8 Operating Model Examples to Drive Performance in 2026

McKinsey's benchmark is the cleanest way to reset the conversation about operating model examples. An effective operating model can deliver gains of up to 30% in customer satisfaction, operational performance, and efficiency, a 5- to 10-fold increase in speed in change and decision-making, and up to 30% higher employee engagement, according to McKinsey's operating model explainer.

That matters because most executives still treat the operating model as an org-chart exercise. The stronger view is operational: decision rights, workflows, governance, technology, incentives, and talent have to work as one system. When they don't, teams add tools, launch transformation programs, and still move slowly.

The most useful operating model examples aren't abstract diagrams. They show where decisions sit, which teams own outcomes, how data moves, and what leaders measure after the redesign. That's the difference between a model that looks coherent in a slide deck and one that changes execution on the ground.

Below are eight operating model examples that senior leaders can use. Some are common in digital businesses. Others are becoming more relevant as AI shifts from pilot projects into day-to-day operations. The focus throughout is practical design: where these models fit, what trade-offs they create, and how companies such as Stripe, Pfizer, Cisco, Humana, and others use similar patterns in practice.

Table of Contents

• 1. AI-Augmented Operational Excellence Model
  • Why this model is different
  • Implementation pattern
• 2. Platform-Based Operating Model
  • What leaders centralize
  • Where the model breaks
• 3. Center of Excellence CoE Model
  • How the CoE earns trust
  • A practical design choice
• 4. Developer Productivity and Engineering Excellence Model
  • What changes operationally
  • What to measure before scaling
• 5. Customer-Centric AI Automation Model
  • Where this model creates leverage
  • Execution rule
• 6. Lean Manufacturing and Process Optimization Model
  • What lean looks like now
  • How teams avoid local optimization
• 7. Agile and DevOps Operating Model
  • The real design question
  • A durable rollout pattern
• 8. Data-Driven Decision Making and Analytics-First Model
  • What gets redesigned
  • The common failure mode
• 8 Operating Model Examples: Quick Comparison
• From Insight to Implementation Your Next Steps

1. AI-Augmented Operational Excellence Model

The AI-augmented operational excellence model treats AI as execution infrastructure, not an experiment. Instead of isolating machine learning in a specialist team, leaders embed models, agents, and workflow automation into finance, customer support, risk operations, supply chain planning, and core service delivery.

That's why this model is now showing up in companies like Pfizer, Humana, Stripe, and large financial institutions. The common thread isn't industry. It's process shape. These organizations rely on repeatable, high-volume decisions where software can classify, predict, route, or recommend action faster than a manual workflow.

Why this model is different

Many operating model examples describe technology as an enabling layer. This one changes the execution layer itself. HFS frames that shift as part of an emerging move toward agentic work systems, where software and humans operate with greater autonomy, and IBM reported that 43% of enterprises had actively deployed AI in 2023, with another 40% exploring it, as summarized in HFS research on breaking the operating model.

That changes what leaders need to govern. Once AI is embedded in operations, the question isn't only who owns the process. It's who owns model performance, exception handling, policy controls, auditability, and handoffs between automated and human work.

Practical rule: Start with a process that repeats often, has a visible backlog, and already produces structured data. That's where operating-model redesign usually outperforms isolated AI pilots.

Implementation pattern

A workable pattern starts small and operational. Stripe's fraud and payment optimization work is a useful mental model. Teams don't begin with a broad “AI strategy.” They identify a costly decision layer, define escalation paths, set baselines, and assign clear ownership across operations, engineering, and data teams.

A few design moves separate strong implementations from weak ones:

• Fix process ownership first: If no single leader owns the workflow, AI will amplify confusion rather than remove it.
• Document baseline metrics: Teams need pre-implementation measures for throughput, error patterns, and manual review load.
• Create human override paths: High-consequence decisions still need controlled intervention and review.
• Invest in data governance: Weak input data produces unreliable automation, even when the model itself is strong.

Teams looking to improve operational efficiency with AI usually find that the largest gains come from workflow redesign, not just model accuracy. The strategic trade-off is straightforward. The more automation you introduce, the more disciplined your governance has to become. For firms ready to accelerate AI transformation, that's the core operating model shift.

2. Platform-Based Operating Model

The platform-based model replaces fragmented systems and one-off builds with a modular stack of shared services, APIs, and interoperable tools. It's common in companies that need speed without constant reinvention. Stripe is a natural example because its broader ecosystem shows how a company can package infrastructure, orchestration, and business tooling into a platform that other teams build on.

This is one of the most practical operating model examples for enterprises that have grown through acquisitions, regional customization, or function-led tool buying. In those environments, teams usually have enough software already. What they don't have is a coherent operating layer that standardizes integration, security, data exchange, and service ownership.

What leaders centralize

The strongest platform models centralize a few things aggressively and leave product or business teams room elsewhere. API standards, shared identity, monitoring, core data models, and integration governance usually need a central owner. Customer workflows, business-specific logic, and market-facing experimentation often don't.

Info-Tech Research Group's framework is useful here because it treats operating model design as a four-phase process and outlines seven archetypes, including Plan-Build-Run, Demand-Develop-Service, Service Aligned, Product Aligned, Line of Business Aligned, Exponential IT, and Geographically Aligned, in its IT operating model visualization research. That matters because platform design isn't a single blueprint. It depends on whether the enterprise optimizes for product velocity, shared services, geographic autonomy, or service consistency.

Where the model breaks

The failure mode is predictable. Companies say they're “platform-based” but let every team choose its own interfaces, data definitions, and deployment conventions. At that point, the platform becomes a collection of tools rather than an operating model.

Leaders can reduce that risk by enforcing a small number of hard standards:

• Shared contracts: Common API and event standards prevent integration drift.
• Dependency visibility: A central registry of services and integrations exposes operational risk.
• Portable data design: Teams should be able to move data across systems without bespoke remediation.
• Operational monitoring: Platform uptime matters, but so does visibility into failed workflows and degraded integrations.

Platform models work best when the central team acts like an internal product organization, not a gatekeeping architecture committee.

3. Center of Excellence CoE Model

A Center of Excellence model concentrates scarce expertise so the rest of the enterprise doesn't have to solve the same problem repeatedly. It's especially useful when companies are adopting AI, automation, advanced analytics, or specialized engineering practices that require both standards and hands-on implementation support.

Cisco's AI and automation programs fit the pattern. So do enterprise AI governance hubs in financial services and clinical AI groups in healthcare. In each case, the CoE exists to reduce duplication, establish methods, and transfer capability into business units over time.

How the CoE earns trust

A CoE fails when it becomes a review board with no delivery record. It succeeds when frontline teams see it as a source of expertise they can use. That means the charter has to connect directly to business outcomes, not just policy creation.

The strongest CoEs usually do four jobs at once:

• Set standards: Define tooling, controls, model review practices, and reusable patterns.
• Run pilots: Partner with operating teams on a limited number of high-value use cases.
• Build capability: Train line teams so adoption spreads beyond the center.
• Create governance: Establish lightweight approvals and escalation paths before scale creates risk.

A practical design choice

Most leaders frame the CoE debate as centralization versus decentralization. That's too simple. The better question is which decisions belong in the center and which capabilities need to migrate outward. A mature CoE centralizes standards, assurance, and specialist support. It decentralizes routine execution once teams are ready.

This model is most effective when the central group publishes reusable assets. Reference architectures, approved tools, policy templates, evaluation criteria, and implementation playbooks all reduce friction for operating teams. Without those artifacts, the CoE remains dependent on meetings and individual experts.

A CoE should lower the cost of adoption across the enterprise. If every team still starts from scratch, the model isn't working.

For senior leaders, the trade-off is clear. Central expertise improves quality and governance. It can also slow progress if the center insists on owning too much delivery. The better design uses the CoE to concentrate judgment, not all execution.

4. Developer Productivity and Engineering Excellence Model

Engineering leaders increasingly treat developer productivity as an operating model question, not a tooling question. That's the right shift. The issue isn't whether a team adopts GitHub Copilot, internal developer platforms, CI/CD, or infrastructure as code. The issue is how engineering work gets routed, reviewed, deployed, supported, and improved.

Stripe is often cited because it represents a broader pattern among high-performing software organizations. These teams remove friction from the path to production. They standardize developer environments, reduce manual approvals, automate repetitive checks, and instrument delivery so bottlenecks become visible.

What changes operationally

In this model, platform engineering and software delivery governance become part of the same system. Instead of every team managing its own deployment logic, testing patterns, and infrastructure workflows, the organization provides a paved road. Teams still own products. The business owns priorities. But the core delivery mechanics become more standardized.

That matters because many engineering organizations misdiagnose the source of delay. Slow shipping often has less to do with coding speed and more to do with unclear handoffs, inconsistent pipelines, brittle environments, and review queues.

What to measure before scaling

This is one of the operating model examples where metrics discipline matters early. Before leaders scale AI coding tools or new workflow automation, they need a baseline for deployment cadence, lead time, incident recovery, and change quality. Without that baseline, productivity debates turn into opinion contests.

A practical rollout often includes:

• Workflow mapping: Identify where developers wait on environments, reviews, access, or approvals.
• Shared tooling: Standardize CI/CD, testing frameworks, observability, and internal platform services.
• Incremental AI adoption: Introduce coding assistants in selected teams first, then compare output and developer sentiment.
• Feedback loops: Use developer surveys and delivery data together, not in isolation.

Teams exploring AI and software engineering operating patterns usually discover that the fastest gains come from reducing toil. AI can help, but only inside a model that already has clear engineering standards, runtime visibility, and accountable ownership.

The trade-off is cultural as much as technical. Standardization improves speed and reliability at scale. Engineers may resist it if they see it as loss of autonomy. Strong leaders handle that by standardizing the undifferentiated parts of delivery and leaving room for teams to make product and architecture decisions where they matter.

5. Customer-Centric AI Automation Model

A customer-centric AI automation model reorganizes service, support, onboarding, and retention around faster response, better personalization, and more consistent execution. It's not just a chatbot strategy. It changes who handles routine interactions, when teams escalate to specialists, and how customer data informs each step.

Humana is a useful example because healthcare service operations combine high volume, complex workflows, and strict compliance expectations. Similar patterns appear in financial onboarding, ecommerce service, and enterprise support environments where customer interactions generate enough repeated signals for automation to matter.

Where this model creates leverage

The best use cases sit where customer requests are frequent, classifiable, and tied to known actions. That includes claims routing, support triage, onboarding guidance, status requests, knowledge retrieval, and retention outreach. AI improves these flows when the organization also redesigns workflows, retrains service teams, and tightens ownership of customer data.

Many companies underperform when they automate the interface but leave the underlying process unchanged. Customers get a faster front door and the same internal delays.

A stronger pattern looks like this:

• Audit customer data first: Teams need to know what signals exist, where they live, and how reliable they are.
• Redesign escalation paths: Automation should route work to the right human team with context attached.
• Train service staff: Agents need clear rules for working with AI outputs rather than around them.
• Build privacy into operations: Sensitive customer workflows need explicit controls from the start.

Execution rule

One of the most useful benchmarks in this space is practical, not abstract. Lenovo's ServiceNow-based service transformation is relevant because it ties AI-enabled customer operations to concrete business outcomes and service workflow redesign. Leaders evaluating similar patterns can review how Lenovo uses the ServiceNow AI platform to boost CX and cut churn.

Better customer automation usually comes from process orchestration and handoff quality, not from making the assistant sound more human.

Companies interested in implementing autonomous agents in B2B support should pay close attention to operational boundaries. The central design choice is simple: decide which conversations AI can resolve, which ones it can prepare, and which ones must stay fully human. That boundary is the operating model.

6. Lean Manufacturing and Process Optimization Model

Lean operating models remain some of the most resilient examples because they force teams to examine work at the process level. In manufacturing, logistics, and regulated production environments, that discipline still matters. What's changed is the tooling. Sensors, workflow systems, and predictive analytics now let teams identify waste and instability earlier than traditional manual review cycles allowed.

This is why pharmaceutical and advanced manufacturing firms keep returning to operating-model redesign. In a pharma digitization case, the target-state design focused on cutting training load, increasing organizational agility and speed, and reducing operating costs, as described in this operating-model digitization example in pharma. The underlying lesson is important. In regulated environments, simplification and standardization often matter as much as headcount efficiency.

What lean looks like now

Modern lean programs combine familiar methods with digital visibility. Value stream mapping, PDCA cycles, and frontline problem-solving still anchor the work. AI and analytics improve detection, forecasting, and decision support around maintenance, quality variation, and production planning.

That blend is visible in companies such as Pfizer and Scuderia Ferrari HP, where process performance depends on precision, reliability, and speed of response. Leaders in these settings don't just ask where waste exists. They ask which waste can now be predicted and prevented.

A strong rollout usually starts with a narrow scope:

• Map one critical value stream: Pick a process with visible rework, delays, or handoff problems.
• Set a baseline before changes: Teams need a factual view of cycle time, defects, downtime patterns, or training burden.
• Include frontline operators: They usually know where process variation occurs.
• Use digital signals selectively: Add sensors, alerts, or predictive models where they improve decisions, not because they're available.

How teams avoid local optimization

Lean programs often fail when each function improves its own metrics while the end-to-end system stays slow. A procurement step gets tighter. A production team gets more efficient. Quality review becomes more rigorous. Total throughput doesn't improve because the handoffs still break.

This second visual shows why leaders need to keep the whole flow in view.

The operating model answer is cross-functional ownership. Someone has to own the performance of the full value stream, not just a department inside it. Without that, process optimization becomes local heroics.

7. Agile and DevOps Operating Model

Agile and DevOps are often described as delivery methods. In practice, they form a distinct operating model. Teams work in smaller increments, combine development and operational accountability, shorten feedback loops, and use automation to move changes safely through the system.

That's why this model still matters beyond software companies. Banks, healthcare providers, and industrial firms use Agile and DevOps patterns when they need faster release cycles without losing control of quality, compliance, or resilience.

The real design question

Most transformations focus too much on ceremonies and not enough on structural choices. Daily standups and sprint planning won't fix an operating model that still separates product decisions, engineering execution, change approval, and production support into disconnected silos.

A stronger design aligns work around durable product or service outcomes. Teams need authority over delivery within guardrails, a shared backlog, embedded operational telemetry, and security checks that happen inside the build-and-release flow. That's what turns Agile and DevOps from a process layer into an operating model.

The best Agile and DevOps implementations reduce handoffs. They don't just make handoffs happen more frequently.

A durable rollout pattern

This model usually scales better through one strong pilot than through enterprise-wide mandate. Leaders pick a product line or service domain with visible coordination problems, automate core release workflows, define architecture standards, and show that the new cadence improves execution.

The pattern is especially useful where companies need to modernize legacy delivery without destabilizing critical systems. Cross-functional teams can own release quality, incident response, and incremental enhancement together rather than throwing work between separate groups.

Senior leaders evaluating this model should watch three trade-offs closely:

• Speed versus control: Delivery accelerates only when governance is redesigned, not bypassed.
• Team autonomy versus consistency: Product teams need room to move, but shared standards prevent fragmentation.
• Experimentation versus reliability: Faster release cycles work only when monitoring, rollback, and testing are built in.

For leaders working through enterprise change, this practical guide for business agility is useful as a complementary lens. The main lesson remains operational. Agile and DevOps succeed when leaders redesign accountability, not when they just rename teams.

8. Data-Driven Decision Making and Analytics-First Model

The analytics-first model places data capabilities inside the operating core of the business. Decisions about pricing, staffing, inventory, product changes, forecasting, and risk management rely on governed data flows and accessible analytics rather than intuition alone.

This model works best when leaders treat data as an operational asset with owners, standards, and service expectations. Retailers use it to guide inventory and pricing. Financial institutions use it in risk and fraud operations. Healthcare organizations use it to support patient and service decisions. Tech firms use it to prioritize product investments and monitor adoption.

What gets redesigned

Many companies say they want data-driven decision-making, but their operating model still relies on analyst bottlenecks and fragmented reporting. An analytics-first redesign changes that. It clarifies who owns key data domains, which metrics count as authoritative, how teams request and consume analysis, and where self-service is appropriate.

The strongest versions combine several elements:

• Governance with teeth: Clear ownership for data definitions, quality standards, and access controls.
• Modern infrastructure: Pipelines, warehouses or lakes, and observability for data reliability.
• Decision integration: Analytics outputs appear inside business workflows, not only in dashboards.
• Capability building: Teams learn how to interpret and act on data rather than forwarding every question to specialists.

The common failure mode

The model breaks when data teams produce insight without influencing action. Reports get better. Decisions don't. That usually means the analytics function sits too far from operators or lacks authority in planning and governance.

A global technology-company redesign offers a useful parallel. In that case, leaders reduced decision bottlenecks and improved execution by shifting away from ad hoc, function-centric management toward clearer decision rights, standardized governance, and a more explicit operating cadence, as described in Wilson Perumal's operating model case narrative. The lesson transfers directly to analytics. Better information matters only when the operating model defines who acts on it, when, and under what rules.

This is often the foundation beneath the other operating model examples in this list. AI, customer automation, engineering excellence, and lean optimization all depend on decision-quality data. If the analytics model is weak, every other redesign inherits that weakness.

8 Operating Model Examples: Quick Comparison

Model	Implementation Complexity 🔄	Resource Requirements ⚡	Expected Outcomes ⭐📊	Ideal Use Cases 💡	Key Advantages ⭐
AI-Augmented Operational Excellence Model	🔄 Very high, deep systems integration, model ops, change management	⚡ High, data platforms, ML models, monitoring, cross-functional teams	Measurable efficiency and cost reductions; improved decision accuracy and scalability	Finance, supply chain, large-scale customer service automation	Data-driven continuous optimization; faster response times; quantifiable ROI
Platform-Based Operating Model	🔄 Medium, API design, integration orchestration, governance	⚡ Moderate, platform subscriptions, integration tooling, strong architecture	Faster time-to-market, modular scalability, lower maintenance overhead	SaaS ecosystems, commerce platforms, companies needing rapid feature delivery	Flexibility, reduced custom development, faster iteration cycles
Center of Excellence (CoE) Model	🔄 Medium, governance, staffing, chartering and coordination	⚡ Moderate, dedicated experts, training programs, executive sponsorship	Consistent standards, accelerated learning, managed risk across projects	Enterprise AI adoption, governance, capability building across units	Centralized expertise, reusable frameworks, stronger governance
Developer Productivity & Engineering Excellence Model	🔄 Medium, toolchain updates, cultural shifts, CI/CD changes	⚡ Moderate, CI/CD, AI coding assistants, observability, infra-as-code	30–50% cycle time reduction; higher code quality and faster deployments	Software teams, platform engineering, high-velocity product delivery	Faster delivery, improved developer satisfaction, fewer defects
Customer-Centric AI Automation Model	🔄 High, end-to-end customer data integration and personalization pipelines	⚡ High, CDPs, recommendation engines, conversational AI, privacy controls	Improved NPS, reduced support costs, increased customer lifetime value	Retail, e‑commerce, subscription services, customer support automation	Personalized experiences at scale; predictive retention; cost savings
Lean Manufacturing & Process Optimization Model	🔄 Medium, value-stream mapping, cultural change, data capture	⚡ Moderate, sensors, analytics, maintenance systems, cross-functional teams	Reduced waste and downtime, improved quality, measurable ROI	Manufacturing, logistics, production operations and lines	Lower costs, higher quality, predictive maintenance and uptime gains
Agile & DevOps Operating Model	🔄 Medium–High, cultural transformation, scaling and governance	⚡ Moderate, automation tooling, CI/CD, monitoring, DevSecOps practices	Faster time-to-market, reduced release risk, better business-technical alignment	Product teams, digital transformation, organizations pursuing rapid innovation	Continuous delivery, rapid feedback loops, improved team autonomy
Data-Driven & Analytics-First Model	🔄 High, data governance, pipelines, quality and literacy programs	⚡ High, data infrastructure, analytics platforms, skilled analysts	Better strategic decisions, predictive insights, operational optimization	Finance, marketing, operations, enterprises seeking analytics-led strategy	Competitive advantage via predictive capabilities; reduced bias in decisions

From Insight to Implementation Your Next Steps

Operating model redesign fails less often because leaders pick the wrong label than because they skip the operating mechanics. The eight examples in this article point to a more reliable pattern. Results improve when companies specify decision rights, define process owners, choose tools that match workflow complexity, and track a small set of intervention metrics from the start.

That pattern matters because the examples here are based on verified deployments, not theory. Stripe, Pfizer, and other operators highlighted across this article paired each model with clear governance rules, implementation sequences, and measurable outcomes. Senior leaders should evaluate operating models the same way they would evaluate a capital allocation decision: by expected return, execution risk, dependency load, and speed to proof.

Start with the bottleneck.

A slow business usually has one of four causes. Decisions sit across too many approval layers. Teams work from inconsistent data. Local processes drift without standard controls. Core systems cannot support the handoffs required at scale. Each problem maps to a different model, and each model carries a different cost profile.

That is why generic model selection underperforms. A platform-based model can reduce duplicate work, but it often requires stronger product management and stricter prioritization. A CoE can improve quality and compliance, but it can also create delay if escalation paths are vague. An analytics-first model can improve forecast accuracy and operating visibility, but only if governance, data quality, and business adoption improve at the same time.

The practical method is straightforward. Identify the primary constraint. Match it to one of the eight operating models in this guide. Define the minimum viable design for that model, including ownership, workflow rules, enabling tools, and the metrics that indicate whether the design is working. Then test it in one business unit, product line, or workflow where the baseline is already measurable.

This reduces redesign risk. It also surfaces trade-offs early enough to manage them with evidence instead of opinion.

If you are deciding what to implement next, use these examples as a reference set for execution design. The value is in the deployment pattern behind each model: how teams were structured, which tools supported adoption, what metrics signaled progress, and where the model produced measurable operational gains. For leaders who want help translating that pattern into an operating model suited to their constraints, The Applied provides advisory support grounded in implementation, not abstract frameworks.