McKinsey: Cutting the coordination tax: How agentic AI can reshape workflows
Key takeaways
Individual AI productivity is not translating proportionately into enterprise value. 80% of respondents to McKinsey’s latest Global Survey on AI say AI has improved individual productivity, but only 37% attribute any EBIT impact to their organisation’s AI use.
The overlooked opportunity sits between workflow steps. McKinsey calls the cost of human mediated handoffs between teams, functions and systems the coordination interface tax.
Coordination can consume 35% to 60% of total work time in knowledge intensive organisations. McKinsey is careful that this is a convergent estimate from several studies rather than one universally measured figure.
Traditional automation optimised the boxes on the process map. Agentic AI creates an opportunity to redesign the arrows between them.
Agentic AI can increasingly handle three forms of work at workflow interfaces: verification, reconciliation and routing, while escalating exceptions that genuinely require human judgement.
In one industrial workflow studied by McKinsey, interface latency was nine to 36 times longer than the processing time inside the workflow steps.
The same workflow contained nine to 18 days of coordination latency compared with only 12 to 24 hours of actual processing.
McKinsey estimated the coordination interface tax for that workflow at $140 million to $240 million annually.
In a later transformation at the manufacturer, planning moved from 30 days to three days, 80% became touchless, and reaction time to supply shocks and demand opportunities fell from weeks to less than one day.
McKinsey argues that interface redesign is primarily an organisational redesign challenge rather than a technology challenge. In its experience, around 70% of the effort goes into people and process redesign.
The workforce outcome is not necessarily fewer people. McKinsey describes the opportunity as operating leverage: fewer people coordinating and more people deciding.
Executive summary
Most large organisations have already spent several years deploying AI through copilots, proofs of concept and function specific applications. The problem is that improvements in individual productivity have not translated proportionately into enterprise economics. McKinsey reports that 80% of respondents to its latest Global Survey on AI say AI has improved their individual productivity, while only 37% attribute any EBIT impact to their organisation’s use of AI.
The article argues that one reason lies in where AI is being deployed. Most organisations use AI to make individual workflow steps faster. McKinsey argues that a larger opportunity sits between those steps, where information and decisions move from one team, function or system to another.
McKinsey calls the cost created at these points the coordination interface tax: the total cost an organisation pays for human mediated handoffs between workflow steps. These handoffs include aligning information, checking whether an upstream output is usable downstream, reconciling conflicting constraints, obtaining approval, routing work and waiting for another person to become available.
Traditional automation improved work inside individual process steps but was much less capable of dealing with these interfaces because they often required contextual judgement. McKinsey argues that agentic AI changes this constraint. Agentic systems can increasingly verify an upstream output, reconcile competing constraints and route work to the appropriate next step, while escalating only those cases that genuinely require human judgement.
The result is a different unit of workflow redesign. Instead of beginning with the work inside each box on a process map, organisations can begin with the arrows between the boxes. This matters because McKinsey estimates that coordination activities can consume 35% to 60% of total work time in knowledge intensive organisations.
The cost also extends beyond people visibly responsible for coordination. McKinsey describes three components: dedicated coordination structures, the financial cost of latency, and the opportunity cost of business that never happens because the organisation moves too slowly.
For WORK SELF, the article is particularly relevant because reducing coordination does not eliminate the human role. It changes it. Routine verification and reconciliation increasingly move towards AI while people spend more time on exceptions, judgement, trade offs and decisions.
The workforce challenge is therefore not simply automating tasks. It is defining where human judgement belongs in an AI mediated workflow, when the agent should act independently, when a human should review, and when the system should escalate.
Key stats and quotable claims
80% report that AI has improved individual productivity.
Only 37% attribute at least some EBIT impact to their organisation’s use of AI.
Coordination can consume 35% to 60% of total work time in knowledge intensive organisations.
Visible coordination structures in manufacturing and industrial companies typically account for approximately 3% to 5% of revenue.
Across the companies McKinsey has studied, visible coordination cost typically represents only around one third of the total coordination tax.
In one Fortune 500 industrial workflow, interface latency was nine to 36 times greater than processing time.
Total interface latency was nine to 18 days compared with only 12 to 24 hours of processing.
McKinsey estimated the coordination interface tax for the workflow at $140 million to $240 million annually.
Less than 7% of that amount came from visible people dedicated to coordination.
The remaining 93% appeared through latency and opportunity costs.
A field experiment involving 515 start ups found that firms redesigning workflows across handoffs generated 1.9 times more revenue and were 11 percentage points more likely to acquire paying customers than firms applying identical AI tools within existing workflow steps.
At one manufacturer, approximately 65% of work at a major interface consisted of routine verification, 25% involved constrained reconciliation, and only 10% represented genuine exceptions requiring human expertise.
Planning moved from 30 days to three days.
80% of planning became touchless.
Reaction time to demand and supply changes fell from weeks to less than one day.
More than 150 team members were upskilled into product management, data science and AI engineering roles.
McKinsey says interface redesign typically takes six to 12 months.
Around 70% of investment goes to people and process redesign, 20% to technology and data integration, and 10% to AI model development.
Where the coordination tax is high, McKinsey says measurable payback can occur within one to two years.
Overall summary
The central argument is simple. Organisations have spent decades improving the steps inside workflows. Lean improved flows and removed waste. Business process reengineering redesigned processes. ERP connected data. Process management established ownership. Robotic process automation automated repeatable tasks.
But McKinsey argues that one problem remained across all of those generations: the interfaces between steps still depended heavily on human coordination.
A forecast may be produced instantly, but somebody still needs to check it against capacity. A procurement request may be automatically generated, but somebody still needs to reconcile it against supply constraints. A customer case may move through an automated system, but somebody still needs to decide whether it can proceed, requires review or needs escalation.
Those interactions create waiting, reconciliation, meetings, reviews and handoffs. The cost is difficult to see because individual functions own their own activities, while nobody necessarily owns the space between them.
Agentic AI changes what can happen at those interfaces. McKinsey describes three primary capabilities: verification, determining whether an upstream output satisfies the requirements of the next step; reconciliation, resolving discrepancies between competing constraints; and routing, deciding where work should go next and escalating cases outside defined boundaries.
The importance for the workforce is that this changes the employee’s role. Instead of reviewing every transaction, the employee increasingly handles transactions requiring genuine judgement. Instead of manually reconciling routine information, the employee manages exceptions. Instead of coordinating work, the employee spends more time making decisions.
McKinsey summarises the operating model shift particularly well: fewer people coordinating and more people deciding.
Deep dive
The gap between individual productivity and enterprise value
Most large organizations have spent the past several years investing heavily in AI. They have launched copilots, built proofs of concept, and are increasingly scaling AI across individual functions. Eighty percent of respondents to McKinsey’s latest Global Survey on AI report that AI has improved their individual productivity. Yet only 37 percent attribute any EBIT impact to their organization’s use of it.
Why? The problem is less about the sophistication of the technology than where it has been deployed. Most organizations use AI to make individual workflow steps more efficient. A larger opportunity lies elsewhere: in the coordination that takes place between those steps.
Over the past several years, organizations have learned another lesson, as well: AI rarely creates enterprise value by improving isolated tasks. The biggest gains come from reimagining end to end workflows around what the technology makes possible. That insight is now well established. The more difficult question is how. Where should leaders begin retooling complex workflows? Which parts of a workflow deserve the most attention? Why do some redesign efforts fundamentally change performance while others deliver only incremental improvements?
One answer is that rather than focusing on the work performed inside each process step, leaders should begin at the interfaces where work passes from one team, function, or system to the next. Those interfaces are where organizations confront what McKinsey calls the coordination interface tax, the total cost an organization pays for human mediated handoffs between workflow steps.
The coordination interface tax
Such interactions have been largely ignored by previous generations of automation. While those efforts excelled at making individual steps faster and more efficient, they could not reliably perform the judgment required at the interfaces between them.
Agentic AI changes that. In McKinsey’s experience, it is the first generation of technology capable of automating much of the verification, reconciliation, and routing that previously required human coordination. The opportunity is not only to make existing workflows more efficient but to redesign workflows in ways that reduce the hidden costs of coordination.
Most companies can draw their workflows on a whiteboard: order to cash, procure to pay, demand to production. After decades of process improvement, the steps within these workflows are designed, measured, and optimized. Yet, across knowledge intensive organizations, coordination, aligning, verifying, reconciling, waiting, and handing work from one step to the next, consumes 35 to 60 percent of total work time.
McKinsey notes that the 35% to 60% range is a convergent estimate drawn from studies using different methods and definitions rather than a single measured figure. But the underlying problem is that the cost created at these interfaces is often difficult to see.
No function owns the interfaces. The demand planning team is responsible for its forecast. The supply planning team owns its allocation. But nobody is accountable for the gap between them, where the forecast waits for reconciliation against capacity constraints. That gap is organizational white space, unowned and unmeasured.
Where the coordination tax hides
The coordination tax hides in three places, only one of which appears as an item in selling, general, and administrative expenses.
The visible portion is people and structures dedicated to coordination: planning teams, project management offices, governance forums, and review meetings. In manufacturing and industrial companies, this typically runs 3 to 5 percent of revenue, McKinsey research has found. Traditional cost programs target it regularly.
Across the companies McKinsey has studied, that visible cost typically represents only about one third of the total coordination tax. The remainder hides in places that no cost program has historically addressed.
The second portion can be found in cycle time and working capital. Every day of latency at a coordination interface carries a financial cost, such as inventory held longer, receivables collected later, and capacity committed but not yet producing revenue.
The third portion appears nowhere in financial statements. It consists of the revenue not captured because the organization could not coordinate fast enough, such as products launched months later because engineering to production handoffs added delay, or market share not won because pricing decisions traversed too many approval layers.
This is the largest portion and the least visible, because it represents what did not happen. No executive receives a report showing revenue lost to coordination latency. The costs may be real, but the measurement is absent.
What the coordination tax looks like in practice
McKinsey recently measured actual latency at each coordination interface in a standard demand to production workflow at a Fortune 500 industrial manufacturer. The workflow has seven steps. Between them sit six coordination interfaces.
Interface latency exceeds processing time by nine to 36 times. The workflow takes roughly one and a half to two and a half weeks to traverse. The actual work inside the steps takes half a day to a day. The rest is coordination, such as verifying that one step’s output meets the next step’s requirements, reconciling discrepancies, obtaining approvals, and waiting for human availability.
From sensing to planning, interface latency was one to two days while processing time was two to four hours. Planning to supply created three to five days of latency for four to eight hours of processing. Supply to scheduling added one to two days for two to four hours of processing, while scheduling to procurement created another one to three days of latency for one to two hours of actual work.
Procurement to execution added two to five days of interface latency for only one to two hours of processing, while execution to fulfilment created one additional day of latency for two to four hours of processing. Across the workflow, interface latency totalled nine to 18 days, compared with only 12 to 24 hours of processing.
McKinsey estimates that the total coordination interface tax for this single workflow came to $140 million to $240 million annually. The figure comprises latency driven working capital cost, underutilized capacity, and missed revenue from delayed response to demand signals.
At this manufacturer, the visible portion, the people dedicated to coordination, accounted for less than 7 percent of that total, well below the typical one third. The business is capital intensive and carries a high working capital base, so latency cost dominates. The remaining 93 percent appeared as latency and opportunity costs, which showed up nowhere on the income statement.
This is why traditional cost programs miss it. Such programs target the 7 percent they can see: head count, meetings, and reporting layers. The other 93 percent is in cycle time, working capital, asset utilization, and revenue that never materializes because work moves too slowly between functions.
Why the interface matters more than the tool
Research increasingly suggests that these interfaces are where AI has the potential to create the greatest value.
A recent field experiment by researchers at INSEAD and Harvard Business School randomized 515 start ups into two groups, each of which received identical AI tools. One group redesigned workflows across handoff points. The other used AI within existing workflow steps.
Firms that redesigned across interfaces generated 1.9 times more revenue and were 11 percentage points more likely to acquire paying customers. The study involved early stage start ups rather than large enterprises, so the magnitude of the effect may differ at scale. But the finding is striking in its consistency with the industrial evidence: what matters is not simply the sophistication of the AI but where it is deployed.
Why this has never been addressed
Before exploring how to seize the newfound opportunities posed by agentic AI, McKinsey looks at how workflow improvement evolved. The history is one of cumulative progress. Each era solved a real problem and created enormous value. Yet each also left the coordination interfaces largely untouched.
Seeing the flow
Quality movements, lean thinking, and Six Sigma made work visible as a connected system. Organizations learned to map the current state, identify waste, and measure cycle time across functions. The conceptual advance was enormous: for the first time, workers understood their activities as a system rather than a collection of departments.
Redesigning the work
Proponents of business process reengineering often made a bold claim: “Don’t automate, obliterate.” When executed well, BPR delivered cycle time reductions of 50 to 90 percent. Ford reduced its North American accounts payable head count by 80 percent. Hallmark cut time to market by 70 percent. The ambition was transformative, but the unit of redesign was still a step.
Connecting the data
Enterprise resource planning systems created integrated data across functions. For the first time, a transaction could flow across functional boundaries without manual reentry. Implementations delivered inventory reductions of 20 to 50 percent in the first two years. The promise was a single source of truth, but in reality, shared data did not eliminate the need for shared judgment at each handoff.
Owning the outcome
Process management formalized ownership, measurement, and continuous improvement. Organizations created process offices and governance around their end to end flows. The discipline was meaningful, but in McKinsey’s experience, formalizing the interfaces documented rather than reduced their costs.
Automating the task
Robotic process automation targeted the manual effort persisting within individual steps. Process mining revealed where actual execution diverged from design. Targeted tasks were compressed by 30 to 70 percent when applied. But automating a task within a step does not address the verification, reconciliation, and approval work required before that step is handed off to the next.
The pattern across all five eras is clear. Each was driven by a technological advance; each required organizations to redesign around what the technology made possible; and each rewarded those that moved first. But in every case, the unit of optimization was the process step. Those individual steps may have improved, but the arrows between them remained largely unchanged.
In McKinsey’s experience, no previous generation of technology could operate at the interface itself with the judgment required to verify, reconcile, and route work across competing constraints. That capability did not exist. Now it does.
How agentic AI works within interfaces
AI creates value not so much by performing individual tasks as by connecting them into continuous flows that previously required human mediation at each junction.
A copilot that helps a planner write a demand forecast faster does not address the three days that the forecast waits for approval. An RPA bot that automates data entry within a step does not reduce the verification overhead at the boundary where that step is handed off to the next. Despite the individual productivity gains AI tools deliver, the interface remains a constraint.
Agentic AI systems can verify, reconcile, route, and act at workflow handoffs with judgment, at machine speed, and at a fraction of the cost of the human coordination they replace. They operate with the reasoning capability that previous generations of automation lacked: evaluating exceptions, applying contextual rules, and escalating only what genuinely requires human judgment.
Unlike RPA, which executes predetermined rules within a single step, agentic systems can reason across the boundaries between steps. McKinsey describes the mechanism at three levels.
Verification: The system confirms that an upstream step’s output meets the downstream step’s input requirements, including data completeness, format consistency, and threshold compliance.
Reconciliation: The system resolves discrepancies between competing constraints, such as demand against capacity, budget against timeline, and specification against availability.
Routing: The system directs work to the appropriate next step based on contextual evaluation, escalating only those exceptions that exceed its confidence threshold.
Each function previously required a human coordinator who understood both sides of the interface. That individual’s calendar, availability, and cognitive load determined the interface’s latency. When a machine performs these functions, the latency collapses from days to hours or minutes.
What happens to the human role
Consider how such a system might transform the Monday morning experience of a demand planner at a manufacturer. In the past, this executive might arrive at work to find a screen showing last week’s forecast, awaiting manual review.
That is no longer the case. Instead, the system has already ingested new demand signals, reconciled them against current capacity and inventory, flagged three exceptions that exceed its confidence thresholds, and staged a revised production schedule for the remaining 94 percent of items.
The planner’s role is no longer to review every forecast and approve routine decisions. It is to investigate the three anomalies that require judgment. Rather than spending four hours reconciling routine demand against capacity constraints, the planner spends that time on the decisions that genuinely require human expertise, such as responding to a customer requesting an unusual delivery pattern, managing a raw material shortage across product lines, or evaluating the implications of a new product launch with little historical data.
The routine coordination happened overnight, autonomously, at the interfaces. The human worker arrived to find a curated set of decisions that only a human should make.
That is an important workforce shift. Agentic AI is not simply performing the same role faster. It is changing the composition of the role itself.
What an interface redesign produces
The results are operating leverage, not simply a reduction in head count.
Freeport McMoRan raised mill throughput by 5 to 10 percent from the same asset, with no new capital deployed. Toyota did not reduce its workforce when AI compressed resource allocation from weeks to minutes; its planners were redeployed rather than released.
Rather than eliminating people, reducing the coordination tax gives employees back the time they currently lose to coordination overhead. The planner who no longer spends four hours reconciling a demand signal against capacity constraints can spend those hours evaluating scenarios, managing exceptions, and advising the business on trade offs.
An engineer who no longer waits three days for cross functional approval of a design change can iterate in hours. The procurement manager who no longer manually reconciles supplier confirmations with production schedules can focus on supplier relationships and risk management.
Over time, SG&A as a percentage of revenue declines because the same people produce more revenue, not because organizations employ fewer people overall. Roles change composition even where total employment holds: fewer people coordinating and more people deciding. The denominator grows faster than the numerator, which is the source of the operating leverage.
The manufacturer transformation
At the manufacturer McKinsey studied, demand to production planning had often operated on a 30 day cycle spanning six functional domains and more than 75 planning tools, most of them spreadsheets and legacy systems.
Regional sales teams submitted forecasts, central planning reconciled them against manufacturing capacity and supplier constraints, and allocation guides eventually made their way to production. More than 50 planners, each working within a largely siloed scope, spent ten nights or more during each cycle manually adjusting allocation and operating plans as conditions changed.
The work performed within each step was relatively straightforward. Much of the delay was due to information crossing functional boundaries and planners reconciling competing constraints and conflicting KPIs.
The redesign team began by mapping coordination intensity across the workflow’s six interfaces. It focused on the demand supply reconciliation junction, where thousands of part level forecasts were reconciled against hundreds of supplier capacity positions each week and where delays cascaded most severely downstream.
After deconstructing the work performed at that interface, the team discovered that roughly 65 percent of the work consisted of routine verifications, such as checking demand against capacity bands, validating forecast changes against stability thresholds, and confirming part level requirements against orderable configurations.
Another 25 percent involved constrained reconciliation across competing functional KPIs: resolving capacity conflicts, responding to supplier gaps, and rebalancing allocations as conditions changed.
Only about 10 percent represented genuine exceptions requiring human expertise and cross functional negotiation.
This breakdown is important because it shows why human and agent orchestration cannot be defined with a single autonomy rule. Different categories of work need different levels of human involvement.
Establishing the boundaries before introducing the technology
The team spent four months mapping and classifying these tasks and establishing the boundaries for AI autonomy before configuring any technology.
Routine verification within agreed thresholds could proceed without human review. Constrained reconciliations could be handled autonomously within established business rules and cost limits. Genuine exceptions, along with cases where AI confidence fell below threshold, would be routed to a planner with the relevant context and alternatives already assembled.
The sequence matters. Before AI can coordinate work at an interface, the organisation first needs to understand the work itself, determine which activities genuinely require human judgement and establish the boundaries within which AI can act.
Only then should the technology be configured.
Results from the redesign
The results were dramatic. Planning clock speed improved tenfold, from 30 days to three, and 80 percent of planning became touchless. Reaction time to supply shocks and demand opportunities fell from weeks to less than one day.
The more than 50 planners who previously worked within siloed scopes gave way to roughly ten end to end planners who governed a system that processed routine decisions at machine speed, while more than 150 team members were upskilled into product management, data science, and AI engineering roles.
The 75 spreadsheets and legacy tools gave way to a single platform, and the ten late nights of manually adjusting allocations became a five minute optimizer run.
The structural profit impact, validated by the finance function, reached billions within the first two years. McKinsey is careful to note that this figure reflects the full planning transformation across the enterprise, including platform consolidation and a tenfold gain in clock speed, rather than the coordination tax on the single workflow measured earlier.
Why interface redesign cannot belong to one function
This manufacturer succeeded in large part because it understood that interface redesign cannot be delegated to existing functions.
A finance team cannot redesign the planning to procurement interface alone because it owns only one side. A technology team cannot redesign it either, because the redesign is primarily organizational rather than technical.
What is needed is a cross functional team accountable for a specific workflow and empowered to redesign the interfaces within it. Its charter is to separate tasks from individuals and reassemble them into AI mediated chains.
In practice, this requires a COO sponsored team with representation from each function the interface touches, accountable for a single workflow’s end to end cycle time and reporting progress to the executive committee.
The process is sequential. Before AI can coordinate work at an interface, the team must identify the tasks currently performed there, determine which require human judgment and which can be automated, and redesign the flow before introducing technology.
Organizations that deploy AI on top of existing coordination patterns add a new tool without removing the underlying source of delay.
Six priorities for addressing the coordination tax
McKinsey identifies six priorities for leaders attempting to redesign these interfaces.
Map interfaces, not just processes. Make handoff points visible: what information crosses each interface, what verification occurs, how long it takes, and what it costs.
Start where interface density meets core competence. The highest value opportunities combine frequent coordination, moderate judgment requirements, and activities central to the business.
Design for verification, not generation. The value of AI at interfaces lies in verifying and reconciling competing constraints at machine speed.
Invest primarily in people and processes. In McKinsey’s experience, successful redesign efforts devote roughly 70 percent of their effort to organizational redesign rather than technology alone.
Govern by interface quality. Define escalation thresholds, accountability, and the boundaries between human and machine judgment.
Treat delay as a cost. In McKinsey’s experience, every quarter spent postponing interface redesign increases the cost of catching up as competitors accumulate coordination data and operating experience.
Where the investment goes
Interface redesign typically takes six to 12 months, with roughly 70 percent of the investment going to people and process redesign, 20 percent to technology and data integration, and 10 percent to AI model development.
Where the coordination tax is high, organizations can expect measurable payback within one to two years, consistent with the breakeven that leading companies report on technology and AI transformations more broadly.
The investment split itself is revealing. The model represents only a relatively small portion of the transformation effort. The majority sits in redesigning the organisation around what the technology now makes possible.
The next frontier of workflow optimisation
Every major technology wave lowers the cost of something important. Steam reduced the cost of power. Electricity reduced the cost of light and motion. The internet reduced the cost of information. AI is reducing the cost of coordination.
For more than a century, organizations designed themselves around the assumption that coordinating work across functions is expensive, slow, and constrained by human bandwidth. That assumption justified layers, meetings, review cycles, and approval chains. It was not incorrect. It was simply the constraint around which organizations were built.
Agentic AI changes the economics of that constraint. Organizations can now redesign workflows around a dramatically lower cost of coordination, not by improving the work performed within each step, but by reducing the friction between them.
Organizations that eliminate their coordination tax will carry a cost structure that their competitors cannot easily match. The advantage will compound because AI mediated interfaces improve with every coordination event. Every transaction that passes through a redesigned interface generates information that makes the next one faster and more accurate.
It is clear that organizations need to redesign workflows. The question is where to begin.
For four decades, organizations have looked first at the boxes on the process map.
The next generation of redesign will begin with the arrows.
What the report means for workforce transformation
The workforce implication is deeper than simply automating administrative work. McKinsey’s example shows a workflow where 65% of activity at a major interface was routine verification, 25% was constrained reconciliation and only 10% genuinely required human expertise.
Once those activities are separated, the organisation can make different decisions about each one. Routine verification can increasingly move to agents. Reconciliation can move to agents within defined business rules and thresholds. Human employees can concentrate on exceptions, ambiguity, trade offs and judgement.
The employee’s role changes as a result, but so does management. The organisation now needs explicit decisions around what an agent can do independently, what requires human review, who remains accountable for the outcome, what causes an escalation, which human receives that escalation and what context needs to accompany the handoff.
The technology therefore creates a new operating model problem. Removing human coordination only produces value if organisations deliberately replace it with governed human and agent orchestration.
What this means for WORK SELF
This McKinsey article is one of the clearest external articulations of the problem WORK SELF has been describing.
The coordination interface is exactly where the relationship between an agent and an accountable employee becomes operational. McKinsey describes three things agents increasingly do at these interfaces: verify, reconcile and route.
But those capabilities immediately create another set of questions. Which decisions can the agent take independently? Which employee remains accountable? When does human review become mandatory? What threshold triggers escalation? Who owns the case after escalation? What information needs to accompany the handoff?
Those are not simply model questions. They are human agent operating model questions.
Maya Enterprise is designed around that layer. Existing enterprise systems, models and agents can remain in place. The missing layer is governed human context around ownership, review, autonomy and escalation so that an approved agent knows how work should move through the organisation and which human remains accountable when judgement is required.
The article also reinforces another important WORK SELF position. The value case does not depend on removing employees.
McKinsey explicitly frames the outcome as operating leverage and illustrates employees being redeployed into higher value work. The economic opportunity comes from reducing rework, waiting, reconciliation and unnecessary human coordination while moving scarce human attention towards decisions that actually require it.
That is a much more useful way to think about human and agent orchestration than simply asking what percentage of a role can be automated.
Key takeaways
Enterprise AI value increasingly depends on redesigning workflows rather than isolated tasks.
The next large source of inefficiency may sit at the interfaces between functions, teams and systems.
Coordination is expensive because verification, reconciliation, routing and waiting still consume human time.
Agentic AI creates a new opportunity because those activities can increasingly happen at machine speed.
The redesign should begin by separating routine coordination from genuine human judgement.
Human involvement should increasingly concentrate around exceptions, ambiguity and consequential decisions rather than every transaction.
Autonomy, review and escalation boundaries need to be established before the technology is configured.
Interface redesign is primarily an organisational challenge. McKinsey says around 70% of the effort belongs in people and process redesign.
The workforce opportunity is operating leverage: less time coordinating, more time deciding.
The operating model moves from optimising the boxes inside the workflow to governing the arrows between them.
The bottom line
McKinsey’s coordination tax is a useful way of explaining why individual AI productivity can rise while enterprise ROI remains weak. Making each employee faster does not necessarily make the workflow faster if the delay remains in the handoff.
Agentic AI changes that because it can increasingly verify, reconcile and route work between steps. But removing the human from routine coordination does not remove human accountability from the workflow. It makes the boundary more important.
The enterprise now needs to know what the agent can decide, where the human remains accountable, when review is required, what triggers escalation and how the two operate together as one workflow.
That is where workflow redesign becomes human agent orchestration.
Source check against the uploaded McKinsey report: the coordination tax framing, workflow economics, agentic interface mechanism and implementation model above all follow the report’s own sequencing and terminology.
