Why Most Companies Operating Models Fail in Dynamic Markets

Most organisations today operate under an Industrial Operating Model (IOM) while competing in dynamic markets. This fundamental mismatch creates enormous waste, missed opportunities, and frustrated teams. Understanding why this model fails, and what must replace it, is essential for any organisation seeking to deliver value effectively in the 21st century and beyond.

Defining the Two Operating Models

Before we explore why organisations struggle, we must clearly define the two operating models at the heart of this discussion, and more importantly, understand the theory of the business that underlies each one.

Every organization operates on a theory of the business ¹: a set of assumptions about the environment, the organization’s mission, and what it must do to succeed. Understanding the theory of our business begins with two foundational questions: Who is the customer, and what does the customer value? But it doesn’t end there. We must also understand the constraints we operate within, technological, regulatory, resource, competitive, and environmental factors that shape what’s possible. Without explicit answers to these questions, “value delivery” becomes an empty phrase.

Each operating model embodies a fundamentally different theory of the business and makes fundamentally different assumptions about the customer, what they value, and the constraints within which value must be delivered.

Im going to use “ Industrial Operating Model (IOM) ” to refer to what most companies do, and “ Agile Product Operating Model (APOM) ” to refer to what a few companies do.

The Industrial Operating Model

Core assumption: The environment is predictable enough that organisations succeed through efficiency, standardisation, and control.

Underlying beliefs:

• Demand changes slowly.
• Work can be understood and specified upfront.
• Variability signals defects and should be minimised.
• Planning provides more certainty than adaptation.
• Performance improves through specialisation and hierarchical coordination.
• Management’s role is to optimise throughput and control deviation.

Customer and value assumptions: Customers primarily want consistency, reliability, and quantity. Value comes from standardised output delivered at lower cost and with minimal variation. This model fits environments with long product cycles, slow-changing needs, and stable demand.

How the model operates: The Industrial Operating Model is built on Taylorism and Scientific Management ². It separates planning from execution, functions from one another, and thinking from doing. Decision-making flows vertically. Work is governed through predictive plans, fixed scope and resources, detailed procedures, stage gates, and individual accountability. Output is the dominant measure of performance.

Where it works: This theory of the business is not wrong. It performs exceptionally well when its assumptions hold, predictable environments, repetitive work, limited uncertainty, and markets where efficiency and consistency create advantage.

The Agile Product Operating Model

Core assumption: The environment is dynamic enough that organisations can only succeed through continuous learning, rapid adaptation, and proximity to customers.

Underlying beliefs:

• Demand shifts frequently and unpredictably.
• Work cannot be fully known upfront; it emerges through discovery.
• Variability provides information rather than noise.
• Fast feedback is more reliable than detailed prediction.
• Performance comes from empowered, cross-functional teams.
• Clear, adaptive goals at every level provide direction without constraining discovery.
• Success requires continuous alignment to customers, stakeholders, and the environment.
• Management’s role is to design systems that enable learning, innovation, and quick adjustment.

Customer and value assumptions: Customer needs are diverse, evolving, and context-dependent. Value is created by solving specific customer problems, not by producing generic output. Organisations win by learning faster than competitors, adapting their products continuously, and delivering outcomes that matter in the customer’s current context. Where the Industrial Operating Model optimises for volume and repeatability, the Agile Product Operating Model optimises for relevance, impact, and adaptability.

How organisations discover value: Customer value is discovered through direct observation, rapid experimentation, real usage data, and continuous engagement with real users. In dynamic markets, discovery is an ongoing activity rather than an occasional research exercise.

How the model operates: The Agile Product Operating Model aligns with empiricism and continuous value delivery. It organises persistent cross-functional teams around products or value streams, decentralises decision-making to those closest to the work, and measures success through outcomes and customer impact. The model depends on transparency, inspection, and adaptation, supported by short feedback loops, modern engineering practices, and a commitment to technical excellence.

Where it works: This theory of the business succeeds when uncertainty is high, customer needs evolve rapidly, and competitive advantage comes from learning speed, responsiveness, and continuous alignment between the product and the market.

Why This Distinction Matters

These are not simply different management styles, nor is this a story of “modern vs traditional” or “agile vs waterfall” The contrast is contextual, not moral. The Industrial Operating Model fails only when its assumptions stop matching reality, when the environment becomes too uncertain, too complex, and too fast-moving for prediction and control to work effectively.

Understanding the theory behind each model clarifies why transition is so difficult: you’re not just changing processes, you’re challenging fundamental assumptions about how success happens.

The Industrial Operating Model Was Fit for Stable Markets

The Industrial Operating Model was not a mistake. It was brilliantly designed for its context, an environment where its theory of the business held true.

During the Industrial Age, markets were stable and predictable. Demand grew steadily over long periods. Competition was limited. Products had decades-long lifecycles. In textiles, steel production, and large-scale manufacturing, success came from optimizing efficiency and driving down costs through standardization and economies of scale. The assumptions underlying the Industrial Operating Model, predictability, stability, knowable work, matched reality.

Frederick Winslow Taylor developed scientific management for exactly this environment, and Henry Gantt popularised it. When work is repetitive and predictable, breaking it into specialized tasks and optimizing each step makes sense. When markets change slowly, detailed upfront planning works. When products remain unchanged for years, separation of planning from execution causes minimal waste. Variability could be treated as a problem to eliminate because the “right way” was relatively stable.

The Industrial Operating Model delivered extraordinary results in stable markets. It built railways, scaled factories, and powered economic growth throughout the 20th century. The model succeeded because its theory of the business matched the actual environment: success truly did come through efficiency, standardization, and control in predictable work within predictable markets.

Why the Industrial Operating Model Fails in Dynamic Markets

Markets began shifting toward greater complexity and volatility in the early 20th century, and this exposed the limits of the Industrial Operating Model. By the mid-20th century, Toyota demonstrated a different way of working that reintroduced people into the centre of the production system, giving teams direct responsibility for quality, problem-solving, and continuous improvement. The pace of change accelerated again in the 1970s with the microprocessor revolution, and by the 1990s the internet had fundamentally changed how quickly ideas spread and how rapidly markets evolve. Today, AI amplifies this acceleration further, providing tools that enable teams to process information, align with stakeholders, and deliver value at unprecedented speed and scale.

Today, most markets are dynamic, customer needs change rapidly, technology enables new competitors to emerge quickly, and product lifecycles measure in months, not decades. Uncertainty is the norm, not the exception.

But it’s not just market volatility that challenges the Industrial Operating Model. The broader environment in which organizations operate has become fundamentally unstable. Regulatory changes like tariffs reshape entire supply chains overnight. Social movements like the remote work revolution force companies to reconsider workplace models, productivity assumptions, and organizational structures. Political shifts, elections, policy reversals, create cascading effects across industries. Economic shocks, pandemics, financial crises, disrupt assumptions that seemed solid months earlier. These environmental forces don’t just affect markets; they affect an organization’s ability to respond to markets. The confluence of market dynamics and environmental volatility creates both the challenge and the opportunity: organizations that can sense and respond rapidly gain advantage, while those locked into rigid structures fall behind.

The environment has shifted, but the Industrial Operating Model’s theory of the business has not. Organizations continue operating on assumptions of predictability in environments characterized by uncertainty, where both what customers need and how organizations can respond change continuously. This mismatch between theory and reality generates massive waste.

• Planning waste emerges when organisations invest months creating detailed plans that become obsolete within weeks. Requirements gathered at the start of a project no longer reflect market reality by the time delivery begins. Change control boards slow response to learning, turning agility into a bureaucratic exercise.
• Decision-making waste occurs when hierarchical approval chains delay action. By the time a decision escalates through management layers, the information is stale and the opportunity has passed. Teams closest to customers lack authority to respond to what they learn.
• Innovation waste happens when rigid processes and risk-averse cultures kill experimentation. Ideas must fit into predetermined roadmaps. Small experiments that could provide fast feedback are blocked by governance processes designed for large capital investments. Teams cannot explore what customers actually need.
• Human potential waste is perhaps the most painful. Talented people are treated as interchangeable resources, assigned to projects based on availability rather than capability or interest. Individual utilization metrics drive behaviors that undermine team collaboration. Separation of planning from execution means those doing the work cannot apply their insight to improve it.
• Market disconnect waste grows as organisations become insulated from customer reality. Layers of management separate decision-makers from actual users. Success is measured by delivery of planned outputs, regardless of whether those outputs create value. Teams ship what was specified months ago, not what customers need today.

The Industrial Operating Model operates at fundamentally the wrong speed for dynamic markets. While the market iterates daily, the organisation plans annually or bi-annually. While customer needs evolve continuously, the organisation delivers in large batches after months of work. This speed mismatch is not a minor friction, it is a structural incompatibility rooted in an obsolete theory of the business that no longer reflects how markets actually behave.

The Agile Product Operating Model Enables Success in Dynamic Markets

The Agile Product Operating Model structures organisations for speed, learning, and adaptation. Its theory of the business, that success comes through continuous learning and rapid adaptation in complex, changing environments, matches the reality most organizations face today.

At its foundation lies empiricism: the practice of making decisions based on observation and experiment rather than prediction and assumption. Scrum , as a social technology, makes work transparent, creates regular inspection points, and enables rapid adaptation based on what is learned ³. Kanban , as an observability pattern, makes workflow visible and measures actual delivery performance, enabling teams to see and improve their systems.

Decentralization is essential, not optional. Mary Parker Follett recognized nearly a century ago that effective organisations distribute authority to where knowledge resides ⁴. In dynamic markets, that knowledge lives with teams close to customers and technology, not with executives insulated from both. Decentralization enables fast decisions based on current information. It eliminates escalation delays and management bottlenecks that plague hierarchical structures. However, decentralization only works when goals are clear and adaptive at every organizational level, from company strategy through product goals to sprint objectives. This clarity of purpose proves challenging for most companies, particularly because unlike the fixed targets of industrial planning, these goals must evolve as learning occurs while maintaining coherent direction.

Cross-functional, persistent teams form the organisational structure. Each team possesses all skills needed to deliver value without dependencies on other teams or handoffs to specialized functions. Teams stay together over time, building deep capability and psychological safety. They own outcomes, customer value and business impact, not just outputs like features or story points. Today’s teams increasingly include AI team members augmenting human capabilities. Modern AI tools now make it possible to coordinate these teams effectively at scale, maintaining clear alignment with customers and stakeholders while preserving team autonomy and speed.

Fast feedback loops replace predictive planning. Teams deliver working software frequently, ideally continuously, and observe real customer behaviors. Product Managers make decisions based on actual usage data and customer feedback rather than assumptions documented months earlier. Sprint Goals provide coherence and focus while allowing teams to adapt their approach as they learn ⁵. Service Level Expectations establish predictable flow without requiring detailed estimates.

Technical excellence is mandatory, not aspirational. Teams must practice Continuous Integration and Continuous Delivery as Kent Beck and Jez Humble have taught us ⁶. Automated testing provides the safety to move quickly. Infrastructure as Code enables rapid provisioning and recovery. Monitoring and observability reveal how systems behave in production. AI-assisted development accelerates capability building while maintaining quality and enabling teams to focus on higher-value problem-solving. These practices are not tools or processes, they embody a DevOps ethos that breaks down barriers between building and operating software.

The Agile Product Operating Model matches the speed and uncertainty of dynamic markets. It structures organisations to learn and adapt continuously rather than predict and control.

Operating-Model Hygiene: Why Organizations Regress

Many organisations transition away from hierarchical structures only to drift back over time. Start-ups begin as nimble network organisations with direct communication and fast decisions ^{7 8}. As they grow, departments emerge. Managers accumulate authority. Planning processes formalize. Approval chains lengthen. Within a few years, the organisation has recreated the Industrial Operating Model.

This regression is predictable. The Industrial Operating Model’s theory of the business, control through prediction and standardization, is deeply familiar. Most leaders experienced it throughout their careers. Under pressure, people default to known patterns. When faced with coordination challenges, the instinct is to add management layers. When facing uncertainty, the reflex is to demand more detailed plans. The old theory reasserts itself because it feels safer, even when it has become obsolete.

Organizations build up what we might call operating-model cruft, accumulated structures, processes, and behaviors that no longer serve the current context but resist removal. A new approval process gets added to prevent a single failure. It never gets removed when the risk passes. A management layer forms to coordinate between teams. It persists even after teams become more self-sufficient. Individual performance reviews drive behaviors that undermine team collaboration, but changing them feels too risky.

Without deliberate operating-model hygiene, the continuous practice of examining and removing organisational structures, processes, and behaviors that no longer serve their purpose, organisations inevitably regress. This hygiene means regularly asking Drucker’s fundamental question: “If we were not doing this today, would we start?” ⁹ If the answer is no, the practice must be abandoned. Does this approval process still serve us? Does this management layer enable or impede fast feedback? Does this metric distribute authority or concentrate it? Does this structure build team capability or create dependencies?

Operating-model hygiene requires refactoring organisational structures just as we refactor code. Remove processes that no longer serve their purpose. Eliminate management layers that have become bottlenecks. Disband cross-functional coordinating bodies that exist only because teams lack necessary skills. Challenge individual metrics that undermine collaboration.

But Drucker taught that abandonment alone is insufficient, it creates capacity, not capability. Organizations must pair systematic abandonment with purposeful innovation: the disciplined creation of new practices that support the current mission. This means building technical capabilities like Continuous Delivery and automated testing, establishing outcome-focused management practices, creating persistent cross-functional team structures, developing routines for empirical discovery and rapid experimentation, and investing in skills that enable adaptability. Without purposeful innovation to replace what is removed, organizations drift.

This work never ends. Organizations exist in constant tension between forces that drive toward hierarchy and control and forces that enable distributed decision-making and adaptation. Effective operating models depend on continuously pruning what no longer works while intentionally building what the future requires. Without this dual discipline, the Industrial Operating Model reasserts itself.

Making the Transition

Transitioning from Industrial to Agile Product Operating Model is not a simple process change. It requires fundamentally changing your organization’s theory of the business, the core assumptions about how success happens. This demands shifts in structure, culture, measurement, and leadership that reflect the new theory.

Structure must change to persistent, cross-functional teams aligned to value streams or products. Functional departments become communities of practice rather than organisational silos. Project teams and resource pools must go, they are incompatible with the Agile Product Operating Model.

Measurement must change from output to outcome. Evidence-Based Management provides the framework: Current Value, Unrealized Value, Time to Market, and Ability to Innovate. These measures focus on value delivery and system capability, not individual productivity or task completion. Velocity, utilization rates, and other behaviors-distorting metrics must be abandoned ¹⁰.

Leadership must change from command-and-control to system stewardship. Drucker defined management through five responsibilities: setting objectives, organizing work, motivating people, measuring performance, and developing people ¹¹. Under the Industrial Operating Model, managers set objectives through prediction, organize work through functional silos, motivate through supervision and utilization targets, measure output and efficiency, and develop narrow specialization. The Agile Product Operating Model requires different interpretations: managers define clear goals and constraints rather than detailed plans (Product Goals, Sprint Goals), design persistent cross-functional teams as the primary organizational structure, create conditions for mastery and autonomy by removing impediments, measure outcomes and system capability through Evidence-Based Management, and build broad adaptable capability through continuous development ¹². Critically, leaders must maintain goal clarity throughout the organization while allowing those goals to evolve with learning, a discipline most companies find difficult because it requires balancing stability of direction with flexibility of approach. Without explicitly redefining the manager’s role, organizations default to Industrial-era behaviors regardless of their stated intentions.

Culture must change to support transparency, experimentation, and psychological safety. Teams must be able to surface problems without fear of blame. Failure in controlled experiments must be recognized as learning, not punished as incompetence. This cultural shift takes time and consistent leadership behaviors.

Many organisations attempt these changes incrementally, hoping to avoid disruption. This rarely succeeds. The Industrial Operating Model is coherent, its pieces reinforce each other because they all flow from the same theory of the business. Individual performance reviews support functional silos. Functional silos necessitate project structures. Project structures require resource allocation and individual utilization measurement. These elements form an interconnected system, all built on assumptions of predictability and control.

Changing one element while preserving others creates internal contradictions that prevent real transformation. Organizations end up with “Agile teams” reporting through hierarchical management structures, measured by industrial metrics, organized into temporary project assignments. This is not transition, it is the Industrial Operating Model with different terminology. The underlying theory of the business remains unchanged.

Effective transition requires system-level change. The organisation must commit to restructuring around products, establishing cross-functional teams, shifting to outcome-based measurement, and supporting leaders who steward systems rather than control work. This takes months of focused effort, not years of gradual adjustment.

OpenSpace Agile provides a mechanism for system-level change without multi-year programmes or imposed transformation ¹³. Leaders define outcomes and constraints, and the organisation self-organises to design and implement the changes. The people who do the work shape the operating model, which increases ownership, alignment, and speed. A short cadence of Open Space events creates the forum for surfacing problems, proposing solutions, and committing to experiments that reflect the realities of the work.

These experiments run in fast cycles with clear feedback, allowing the organisation to learn what works in its context rather than copying someone else’s playbook. Effective changes get amplified, ineffective ones are dropped, and the operating model evolves through participation rather than prescription. This enables meaningful organisational shifts in months by removing the bottlenecks of traditional change management and allowing those closest to the work to drive improvement.

Call to Action: Redesign Your Operating Model

If your organisation competes in dynamic markets, and most do, the Industrial Operating Model is costing you. Every day. Wasted effort. Missed opportunities. Frustrated teams. Lost customers.

The question is not whether to change, but how quickly you can change.

Start by acknowledging the mismatch. Your operating model embodies a theory of the business designed for different conditions. It is not bad, it is unfit for current context. The assumptions embedded in your structures, processes, and leadership behaviors were built for a world that no longer exists.

Commit to operating-model redesign built on the Agile Product Operating Model. Establish clear, adaptive goals at every level that provide direction without constraining discovery. Structure persistent, cross-functional teams around value streams. Distribute decision-making authority to those teams. Measure outcomes and system capability, not outputs and individual activity. Develop leaders who design systems rather than control work.

Let the organisation shape its own transition. OpenSpace Agile provides the structure: you set the direction, your people design the path through rapid experiments and continuous learning ¹³. This creates change in months, not years.

Implement continuous operating-model hygiene. Regularly examine structures, processes, and behaviors. Remove what no longer serves. Challenge what creates dependencies or delays feedback. Resist the gravitational pull toward hierarchy and control.

This is not easy work. But it is necessary work. The Industrial Operating Model’s theory of the business, success through prediction and control in stable environments, cannot deliver value effectively when environments are uncertain and fast-changing. Organizations that complete this transition, adopting a theory of the business built on learning and adaptation, will thrive. Those that don’t will continue generating waste while wondering why their “Agile transformation” failed.

The choice is yours: maintain an operating model built on assumptions about markets that no longer exist, or redesign your organisation around a theory of the business that matches the reality you face today.

What is one structure or process in your organisation that generates waste because it assumes stable, predictable work, and what would operating-model hygiene lead you to change?

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