What Operating Model Design Actually Means in Practice

Operating model design is often described in abstract terms. It is framed as a question of structures, processes, governance, and capabilities. While none of these are wrong, they rarely help leadership teams understand why execution becomes harder, slower, or riskier as organisations grow.

In practice, a more useful starting point is much simpler.

An operating model describes how work actually gets done.

Not how it is supposed to get done, or how it appears in organisational charts and process maps, but how decisions are made in reality, how work flows across teams, how exceptions are handled, and how accountability operates day to day. Operating model design, in practice, is the discipline of making this reality explicit and shaping it so the organisation can deliver its strategy under real-world conditions.

In most organisations, the operating model has not been deliberately designed end to end. It has evolved. Growth increases volume and complexity. Regulation adds layers of control. Acquisitions introduce parallel ways of working. New products, partners, and technologies create additional handoffs. Local fixes are applied to keep things moving when pressure builds.

Each of these changes is usually sensible in isolation. Over time, however, their accumulation produces complexity, duplication, and ambiguity. Work continues to get done, but increasingly through manual intervention, informal coordination, escalation, and exception handling. Teams rely on experience and goodwill to bridge gaps that were never intentionally designed.

This is not organisational failure. It is normal evolution. The problem arises when an operating model built for an earlier stage becomes a constraint on scale, control, or speed. At that point, execution starts to feel fragile. Outcomes depend more on heroics than on design.

This is where operating model design is often misunderstood.

In practice, it is not a reorganisation exercise. It is not a theoretical target operating model. It is not a technology implementation or a transformation programme. Those interpretations tend to obscure what actually matters.

Operating model design focuses on a small set of fundamentals that already exist in every organisation, whether explicitly or implicitly. It clarifies who can make which decisions and under what conditions. It makes end-to-end ownership visible, especially when things go wrong. It brings discipline to handoffs between teams, functions, and partners. It defines how exceptions are handled rather than left to improvisation. It aligns controls and governance so that they support execution instead of overwhelming it.

These elements are always present. The question is whether they are coherent and workable, or fragmented and fragile.

In practice, effective operating model work does not begin with an abstract target state. It starts from execution. It begins by examining where work slows down, where exceptions accumulate, what repeatedly gets escalated, and where people are compensating for unclear ownership or decision rights.

By starting from how work actually happens today, design decisions are grounded in reality rather than intent. Strategic ambition can then be translated into concrete choices. Leaders can decide what truly needs to scale, where consistency matters more than flexibility, where judgement must remain human, and where technology or automation can genuinely help.

Strategy sets direction. Execution reveals constraints. Operating model design reconciles the two.

For many organisations, this work has become more urgent. Operating models that were previously “good enough” are now being stretched by higher volumes, tighter regulatory scrutiny, more complex partner ecosystems, and greater reliance on platforms and third parties. The introduction of AI has accelerated this pressure.

AI, in particular, acts as a forcing function. It exposes ambiguity in workflows, decisions, and ownership that was previously manageable through manual effort. Where humans could compensate, systems cannot. In this sense, AI does not replace operating models. It makes weak ones visible.

When operating model design is done well, the organisation does not feel transformed. It feels clearer and easier to run. Execution becomes more predictable. Escalation decreases. Control strengthens without paralysing delivery.

Leadership gains better line of sight into how strategy is actually being executed and clearer accountability when it is not. Teams experience fewer grey areas, less rework, and more usable authority. The organisation becomes less dependent on heroics and more resilient under pressure.

A well-designed operating model makes execution workable under scale, regulation, and change.

Most operating models do not fail because they were badly designed. They fail because they were never deliberately designed at all, and because the conditions they now operate under have changed.

Operating model design, in practice, is not about imposing theory. It is about making reality explicit and choosing deliberately how the organisation will operate going forward.