Some organizations navigate change with speed and control, while others stall. The difference often comes down to operating model design, the blueprint for how work flows across people, process, technology, and governance. In an AI-saturated world, operating models perform best when they fit the organization’s context, strategic intent, and real business outcomes.
This article outlines how modern organizations approach operating model design. It focuses on teaming structures and AI-enabled ways of working, drawing on frameworks such as Elabor8’s Teaming Primes of Organizational Design. The central point stays constant: operating models succeed when they match your context and trade-offs are made deliberately.
Why deliberate operating model design matters in the age of AI
An operating model is the engine that turns strategy into execution. It defines how people, processes, technology, and culture work together to deliver value. In a fast-changing environment, deliberate operating model design drives outcomes such as:
- AI-first competitive advantage: applying AI where it improves speed, quality, and decision-making
- Staying on track: aligning teams and decisions to enterprise priorities, supported by AI-enabled performance signals and real-time progress visibility.
- Working smarter: optimizing how you deploy people and resources, streamlining workflows, and improving productivity by shifting routine tasks to AI-assisted automation and agents.
- Adapting with speed: responding to disruption and capturing opportunity through scenario planning, forecasting, and AI-enabled market sensing.
- Designing around the customer: building operating choices that improve experience, consistency, and trust.
- Embedding AI capabilities: placing intelligence into core workflows and defining how humans and AI collaborate in decisions and execution.
- Managing risk: designing governance that monitors compliance, bias, security, and model drift across AI-enabled decisioning.
- Engaging your teams: clarifying roles, strengthening collaboration, and reinforcing autonomy with accountability.
The Teaming Primes: a practical lens for organizing the enterprise
The Teaming Primes provide a structured way to design how an organization delivers value. They describe fundamental patterns for organizing work, including shifts towards customer and product alignment and the operating implications of AI-enabled execution. These shifts span a spectrum.
On one end are traditional structures: departments organized around projects, technical components (such as a specific IT system), or business functions. These designs prioritize efficiency within established boundaries. In today’s environment, AI often shows up here as automation and optimization inside the function (for example, using AIOps to stabilize IT operations). The result typically improves internal efficiency and reliability.
On the other end are customer- or product-aligned approaches: structures designed around how value flows to the customer. Organizations may align around customer journeys, products and services, or end-to-end value streams. In these models, AI is designed into the flow of work to improve speed, quality, and decision-making across the system.
A key takeaway from the Teaming Primes is that many organizations recognize misalignment and struggle to correct it. The framework positions the organization as an adaptive system that can continually refocus on value delivery as the business, competitors, and customers change.
Teaming structures: how work gets done
Within any operating model, teaming structures determine how people collaborate and how decisions move. Many organizations are shifting towards flexible, empowered, cross-functional teams that accelerate delivery and improve customer alignment. Common teaming patterns include:
Functional teams: grouped by specialized skills (for example, marketing or engineering).
- Good for: deep expertise, clear roles, operational efficiency.
- Watch out for: siloed thinking, slow cross-functional communication, and limited visibility into the end-to-end customer experience.
Divisional teams: grouped by product line, geography, or customer segment.
- Good for: focus on specific markets or products, faster decision-making within the division.
- Watch out for: duplicated effort, reduced cross-division collaboration, and fragmentation across “mini silos”.
Matrix teams: where people report to more than one leader, such as a functional manager and a project manager.
- Good for: shared expertise across projects, flexibility in resource allocation.
- Watch out for: role ambiguity, competing priorities, and increased coordination overhead.
Cross-functional product teams: small teams with diverse skills that own a product or customer journey end-to-end.
- Good for: rapid iteration, strong customer alignment, higher autonomy, and improved engagement.
- Watch out for: significant cultural change requirements, challenges to traditional management practices, and scaling complexity.
Process- or value stream-aligned teams: organized around an end-to-end value stream (for example, order to cash).
- Good for: optimizing value delivery across multiple functions, reducing hand-offs.
- Watch out for: complex coordination across functions, difficult governance.
Networked/distributed teams: rely on flexible connections and collaboration across geographies and, in some cases, external partners.
- Good for: access to global talent, flexible resourcing, collaboration with external experts.
- Watch out for: requires strong communication practices and tooling, and introduces cultural and time zone coordination challenges.
Taken together, these patterns raise an important question: how is work organized in your own business today, and how well is that serving you? Are you seeing the benefits these structures promise, and are the trade-offs showing up in familiar ways? Understanding where your current model helps or hinders execution sets the foundation for choosing what comes next.
Why context drives operating model choices
The effectiveness of an operating model depends on organizational context. Selecting the right design requires clarity across:
Goals and vision: what outcomes matter most across the short, medium, and long term? Examples include growth, market expansion, innovation, cost leadership, and experience leadership. Innovation-led strategies often benefit from empowered product teams. Efficiency-led strategies often benefit from more standardized, process-driven designs.
Starting point and capabilities: assess strengths and constraints across people, process, technology, and culture. Identify legacy systems and entrenched behaviors that slow change. Clarify current skills and the capability build required to reach your target state.
Industry and market dynamics: how quickly is the market changing, and what do customers and competitors signal? Fast-moving environments typically demand adaptable structures and shorter decision cycles.
Target outcomes: define the measurable results the new operating model must produce, such as faster product launches, improved customer experience, lower cost-to-serve, higher engagement, and stronger innovation throughput.
Culture and leadership: assess readiness for empowerment, experimentation, and distributed decision-making. Strong operating models depend on leaders who reinforce new behaviors and teams who feel safe to learn, iterate, and improve.
Making change stick through people
Operating model design often focuses on structure, process, and technology. Implementation succeeds through people. The model delivers value when teams understand the intent, adopt the behaviors, and change how work gets done.
People resist change when the purpose feels unclear or the shift feels unmanageable. The COM-B model for behavior change is a useful lens. For someone to adopt a new behavior, they need:
- Capability (C): the skills and knowledge to do the behavior.
- Opportunity (O): the right environment, resources, and support.
- Motivation (M): the desire and reason to change.
Using COM-B, focus areas for successful rollout include:
Explain the purpose and benefits (motivation): clearly communicate why the change matters and how it improves outcomes for teams and the enterprise. Connect the operating model to strategy, measurable results, and better day-to-day execution. When teams see the value and understand the direction, motivation rises.
Equip teams with skills (capability): new operating models demand new behaviors and, increasingly, AI-enabled ways of working. Invest in training that covers collaboration rituals, agile delivery practices, data fluency, AI literacy (ethical use of generative AI), and AI oversight (how leaders validate and govern agent outputs). Reinforce the human skills that make cross-functional delivery work, such as feedback and active listening.
Set up the environment for success (opportunity): skills scale when the environment reinforces them. That includes:
- New processes: redesign workflows to fit the new structure, including hand-offs, decision rights, and where AI agents support decisions.
- Supportive technology: provide the tools people need to collaborate, work transparently, and access the right data.
- Clear roles and responsibilities: define who owns what so teams can act with confidence.
- Remove friction: address physical and social barriers that block adoption by updating policies, aligning incentives, and replacing outdated habits.
- Sustain motivation: after launch, reinforce commitment through empowerment, leadership attention, and visible support mechanisms.
- Lead by example: leaders model the behaviors the operating model requires.
- Safe space to try: create a culture that supports experimentation, learning, and constructive feedback without fear.
- Recognize and reward: celebrate progress and reward teams for adopting new ways of working.
- Listen and adapt: gather feedback on what works, identify friction, and use what you learn to refine the model.
Designing with purpose and strategic intent
Designing and implementing a modern operating model is an iterative process:
- Assess the current state: understand where you are today.
- Set guiding principles: define the design rules anchored to strategy and outcomes. Use them to steer every operating model decision.
- Test and learn: run smaller-scale pilots for new structures and ways of working, then iterate based on evidence.
- Improve continuously: review and refine the operating model as conditions change across the enterprise and the market.
With a deliberate, iterative approach and frameworks such as Elabor8’s Teaming Primes, organizations can design operating models that fit their context and accelerate progress towards strategic goals.
The goal is clarity on who you are, where you are headed, and how you organize to deliver outcomes on that path. People make the model real through daily decisions and execution.
Do you see room for improvement in your current operating model?
Let’s co-create what comes next.
