Most organisations still operate as if their environment were stable and predictable. Their structures, planning processes, governance, and performance measures all reflect the logic of an Industrial Operating Model. Yet the reality they face is dynamic, uncertain, and shaped by rapid shifts in technology and customer demand. The result is a growing mismatch between how organisations are designed and what their environment requires.
This tension is the core issue. The Industrial Operating Model assumes that work is knowable upfront, variation is harmful, planning provides control, and management coordinates through hierarchy. These assumptions created advantage in stable markets. They do not hold in dynamic ones. When leaders rely on them anyway, they unintentionally introduce delays, rework, and systemic fragility.
Organisations competing in dynamic contexts need a different theory of the business. The Agile Product Operating Model starts with a simple assertion, success depends on learning, adaptation, and continuous alignment with customers. It is built on empiricism, short feedback loops, and clear accountabilities. It uses Scrum as a social technology for transparency, inspection, and adaptation. It uses Kanban to make flow visible and measurable. It depends on leaders who create the enabling constraints for teams to make decisions close to the work.
The contrast between these two models is not stylistic. It is structural. The Industrial Operating Model places planning and control at the centre. The Agile Product Operating Model places learning and adaptation at the centre. Both are coherent. Only one matches today’s environment.
Leaders often underestimate how deeply the Industrial Operating Model shapes behaviour. Functional silos, project-based funding, stage gates, individual performance metrics, and hierarchical approvals all reinforce predict-and-control assumptions. These structures make adaptation slow, reduce transparency, and weaken accountability for outcomes. They create the illusion of control while increasing the organisation’s exposure to change.
Shifting to the Agile Product Operating Model requires more than adopting new practices. It requires making the organisation’s theory of the business explicit, testing its assumptions, and redesigning the system of work accordingly. Measurement must shift from output to outcome. Teams must be organised around products or value streams, not projects. Decision-making authority must move to the people closest to customers and technology. Evidence-Based Management provides the measures that allow leaders to understand value, capability, and improvement opportunities.
Scrum reinforces this shift through clarity of purpose and accountability. The Product Goal and Sprint Goal focus the organisation on outcomes rather than tasks. Transparency ensures leaders see real progress rather than reported progress. Inspection and adaptation create the cadence for continuous learning. These are not ceremonies; they are mechanisms for operating in uncertainty.
The transition becomes difficult when organisations try to mix elements of both models. Cross-functional teams constrained by project funding, “Agile delivery” governed by industrial gates, or adaptive goals managed through fixed-scope roadmaps produce friction and confusion. Leaders experience poor results because the organisation is operating with two incompatible theories of the business. The underlying logic of prediction quietly overrides attempts at adaptation.
What leaders must confront is the need for coherence. Structures, measures, leadership behaviours, and team accountabilities must all align to the same operating model. This is not about adopting specific frameworks. It is about aligning the organisation around a clear understanding of how value is created and how decisions should be made.
This shift requires discipline. Leaders must examine which structures and processes assume predictability and which support adaptation. They must remove those that slow feedback or dilute accountability. They must support teams with the technical capabilities, such as continuous integration and continuous delivery, that enable empirical control at scale. They must maintain clarity of goals while allowing plans to evolve as learning occurs.
The organisations that make this shift gain the ability to sense and respond quickly. They reduce waste caused by obsolete plans, avoid decision-making bottlenecks, and stay closer to customer needs. They build systems that learn continuously rather than operate on outdated assumptions.
The starting point is recognising that an operating model designed for stable markets cannot succeed in dynamic ones. Once that is acknowledged, the path becomes clearer. Leaders can redesign their systems of work around empiricism, customer value, and adaptive structures that reflect the environment they are actually operating in.
What is one assumption in your organisation’s current operating model that no longer matches the environment you compete in, and what would you change if you stopped treating that assumption as true?
Extracted from Why Most Companies' Operating Models Fail in Dynamic Markets