Operating-model redesign is one of those phrases that sounds precise until someone asks what is actually being redesigned.
Are we changing the organisation chart? Centralising marketing capabilities? Introducing cross-functional teams? Redefining the relationship between marketing and sales? Building an AI-enabled content engine? Or changing who decides what, based on which information, at what point in the customer process?
All of these can be part of the answer. But none is a sufficient starting point.
My current working hypothesis is that commercial operating models should not primarily be designed by sector. They should be designed around how the organisation creates commercial value.
Two companies in the same industry may therefore need fundamentally different operating models. One software company may grow mainly through product-led adoption, while another depends on a small number of complex enterprise deals. One financial-services organisation may serve customers largely through digital transactions, while another relies on local advisers and long-term relationships.
The relevant questions are more structural:
- Is value created through volume or through complex, high-value interactions?
- Is the relationship transactional or relational?
- Does the organisation reach customers directly or through partners?
- Is revenue generated once, repeatedly or through increasing usage?
- Is commercial execution centrally controlled or distributed across local teams?
- How much human judgement, trust and contextual knowledge does the process require?
These dimensions provide a more useful basis for operating-model redesign than a generic industry benchmark.
Fifteen commercial operating-model archetypes
The following archetypes are not fixed categories. They are design lenses. Most organisations combine several, although one or two usually dominate the commercial logic.
| Archetype | Commercial value logic | Primary operating-model requirement | Illustrative example |
|---|---|---|---|
| 1. High-Volume Transactional | Large numbers of relatively low-value transactions | Standardised processes, automated decisioning, continuous experimentation and low cost-to-serve | Amazon retail |
| 2. Omnichannel Retail | Customers move between stores, websites, apps and service channels | Shared customer, product and inventory context across channels | IKEA |
| 3. Direct-to-Consumer Brand | The brand owns most of the acquisition, transaction and customer relationship | Integration of brand, performance marketing, commerce, service and first-party customer data | Tesla |
| 4. Complex B2B / Account-Based | A limited number of high-value opportunities involving long cycles and multiple stakeholders | Account-level coordination, shared opportunity intelligence and significant human judgement | SAP |
| 5. Distributed Relationship-Led Advisory | Local or individual advisers own much of the customer relationship | Central capabilities and controls combined with local context, trust and professional autonomy | Edward Jones |
| 6. Professional Services / Expert-Led | Professional expertise is both the product and the basis for commercial growth | Close integration between business development, knowledge, staffing, reputation and delivery | McKinsey |
| 7. Subscription / Recurring Revenue | Acquisition starts the relationship; adoption, renewal and expansion determine lifetime value | End-to-end lifecycle ownership rather than separate acquisition and retention silos | Adobe |
| 8. Product-Led Growth | Product usage drives acquisition, activation and expansion | Shared product and commercial signals, with selective human intervention | Atlassian |
| 9. Marketplace / Two-Sided Platform | Value depends on developing and balancing both demand and supply | Management of liquidity, trust, incentives and experience on both sides of the platform | Booking.com |
| 10. Partner- or Channel-Led | Partners perform a substantial part of acquisition, sales or delivery | Partner enablement, shared planning, incentives and visibility without direct operational control | Cisco |
| 11. Consumer Brand / Indirect Distribution | Marketing creates preference, while retailers or distributors complete most transactions | Brand and demand creation combined with retailer collaboration and indirect customer insight | Unilever |
| 12. Franchise / Federated Commercial Model | A central brand and proposition are executed largely by local entrepreneurs | Clear standards, shared tooling and transparency while preserving local ownership | McDonald’s |
| 13. Usage-Based / Consumption Model | Revenue increases with actual customer usage | Usage sensing, adoption management, transparent consumption and expansion interventions | AWS |
| 14. Ecosystem-Led Platform | Growth is created partly by developers, partners, applications and complementary propositions | Platform governance, ecosystem incentives and partner or developer success | Salesforce |
| 15. Regulated Relationship-Based | Advice and trust are commercially important, but decisions are constrained by regulation and controls | Evidence, traceability, compliant communication and explicit human accountability | Rabobank |
The examples are shorthand rather than definitive classifications. Amazon, for instance, also operates marketplace, subscription and ecosystem models. Salesforce combines subscription, account-based selling and ecosystem-led growth. The point is not to place a company permanently in one box. It is to identify the logic that should dominate a particular commercial process.
The archetype determines the design questions
Once the dominant archetype is explicit, operating-model redesign becomes much more concrete.
A high-volume transactional organisation needs to decide which commercial decisions can be standardised and automated. It will typically optimise for speed, conversion, consistency and marginal cost. Centralised data, decisioning and experimentation capabilities may create substantial value.
A distributed advisory organisation faces a different problem. It needs to strengthen advisers with better information and tools without stripping them of the contextual judgement on which the relationship depends. Excessive centralisation may improve consistency while weakening local ownership and customer trust.
A subscription business cannot organise marketing primarily around acquisition campaigns. It needs shared responsibility for activation, adoption, retention and expansion. That affects targets, team boundaries, customer data, measurement and the relationship between marketing, sales, product and service.
A marketplace must optimise two customer systems simultaneously. Increasing demand without sufficient supply can damage the experience. Increasing supply without demand reduces partner value. Its operating model therefore needs mechanisms for balancing interests, not merely separate marketing plans for each audience.
These are materially different management problems. A single “best-practice marketing operating model” cannot resolve all of them.
What is actually being redesigned?
For a CMO or CCO, operating-model redesign should make at least six choices explicit.
1. The decision architecture
Which decisions are made centrally, locally or automatically? Which require professional judgement? Who can override a recommendation, approve an exception or accept a commercial risk?
This is more important than where a capability appears on the organisation chart.
2. Ownership of the customer process
Who owns the outcome across acquisition, conversion, onboarding, adoption, service, renewal and expansion?
Many organisations have strong functional teams but weak ownership of the customer process between them. Their operating model optimises activities while leaving the commercial outcome fragmented.
3. The information architecture
What customer, product, interaction, partner and usage context must be available for each decision? Where is that context captured, and who is responsible for its quality?
The required information differs by archetype. A retailer may depend heavily on real-time behavioural and inventory signals. An account-based organisation needs stakeholder, relationship and opportunity context that cannot be reduced to digital interactions alone.
4. The capability model
Which capabilities are differentiating enough to build internally? Which should be shared across business units? Which should remain close to markets, accounts or customers?
This includes analytics, content, campaign execution, customer decisioning, sales enablement, experimentation, AI engineering, marketing operations and commercial measurement.
5. The performance system
What behaviour do targets and metrics encourage?
A subscription model managed predominantly through acquisition metrics will underinvest in adoption and retention. A partner-led organisation measured only on direct leads may systematically undervalue partner influence. A distributed advisory model that optimises adviser productivity too aggressively may reduce the time required to understand complex customer needs.
6. The boundaries between standardisation and autonomy
Standardisation creates scale, reuse and control. Local autonomy creates relevance, speed and ownership. Both are rational objectives.
The design challenge is to determine which elements must be common and which can vary: data definitions, customer policies, brand standards, models, workflows, offers, channel choices, content or individual customer decisions.
AI does not remove the differences between archetypes
AI is sometimes presented as if it leads every organisation towards the same autonomous commercial model. In practice, it makes the underlying commercial logic more important.
In high-volume transactional models, AI may take a substantial role in real-time decisioning, personalisation and automated execution.
In complex B2B, professional-services and advisory models, its role is more likely to be improving preparation, identifying signals, assembling context and supporting judgement. The commercial relationship remains partly dependent on people who can interpret ambiguity and take responsibility.
In subscription and usage-based models, AI can continuously identify adoption barriers, churn risks and expansion opportunities. But optimising every interaction for short-term usage may conflict with customer trust or long-term value.
In partner, franchise and ecosystem models, AI can improve coordination without giving the central organisation direct authority over every commercial action. Incentives, data rights and decision boundaries remain essential.
And in regulated relationship-based models, greater technical capability may increase rather than reduce the need for traceability, controls and clearly assigned accountability.
The relevant question is therefore not simply where AI can automate commercial work. It is which decisions the operating model should allow AI to influence or execute, given the way value, trust and responsibility are created.
Start with a primary and secondary archetype
Most organisations should not attempt to capture their entire complexity in one model. A more useful first step is to identify:
1. the primary archetype governing the largest or most strategically important source of commercial value;
2. a secondary archetype that introduces a materially different requirement;
3. the tension between the two.
A bank might combine regulated relationship-based, distributed advisory and omnichannel characteristics. An enterprise software company might combine complex B2B, recurring revenue and ecosystem-led growth. A manufacturer might combine indirect distribution, partner-led selling and account-based commercial processes.
Those combinations expose the real design choices. Should customer intelligence be centralised while customer decisions remain local? Should product usage trigger an automated intervention or create a signal for an account team? Should the central brand prescribe campaigns or provide reusable capabilities to local operators?
That is where operating-model redesign becomes tangible.
The purpose of the archetypes is not to label an organisation. It is to create agreement about where commercial value comes from, who owns the customer relationship, which decisions require scale, which require context and where standardisation helps or harms.
Without that clarity, operating-model redesign quickly becomes a discussion about boxes, tools and headcount.
With it, the CMO and CCO can begin designing the commercial system the organisation actually needs.