Builders, Sellers, Measurers: Why AI Is Restructuring Every Company Whether They Plan For It or Not
Artificial intelligence is fundamentally restructuring the assumptions organisations use to justify how they coordinate work, allocate decisions, and design hierarchies, rather than simply eliminating jobs. Just as electricity reshaped factory layouts and computers dismantled paper-based bureaucracies, AI challenges why certain roles, layers, and processes exist at all, making organisational design the real frontier of disruption.
AI is not eliminating work so much as changing what organisations optimise for. Companies that once grew by adding layers of management, reporting and coordination are beginning to grow by adding builders and customer-facing experts instead, with AI quietly removing much of the organisational overhead that previously sat between them. The businesses that understand this first will look structurally very different from those still debating whether artificial intelligence is a threat or an opportunity.
The dominant narrative frames this as job displacement: a technology company restructures, a chief executive explains that AI has transformed productivity, and commentators immediately conclude that software developers, analysts or managers are becoming obsolete. That framing misses what may be the most significant economic shift underway. AI is not primarily changing who works; it is changing why organisations looked the way they did in the first place, and what it now costs to keep them looking that way. Every major technological revolution has reshaped organisations long before it reshaped labour markets. Steam power, electricity, computing and the internet each removed a fundamental economic constraint and forced businesses to reorganise around a new reality. Artificial intelligence appears to be removing a different constraint altogether, namely the cost of coordination, and that changes nearly everything about how large institutions justify their shape.
1. Every Technological Revolution Changes Organisational Design
It is tempting to think of technological progress as a sequence of better tools, yet history consistently suggests that tools are only the visible outcome. The deeper transformation occurs when a technology makes something dramatically cheaper, allowing organisations to abandon structures that were previously essential. Steam power made large-scale manufacturing economical and displaced cottage industries over the following decades. Electricity freed factories from central drive shafts, allowing production lines to be organised around efficiency instead of mechanical proximity. Computers made calculation almost free, eliminating armies of clerks whose days were spent on arithmetic that now takes milliseconds. The internet removed geography as a barrier to commerce, while cloud computing removed infrastructure as a prerequisite for building software businesses. None of these revolutions simply improved existing organisations — each replaced the assumptions upon which those organisations had been built, and the businesses that understood the distinction thrived while those that treated new technology as a faster version of the old way eventually did not. Artificial intelligence appears to be following exactly the same pattern.
2. Modern Organisations Were Built Around Expensive Communication
Large organisations are often criticised for bureaucracy, endless meetings and layers of management, as though these structures developed because executives enjoyed process for its own sake. The reality is rather more practical. Bureaucracy exists because communication has historically been slow, fragmented and expensive, and the roles that constitute it did not appear because organisations were poorly run but because organisations had no alternative.
An executive responsible for twenty thousand employees cannot observe everything personally, which means information has to travel through the organisation before decisions can be made, and someone has to carry it. Managers collect status updates because systems cannot report their own state clearly. Programme offices exist because no single person can hold the dependencies of a large delivery portfolio in their head. Governance teams prepare committee packs because senior leaders cannot read raw operational data across dozens of systems before a board meeting. Finance teams reconcile accounts because the underlying platforms do not agree with each other automatically, and steering committees exist because alignment between functions requires a structured venue in which people with different information can reach shared understanding. None of these roles were invented frivolously — each emerged to solve a real problem created by the cost and fragmentation of information, and the measurer category as a proportion of total headcount grew in direct proportion to organisational complexity because complexity created information gaps and information gaps required people to fill them.
Artificial intelligence challenges the assumption that this infrastructure must continue to exist in its current form. According to BCG research published in 2026, organisations that redesign their operating models around AI are achieving up to 60 percent cost reduction and 80 percent cycle time reduction, and the gap between those results and the outcomes of incremental adoption is not a matter of degree but a matter of structural choice. The reason those numbers are possible is that AI can now perform much of the information assembly, translation and distribution that entire departments previously existed to do manually.
3. The Hidden Coordination Tax
Every organisation pays what might be called a coordination tax. As companies grow, an increasing proportion of employees spend their time helping other people work rather than directly creating value for customers — some build products, some sell them, some support customers, and many others coordinate the work of everyone else through reporting, scheduling, governance, planning, documentation, approvals, portfolio management and communication. These activities have historically been valuable precisely because information was expensive to collect and distribute, but artificial intelligence is rapidly reducing those costs. An AI system connected to operational telemetry, engineering systems, customer support platforms, financial data and documentation repositories can answer questions that previously required days of manual effort — explaining why a deployment failed, identifying affected customers, summarising operational risks, analysing financial performance and retrieving institutional knowledge without requiring multiple people to prepare reports for multiple meetings. The information itself increasingly becomes available on demand, continuously and at negligible marginal cost, and if coordination becomes inexpensive then organisations built around expensive coordination begin to look structurally inefficient in ways that are difficult to ignore when a competitor has already reorganised itself around the new reality.
Research published in February 2026 under the title AI as Coordination-Compressing Capital formalises this intuition, arguing that AI reduces coordination costs in ways that expand management spans of control and enable new task creation, and that firms adopting such tools are demonstrably flattening hierarchies as a result. The distributional consequences depend on who controls the organisational elasticity that follows.
4. Cloudflare and the Builders, Sellers, Measurers Framework
When Cloudflare cut approximately 1,100 jobs in May 2026, the headline interpretation was straightforward: a technology company was replacing workers with AI. What followed complicated that reading considerably. According to BNP Paribas analysis of LinkedIn data, Cloudflare’s engineering headcount grew from 1,308 to 1,894 in the weeks after the cuts, a 45 percent increase in the function the company considers most central to its value creation even as total headcount shrank by roughly a fifth. CEO Matthew Prince confirmed the trend and offered a framework for understanding it, dividing almost every large organisation into three groups: builders who make the product, sellers who bring in revenue, and measurers who track, report and coordinate the work of the first two groups.
It is worth being precise about what a measurer is, because the word is easy to misread. A measurer is someone whose primary organisational value comes from observing, reporting, coordinating or validating work created by others rather than directly creating products or revenue — a definition that immediately removes ambiguity about where the boundary sits, because it is not a job title but a description of what someone’s output actually is. A finance analyst who produces dashboards summarising other people’s work is a measurer, while a quantitative trader whose models directly generate revenue is not. A programme manager who coordinates delivery across teams is a measurer, while an engineer who builds the delivery pipeline is not. The distinction is not about seniority or salary but about whether the work creates value or describes value that others have created.
The roles cut at Cloudflare fell overwhelmingly into the measurer category: middle managers, operations staff, finance analysts and marketing coordinators whose primary function AI agents can increasingly approximate. Prince’s logic runs in the opposite direction from the dominant narrative — if his engineers become more productive with AI, he would hire more of them rather than fewer, because each additional engineer now generates greater returns. Cloudflare’s Q1 2026 revenue grew 34 percent year on year to $640 million, and the company added a record number of enterprise customers through the same period in which it shed those roles, making clear that the restructuring was not driven by financial weakness.
GitLab followed a strikingly similar path in May 2026, announcing a seven percent workforce reduction alongside the removal of up to three layers of management in certain functions and a reorganisation of its research and development division into roughly 60 smaller autonomous teams. CEO Bill Staples framed it as preparation for the agentic era, in which the company’s own internal reviews, approvals and handoffs would be automated by AI agents, with savings reinvested into the engineering capability that generates customer value. The pattern repeating across two companies in the same month, with nearly identical logic and structural outcomes, is harder to dismiss as coincidence than a single data point would be.
5. AI Is Replacing Assumptions, Not Professions
Perhaps the biggest misunderstanding surrounding artificial intelligence is the belief that it replaces professions directly. Computers did not replace accountants, the internet did not replace retailers, and cloud computing did not replace IT departments. Instead, each technology replaced the assumptions about how those professions needed to operate, producing profound changes in what those professionals actually did for a living and in the organisational structures built to support them. Artificial intelligence appears to be replacing a different assumption. For decades businesses have accepted that moving information between people is expensive and have built organisational structures accordingly, with entire layers of administration emerging because somebody needed to gather information, summarise it, distribute it and ensure everyone shared the same understanding before a decision could be reached. If AI performs much of that coordination continuously and at negligible cost, then many of those assumptions become unnecessary, which is a structural transformation rather than a technological one and explains why the phenomenon cannot be adequately described as job displacement alone. The IMD Business School’s analysis of 2026 AI trends observes that middle management roles are expected to shrink as AI systems take over reporting, forecasting and coordination tasks, with analysts projecting a ten to twenty percent reduction in traditional middle management positions by the end of 2026.
It is also worth noting that measurement is not uniformly administrative, because in certain domains measurement is the product. Fraud detection, cyber monitoring, quantitative trading, observability engineering and scientific research are all measurement-intensive activities that directly generate business value or prevent loss, and these functions are builders operating in measurement-shaped domains rather than overhead. The relevant distinction is not whether someone works with data, models or reports but whether their output directly creates value or primarily describes the value that others have created — measurement that generates revenue, reduces loss or enables the product remains part of building, while measurement that exists primarily to observe the organisation increasingly becomes something AI does automatically.
This distinction matters particularly in regulated industries. Banks, healthcare providers, aviation operators and government agencies cannot simply eliminate measurement functions because those functions are legally mandated, but what changes is the human role within them. AI increasingly performs the mechanical production of compliance output, risk reports and audit trails, while the humans who remain in those functions shift from being producers of measurement to reviewers and interpreters of it. The measurement does not disappear but the labour required to produce it does.
Banking illustrates this particularly clearly. Entire departments exist to reconcile systems, prepare governance packs, aggregate operational metrics and coordinate information between technology, operations and risk functions, and much of this work exists not because it is intrinsically valuable but because today’s systems cannot continuously explain themselves in terms that decision-makers can act on. A risk committee meets monthly because assembling the required picture of the organisation’s exposure takes weeks of manual effort. A technology steering committee convenes because status cannot be read directly from the systems that produce it. As AI gains direct access to operational data across these systems, many of these coordination activities become automated rather than manual, the humans involved shift from producing the picture to interpreting and acting on it, and the governance obligation remains while the infrastructure required to discharge it changes substantially.
None of this means that management disappears or that leadership becomes less important — if anything it becomes more important. Great organisations still need people who exercise judgment under uncertainty, set direction, resolve conflict and accept accountability for outcomes. What disappears is not management itself but coordination work that exists only because humans previously had to move information manually between teams, and the distinction matters because conflating the two produces a misreading of what AI is actually doing to organisational structures. It is not removing the need for experienced leaders but removing the administrative infrastructure that surrounded them, and in doing so it is making the quality of leadership more visible rather than less.
6. Why Builders Become More Valuable
One of the most persistent assumptions surrounding AI is that greater productivity inevitably leads to fewer employees, but that conclusion only follows if demand remains constant, and history suggests something rather different. When productive capacity increases, organisations frequently expand investment in the activities that generate value because each additional contributor now produces greater returns.
A useful way to think about this is what might be called the value creation ratio: the proportion of an organisation’s total headcount that is directly involved in creating or converting value for customers, as opposed to supporting, coordinating and reporting on that creation. Builders create value, sellers convert it into revenue, and everyone else exists to support those two groups. As AI makes that support function dramatically more efficient the ratio shifts, and the companies that benefit most from AI are therefore not necessarily those that reduce total headcount but those that continuously increase the proportion of employees directly involved in creating or selling something customers pay for — which is a profoundly different optimisation target from the one most large organisations have historically pursued.
Prince’s stated reasoning at Cloudflare makes this explicit: AI amplifies the output of people who build and sell, which means hiring more builders becomes more attractive rather than less as AI capability grows, and the return on investment of the builder category improves precisely because coordination and reporting consume less of their time. TrueUp, a platform that tracks technology hiring, reports that open technology roles were up fourteen percent in 2026 compared with the previous year, with hardware engineering positions growing at more than fifty percent. The gains are concentrated in technical and product roles while openings in operations, human resources and general management have declined, and companies are hiring more people who build things and fewer people who manage the people who build things.
7. The Ford Counterexample and the Limits of the Thesis
The structural argument for AI replacing coordination roles is compelling, but the picture has an important complication that deserves equal weight. Ford Motor Company spent several years reducing its reliance on experienced engineers, replacing human quality inspection with AI driven automated systems as part of a broader reduction in its salaried workforce that eventually shed approximately 5,300 positions from its 2020 peak. The results were damaging. Ford’s VP of vehicle hardware engineering, Charles Poon, acknowledged publicly in June 2026 that the company had mistakenly believed it could introduce AI, ingest existing design requirements and thereby produce a high-quality product. The fundamental problem was not that the AI was technically broken but that experienced engineers had left before they could transfer their institutional knowledge into the systems intended to replace them. Without decades of engineering judgment encoded in training data, Ford’s automated tools amplified weak inputs rather than catching design flaws, and the company subsequently hired, rehired or promoted 350 veteran engineers described internally as gray beards to mentor younger staff, rebuild the data pipelines feeding AI training, and refine the automated systems they were originally supposed to be supplanted by. Following that investment, Ford reached the top of JD Power’s initial quality ranking among mainstream brands for the first time in sixteen years.
The lesson is not that AI cannot replace certain human functions but that the sequence matters enormously. Institutional knowledge must be transferred before it can be encoded, and encoding it requires the people who hold it to be present and engaged, which means organisations that remove experienced practitioners before their knowledge has been adequately captured will find themselves with AI systems of limited use and a pipeline of junior talent that has never had the opportunity to learn the things those systems were supposed to know.
8. The Apprenticeship Problem
There is a genuine concern that deserves more serious attention than it currently receives in most discussions of AI and the labour market. Every profession depends upon apprentices, with junior engineers becoming experienced architects by solving thousands of relatively small problems over many years, lawyers becoming trusted advisers by handling routine cases before tackling complex litigation, and accountants developing expertise by mastering everyday financial work before taking responsibility for strategic decisions. The routine work that feels most automatable is also, in most professions, the substrate on which expertise is built, and artificial intelligence increasingly performs much of that routine work.
The instinctive organisational response has been to reduce junior hiring and concentrate AI tooling in the hands of experienced practitioners who are less likely to make amplified mistakes. Amazon moved in this direction after a run of significant production outages, requiring senior engineer sign-off as a risk control, and the logic is understandable because AI does not just amplify good judgment — it amplifies whatever judgment is present, which means a junior engineer operating in a complex, poorly bounded system with AI assistance can cause damage at a speed and scale that was previously impossible.
But there is a less visible and more dangerous version of this problem. Research published in June 2026 in the Journal of Higher Education Theory and Practice argues that the informal post-degree apprenticeship system that historically completed graduate formation no longer reliably exists, and that the architecture of the undergraduate degree is structurally incapable of replacing it through curricular revision alone. ADP payroll data analysed by Brynjolfsson, Chandar and Chen found a thirteen percent relative decline in employment for workers aged twenty-two to twenty-five in AI-exposed occupations since late 2022, while employment for experienced workers remained stable. Hosseini and Lichtinger, analysing sixty-two million resumes across 285,000 American firms, found that AI-adopting companies reduced junior hiring by nine to ten percent within six quarters, driven entirely by reduced hiring rather than increased departures, with senior hiring unaffected. The career ladder is not collapsing from the top down but being eroded from the bottom up.
The response to this should not be to accept it as inevitable, because the correct diagnosis is that AI amplifying junior mistakes is a symptom of an environment problem rather than a people problem. If the blast radius of a mistake is catastrophically large, the solution is to reduce the blast radius rather than stop hiring the people most likely to learn from making mistakes — which means better guard rails, better automated testing, better standards, better mentorship structures, and crucially better architectural boundaries that limit how far a mistake can travel before it is caught. An organisation operating on a large shared monolithic database, where a poorly written query or an ill-considered migration can affect dozens of teams simultaneously, is not a safe environment for AI-amplified junior work regardless of how capable those juniors are. The monolith is not just technical debt but a cap on how aggressively the organisation can distribute AI tooling and how fast it can develop new engineers into productive autonomy.
The practical implication is that domain decomposition and junior talent development are not independent workstreams but the same investment viewed from different angles. Each domain boundary that gets properly established creates a bounded environment where junior engineers can operate with AI tooling, make mistakes, learn from them and be mentored by seniors without the consequences propagating across the organisation. The organisations that understand this sequencing will build sustainable engineering capability, while those that take the easier path of simply restricting AI access to experienced staff will find themselves a decade from now in the position Ford found itself in June 2026: dependent on practitioners whose knowledge was never transferred, facing a generation of engineers who were never given the conditions to develop it. The career ladder is not broken beyond repair but has been made fragile by organisations that treated the symptoms rather than the cause, and it can be rebuilt by those willing to do the harder architectural and cultural work that makes junior talent viable again.
9. Conway’s Law May Be Entering a New Era
Software architects have long quoted Conway’s Law, the 1968 observation by Melvin Conway that any organisation designing a system will inevitably produce a design whose structure mirrors its communication structure. For decades this proved remarkably accurate, as communication barriers naturally became architectural boundaries, monolithic IT structures reflected monolithic management hierarchies, and microservices architectures tended to emerge from organisations already structured around small autonomous teams. Artificial intelligence introduces an intriguing possibility here: if communication becomes dramatically easier, cheaper and more immediate across organisational boundaries, then perhaps software architectures no longer need to mirror complex organisational hierarchies quite as closely as they once did. GitLab’s restructuring into 60 autonomous teams is explicitly designed around this logic — by removing management layers and giving small teams end-to-end ownership, the company is attempting to apply what architects call the Inverse Conway Maneuver at organisational scale, deliberately restructuring itself to produce the architecture it wants rather than the one it inherited from historical communication constraints. Conway’s Law does not become incorrect as this happens; the organisations themselves evolve, and as they do, the architectural patterns they produce evolve with them.
10. Separating Transformation from Storytelling
There is also a reason for caution whenever companies attribute restructuring entirely to artificial intelligence. Some organisations undoubtedly are redesigning themselves around AI, but others are responding to slower growth, previous overhiring or changing market conditions while recognising that AI narratives are currently rewarded by investors and generate more favourable coverage than conventional cost reduction. The pattern of Atlassian following GitLab’s playbook in March 2026 with nearly identical language, both seeing their stock decline after the announcements, suggests the market has become sophisticated enough to notice the difference between genuine operational reinvention and the repackaging of ordinary cost cutting in technological framing — a phenomenon Sam Altman has himself described as AI washing. The coming years will require separating businesses that are fundamentally redesigning how they operate from those simply using AI as a convenient explanation for headcount reduction, because only the first group is likely to enjoy lasting competitive advantages, while the second will find itself having reduced coordination capacity without having built the builder capacity to replace it, which is a structurally worse position than the one they started from.
11. The Lag Problem
There is a transition cost that most commentary on AI organisational design ignores entirely and that deserves its own treatment. Almost every large organisation will not respond to AI by immediately dismantling its measurement infrastructure but will instead add AI tooling to existing structures, keep every reporting layer, retain every approval committee and maintain every governance process, with the result that at least initially there is more bureaucracy rather than less. Coordinators now have AI-assisted dashboards, programme offices now generate automated status reports, and governance committees receive AI-summarised risk assessments, but none of the underlying structure changes — it simply runs faster and produces more output, most of which nobody needed in the first place.
This creates a temporary period during which AI makes organisations slower and more expensive before the structural pressure eventually becomes impossible to ignore. The coordination layer now has powerful tools and institutional legitimacy, which makes removing it politically harder rather than easier because the people within it can point to measurable productivity gains. The organisation has essentially automated the production of output that was already of marginal value, and the cost of that output has fallen just enough to defer the harder conversation about whether it should exist at all. The organisations that move fastest through this transition are those whose leadership understands from the outset that the goal is not to make the existing structure more efficient but to ask whether the existing structure would have been built at all if AI had existed when the company was founded — and those are very different questions, with the second one considerably more uncomfortable to answer honestly.
12. The Next Generation of Organisations
The organisations that thrive over the coming decade are unlikely to be those with the fewest employees but far more likely to be those with the greatest concentration of people directly creating customer value: builders, designers, engineers, researchers, product managers, sales professionals and customer specialists whose work compounds in proportion to the quality of the people doing it. As coordination becomes progressively cheaper, competitive advantage shifts toward those who invent, create, solve and build, because artificial intelligence performs much of the information movement automatically and at a cost that trends toward zero. This is not a story about replacing people but about what happens when one of the most persistent constraints on how organisations are structured quietly disappears, and about which organisations understand what that means before their competitors do — a story that also carries genuine obligations to invest in apprenticeship pathways, to sequence AI adoption carefully enough that institutional knowledge is encoded before it walks out the door, and to resist the temptation to treat structural transformation as an exercise in counting heads.
13. Final Thoughts
Every industrial revolution reduced the cost of producing something fundamental. Steam reduced the cost of physical labour, computing reduced the cost of calculation, and the internet reduced the cost of distribution. Artificial intelligence is reducing the cost of organisational coordination, and that changes everything about how large institutions justify their shape.
Twenty years ago companies competed by scaling headcount. Today they compete by scaling decision quality, and the organisations that win over the next decade will not necessarily employ fewer people but will employ a much higher proportion of people who directly create value for customers, supported by AI systems that handle the coordination, reporting and information assembly that previously required entire layers of management to perform manually. Companies built primarily around measuring themselves will find that shape increasingly difficult to defend, while companies built around building and selling will find they can grow faster, develop talent more sustainably and deploy capital more effectively than competitors still running the old model. The headlines asking whether AI will take your job are asking the wrong question. The right question is whether the assumptions your organisation was built on are still true, and what you are going to do now that many of them are not.
References
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