assumptions.
Agent-based coordination introduces step-change effects. Once a coordination layer is automated, adjacent layers often follow. What begins as a pilot in procurement can ripple into finance, compliance and operations.
Boards should focus on two priorities.
First, assess where coordination overhead dominates your cost structure. Those domains are the most exposed to agent-based compression.
Second, invest in capability redesign before role reduction. Organisations that retrain and reallocate talent toward oversight, orchestration and design will navigate disruption more effectively than those that treat AI primarily as a cost-cutting lever.
The internet reorganised industries over a decade. Agentic AI may reorganise employment in a fraction of that time.
Not because it works harder.
But because it works together.
