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AI is unlikely to erase middle management in one stroke, but it can compress the hierarchy by taking over routine coordination and giving front-line employees their own software agents to supervise. Goldman Sachs executives and labor analysts describe a shift in which human managers spend less time routing information and monitoring repetitive analysis, and more time on judgment, coaching, accountability, specialist knowledge and relationships. The number of jobs affected, and the speed of change, remain uncertain.
Kevin Sneader, Goldman Sachs president of Asia Pacific ex-Japan, summarized the emerging model at the Milken Institute Asia Summit on October 8, 2026: “When our young folks now start work, they’re managing agents. They have to make the most of this virtual army that they’ve now got.”
What does Goldman Sachs actually predict about middle management?
Goldman Sachs is describing a possible redesign of management work, not announcing a forecast that a fixed number of managers will be dismissed. Its analysis separates exposure to AI from full job elimination: a role can contain automatable tasks while the occupation continues, with people using AI to increase their output.
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The clearest organizational change is a shorter chain between employees and senior leaders. If software agents can prepare analyses, track deadlines, draft updates and flag exceptions, a manager who mainly coordinates those activities adds less value as a separate layer. Junior employees may instead direct several agents themselves, while experienced managers concentrate on decisions that require context, responsibility and influence.
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“That management task no longer sits with the middle manager; it sits with the front line,” Sneader said, according to a Moneycontrol report dated October 8, 2026.
Goldman’s labor research places middle-management work in a difficult but unresolved category: the next layer of jobs exposed after many physical tasks is work involving judgment. Agentic AI can perform parts of that work, yet the outcome depends on how reliably systems handle ambiguity, exceptions, confidential information and consequences for customers or employees.
Which management tasks can AI automate first?
AI is most likely to absorb repeatable coordination and monitoring before it replaces managers who own difficult decisions. The practical test is whether a task has a clear objective, standardized inputs, a measurable output and a tolerable cost when the system is wrong.
Routine information flow
Agents can collect status updates, reconcile spreadsheets, summarize meetings, distribute action items and remind owners about deadlines. These activities often consume a manager’s week without requiring the manager’s personal judgment. A human still needs to set the rules, inspect unusual results and decide what happens when priorities conflict.
Scheduling, allocation and workflow control
Software can assign cases, balance workloads, propose staffing rosters and route approvals using policies supplied by the organization. This can reduce the need for a coordinator whose main role is moving work from one queue to another. It does not remove accountability for understaffing, unfair assignments, missed service levels or a policy that produces bad outcomes.
Standard analysis and reporting
An agent can query approved data, produce a first-pass forecast, compare performance with a target and generate a management dashboard. The substitution risk rises when the analysis is repetitive and the data is clean. It falls when the manager must select a model, challenge its assumptions, combine proprietary context with incomplete evidence or explain a high-stakes recommendation.
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Exception detection
Continuous monitoring can flag unusual transactions, quality failures, customer complaints or schedule slippage earlier than a person reviewing a weekly report. Detection is easier to automate than resolution. Managers remain responsible for deciding whether an alert is a real problem, who should act and whether the underlying process needs redesign.
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| Management activity | What an AI agent can do | Human work that remains | Likely effect |
|---|---|---|---|
| Collecting updates | Ask contributors for progress and summarize responses | Resolve conflicting priorities and negotiate commitments | High substitution of routine effort |
| Work assignment | Recommend queues, owners and deadlines from rules and data | Handle fairness, capability gaps and political or customer constraints | Augmentation with selective substitution |
| Performance reporting | Produce dashboards, variance analysis and drafts | Interpret causes, set consequences and communicate decisions | Mostly augmentation |
| Risk monitoring | Scan events and flag anomalies continuously | Judge severity, investigate and accept accountability | Augmentation |
| Coaching and conflict resolution | Suggest prompts or summarize documented history | Build trust, read emotion and make ethically difficult calls | Low near-term substitution |
Are middle managers at risk from AI agents?
Yes, particularly when a manager’s job is dominated by coordination, reporting and supervision of standardized analytical work. The risk is lower when the role combines judgment with coaching, domain expertise, relationship management, physical presence or formal accountability.
Goldman Sachs Global Investment Research estimated in 2025 that roughly two-thirds of current jobs are exposed to some degree of AI automation and that generative AI could substitute for up to one-fourth of current work. “Exposure” means that tasks may be changed or assisted; it is not a count of jobs certain to disappear. Goldman Sachs Research estimated in 2026 that about 300 million jobs globally are exposed to AI automation and that, in a base case, 6–7% of workers could be displaced during an adoption transition of approximately ten years. Those are scenario estimates, not a timetable for every company or country.
A manager can therefore face a serious redesign without losing employment. One position may cover a larger team because agents handle routine follow-up. Another may move into model oversight, compliance, training, customer work or a specialist role. Redundancy becomes more plausible when an organization can remove an entire layer and still maintain decision quality, controls and relationships.
What happens when junior employees manage AI agents?
Front-line supervision of agents changes what “entry-level management” means. A new employee may not have direct reports, yet may be responsible for configuring agents, checking their work, escalating failures and combining their outputs into a decision.
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- Define the assignment. The employee states the goal, constraints, permitted data and success measure. Vague instructions produce plausible but unusable work.
- Choose and configure agents. The worker selects approved tools, gives them access only to necessary systems and sets limits on actions such as sending messages, changing records or approving payments.
- Review the evidence. Outputs are checked against source data, calculations, policy and known edge cases. A confident answer is not evidence that the underlying process was sound.
- Escalate exceptions. The employee routes legal, safety, personnel, financial or customer-impacting issues to a person with the right authority.
- Record decisions and learn. Prompts, approvals, errors and corrections are logged so the team can improve the workflow and demonstrate who was accountable.
This model gives junior staff more leverage but also more responsibility. Organizations must provide training, access controls and a clear rule that delegating a task to an agent does not delegate accountability.
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Will AI flatten the corporate hierarchy?
It could flatten some layers, but the result depends on adoption speed, error tolerance and the value of human relationships. A gradual transition gives companies time to redeploy managers; a rapid, front-loaded transition creates greater short-term disruption.
| Adoption pattern | Likely organizational change | Pressure on middle managers | Main risk |
|---|---|---|---|
| Gradual, task by task | Managers use agents, then teams are redesigned as evidence accumulates | Redeployment and selective layer reduction | Uneven practices and a long period of role ambiguity |
| Front-loaded deployment | Many coordination processes are automated at once | Fewer supervisory positions in the short term | Loss of institutional knowledge, weak controls and displaced workers |
| Augmentation-led | Agents increase each manager’s span of control without removing layers | Higher expectations and broader responsibilities | Overloaded managers who cannot review automated work properly |
| Substitution-led | Standardized teams and reporting lines are consolidated | Highest risk for coordination-heavy roles | Decisions become faster but less explainable or less trusted |
A flatter chart is not automatically a better organization. If senior leaders receive more direct reports but lack time for coaching and context, the company may simply move bottlenecks upward. The useful measure is whether decisions improve, customers receive better service and controls remain effective—not whether the org chart has fewer boxes.
Which managers are most resilient or complementary to AI?
Resilience comes from the parts of management that are difficult to specify, verify or hand to a machine. Goldman’s examples distinguish substitution from augmentation; construction managers, for example, are described as having high augmentation potential because their work combines planning with physical sites, changing conditions and coordination among people.
- Judgment-intensive roles: Leaders who make trade-offs with incomplete information and accept consequences are harder to replace than people who only compile information.
- High social interaction: Coaching, conflict resolution, negotiation, hiring and client relationships depend on trust, emotion and credibility.
- Domain expertise: Knowledge of a regulated process, technical system or customer context helps a manager detect when an AI answer is subtly wrong.
- Governance and accountability: Someone must approve policies, investigate incidents, protect confidential data and explain decisions to auditors, employees or customers.
- Physical or operational presence: Work involving sites, equipment, safety and rapidly changing conditions often benefits from AI assistance without becoming fully virtual.
- Proprietary workflows and data: Managers who turn unique company knowledge into reliable processes can make AI more valuable rather than obsolete.
By contrast, exposure rises when a role has a narrow span of control, highly standardized inputs, little discretion and outputs that can be checked automatically. These are tendencies, not guarantees; a supposedly routine process can carry hidden legal or reputational risk.
How should companies redeploy middle managers instead of simply removing them?
Redeployment works when the organization identifies valuable human decisions before automating the surrounding tasks. A practical program has five parts.
- Map tasks, not titles. Break each manager’s week into coordination, analysis, decisions, coaching, relationship work and administration. Estimate frequency, error cost and required access for each task.
- Classify substitution versus augmentation. Automate low-risk, repeatable steps first; design review points for tasks where a wrong answer could harm a person, customer or balance sheet.
- Redesign spans of control deliberately. If agents let one manager support more employees, set a limit that preserves time for one-to-one conversations and incident review.
- Create new accountability roles. Model owners, workflow designers, quality reviewers and AI-governance leads can absorb expertise from managers whose former coordination work is shrinking.
- Measure outcomes. Track decision quality, cycle time, employee development, customer results, error rates and escalation volume. Headcount reduction alone cannot show that the redesign worked.
Managers should be involved in these changes because they know where informal work, exceptions and trust relationships sit. Removing that knowledge before automating a process can make the system look efficient while increasing hidden failures.
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What should a current or aspiring manager do now?
Build capabilities that increase the value of AI without surrendering professional judgment. The goal is not to become a prompt specialist alone; it is to become the person who can turn automated output into a safe, useful decision.
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- Practice writing measurable objectives, constraints and acceptance tests for delegated work.
- Develop a review habit: verify sources, reproduce important calculations and test unusual cases.
- Strengthen coaching, negotiation, presentation and conflict skills that software cannot reliably perform.
- Document domain rules and exceptions so agents can use them without exposing confidential information.
- Volunteer for workflow redesign or governance projects, where you can demonstrate judgment and accountability.
- Keep a record of measurable improvements in quality, speed, risk reduction or employee development rather than only listing tools used.
Employees entering the workforce should expect management experience to include supervising automated work. They can gain an advantage by learning when to trust an agent, when to challenge it and when to escalate to a human authority.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How certain are Goldman Sachs’ numbers?
They are useful for framing scale, not for predicting an individual employer’s headcount. The “roughly two-thirds” exposure estimate and “up to one-fourth” substitution estimate were published by Goldman Sachs Global Investment Research in 2025. The 2026 estimate of about 300 million globally exposed jobs and the base case of 6–7% worker displacement over an approximately ten-year transition come from Goldman Sachs Research.
Different studies can produce different totals because they define exposure, tasks, occupations, geography and adoption assumptions differently. Actual results will depend on technology reliability, regulation, wages, investment, worker bargaining power and whether companies use productivity gains to expand output or reduce labor. Treat the figures as scenarios with uncertainty bands, not promises that a particular management layer will vanish by a particular year.
What does the shift mean for the wider labor market?
AI can raise productivity while still causing painful transitions. When agents let a small team serve more customers, demand may grow and create new work; when output stays fixed, the same capability can reduce the number of people needed. Workers displaced from coordination roles may move into specialist, coaching, governance or client-facing jobs, but that movement requires training and available vacancies.
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How can you tell whether your own management task is being replaced or augmented?
Ask four questions before assuming either outcome: Can an agent complete the task with standardized data? Is the cost of a mistake acceptable without a human review? Does success depend on trust, negotiation or physical context? And can the organization redeploy the person’s expertise to a higher-value decision?
If the first two answers are yes and the last two are no, substitution pressure is high. If the task requires judgment, relationships or accountability, expect augmentation—provided the organization invests in reliable data, clear controls and time to review automated work.
Frequently Asked Questions
Does AI exposure mean my manager job will disappear?
No. Exposure describes tasks that technology may change, not a guaranteed job loss. A manager may keep the role while supervising more people, reviewing agent outputs or moving into governance, coaching or specialist work.
Why might construction managers benefit from AI instead of being replaced by it?
Construction work combines schedules and budgets with changing physical conditions, safety decisions and coordination among crews. AI can improve planning and monitoring, while on-site judgment and accountability remain difficult to automate.
What protections should employees ask for when their teams adopt AI agents?
Ask who approves agent actions, what data the system can access, how errors are reported and who makes the final decision. Clear logs, appeal routes and training reduce the risk that workers are blamed for an automated action they could not inspect.
Could a flatter hierarchy reduce promotion opportunities?
It might remove some traditional supervisory steps, especially in coordination-heavy departments. New paths can emerge in workflow design, model oversight, domain expertise and people development, but employers need to define and fund those paths rather than assuming they will appear automatically.
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