Why Every Company Now Looks the Same

Jason Williamson • • 23 min read
organizational-design innovation corporate-culture digital-transformation
Table of Contents

The conference room in Dallas could have been anywhere. Not in the banal sense that all conference rooms share certain features — tables, chairs, the inevitable speakerphone spider — but in the more unsettling sense that this particular room, with its exposed brick, Edison bulbs, and motivational typography (“ITERATE FEARLESSLY”), had been precisely replicated in Stockholm, Singapore, and Santiago. The founder across from me, a former JPMorgan analyst turned insurtech entrepreneur, gestured at the reclaimed wood accent wall. “We hired a design firm to make our space ‘unique,’” she said, making air quotes. “They delivered the same mood board they’d apparently given to half of Dallas. Terracotta planters, mid-century furniture, a neon sign saying ‘HUSTLE.’ I call it the mid-century-modern brain.”

She wasn’t wrong. Walking through the city’s innovation district that morning, I’d counted four identical “living walls” of succulents, three variations on the same brass-and-marble reception desk, and enough Eames chair knockoffs to furnish a small museum. But the aesthetic convergence was merely the visible symptom of something deeper. When she pulled up her company’s OKR dashboard — the same software platform used by her last three employers — I watched her navigate through performance metrics, sprint velocities, and employee engagement scores that could have belonged to any firm, in any industry, anywhere.

“Sometimes I forget which company I’m looking at,” she admitted, toggling between browser tabs. “The KPIs are identical. The terminology is identical. Last week, our board asked why our ‘employee net promoter score’ was two points below the venture portfolio median. Not whether it mattered for an insurance startup. Just why we were below median.”

This is the paradox of contemporary capitalism: the very technologies that promised to unleash a thousand flowers of organizational innovation have instead produced a monoculture. As Jon Askonas observed in his diagnosis of the emerging “control society,” we’ve built tools of infinite possibility and used them to create infinite sameness. But where Askonas focused on how algorithmic systems shape individual behavior and political life, the workplace offers an even starker illustration of convergence. Every strategy deck deploys the same frameworks. Every reorg follows the same playbook. Every performance review cycles through the same rubrics. It’s as if the entire corporate world is running on a single operating system — and nobody remembers who installed it.

The question isn’t simply why this happened, though that story alone would be worth telling. The deeper puzzle is why it persists. In competitive markets, differentiation should be an advantage. Yet from Mumbai’s financial district to Munich’s industrial corridor, firms are actively choosing to become more alike. They adopt identical structures, implement identical processes, and measure success through identical lenses. The result is a curious inversion of capitalism’s core promise: instead of competition driving diversity, it now enforces conformity.

To understand this convergence, we need to excavate three layers of explanation, each building on the last. The first is historical: how the dream of rational organization, from Weber’s bureaucracy through corporate America’s postwar expansion, created the conceptual infrastructure for standardization. The second is institutional: how specific mechanisms — from venture capital due diligence to consulting firm templates — transform possibility into prescription. The third is technological: how software platforms don’t just enable standardization but encode and enforce it, creating what we might call “protocol capitalism.”

From Iron Cage to Control Protocol

Max Weber saw it coming, though he couldn’t have imagined the specific form it would take. Writing in the 1890s as Germany industrialized, Weber watched traditional forms of authority — charismatic leaders, inherited positions, local customs — give way to something new: bureaucratic rationality. This wasn’t merely about organizations getting larger or more complex. It was about a fundamental shift in how humans coordinated action. Rules replaced relationships. Procedures replaced judgment. The “iron cage” of rationalization would, Weber predicted, eventually enclose all of modern life.

But Weber’s cage was still made of paper and human habit. Forms might be standardized, but they were filled out by hand. Procedures might be codified, but they were interpreted by people. The rationalization he described was powerful but incomplete — it could shape behavior but couldn’t fully control it. Workers retained what James Scott would later call “mētis”: the local, contextual knowledge that makes organizations actually function. The gap between formal rules and informal practice was where human agency lived.

By 1983, organizational theorists Paul DiMaggio and Walter Powell noticed something odd. Organizations were becoming more similar not through direct coordination but through indirect pressure. Their landmark paper “The Iron Cage Revisited” identified three mechanisms of what they called “institutional isomorphism” — a fancy term for why organizations in the same field start to look alike:

Coercive isomorphism came from formal pressure: regulations, standards, legal requirements. If every bank had to meet the same capital requirements, they’d develop similar structures to manage them. Normative isomorphism came from professionalization: when managers all attended the same business schools and read the same journals, they imported the same ideas. Mimetic isomorphism came from uncertainty: when unclear about best practices, organizations copied apparently successful peers.

These forces were powerful but slow. Ideas spread through conferences and consultants, taking years or decades to saturate a field. A management innovation like the multidivisional corporation took nearly half a century to become standard. Even then, significant variation persisted. General Motors and Ford might both adopt divisional structures, but they’d implement them differently, shaped by their specific histories and cultures.

The digital transformation changed both the speed and totality of convergence. What emerged wasn’t just Weber’s bureaucratic rationalization or DiMaggio and Powell’s institutional isomorphism, but something qualitatively different: algorithmic standardization. When work flows through software platforms, possibilities narrow to what the code permits. When performance is tracked through dashboards, only what’s measured matters. When algorithms allocate tasks and evaluate outputs, human judgment doesn’t just diminish — it disappears into the background entirely.

The OECD’s research into algorithmic management reveals the scale of this transformation, with a significant portion of surveyed firms across multiple nations now using algorithmic systems for work allocation. This isn’t the factory floor Taylorism of a century ago, where efficiency experts timed workers with stopwatches. It’s something more subtle and more total: software that decides who works on what, when, and how, often without any human understanding why. The algorithm becomes the manager, and its logic becomes the organization’s logic.

This is where Askonas’s concept of “control protocols” becomes essential. Unlike older forms of bureaucratic control, which operated through rules and hierarchies, protocol control operates through technical standards and default settings. It doesn’t tell you what to do — it shapes what it’s possible to do. When every company uses the same project management software, with the same workflow options and the same reporting templates, convergence isn’t a choice. It’s an emergent property of the system.

The Three Engines of Corporate Convergence

Understanding how we arrived at this moment requires examining three interlocking mechanisms that drive organizational sameness. Each operates at a different level — financial, professional, and technical — but they reinforce each other in ways that make divergence not just difficult but often irrational.

Financial Risk-Averse Signaling

In a conference room overlooking Sand Hill Road, a veteran venture capitalist explained why every pitch deck looks identical. “Founders think we want innovation,” he said, pulling up a folder of recent presentations. “We do — in product. But in everything else, we want pattern recognition. Same metrics, same milestones, same mental models. When I see a company tracking unusual KPIs, my first thought isn’t ‘how creative.’ It’s ‘what are they hiding?’”

This investor’s candor illuminates a core paradox of contemporary capitalism. Markets theoretically reward differentiation, but market intermediaries — VCs, analysts, rating agencies — reward conformity. They’ve developed sophisticated tools for evaluating companies, but those tools assume standardized inputs. A firm that reports its performance differently isn’t just harder to evaluate; it’s suspect.

Research from Stanford’s Graduate School of Business explores this “Unlocking the Iron Cage” phenomenon, examining how financial pressures drive organizational conformity. Companies that adopt standard frameworks and metrics find it easier to communicate with investors, even when those frameworks may not perfectly capture their business model. As one founder told researchers: “We switched to OKRs not because they fit our business, but because investors kept asking why we didn’t use them.”

This financial pressure cascades through the economy. Public companies face it from analysts who build models assuming standard metrics. Private equity portfolio companies face it from partners who want comparable dashboards across holdings. Even family-owned businesses face it from lenders who’ve standardized their underwriting criteria. The result is what we might call the “capital markets convergence machine” — a system that transforms organizational diversity into standardized legibility.

Consulting & Playbook Diffusion

The second engine operates through the professional networks that Weber identified but at unprecedented scale and speed. Modern consulting firms don’t just advise organizations — they reprogram them. When McKinsey develops a “transformation playbook” for one client, it becomes the template for entire industries. When Deloitte implements SAFe (Scaled Agile Framework) at a bank, the same framework spreads to insurers, retailers, and government agencies.

According to industry reports and Atlassian’s own documentation, vast numbers of enterprises have adopted standardized frameworks like Scrum methodology and the Spotify model, with Atlassian noting that “hundreds” of enterprises have implemented variations of Spotify’s squad-based structure. But here’s the revealing detail: relatively few report modifying these frameworks significantly for their context. They’re not adapting Agile principles to their specific needs — they’re importing wholesale frameworks, complete with roles (Scrum Master, Product Owner), ceremonies (daily standups, retrospectives), and artifacts (backlogs, burndown charts).

A senior partner at one of the Big Four consulting firms, speaking on condition of anonymity, described the process: “We used to customize everything. Spent months studying a client’s specific context, designing bespoke solutions. Now? We have accelerators — pre-built frameworks we configure slightly. Same workshop agenda whether it’s mining or media. Same maturity models. Same roadmaps. Clients love it because it’s ‘proven.’ We love it because it’s profitable. Nobody asks whether it fits.”

This standardization through professional services wouldn’t be possible without a supporting infrastructure of certification programs, methodology frameworks, and software tools. When the Project Management Institute certifies professionals in PMBOK (Project Management Body of Knowledge), they’re not just teaching skills — they’re programming organizational DNA. When Spotify publishes its “squad model” of autonomous teams, it becomes religious doctrine, implemented faithfully even in organizations with completely different cultures and constraints.

Platform & Protocol Lock-In

The third and most powerful engine is technological. When work moves onto platforms, possibilities collapse to what the platform permits. This isn’t a bug — it’s the core feature. Standardization is what makes platforms valuable. Salesforce works because every sales process can be decomposed into leads, opportunities, and accounts. Workday works because every HR process can be encoded into workflows and approvals. The promise of these systems is efficiency through standardization. The price is organizational convergence.

A Fortune 500 CHRO described discovering this constraint: “We wanted to redesign our performance review process. Move away from annual ratings toward continuous feedback. Then we opened Workday and realized — that’s not how the system works. You can tweak the rating scale, change the labels, adjust the frequency. But the fundamental logic is locked. To do what we wanted would mean abandoning a $50 million investment and retraining thousands of managers. So we kept the old process and called it ‘reimagined.’”

This technical lock-in operates at every level. Individual workers experience it when they can’t customize their dashboards or modify their workflows. Teams experience it when collaboration tools dictate communication patterns. Organizations experience it when enterprise platforms define possible structures. The phenomenon is so pervasive that researchers have begun studying what they call “technical isomorphism” — convergence driven not by choice or pressure but by the constraints of shared technical infrastructure.

The most insidious aspect is how these constraints become naturalized. After using JIRA for sprint planning, teams start thinking in two-week cycles. After using Slack for communication, organizations develop channel-based cultures. After implementing OKRs in software, strategic thinking narrows to what fits the quarterly objective format. The tool shapes the practice, and the practice shapes the organization, until nobody remembers that other ways were once possible.

Evidence of “Template Capitalism”

The convergence these engines produce isn’t anecdotal — it’s observable across industries, though comprehensive data remains frustratingly scarce. Industry observers and organizational researchers point to several patterns that suggest the depth of what we might call “template capitalism”: the reduction of diverse organizational forms to a handful of standardized patterns.

Start with the physical environment. As cultural critics like Alex Murrell have documented in analyses of “the age of average,” workspace design has converged dramatically. Walk through corporate offices from Tokyo to Toronto and you’ll find the same aesthetic: exposed brick, open floor plans, collaborative spaces with whimsical names, and the ubiquitous ping-pong table. Architecture firms report that clients increasingly request designs that match competitor spaces rather than reflect unique organizational cultures.

Organizational structures show similar convergence. The rapid spread of the “Spotify model” — autonomous squads grouped into tribes, with chapters and guilds providing functional expertise — illustrates how quickly templates propagate. This is particularly striking given that Spotify itself has moved away from many elements of its famous model. But the template has taken on a life of its own, spreading through conference talks, Medium posts, and consulting decks until it became organizational orthodoxy.

Management practices exhibit perhaps the starkest convergence. Based on industry surveys and consulting firm reports, the vast majority of large companies have adopted remarkably similar practices:

  • OKRs (Objectives and Key Results) originally from Intel
  • Agile methodologies spreading far beyond IT departments
  • Net Promoter Score as a primary customer metric
  • Variants of Amazon’s “working backwards” document process
  • Innovation time policies modeled on Google’s “20% time”

These aren’t just similar practices — they’re often identical implementations. Consulting firms report that clients frequently use the exact same templates, downloaded from the same online sources. The language is identical. The metrics are identical. Even the examples used in training are identical.

Perhaps most telling is the convergence in strategic thinking itself. Read through annual reports and strategic plans from major corporations and you’ll find remarkable similarity in both language and logic. The same strategic priorities appear repeatedly: digital transformation, customer-centricity, operational excellence, innovation culture, and sustainable growth. While the specific tactics may vary, the underlying frameworks and mental models have converged dramatically.

Yet complete convergence remains elusive. Pockets of resistance persist, and some achieve remarkable success. Understanding these exceptions helps illuminate both the power of convergence pressures and the potential for escape.

Consider a Nordic biotech firm I’ll call “Bjørn Bio” that deliberately rejected standard management frameworks. No OKRs, no Agile ceremonies, no performance ratings. Instead, they use long-form narrative memos for strategic planning, relationship-based project allocation, and qualitative peer feedback. “We tried implementing Scrum,” the founder explained. “Within three months, our best researchers were spending more time updating JIRA than running experiments. So we threw it all out.”

The results have been noteworthy. Despite — or perhaps because of — their organizational idiosyncrasies, the firm has consistently outperformed sector benchmarks. Their drug development timeline runs faster than industry average. Employee retention exceeds industry norms. They’ve become a case study in what organizational theorist David Deephouse calls “optimal distinctiveness” — the sweet spot between conformity and differentiation.

Deephouse’s research into optimal distinctiveness reveals an inverted U-shaped relationship between strategic differentiation and performance. Companies that conform completely to industry norms underperform — they compete on execution alone, with no structural advantages. But companies that diverge too dramatically also underperform — they forfeit legitimacy, struggle to attract resources, and can’t leverage industry infrastructure. The optimal position is moderate differentiation: similar enough to be understood, different enough to be valuable.

This theory helps explain why certain companies successfully resist convergence. They typically share three characteristics:

Strong founding cultures that predate modern management orthodoxy. Bridgewater Associates’ radical transparency, W.L. Gore’s lattice organization, Haier’s self-managed microenterprises — these models emerged from specific contexts and founding philosophies, not consulting frameworks.

Business models that create space for experimentation. High-margin businesses, founder-controlled firms, and companies in emerging sectors face less pressure to conform. They can afford the efficiency losses of divergence because they’re not competing on operational optimization alone.

Leaders willing to defend difference. Every successful organizational innovator tells stories of resisting pressure — from boards demanding “industry best practices,” from employees wanting familiar frameworks, from partners requiring standardized interfaces. Maintaining distinctiveness requires constant, active resistance.

But these exceptions prove the rule. For every firm that successfully maintains organizational distinctiveness, there are hundreds running identical processes. The successes are notable precisely because they’re so rare.

What It Means for Innovation, Inequality, and Worker Agency

The convergence toward template capitalism isn’t merely an organizational curiosity — it reshapes fundamental aspects of economic life. Three consequences deserve particular attention.

Innovation Lag

When every company optimizes for the same metrics using the same methods, breakthrough innovation becomes structurally improbable. This isn’t about individual creativity — plenty of brilliant people work within these systems. It’s about organizational capability. Innovation requires slack, variance, and tolerance for failure. Template capitalism eliminates all three.

Consider how machine learning increasingly drives strategic decisions. These models train on historical data — what worked yesterday across many companies. By definition, they converge toward past median performance. As one AI researcher explained: “We’re building systems that are extraordinarily good at pattern matching and catastrophically bad at pattern breaking. When those systems start making organizational decisions, you get regression to the mean as a design principle.”

The innovation that does occur increasingly happens at the margins — in startups not yet captured by convergence pressures, in research labs with unusual autonomy, in geographic peripheries where templates haven’t fully penetrated. But as these spaces shrink, so does the possibility for fundamental breakthroughs. We may be entering what economist Tyler Cowen calls the “great stagnation” not because we’ve exhausted nature’s secrets but because we’ve standardized the organizations meant to discover them.

Inequality

Standardization enables a particularly pernicious form of inequality. When every company uses the same roles, processes, and metrics, human work becomes perfectly comparable and thus perfectly commodifiable. A “Product Manager Level 3” at one company can be swapped for another with minimal friction. This liquidity benefits companies — they can tap global labor markets, benchmark compensation precisely, and replace workers easily. But it devastates worker bargaining power.

The standardization also facilitates offshoring and automation. When work is decomposed into standardized units, it can be relocated to wherever labor is cheapest or replaced by software when feasible. The OECD’s research on algorithmic management points to concerning trends in wage dispersion and contingent labor usage among firms using such systems. The templates don’t just organize work — they reorganize power.

Moreover, standardization creates winner-take-all dynamics in labor markets. If every company wants the same skills and experiences, workers who fit the template perfectly command premiums while those who don’t are excluded entirely. A software engineer who knows the right frameworks can work anywhere; one with idiosyncratic skills, however valuable, struggles to find roles that match. The result is bifurcation: a privileged class of template-compatible workers and a growing precariat of everyone else.

Worker Agency

Perhaps most troubling is how template capitalism erodes human agency within organizations. When algorithms assign tasks, software enforces processes, and dashboards define success, the space for individual judgment collapses. Workers become what philosopher Yuk Hui calls “algorithmic subjects” — agents whose agency is pre-structured by technical systems.

This isn’t the deskilling that Harry Braverman described in manufacturing, where complex craft knowledge was decomposed into simple tasks. It’s more subtle: the replacement of contextual judgment with procedural compliance. A manager can’t decide to skip a retrospective if the Agile framework demands it. A salesperson can’t pursue an opportunity that doesn’t fit Salesforce’s pipeline stages. A designer can’t propose a solution that the project management tool can’t track.

The erosion happens gradually. First, the system makes suggestions — “best practices” based on aggregated data. Then it provides templates — pre-structured ways of working that save time. Then it enforces compliance — workflows that can’t be bypassed, metrics that must be met. Eventually, workers internalize the system’s logic. They stop imagining alternatives because alternatives seem not just impractical but inconceivable.

Ethnographic studies of tech workers reveal concerning patterns: employees using algorithmic management systems show less variation in problem-solving approaches and are less likely to propose novel solutions. They haven’t become less capable — they’ve been trained to think within the system’s constraints. As one developer put it: “I used to architect solutions. Now I configure services.”

Escape Routes: Five Playbooks for Re-introducing Difference

Diagnosing template capitalism is one thing; escaping it is another. The convergence pressures are real, powerful, and often rational for individual organizations. But the collective result — an economy of clones — serves no one well. Here are five concrete strategies for reintroducing organizational diversity.

1. Regulate for Slack

Governments already incentivize R&D through tax credits, but these typically focus on technological innovation. We need equivalent incentives for organizational innovation. Imagine tax benefits for companies that:

  • Maintain metrics not used by industry peers
  • Develop proprietary management frameworks
  • Allocate significant workforce time to unstructured experimentation
  • Demonstrate novel organizational structures in regulatory filings

Singapore’s Economic Development Board has begun exploring such approaches, offering grants to companies that can demonstrate “management innovation” alongside technical innovation. While still experimental, early results suggest participating firms develop distinctive capabilities that translate to market advantages.

2. Protocol Hygiene Audits

Just as companies audit financial controls and cybersecurity, they need regular audits of their technical and procedural constraints. A protocol hygiene audit would:

  • Map all software systems that structure work
  • Identify default settings and unmodified templates
  • Calculate the percentage of processes that are externally determined
  • Recommend areas for conscious divergence

The audit wouldn’t advocate abandoning all standards — that would be chaos. Instead, it would surface unconscious convergence and create space for intentional choice. One European bank that piloted this approach discovered that the vast majority of their processes were software defaults they’d never consciously chosen. Modifying even a subset led to improvements in both efficiency and innovation.

3. Risk-Portfolio Governance

Venture capitalists manage portfolios expecting most investments to fail but a few to generate outsized returns. Companies need similar thinking about organizational design. This means:

  • Explicitly allocating resources to organizational experiments that violate industry norms
  • Measuring success over longer timeframes that capture innovation benefits
  • Protecting experimental units from convergence pressure
  • Learning from failures without abandoning the portfolio approach

Amazon’s “two-pizza teams” and Google’s “Area 120” represent primitive versions of this idea, but implemented half-heartedly. True risk-portfolio governance would accept that most organizational experiments will underperform on standard metrics while potentially discovering breakthrough approaches.

4. Data-Diversity Injection in AI

As algorithmic decision-making proliferates, we need technical interventions to prevent convergence. Machine learning systems should be required to:

  • Include training data from organizational outliers, not just industry medians
  • Weight novel approaches more heavily than common patterns
  • Generate multiple strategic options, including some that violate conventional wisdom
  • Flag when recommendations converge too closely with competitor actions

This isn’t just about fairness or bias — it’s about maintaining economic dynamism. If every firm’s AI strategist learns from the same data and optimizes for the same outcomes, we’ll converge toward a steady state that benefits no one. Diversity injection would be a form of algorithmic antitrust — preventing the monopolization of organizational possibility.

5. Legitimacy vs. Performance Dashboards

The core insight from optimal distinctiveness theory is that organizations face two competing pressures: performing effectively and maintaining legitimacy. Currently, most measurement systems conflate these, using the same metrics for both. Organizations need dual dashboards:

Legitimacy metrics would track conformity to stakeholder expectations:

  • Standard KPIs that enable comparison
  • Compliance with industry frameworks
  • Adoption of recognized best practices

Performance metrics would track actual value creation:

  • Customer outcomes independent of satisfaction scores
  • Innovation measures beyond patent counts
  • Employee contributions beyond productivity metrics

By making the tension explicit, organizations can consciously choose when to conform and when to diverge. A biotech might maintain standard financial reporting (legitimacy) while using completely novel research processes (performance). A retailer might adopt industry-standard supply chain practices (legitimacy) while pioneering new customer interaction models (performance).

Return to Dallas: The Possibility of Difference

Six months after our first conversation, I returned to the Dallas innovation district. The founder I’d met had embarked on her own escape attempt. The mid-century-modern furniture was gone, replaced by an eclectic mix that employees had chosen themselves. The motivational posters had been replaced by whiteboards covered in diagrams I couldn’t parse — some proprietary system they’d developed for project management.

“We killed OKRs,” she said, with the slight smile of someone who’d committed organizational heresy and survived. “Also Scrum, NPS, and our performance management system. We build everything from first principles now. Ask what we’re actually trying to accomplish, then design the minimal process that achieves it.”

The results had been mixed but fascinating. Traditional productivity metrics had declined. But they’d launched products that competitors hadn’t imagined, hired engineers that larger firms had rejected for being “non-standard,” and dramatically improved employee retention. Their series B fundraising had been challenging — investors kept asking for metrics that didn’t exist — but they’d eventually found backers intrigued by their divergence.

“The hardest part isn’t designing new systems,” she reflected. “It’s resisting the pull back to normal. Every new hire asks why we don’t use JIRA. Every board meeting includes suggestions for ‘proven frameworks.’ Every customer wonders why our processes seem weird. You have to defend difference every single day.”

This is the challenge and opportunity of our moment. Template capitalism isn’t inevitable — it’s a choice we’ve collectively made and could collectively unmake. The tools of convergence could, with conscious effort, become tools of divergence. The same digital technologies that enable standardization could enable mass customization of organizational forms. The same global connectivity that spreads templates could spread alternatives.

But it requires recognizing that organizational monoculture is not just an aesthetic problem — it’s an existential threat to economic dynamism. When every company becomes the same company, we lose not just diversity but possibility itself. The future depends on our willingness to resist the template, to defend difference, to imagine organizations as varied as the humans who comprise them.

The founder walked me out through their redesigned office. In place of the ubiquitous neon “HUSTLE” sign, someone had hung a handwritten note: “Build something that couldn’t exist anywhere else.” It wasn’t professionally designed. It wouldn’t photograph well for Instagram. It was perfect.

What’s your experience with organizational convergence? Have you seen companies successfully maintain their distinctiveness, or witnessed the pull toward standardization? Share your thoughts at info@eudexio.com.