Operating Model: What Needs to Be in Place for Strategy to Work

Clarifying What an Operating Model Is—and Why It Goes Far Beyond the Organisational Chart

An operating model is the integrated set of choices about how work is actually carried out in the organisation—from who decides what, to how information flows, which technologies support operations, and how different actors coordinate to deliver the value proposition. In practice, it translates strategy into concrete decisions about processes, roles, governance, information, and technology, creating the “operating system” that enables strategic ambition to materialise into tangible results.

While strategy primarily answers the “why” and the “what”—why the organisation exists, which segments it wants to serve, what value proposition it offers, and what competitive positioning it pursues—the operating model answers the “how” and the “who” in an integrated way: how critical work is designed, executed, and improved, and who holds authority, responsibility, and resources to do so. Strategy sets the intent and the playing field; the operating model defines the practical rules of the game, the team’s configuration on the field, and how the ball moves.

The organisational chart, on the other hand, only addresses a fraction of the “who”: it clarifies formal reporting lines, organisational units, and, in some cases, hierarchical levels and spans of control. It is a static representation of formal authority relationships, not of how work actually happens. The same organisational chart can support radically different operating models, depending on decision flows, coordination mechanisms, process design, and the role assigned to technology. Treating the organisational chart as synonymous with the operating model often leads to superficial changes with little impact on execution.

Processes, in isolation, mainly address the “how” from a functional or departmental perspective: they describe activities, task sequences, inputs, and outputs. Without clarifying who decides, based on what information, how priorities are articulated between areas, and how trade-offs are managed, process design tends to generate voluminous documentation but little behavioural change. The operating model integrates end-to-end processes with roles, governance, and technological infrastructure, ensuring that conceptual design is executable on the ground.

A robust operating model starts by making critical decision flows explicit: which decisions are made at which levels, with what cadence, according to what criteria, and with what supporting data. Clear allocation of decision rights on topics such as pricing, investments, capacity allocation, product development, or risk management tends to reduce approval cycles, latent conflicts, and rework, while increasing individual and collective accountability (MGI).

Secondly, end-to-end process design focuses on how work “crosses” functions and units, from the initial trigger to the final customer outcome. Rather than optimising departments in isolation, an effective operating model identifies “value streams” and builds processes that minimise handovers, waiting times, and ambiguity of ownership. This end-to-end view is particularly critical in omnichannel contexts, global value chains, or complex public services, where the customer perceives only the integrated experience, not the internal boundaries.

Roles and responsibilities are the third structural component: they define who is accountable for results, who executes which tasks, and who is consulted or informed in each relevant decision. Lack of clarity in this dimension tends to create grey areas (“no one” is clearly responsible) and overlaps (“everyone” feels ownership of a topic), directly impacting the speed and quality of response. A quality operating model translates strategic ambitions into role profiles, critical capabilities, and staffing models consistent with actual workload.

Coordination and governance mechanisms form the fourth core dimension: committees, decision forums, planning rituals, performance monitoring routines, conflict escalation rules, and cross-functional alignment mechanisms. These elements define how the organisation makes collective decisions, manages dependencies between areas, and reacts to deviations from the plan. Without a clear governance architecture, decisions tend to concentrate in a few individuals, be constantly reopened, or be made ad hoc, which weakens execution and makes the organisation vulnerable to external shocks (OECD).

Finally, information infrastructure and supporting technology determine what data is available, at what quality and timing, and what automation is possible. A modern operating model specifies which systems support each macro-process, how data is captured and integrated, what analyses are accessible to decision-makers, and which interactions with clients and partners are digitalised. Coherence between organisational design and technological architecture is critical: systems designed for an outdated functional reality tend to block new ways of working and perpetuate information silos.

The quality of an operating model can first be assessed by its degree of alignment with strategic ambition: different strategic priorities (leading innovation, scaling efficiently, delivering service excellence, reducing systemic risk) require distinct configurations of decisions, processes, and governance. When the operating model does not reflect these trade-offs, permanent internal tensions, contradictory objectives, and scattered initiatives arise, even when the strategy is well formulated.

A second criterion is the effective focus on the customer or end user. High-quality operating models use “backward design”: they start from the target customer experience and design decisions, processes, and information flows from that point, rather than extrapolating from historical internal structures. This approach tends to reduce cycle times, errors, and unnecessary effort, while simplifying the customer experience and increasing the likelihood of capturing economic or social value (European Commission).

Internal coherence between dimensions is another central attribute: decision flows, processes, roles, governance, and technology must be mutually consistent. A model that theoretically empowers local teams but in practice maintains centralised information systems and multiple approval forums generates organisational dissonance and defensive behaviours. The greater the coherence, the lower the organisational friction, the less need for micromanagement, and the more predictable execution becomes.

Operational simplicity is a frequently underestimated criterion: an effective operating model eliminates unnecessary complexity in hierarchical levels, decision forums, process exceptions, and product or service variants. Evidence suggests that, beyond a certain point, internal complexity grows faster than the marginal value generated, creating coordination costs, confusion, and decision-making delays that erode competitive advantage (MGI). Simplicity is not the same as simplism: it results from deliberate choices about what not to do and what not to tolerate as exceptions.

The ability to adapt and scale completes the set of criteria: a robust operating model allows for absorbing growth, regulatory changes, or demand shocks with incremental adjustments, without constant reengineering. This implies modularity in processes, degrees of freedom in roles, rapid feedback mechanisms between front and back office, and configurable technologies. Overly rigid models work well on “sunny days” but collapse under stress; excessively fluid models tend to lose consistency and discipline in execution.

In practice, the concept of the operating model often fails because it is used as a buzzword detached from concrete implications. It is common to find presentations where “redesigning the operating model” means only changing some titles, merging or splitting departments, and redrawing boxes and reporting lines, without addressing the real issues: who decides, based on what information, with what incentives, and with what learning mechanisms. The result is cosmetic reorganisations that generate political wear and tear but little operational impact.

Another typical source of failure is conducting operating model projects as abstract exercises disconnected from reality on the ground. Models designed in meeting rooms, without direct observation of real work, tend to overestimate capabilities, underestimate constraints, and ignore contextual variations between geographies, business units, or services. When the new model meets operational reality, exceptions, parallel solutions, and informal “shortcuts” multiply, quickly eroding the original intent.

The absence of clear metrics for operating model performance also contributes to failure. Many organisations measure financial results and commercial indicators but do not monitor the quality of the operating system itself: decision times on critical topics, number of interfaces per process, coordination effort between areas, degree of automation of repetitive tasks, or percentage of decisions supported by structured data. Without an “operating model health dashboard,” dysfunctions accumulate invisibly until they manifest in more visible execution crises (Banco de Portugal; IMF).

Finally, it is common for operating model initiatives to be fragmented by function or theme—a process project in one area, a governance review in another, a technology implementation in a third silo—without an integrating architecture to ensure coherence. This incremental and uncoordinated approach tends to produce overlaps, gaps, and contradictory rules, increasing complexity and reducing execution capacity. An effective operating model requires systemic decisions anchored in clear strategic priorities and a cross-cutting vision of how work is articulated to deliver value to the customer.

Critical Components of the Operating Model: Decisions, Processes, Responsibilities, and Coordination

Decision architecture is the primary determinant of execution capacity, as it defines who has authority over which choices, with what information, and at what points in the management cycle. For strategic decisions, the focus is on irreversible or far-reaching choices—business portfolio, capital allocation, major M&A moves, geographic positioning—typically concentrated in boards and executive teams, with quarterly or annual cadences. Tactical decisions translate strategy into concrete plans for marketing, capacity, pricing, operational footprint, or investment priorities, requiring monthly or quarterly forums with broader participation from unit leaders and central functions. Operational decisions, in turn, are high-frequency and have immediate impact on customer service and daily efficiency, and should be delegated as much as possible to the front line, with daily or weekly rhythms and simple escalation rules. How these three levels are articulated—which topics are escalated, which remain at the front line, and according to which criteria—tends to determine whether the organisation acts quickly and consistently or becomes trapped in cycles of rework and micromanagement.

Disciplined design of decision architecture involves explicitly mapping the decisions critical to strategy and linking them to owners, data, and concrete rituals. Instead of generically discussing “investments” or “innovation,” leadership can list recurring high-impact decisions, such as “entering or not new segments,” “launching or discontinuing products,” “setting reference prices,” “distributing capacity across channels,” or “prioritising technology initiatives.” For each decision, it is clarified who recommends, who decides, who executes, and who has veto rights, as well as which dashboards and analyses are required and the cadence at which the decision is reviewed. When this matrix does not exist, typical patterns emerge: decisions that “float” between areas without conclusion; topics unnecessarily escalated to the CEO; or, at the other extreme, decisions fragmented by function, generating local solutions inconsistent with strategic direction.

Decision forums serve as the social infrastructure of this architecture, conditioning the quality and speed of choices. In high-performing organisations, it is common to find a limited number of strategic and tactical committees with clear mandates, agendas anchored in concrete decisions, and explicit rules for information preparation (MGI). In these forums, time is protected to discuss trade-offs and risks rather than descriptive reports; materials circulate in advance; and decisions are recorded with responsible parties and deadlines. In contrast, environments saturated with unstructured meetings create the illusion of alignment but leave core decisions vague, fuelling informal conflicts later. The discipline of “one meeting, one decision owner” reduces ambiguity and accelerates the transition from debate to execution.

End-to-end process design converts these decisions into predictable workflows that cross functions and organisational units. Processes such as product development, customer acquisition, claims management, credit granting, supply chain, or after-sales span marketing, sales, operations, risk, IT, and finance; when designed solely by functional silos, redundant steps, responsibility handovers, and information blind spots accumulate (OECD). Mapping critical flows from the customer and economic outcome perspective, rather than internal hierarchy, allows identification of non-value-adding activities, bottlenecks, and poorly defined interfaces. The impact on execution is direct: clear processes reduce approval cycles, improve predictability, and free up management capacity for truly strategic topics.

For these processes to function as stable “value lines,” it is crucial to assign process owners with cross-functional authority. A product development process owner, for example, should have an explicit mandate to harmonise priorities between marketing, R&D, operations, risk, and IT, as well as responsibility for global indicators such as time-to-market, commercial success, and margins. Without this role, each function optimises its part of the flow—maximising technical quality, minimising risk, reducing unit cost—even if the systemic result is chronic delays, products misaligned with the market, or hidden costs in exception management. Creating process communities that bring together representatives from key areas is a practical mechanism to resolve tensions, prioritise improvements, and keep flows aligned with strategy.

Defining performance metrics associated with each process is the mechanism that closes the loop between design and execution. Indicators such as cycle time, rework rate, NPS by stage, cost per processed unit, conversion rate, or compliance with internal SLAs allow quantification of friction and guide continuous improvement initiatives. The output of the operating model ceases to be mere task completion and becomes measurable performance along the value chain. When these metrics are transparent and comparable between teams or units, internal benchmarks are created that encourage learning and the spread of best practices, reducing dependence on centralised interventions.

At the individual level, clarifying roles, responsibilities, and accountabilities translates decision architecture and processes into concrete behaviours. Frameworks such as RACI (Responsible, Accountable, Consulted, Informed) help make explicit who executes, who is accountable for the final result, who should be consulted before deciding, and who only needs to be informed. Rigorous use of such tools in critical decisions and processes reduces two common pathologies: decisions with too many “owners,” where no one truly assumes the outcome; and grey areas where important topics fall between areas because each assumes the other is responsible. Prior clarification of accountabilities tends to reduce personal conflicts by shifting the debate from “who told whom to do what” to “who was designed to decide.”

The link between formal responsibilities and incentive systems is the step that ensures coherence between the model on paper and behaviours on the ground. When objectives and performance evaluation remain organised exclusively by function, even after defining process or cross-decision owners, the result is often destructive tension: leaders are held accountable for systemic indicators without real power to move resources or influence others’ priorities. Conversely, including metrics for collaboration, end-to-end process quality, and overall value creation in individual objectives—especially for middle management—increases the likelihood that teams internalise the logic of the operating model, not just that of their immediate silo.

Coordination and governance mechanisms are the “glue” that keeps decisions, processes, and responsibilities aligned in complex environments. Portfolio committees, integrated capacity planning forums, sales & operations planning (S&OP) meetings, steering committees for critical programmes, or weekly performance review rituals serve as synchronisation points between areas. Their value lies not in the number of meetings but in the clarity of mandate: which decisions can be made there, which dependencies must be resolved, and which priority conflicts are arbitrated at that level. Effective governance reduces the volume of ad hoc escalations to the top and, at the same time, prevents structural topics from being trapped in local negotiations between functions.

Escalation rules and internal service level agreements (SLAs) are operational instruments that stabilise this coordination day-to-day. Clear rules on when an operational topic should escalate to the tactical level, and when a tactical impasse should be taken to a strategic forum, prevent both indecision blockages and leadership overload with micro-details. In parallel, SLAs between areas—for example, IT response times to business requests, maximum credit approval times, finance data delivery windows to commercial units—create mutual expectations and enable predictive management of dependencies. When these agreements are monitored transparently, discussions shift from generic accusations (“IT never responds,” “the business always asks at the last minute”) to objective management of capacity and priority.

The support infrastructure—data, systems, and tools—decisively conditions the quality, speed, and predictability of the entire model but should be treated as an enabler, not the design itself. Strategic and tactical decisions require integrated information on customers, costs, risks, and capacity; if data is scattered across multiple unreconciled systems, strategic discussion often devolves into disputes over “what is the right number” instead of directional choices. Consolidating critical data repositories and defining “single sources of truth” for key indicators make decisions reproducible and auditable, reducing dependence on ad hoc analyses produced by specific individuals (European Commission; MGI).

Finally, the choice and configuration of systems and tools should derive from the requirements of the operating model, not the other way around. Workflow platforms, CRMs, ERPs, collaboration tools, or analytics solutions only reinforce execution capacity when they mirror already clarified decisions, processes, and responsibilities. When technology is treated as a substitute for organisational design, it is common to see organisations digitalising poorly designed processes, crystallising inefficiencies, and creating additional complexity. In contrast, when the sequence is respected—clarify decision architecture, map end-to-end processes, define accountabilities, specify coordination mechanisms—technology acts as a multiplier: it automates low-value routines, makes relevant performance metrics visible, and institutionalises best practices, freeing managers for decisions that truly require judgement.

Signs of Operating Model Failure: Friction, Rework, and Blocked Capacity

Persistent operational friction between areas is one of the most reliable symptoms of a dysfunctional operating model. When commercial, operations, finance, technology, or risk teams enter repeated cycles of dispute over “who decides what” or “who does what,” the problem is rarely just relational; it tends to reflect unclear mandates, blurred boundaries between functions, and lack of explicit decision criteria. Without a transparent matrix of decision domains, autonomy levels, and escalation tiers, every relevant dossier becomes an ad hoc negotiation, consuming management time and straining relationships. The result is predictable: endless alignments, agendas overloaded with coordination meetings, and decisions unnecessarily “escalating” up the hierarchy because no one feels secure enough to make a final decision.

This pattern of decisions “escalating” is particularly destructive in contexts that require speed and proximity to the customer. When front-line teams do not know the limits of their mandate, they transfer risk to the hierarchy by delaying or not deciding, which lengthens response cycles and increases opportunity cost. In highly competitive sectors, this slowness translates into missed market windows, inability to respond to competitor proposals, and erosion of customer trust, who come to perceive the organisation as slow and unresponsive. Evidence in large organisations suggests that lack of clarity on accountabilities and decision rights is one of the main determinants of delays in critical decisions (MGI; OECD).

Recurring rework is another unequivocal sign that the operating model is not supporting execution. When multiple versions of the same document circulate, successive corrections are made to the same deliverables, and there is frequent need to “start from scratch” with analyses or proposals, the cause is usually less about individual competence and more about process architecture. Poorly defined processes, without clear end-to-end flows, lead to divergent interpretations of what is expected at each stage, causing teams to produce outputs that do not meet the standard required by the next step. This asymmetry of expectations generates unproductive iteration cycles, where each new review adds fragments of quality but does not eliminate the structural waste of effort.

A typical mechanism that fuels rework is the existence of redundant checkpoints and multiple validation instances, often created in response to past incidents. Instead of correcting the root cause (clarification of criteria, quality standards, reference data), organisations add “checks” and “gates” that multiply handoffs and dilute the notion of process ownership. When no one is clearly responsible for end-to-end performance, each function optimises its share, even if that means sending work back upstream or demanding late-stage rework. The net effect is increased lead time, inflated by waits, rework, and successive corrections, without proportional improvement in quality as perceived by the internal or external customer.

The absence of clear process owners further aggravates inefficiency, as it removes the incentive to resolve structural blockages. Without an identified accountable for the final result of a process (for example, “time-to-cash,” “product launch,” “customer onboarding”), problems are treated as isolated incidents rather than recurring symptoms of design. This fragmentation prevents identification of systemic causes, such as redundant information requirements, duplicate validations between functions, or activity sequences that could be parallelised. Conversely, when there is end-to-end ownership, those owners tend to redesign flows, eliminate non-value-adding steps, and formalise clear acceptance criteria at each stage, significantly reducing rework (European Commission; Banco de Portugal).

Blocked capacity and chronic bottlenecks reveal another type of operating model failure: the combination of excessive centralisation of decisions with imbalances in workload between functions. Critically overloaded teams—such as legal, IT, risk, compliance, or financial reporting—become “bottlenecks” that limit the organisation’s maximum throughput. When a small group of key people concentrate knowledge, authority, and access to systems or approvals, the entire value flow depends on their availability. The consequence is queues of requests, accumulated delays, and reactive short-term prioritisation, instead of a structured work pipeline aligned with strategic priorities.

This blockage is amplified by the tendency to use approvals as a substitute for clarifying criteria. Instead of making decision rules and autonomy limits explicit ex ante, many organisations require formal approvals from higher levels for a wide range of operational decisions. This centralisation, often motivated by legitimate risk or control concerns, ends up creating a “stopped assembly line” effect: even when upstream and downstream teams have capacity, the process is immobilised waiting for a signature or opinion from an overloaded function. Evidence in large companies shows that reducing the number of approvals and clarifying decision guidelines tends to significantly shorten lead times for critical processes without materially increasing risk incidents (IMF; MGI).

Imbalance in workload between functions and profiles is another indicator of poor design. When some teams systematically operate above their sustainable capacity, while others have idle margins or focus on low-value tasks, the problem is rarely solved by “more resources” alone. It is often a sign that processes were designed without considering real capacities, that the initiative portfolio was not matched to installed capacity, or that there are no robust mechanisms for integrated resource planning between areas. This asymmetry feeds a vicious cycle: overloaded teams accumulate backlog, lose talent to burnout, and see quality degrade, which in turn increases rework and worsens bottlenecks.

Misalignment between declared strategic priorities and day-to-day activities is perhaps the most visible sign to a CEO that the operating model is not fulfilling its function. Strategic initiatives that do not progress, OKRs or multi-year plans that remain on paper, and critical projects subject to repeated delays indicate that there are no effective mechanisms for cascading objectives into operational plans or for intentional resource reallocation. When teams spend most of their time responding to ad hoc requests, urgent issues, or operational “fires,” it reveals that the work portfolio is determined by the loudest requests, not by a clear hierarchy of priorities aligned with strategy.

The absence of structured mechanisms for cascading objectives results in teams not knowing how their daily work contributes to the organisation’s critical goals. Without operational objectives directly linked to strategic outcomes, any task can seem legitimate, creating an environment where it is difficult to refuse requests and almost impossible to manage trade-offs transparently. Similarly, the lack of regular portfolio review forums and resource reallocation leads to new initiatives being launched on top of already high workloads, without discontinuing lower-value activities. The result is dispersed focus, team fatigue, and stakeholder frustration, as tangible progress on announced priorities is not seen.

This strategic-operational misalignment is particularly acute in organisations with multiple competing priorities. Without an explicit mechanism for clarifying sequencing (what to do first), initiative selection criteria, and limiting work in progress, the system enters chronic overload. Each new priority absorbs management time, but in the absence of divestment elsewhere, the effective impact is diluted. Evidence from transformation programmes shows that organisations that actively limit the number of critical initiatives in progress tend to have materially higher completion and impact rates than those that try to do “everything at once” (MGI; European Commission).

Quantitative and qualitative indicators provide a disciplined way to diagnose operating model failures beyond anecdotal perceptions. Metrics such as decision time for specific classes of decisions (e.g., investment approvals, product launches, resolution of critical complaints), lead time for end-to-end processes relevant to the customer (onboarding, credit granting, proposal responses), and number of handoffs per process are good proxies for friction and unnecessary complexity. Accumulation of handoffs tends to correlate with higher error risk, information loss, and rework, signalling clear opportunities for flow simplification and reassignment of responsibilities (OECD; Banco de Portugal).

Internal qualitative indicators, such as satisfaction indices between areas, perceived fairness in workload distribution, and quality of support from central functions, help capture frictions that have not yet translated into financial metrics but erode collaboration and trust. High churn rates in key functions, especially in teams that concentrate critical knowledge or operate in “permanent urgency mode,” are signs that the system is extracting value from talent unsustainably. In parallel, recurring customer feedback about slowness, inconsistent responses between contacts, the need to “explain the same thing several times,” or perceptions of excessive bureaucracy provide a direct external reading of the costs of a poorly designed operating model. For decision-makers, systematically tracking this integrated set of indicators allows differentiation between individual performance issues and structural failures of the operating model, and prioritisation of interventions based on evidence, not just intuition.

Design Errors: Organising to Report Instead of Organising to Execute

In many reorganisations, the starting point remains the formal organisational chart, treated as the “central piece” of the operating model. The debate focuses on who reports to whom, how many hierarchical levels exist between the front line and the CEO, and how to redistribute formal “status” between functions, relegating real value chains, critical handovers, and information flows that underpin daily execution to the background. This focus on hierarchical architecture, rather than work architecture, tends to generate visually elegant structures but with dissonance between what is designed on paper and what teams need to coordinate decisions, share data, and resolve exceptions in a timely manner.

The typical result of chart-centric projects is the creation of “hierarchical islands” with clear command lines but weak interfaces between them. Formal boundaries are well defined, but cross-functional cooperation mechanisms remain implicit or dependent on personal relationships, making performance highly idiosyncratic: it works while certain people are present and cooperate informally, but degrades with turnover or conflicting priorities. This asymmetry between vertical clarity and horizontal opacity translates into long response times for decisions that cross functions, recurring conflicts over “who is in charge of what,” and difficulty identifying clear owners for end-to-end outcomes such as product launches, client integrations, or resolution of critical incidents.

Another recurring structural error is designing the organisation around specific individuals instead of well-defined, replicable roles. In contexts of strong dependence on senior individuals or “star talent,” it is common to crystallise structures to accommodate personal preferences (e.g., creating bespoke departments to ensure direct reporting to the top or avoid peer conflicts), multiplying exceptions to formal design criteria. This personalisation may solve short-term political issues but weakens the logic of roles, making it difficult to replace leaders without redesigning the structure, scale teams while maintaining coherent responsibilities, or compare performance between units with theoretically similar functions.

Designing around individuals also distorts the balance between span of control, technical depth, and effective time for leadership. When a role is defined “to fit” a particular person, it tends to accumulate incongruent domains (e.g., combining areas the person knows, even if they do not make sense from a value chain perspective), creating excessive dependence on a single decision-maker and reducing organisational resilience. In practice, the organisation becomes vulnerable to absences, leadership changes, or conflicts of interest, because roles are not modular, responsibility boundaries are not intuitive for new leaders, and authority distribution is not anchored in stable criteria of risk, materiality, and interdependence.

Functional fragmentation without an end-to-end view amplifies these problems, especially in models where marketing, sales, operations, IT, and customer support are optimised in silos with well-defined local metrics but without a clear “owner” of the complete customer journey or product economic cycle. Each function tends to maximise its own performance (campaigns launched, leads generated, orders processed, tickets resolved), even if this introduces downstream friction, because incentives and accountability are rarely anchored in integrative indicators such as end-to-end response time, total cost per resolved case, or customer satisfaction throughout the interaction (MGI; European Commission). The final experience becomes fragmented: the customer perceives multiple entities interacting with them, with no continuity or institutional memory.

This logic of local optimisation also creates grey areas of responsibility: incidents involving several functions generate disputes over “whose problem it is,” delaying corrective actions and diluting ownership of overall results. Without explicit roles to orchestrate end-to-end flows—for example, journey, segment, or product owners with real authority over priorities and trade-offs—the organisation tends to manage exceptions by successive escalation up the hierarchy, unnecessarily occupying the top, slowing resolution, and encouraging defensive behaviours. The absence of cross-functional accountability thus translates into more ad hoc meetings, proliferation of coordination emails, and difficulty closing decisions involving multiple teams.

Opaque and inflated governance often emerges as a spontaneous response to this fragmentation: to manage conflicts between silos, committees, forums, and thematic “boards” are created to align priorities without redesigning the underlying model. Over time, the governance layer becomes dense, with recurring meetings aggregating multiple hierarchical levels, vague agendas, and unclear mandates about what decisions each forum can actually make. This generates redundant deliberations (the same topic is discussed in several forums without conclusion), decisions conditioned by who is present rather than formal role, and a decision cycle that drags on as topics are referred from forum to forum until they find a sufficiently strong “sponsor.”

When governance does not clearly distinguish between alignment, decision, and escalation forums, managers spend much of their time in meetings that produce recommendations but not binding decisions, forcing discussions to be reopened elsewhere. Execution speed suffers doubly: first, because decision time is extended; second, because ambiguity about who decided what makes subsequent accountability difficult and encourages constant revisiting of already made decisions. In regulated or politically sensitive contexts, this opacity increases the risk of inconsistent decisions, insufficient documentation of rationale, and a general sense of “governance as theatre,” where form prevails over actual decision and implementation capacity.

An additional error with structural impact is misalignment between the operating model design and the organisation’s specific strategy, often motivated by the temptation to copy models from reference companies, regardless of maturity, regulatory context, portfolio complexity, or geographic spread. “Copy-paste” structures from complex global organisations are replicated in smaller or still developing companies, introducing unnecessary layers of coordination, premature specialisations, and decision forums whose fixed cost in time and energy exceeds the value they can generate at the current stage of business evolution (OECD; Banco de Portugal).

Copying models also ignores business model and local context specificities, such as required proximity to the regulator, the relative importance of physical vs. digital channels, concentration of key clients, or heterogeneity of national markets. Structures that work in organisations with highly diversified portfolios or widely dispersed geographies become rigid and over-engineered in more focused businesses, hindering agility in adjusting offers, prices, or processes. Conversely, adopting overly simple structures by imitating “asset-light” companies in asset-intensive or regulatory-risk businesses tends to underestimate needs for control, separation of functions, and risk management, creating operational and reputational vulnerabilities that are difficult to correct later.

Taken together, these design errors converge in a pattern: organising primarily to report, accommodate individuals, and replicate others’ models, instead of organising to execute what is strategic, at the pace and with the reliability that the business model and context demand. Reversing this pattern means putting end-to-end execution logic, scalable role clarity, and contextual fit at the centre of organisational decision-making, using the organisational chart, governance, and individual preferences as variables to optimise for that logic, not as the implicit starting point for any reorganisation.

How the Operating Model Translates Strategy into Routines, Metrics, and Daily Decisions

Effective translation of strategy into practice begins with a structured and disciplined breakdown of top-level objectives into concrete targets by unit, function, and process. Instead of generic statements, the operating model requires making explicit, for each strategic objective, the expected business outcomes (e.g., margin by segment, cycle time, perceived quality), who is responsible for delivering them, and which operational variables determine them. This chain transforms an ambition such as “grow profitably in a priority segment” into business line objectives, commercial productivity targets, service level specifications, and capacity requirements in support processes, enabling each team to know which variable to move and by how much.

The next step is to convert these business objectives into operational metrics and leading indicators associated with concrete processes. The operating model establishes, for each critical process, a logical chain linking controllable drivers (e.g., useful contact rate, lead response time, approval cycle time, first-time-right) to higher-level outcomes (revenue, cost, risk, satisfaction). This structure requires distinguishing between effort indicators, decision quality, and flow performance, avoiding decorative dashboards and focusing measurement on a few numbers that explain result variability. By making these chains explicit, management can act on the proximate cause of deviations, rather than reacting belatedly to failures in aggregate financial indicators.

A frequently underestimated dimension is the link between metrics and concrete initiatives. A robust operating model does not just measure; it establishes, for each identified gap between targets and expected performance, a set of prioritised initiatives with owners and clear milestones, integrated into the management calendar. Thus, moving from “result below target” to “clear action plan” does not depend on the individual proactivity of a manager but on a systematic mechanism: whenever a given leading indicator deviates from a predefined band, a cycle of diagnosis and action is triggered, with explicit resource allocation and review of the expected impact on outcome indicators.

The operating model also acts in the selection of recurring decisions that most influence strategy realisation, preventing the management agenda from being colonised by low-materiality topics. The organisation explicitly identifies which decisions have disproportionate impact on value creation and risk taken, such as defining the client and product mix, pricing and discount rules, channel prioritisation, investments in capacity and technology, and service level concessions. For each of these critical decisions, it is clarified who decides, what alternatives can be considered, what minimum information must be available, and what trade-off criteria apply between growth, profitability, risk, and customer experience.

This clarification of ownership and decision criteria is the mechanism through which strategy ceases to be an abstract principle and starts to guide daily choices. Instead of relying on idiosyncratic interpretations, the operating model defines guardrails: for example, discount limits by segment and channel, thresholds for investments requiring higher-level approval, or rules for accepting clients with certain risk profiles. These guardrails reduce unwanted variability, free up top management capacity, and increase result predictability, while preserving enough degrees of freedom to adapt decisions to specific contexts.

Management rituals are the recurring vehicle that keeps this architecture alive and aligned with strategy. An annual planning cycle sets the major goals, allocates resources, and establishes transformation priorities, but it is the monthly, weekly, and daily cadences that ensure effective convergence. Monthly and quarterly performance meetings combine structured review of key indicators with systematic discussion of emerging risks and resource reallocation decisions, ensuring that units do not crystallise plans misaligned with evolving context (MGI; OECD).

At a more tactical level, daily and weekly management systems introduce discipline in monitoring critical operational variables directly linked to business results. Front-office and operations teams review, in short cycles, a few leading indicators that reflect real work flow, execution quality, and main blockages. These moments include, by design, identification of impediments, clear assignment of responsible parties for their removal, and structured escalation of topics beyond the team’s level. The line manager’s role becomes less about reporting and more about orchestrating resources and decisions to eliminate sources of variability that prevent consistent delivery of the value proposition.

The value of management rituals depends on how they are designed, not just their frequency. A consistent operating model defines, for each type of meeting, the main purpose (decide, prioritise, monitor, learn), mandatory inputs (data, analyses, proposals), the decisions that can and should be made in that forum, and what is explicitly excluded. This agenda engineering avoids redundancies, reduces time spent in meetings without outcomes, and focuses leadership attention on the few topics with real strategic impact. Additionally, the review cadence makes it predictable when structural topics will be discussed, reducing reactive ad hoc decisions and increasing the temporal coherence of choices.

The link between measurement and continuous feedback is another key mechanism connecting strategy to operations. A well-designed metrics system is organised around an architecture that combines lagging indicators (such as operating result, NPS, churn, impairments) with leading process and capacity indicators (such as data quality, process stability, installed vs. utilised capacity, engagement, and turnover of critical talent). This integration allows deviations to be anticipated before they appear in financial results and reinforces accountability for drivers under teams’ direct control, rather than focusing evaluation solely on results exposed to exogenous factors (IMF; Banco de Portugal).

Continuous feedback becomes operational when metrics feed rapid correction loops at different time horizons. In the short term, operational deviations trigger micro-adjustments in schedules, portfolio priorities, or process parameters. On a quarterly horizon, persistent variation patterns lead to process design reviews, risk policy changes, or pricing rule adjustments. In the long term, accumulated evidence on capability and strategic initiative performance leads to decisions on where to reinforce, simplify, or discontinue business lines. The management control function acts as the integrator of these loops, translating dispersed data into causal narratives that support reallocation of capital, talent, and leadership attention.

The operating model also incorporates explicit mechanisms for organisational learning, where feedback serves not only to correct but to adjust the very way of working. Post-implementation reviews of relevant initiatives, structured analyses of critical incidents, and formal after-action review cycles allow isolated events to be transformed into changes in norms, procedures, and decision criteria. Thus, the organisation iteratively updates the model based on experience, rather than accumulating ad hoc rules and exceptions that, over time, make execution more complex and opaque.

The capabilities and culture component acts as the practical extension of the operating model, ensuring that defined roles can actually be performed at the level of demand implied by the strategy. Based on analysis of critical decisions and new target processes, the organisation identifies a limited set of distinctive competencies by function family, covering not only technical skills but also analytical capabilities, decision-making under uncertainty, cross-functional collaboration, and leadership of autonomous teams. This clarification prevents training from being generic and scattered, directing development investment to the points that unlock execution.

Formal development mechanisms operationalise this capability ambition. Programmes focused on real decisions and processes, on-the-job coaching in critical meetings (such as pricing committees, operations performance reviews, project steering), and role designs that expose managers to cross-cutting problems accelerate the building of relevant competencies for the target model. In parallel, progression and performance evaluation criteria are adjusted to reflect the desired operational behaviours, such as cross-functional collaboration, process simplification ability, data rigour, and discipline in executing commitments.

In this framework, culture ceases to be treated as a diffuse topic and becomes a set of behavioural expectations anchored in the formal mechanisms of the operating model. The way decisions are made in governance forums, how exceptions to policies are handled, how metric deviations are addressed, and how conduct is rewarded or sanctioned in tense situations sends clear signals about what is acceptable and valued. By aligning incentives, rituals, promotion criteria, and recognition with strategy and process design, the organisation reduces dissonance between what it claims is important and what is actually rewarded, increasing the likelihood that daily behaviours consistently align with long-term objectives.

Application in Different Contexts: Fast-Growing SMEs Versus Multi-Business Groups

In a fast-growing SME, the operating model tends to emerge implicitly, anchored in the proximity between founders and teams and the ability to make quick decisions. As scale increases, the same logic that enabled speed starts to generate bottlenecks: critical decisions become concentrated in a few people, information circulates through informal channels, and the absence of clear rules multiplies exceptions. The central challenge becomes transforming a model of “coordination by trust and presence” into one of “coordination by explicit mechanisms,” without eliminating the agility that enabled initial growth.

Founder dependence typically manifests in three areas: relevant commercial decisions remain centralised, conflicts between functions are resolved by direct appeal to the founder, and priority changes arise opportunistically rather than through a clear process. This pattern tends to generate decision fatigue at the top, uncertainty in teams, and rework cycles, as directions change faster than processes can absorb. Explicit clarification of which decisions escalate to founders, which are delegated, and which are automated becomes the first structuring move for the operating model in an accelerating SME.

Process informality, often seen as a competitive advantage, starts to incur visible costs as the customer base grows and product or geographic diversity increases. Without minimally documented end-to-end processes, quality becomes inconsistent, onboarding of new employees is slow, and incident resolution depends on the “history” of a few key people. Evidence from high-growth SMEs suggests that selective systematisation of 5–10 critical processes (e.g., lead-to-order, order-to-cash, incident management, product development, hiring) is a decisive factor for scaling predictably (MGI; OECD).

The absence of formal governance often translates into unproductive meetings, unrecorded decisions, and strategic topics mixed with operational issues. In fast-growth contexts, it is common for the same forum to discuss everything from launching a new country to the details of a disputed invoice, without prioritisation criteria. Transitioning to a more robust operating model involves designing a simple governance architecture: 2–3 well-defined regular forums (e.g., weekly executive board, commercial forum, product forum), each with a clear mandate, decision types, fixed participants, and preparation and follow-up rituals.

In SMEs, structuring choices for leadership roles are particularly critical because each first-level position tends to concentrate a very broad range of topics. Clarifying who is responsible for P&L, who holds resource allocation decisions, and who leads cross-functional capabilities (such as technology or people) reduces boundary conflicts and accelerates execution. In many cases, explicitly creating a “de facto COO” or an operations lead who absorbs day-to-day coordination frees founders for strategic direction, partnerships, and capital decisions.

In a context of limited resources, prioritising a few critical processes becomes an operational portfolio choice. Instead of trying to “formalise everything,” leaders define which flows most impact revenue, customer experience, or risk, and focus end-to-end design, metrics, and responsibility clarification efforts there. This selective focus allows proof of concept for operational discipline, quick wins (e.g., reduced sales cycle time or billing errors), and creates an internal narrative that the operating model is a business lever, not a bureaucratic exercise.

Institutionalising simple management rituals is another high-impact mechanism in SMEs. Daily 15-minute meetings in critical teams, weekly pipeline reviews with explicit criteria, monthly operational performance review cycles, and quarterly strategic alignment moments serve as the organisation’s “rhythmic skeleton.” These rituals, when associated with a short set of indicators and standard decisions for deviations, reduce dependence on ad hoc founder interventions and create predictability for teams.

Cross-functional coordination in growing SMEs tends to be more effective when supported by simple mechanisms rather than complex formal structures. Temporary working groups with clear mandates to solve specific problems (e.g., reduce churn, accelerate project implementation), process owners spanning functions, and explicit “touchpoints” between areas (e.g., commercial–operations, product–technology) help mitigate emerging silos before heavier restructurings are needed. The gain lies in reducing grey areas without overloading the organisation with reporting layers.

In a corporate group with multiple areas or businesses, the underlying problem is different: the operating model must manage the structural tension between capturing synergies and preserving local autonomy. The holding or corporate centre can be either a value catalyst—through platforms, talent, and capital discipline—or a source of complexity, duplicated work, and decision-making delays. The quality of centre-business model design explains much of the difference between groups that create consistent value and those that merely aggregate financial results (IMF; European Commission).

The holding’s role can take different logics, often summarised in three archetypes: “operator,” when the centre defines in detail how businesses operate, manages key functions in an integrated way, and intervenes in execution; “orchestrator,” when the centre sets directions, guardrails, and common platforms, promoting synergies but leaving operational management to the businesses; and “investor,” when the holding acts essentially as a capital allocator and portfolio manager, with limited operational intervention. The choice of archetype profoundly affects the degree of decision centralisation, configuration of corporate functions, and talent profile required at the centre.

Defining what is shared versus specific to each business involves granular decisions about capabilities and processes. Functions such as treasury, tax, indirect procurement, core technology, and data platforms tend to benefit from economies of scale and scope, justifying shared services or common platforms. In contrast, activities directly linked to the end customer, product development, or pricing often require market proximity and flexibility, being more effective when designed at the business level. Evidence suggests that more successful groups periodically revisit these boundaries, adjusting the degree of centralisation according to business maturity and technological evolution (MGI; Banco de Portugal).

Well-designed shared services function as “factories of cross-functional processes” with clear standards, agreed service levels, and priority governance. Poorly designed, they become cost centres perceived as bureaucratic, with long response times and a focus on internal efficiency at the expense of business value. The key is to make the service contract explicit, define conflict escalation mechanisms, and ensure that metrics combine efficiency (cost, productivity) with business impact (response time, perceived quality), avoiding demand being entirely at the centre’s mercy.

Common data and technology platforms are now a central axis of the operating model in multi-business groups. A shared data architecture, with common models and definitions, enables performance comparability, consolidated risk management, and reuse of analytical assets. At the same time, excessive centralisation of technology can hinder local experimentation and the ability to quickly adapt solutions to each business’s specific needs. Effective design combines shared core components (e.g., identity, master data, cloud infrastructure) with application and configuration layers closer to the businesses, managed in joint governance.

Portfolio and capital governance in a multi-business group is, in essence, an operating model mechanism applied to large-scale decisions. Explicit criteria for business entry and exit, investment time horizons, expected return thresholds, and centre support rules (e.g., when a turnaround business receives reinforcement versus is divested) reduce the risk of reactive and politicised capital allocation. Regular portfolio review forums, with comparable data and alternative scenarios, allow rebalancing the portfolio according to sector opportunities, business maturity, and risk constraints.

Cross-cutting lessons between fast-growing SMEs and multi-business groups converge on some design principles that tend to be stable. Clarity on who decides what, based on what information and over what time horizon, remains the critical variable in both contexts. The focus on end-to-end processes, rather than local functional optimisation, is equally decisive—whether to reduce rework in an SME or to capture synergies in a shared value chain in a group. Unequivocal accountability for results and process integrity is what enables structures, forums, and platforms to be effectively used, not just designed.

At the same time, operating model elements vary significantly with scale, degree of regulation, geographic spread, and portfolio diversity. In highly regulated contexts, compliance and risk mechanisms tend to be more centralised and prescriptive; in multiple geographies, governance models must integrate different legal and cultural regimes, balancing global standards and local adaptations; in highly heterogeneous portfolios, the holding logic often approaches that of an investor, with emphasis on capital discipline and less on operational integration. This variation makes it clear that there is no single “optimal” model; what distinguishes high-performing organisations is the ability to continuously adjust the operating model to strategy, growth trajectory, and external conditions.

Operating Model Diagnostic Tools and Intervention Priorities for Executives

A serious operating model diagnosis starts by making explicit “how work actually happens,” as opposed to what is formally designed. A robust diagnostic map organises analysis into six interlinked dimensions: decisions, processes, roles, coordination, data/technology, and metrics. For decisions, it assesses who decides what, based on what information, at what cadence, and with what degree of intermediate levels. For processes, it observes the end-to-end journey of critical flows (e.g., “from order to cash”), identifying cycle times, variation between teams, and points where work accumulates. In the roles dimension, it examines clarity, overlaps, and responsibility gaps, especially at function interfaces. Coordination is analysed through existing forums, planning rituals, and conflict resolution mechanisms. For data/technology, it assesses whether applications and data sources actually support defined decisions and flows. Finally, for metrics, it tests whether indicators reinforce desired behaviours or, on the contrary, create contradictory incentives.

A credible assessment requires disciplined combination of three sources: quantitative data, structured interviews, and on-the-ground observation walks. Quantitative data provides evidence on volumes, response times, errors, rework levels, customer satisfaction, and unit costs, enabling localisation of “hot spots” with economic and service impact (MGI; Banco de Portugal). Interviews with executives and middle managers help understand perceptions of where execution is blocked, where autonomy is insufficient or excessive, and what tensions exist between functions. Observation walks—accompanying teams in real work contexts, in back office, operations, shops, factories, or contact centres—expose discrepancies between the “on paper” process and the real process, revealing micro-decisions, workarounds, and informal dependencies that rarely surface in formal meetings.

To make this analysis actionable, it is useful to use a set of tools and frameworks that structure the diagnosis and reduce ambiguity. A decision rights matrix clarifies, for each relevant decision, who recommends, who decides, who is consulted, and who is informed, allowing identification of decisions that are excessively escalated or, conversely, fragmented due to lack of clear ownership. End-to-end process mapping, with enough granularity to capture systems, actors, and waiting times, reveals redundancies, unnecessary approval loops, and critical technological dependencies. Handoff analysis—each time work passes from one team or function to another—makes visible structural delays, information loss, and frequent conflict zones between areas.

In critical functions, a well-constructed RACI (Responsible, Accountable, Consulted, Informed) acts as a fine-tuning instrument between alignment and agility. Assigning too many “Consulted” and “Informed” turns simple decisions into lengthy processes; restricting the intervention circle too much can compromise feasibility or acceptance. Mapping committees and meeting cadences provides another powerful lens: identifying at which levels strategic, tactical, and operational topics are discussed, how often, with what quality of information, and with what resulting decisions. It is common to find redundant forums, unfocused agendas, and lack of clear decision owners, signalling an operating model that generates coordination effort without corresponding speed or decision quality.

Defining intervention priorities should be guided by impact on critical flows, rather than functional affinity or political convenience. The starting point is to select 3 to 5 flows that concentrate value and risk: typically, customer journeys (acquisition, onboarding, service, renewal), financial cycles (budgeting, investment, pricing, collections), and operational chains with high cost or regulatory complexity. For each flow, the potential improvement impact is estimated in terms of revenue (e.g., increased conversion, higher retention), margin (reduction of direct costs and rework), and risk (lower operational or regulatory exposure), using historical data and prudent benchmarking (OECD; European Commission; IMF).

Within these critical flows, balancing quick wins and structural changes increases the likelihood of success. Quick wins typically involve decision clarifications, approval simplification, minor metric adjustments, or committee reconfiguration, with low change cost and visible impact in weeks or a few months. Structural changes may require process redesign, core system reconfiguration, reporting line redefinition, or creation of new business units, with longer execution horizons and higher risk of disruption. Selection should explicitly consider the cost of change (financial investment, management effort, risk of team saturation) versus expected benefit, avoiding both bias for “glamour” initiatives with uncertain return and narrow focus on marginal gains that do not alter structural execution capacity.

Transformation governance of the operating model is itself a test of the model. Executive sponsorship must be clear, visible, and translated into concrete decisions on priorities, resource allocation, and trade-offs between units. Ideally, the CEO and COO jointly assume accountability for the transformation, with systematic involvement of the CFO to ensure economic discipline in choices. A dedicated team—PMO or Transformation Office—is responsible for orchestrating initiatives, managing interdependencies, ensuring methodological consistency, and monitoring impact, with a mandate to challenge functional areas when change commitments are not met.

Implementation phases tend to follow a three-stage logic: pilots, roll-out, and institutionalisation. Pilots allow testing new ways of making decisions, processes, and coordination mechanisms in controlled segments (e.g., a region, product line, or operational unit), gathering evidence on impacts and side effects. In the roll-out phase, validated solutions are scaled with a clear plan for technological dependencies, training, and metric changes, ensuring that immature practices are not exported organisation-wide. Institutionalisation involves reviewing policies, operating manuals, job descriptions, incentive systems, and planning processes to avoid “elastic effects” where the organisation silently reverts to the previous model after project pressure ends.

Impact monitoring mechanisms should combine outcome indicators (cycle time, NPS, unit cost, error, margins) with organisational health metrics (engagement, perceived role clarity, perceived quality of cross-functional collaboration). Simple dashboards, reviewed at defined executive cadences, allow rapid detection of deviations, identification of where the new model is not being adopted, and decisions on reinforcements or corrections. Collecting structured qualitative feedback, through short surveys or focus groups with managers and front-line teams, helps to understand whether the operating model is experienced on the ground as something that simplifies work or as another bureaucratic layer.

For top decision-makers, the main decision is how to frame the topic on the board and executive team agenda. Addressing the operating model only as a by-product of a formal reorganisation reduces space for value creation discussions and tends to focus debate on reporting lines and titles. A more productive alternative is to position it as a lever to realise already made strategic decisions: entering new markets, accelerating digital, repositioning the portfolio, capital efficiency, or improving customer experience. The board can, for example, require a clear view of how the decision, process, and metric model will be adjusted to support agreed strategic objectives before approving significant investments in technology or M&A.

Articulation with strategy and organisational structure reviews requires sequential discipline. Strategy defines “where to play” and “how to win”; the operating model answers “how to deliver”; the organisational structure makes visible who is responsible for what. In strategic cycles, it is prudent to explicitly reserve time to discuss operating model changes implied by the new agenda, rather than treating the topic as an implementation detail. When a structural reorganisation is inevitable, the design of units and reporting lines should be a consequence of already made decisions on critical flows, decision rights, and shared service models, not the other way around.

Finally, operating model transformation must be anchored to talent, technology, and culture initiatives to produce lasting results. For talent, this means aligning profiles, succession plans, and development with new decision and collaboration requirements (e.g., more analytical capacity in front-office roles, more product management skills in IT). For technology, it means prioritising investments that remove visible constraints in critical flows and decision rights, avoiding digitalising poorly designed processes. For culture, it means making explicit the behaviours expected in the new model—for example, greater data transparency, rapid escalation of blockages, end-to-end responsibility—and reinforcing them through leadership examples, recognition, and accountability mechanisms. This chain increases the likelihood that the operating model ceases to be a conceptual exercise and becomes a tangible operational competitive advantage.

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