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The AI Layoff Story Is Too Simple

AI is not simply replacing jobs. It is compressing teams, shifting work outside firms, concentrating implementation expertise, and changing who holds authority. The early workforce struggle is over who controls the redesign—and whether workers can contest it.

Mark Howard

— 12 min read
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AI is beginning to change the architecture of the firm—even as it remains unclear whether or when direct job replacement will become widespread

Rumors of the human-powered economy’s imminent death appear to have been exaggerated.

At least for now.

The arrival of generative AI produced no shortage of predictions that machines would soon perform most economically useful work. The first broad evidence does not look much like the opening chapter of an economy without workers.

A new analysis from the UK Office for National Statistics found that reported AI use among businesses with at least ten employees rose from about 12 percent in late 2023 to about 35 percent in June 2026. That is rapid adoption. But the adoption remains shallow. Only 10 percent of the businesses using AI described that use as extensive. Around half reported no overall change in headcount, and training or retraining existing workers was much more common than replacing them. In a separate question, around 10 percent said they were automating or replacing roles with AI.

This is one country, one survey, and an early point in a long transition. It cannot tell us where the labor market will be in five or ten years. It does show that the immediate story is more complicated than a wave in which AI simply takes one job after another.

That is not necessarily reassuring.

Some large firms are already cutting jobs while reorganizing around AI. But the more revealing changes are often occurring somewhere between augmentation and direct replacement. Companies are compressing teams, removing management layers, moving work to outside providers, concentrating technical expertise, and changing the mix of workers they expect to need. They may be redesigning the firm around what they believe AI will soon make possible before they can show that an AI system has directly performed every job being eliminated.

The question is therefore not only whether AI is replacing workers.

It is whether AI is changing the kind of organization that firms believe they need—and who retains authority inside the organization that remains.

A company does not need to automate every job to redesign itself around AI

The restructuring announced by monday.com in July captures the ambiguity.

The workplace-software company said it would reduce its workforce by approximately 20 percent while continuing to hire in selected strategic areas. In its public filing, monday.com described the restructuring as part of its strategy to become an “AI Work Platform,” a new product and operating organization built around people and software agents working together. The reorganization is intended to achieve a leaner and more focused operating model, not a simple program of replacing employees with machines.

That distinction matters. There is no public evidence showing that monday.com identified 600 jobs, assigned each job to an AI agent, and then dismissed the corresponding people. Its leaders have described a broader organizational change: fewer management layers, smaller autonomous teams, continued hiring for specialized roles, and a greater emphasis on helping customers implement AI inside their own operations.

The company may be correct that its restructuring is not one-for-one automation. But that does not make AI incidental to the decision. It reveals why the usual test—Did AI directly replace these workers?—is too narrow.

A firm can decide that smaller teams will produce more, fewer managers will be needed, agents will handle parts of routine execution, and specialists should concentrate on integrating AI into customer workflows. None of those changes requires a machine to reproduce the complete job description of each displaced worker.

The org chart may change before the technology can fully justify it.

This is one reason the early workforce evidence needs a more careful vocabulary. AI-related restructuring currently appears to follow at least five pathways.

  • Replacement occurs when AI performs tasks or roles that people previously performed.
  • Compression occurs when fewer workers are expected to produce the same or greater output with AI assistance.
  • Outsourcing moves work to vendors, contractors, offshore operations, or managed systems.
  • Redeployment moves existing workers into different or higher-value roles.
  • Organizational recomposition changes the firm’s layers, skill mix, team structure, decision rights, and division of work between people and machines.

These are not separate boxes. A single restructuring can involve several at once.

The important point is that only the first pathway looks like the familiar image of a machine taking a human job. The other four can change labor power, career opportunities, institutional knowledge, and the distribution of authority just as significantly.

The clearest cases still involve real labor compression

Some cases do look much closer to direct substitution.

Allianz Partners announced that it would eliminate as many as 1,800 jobs in its travel-insurance operations because of increasing AI use. The affected work reportedly includes operations in which AI can handle more customer interactions and routine processing. Allianz has said it will seek to retrain or reassign employees where possible, but the connection between the technology and the planned workforce reduction is unusually explicit.

This is stronger evidence than a general corporate layoff accompanied by a reference to “efficiency.” Management has identified AI as a significant cause of a substantial reduction in a defined operation.

Even here, however, the organizational choice extends beyond replacing a set of manual tasks. Allianz must also decide which customer interactions remain human, when an automated system escalates a case, how exceptions are handled, who monitors quality, and how much service capacity should remain available when an AI system fails. To maintain and ideally improve performance, changes in operations must accompany headcount reductions.

This example displays the broader process of labor compression: fewer people are expected to support a larger volume of work because some portion of production, administration, analysis, or coordination has become cheaper and faster with AI.

Compression can occur even when the remaining workers become more capable. A claims specialist with AI assistance may handle more cases and spend more time on difficult judgments. But if the organization captures most of that capacity by reducing staffing and raising output expectations, the technology expands organizational leverage more than worker agency.

The relevant question is not whether augmentation or replacement occurred. Both may occur at the same time.

The question is who receives the benefit of the additional capacity.

AI can also move work outside the firm

The restructuring at British American Tobacco shows a different pathway.

BAT announced that approximately 9,000 roles would be affected by a cost-reduction and technology transformation program. About 5,500 jobs are expected to be eliminated. Another 3,500 roles have been or will be transferred to outside providers, including Accenture. BAT connected the restructuring to a broader effort to become more agile, technology-enabled, and able to use advanced AI capabilities supplied through strategic partners.

It would be misleading to say that AI will perform all 9,000 jobs. BAT is also responding to declining cigarette volumes, regulatory pressures, changing product markets, and an ordinary corporate desire to reduce costs. AI is one component of a much larger restructuring.

But the combination of workforce reduction, outsourcing, and AI investment shows why looking only for direct job replacement misses an important concentration mechanism.

A job can disappear from a company without disappearing from the economy. Work may move to a consultancy, managed-service provider, lower-cost labor market, or technology platform. The incumbent reduces its permanent workforce while continuing to receive the service through a contract.

That changes the boundary of the firm: what the company knows how to do itself, what it employs people to do, and what it purchases from organizations beyond its walls.

It may also change where power resides.

Workers inside a large company may have career paths, institutional knowledge, legal protections, and sometimes collective representation. Workers at an outside provider may face different wages, bargaining arrangements, and performance systems. Responsibility also becomes easier to divide between client and vendor.

The knowledge needed to operate the function may also move outward. A consultancy or service provider that implements AI across many clients accumulates reusable expertise about workflows, integration, and organizational redesign. Each client receives a service. The provider builds a playbook.

The firm may become leaner while becoming more dependent.

This is why outsourcing should be treated as a distinct AI workforce pathway rather than an accounting detail appended to layoffs. AI may not simply substitute machines for employees. It may help large organizations substitute contractual relationships for internal capacity.

A smaller workforce may be paired with a more specialized core

The emerging pattern is not only fewer workers. It is a different composition of workers.

Thomson Reuters is eliminating part of its existing engineering workforce while planning to add more than 250 net-new engineering positions over the next two years. The company says most of the new jobs will be senior and AI-native.

That is not a simple story of engineering jobs disappearing. Nor is it a neutral exchange of one set of skills for another. It suggests that AI may change the internal ladder through which expertise is built.

A company may need fewer general engineers while competing intensely for senior workers who can design AI systems, evaluate their output, understand a customer’s domain, and place the technology inside consequential workflows. The surviving organization may have a smaller base and a more selective technical core.

The same possibility appears from the other direction at Tata Consultancy Services. TCS plans to create a corps of approximately 5,900 to 8,900 forward-deployed engineers—roughly 1 to 1.5 percent of its workforce—who will work closely with customers to adapt and integrate AI into real operations. The company has not yet said how many of those positions will be filled through internal retraining and how many through external hiring.

The forward-deployed engineer represents a different kind of labor model altogether. These workers are not merely building a general product. They combine technical knowledge with detailed understanding of a customer’s processes, data, constraints, and institutional habits. They operate at the point where a model becomes an organizational system.

That implementation knowledge may be among the most valuable forms of expertise in the AI economy.

It may also become highly concentrated.

A company can give thousands of employees access to AI while depending on a much smaller group to connect the systems, redesign work, set safeguards, and define performance. If that expertise sits inside a vendor, the customer may become more productive without becoming more capable of operating independently.

This is the connection between workforce restructuring and implementation capacity. AI-driven restructuring does more than sort workers according to whether their tasks can be automated. It may create a new organizational hierarchy in the economy between people who simply use AI and the smaller group that designs, integrates, evaluates, and governs the systems everyone else must use.

The pattern at monday.com combines these elements unusually clearly: a substantial workforce reduction, fewer layers, smaller teams, selective hiring, an AI-centered platform strategy, and greater emphasis on customer implementation. The most important change may not be that agents took 600 jobs. It may be that the company has formed a new view of the minimum human organization required to build and sell its products.

If that model succeeds, other enterprise-software vendors will not merely offer customers AI tools. They will offer their own reorganized companies as evidence of what successful AI adoption is supposed to look like.

The vendor becomes both supplier and demonstration project.

Redeployment is real, but it needs evidence

Not every company is presenting AI investment as a reason to reduce its workforce.

SAP has restricted hiring and travel spending to redirect resources toward AI. The company says it wants to avoid another large layoff program by retraining and redeploying existing employees into AI-enhanced work. Hiring will concentrate on selected technical and AI roles while current workers are encouraged to build new capabilities.

This is an important alternative pathway. If firms can preserve institutional knowledge, teach employees to use new systems, and move people into more valuable work, AI could expand capability without requiring the first response to every productivity gain to be a workforce reduction.

The ONS evidence suggests that this is presently more common than direct replacement. Training or retraining existing employees was the most frequently reported way UK businesses were integrating AI-related skills. But the effort remained limited: only 11 percent said that more than half of their workforce had received AI-related training.

That encouraging direction should remain provisional. A credible redeployment claim requires more than a company announcement. We should eventually be able to see how many employees completed training, how many moved into durable new roles, whether their wages and job quality were maintained, and whether the transition preserved a meaningful career path. Hiring freezes and attrition can produce labor compression more slowly than layoffs while leaving the same final organization.

Redeployment is a real pathway. It should not become a euphemism that remains permanently ahead of the evidence.

Workers experience the same transition from very different positions

The workforce debate often focuses on what AI can do. The democratic question is who can see and contest what an employer decides to do with it.

A lawsuit involving 26 Meta employees shows the weakest position: an after-the-fact challenge in which the employer controls most of the evidence.

The workers allege that Meta used productivity scores, AI-usage measures, and AI-assisted systems in ways that disadvantaged employees who had taken medical or family leave. Meta denies that AI selected the employees and says human managers made the decisions. A federal judge declined to block the layoffs, while recognizing that the plaintiffs had raised serious questions about the alleged use of AI.

Whatever the merits, the procedural problem is important.

Workers challenging an AI-assisted employment decision are rarely present when the system is designed, the data selected, or the scores interpreted. They may lose their jobs before obtaining the records needed to understand what happened. Human review offers limited protection if workers cannot see how the system influenced the decision.

Litigation may provide a remedy eventually. It is a poor substitute for visibility before the harm occurs.

Google employees are trying to intervene earlier. More than 4,500 workers signed a petition seeking guaranteed severance, voluntary buyouts before mandatory layoffs, and an end to forced performance-rating distributions. The petition does not create binding rights, and it does not give employees shared authority over Google’s AI strategy. It does represent collective organization aimed at setting procedural terms before the next restructuring is complete.

Collective bargaining can go further. The NewsGuild-CWA now reports approximately 85 to 90 contracts containing explicit AI provisions. Existing agreements can require notice and bargaining before deployment, preserve bargaining-unit work, require human oversight, and create remedies when employers violate the agreed terms.

These provisions do not stop technological change. They alter who participates in defining it.

Illinois has created a still stronger form of prospective protection in one specific professional setting. A new law prohibits administrators from using AI to write teacher evaluations or perform evaluation tasks requiring professional judgment. AI may still assist with administrative work, but evaluators must disclose the tool and its purpose to the teacher. The law makes operational a clear principle before a disputed evaluation occurs: professional judgment remains a human responsibility.

Together, these cases show four increasingly contestable positions from which workers may encounter AI-era restructuring.

A Meta employee may seek evidence after the decision. Google employees are organizing for procedural leverage before another decision. A union contract can turn that leverage into an enforceable right to notice and bargaining. Illinois teachers work under a legal boundary defining something AI may not be allowed to decide.

That progression—from remedy after harm, to collective pressure before harm, to enforceable bargaining rights, to a prospective legal boundary—is a useful measure of worker contestability.

General assurances of “human oversight” are not enough. A person can approve a decision inside a process still defined by management and hidden from the worker affected. Contestability requires information, an opportunity to intervene, and an institution capable of changing the result.

The question is who controls the redesign

The early workforce evidence does not show an economy rapidly shedding the need for human work.

It shows something less dramatic and, in the near term, more consequential.

AI adoption is spreading faster than deep organizational transformation. Most firms are still experimenting, improving existing operations, and training at least some workers. At the same time, a smaller group of influential companies is beginning to reorganize: compressing staffing, changing firm boundaries, reducing layers, concentrating implementation expertise, and selecting a different mix of skills.

Those companies do not have to prove that AI can autonomously perform every eliminated job. They can act on early productivity evidence, investor expectations, competitive pressure, and a belief that the technology will improve quickly. The restructuring may arrive before the causal evidence becomes clean.

This is why the AI workforce question should not be reduced to counting layoffs attributed to automation.

We should ask what work is changing, which roles and entry paths are disappearing, where organizational knowledge is moving, and who gains authority over the new systems. We should distinguish direct replacement from compression, outsourcing, redeployment, and recomposition. Most importantly, we should examine whether workers can see and influence the redesign before its assumptions become embedded in software, contracts, and a new org chart.

Three tests will matter.

Can workers and the public identify which tasks, workflows, and decision rights are changing? Do workers participate before the redesign is finalized, or only receive its consequences? Are there enforceable limits on what AI may decide, what employers must disclose, and what remedies are available?

Rumors of the human-powered economy’s death may have been exaggerated.

The struggle over who controls its reorganization has barely begun.

References and further reading

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