The excitement around Generative AI (GenAI) has reached boardrooms, budgets, and business units. But enthusiasm does not equal execution. Most organizations launch GenAI initiatives with fanfare, but few extract consistent value. The failure is structural, not strategic. It stems from a lack of operational clarity—no defined architecture, no clear roles, no enforced governance, and no mechanism to scale what works.
Enter the GenAI Operating Model framework. This is not another layer of abstraction. It is the scaffolding required to deliver repeatable, measurable GenAI value across the enterprise. It replaces scattered pilots with structured progress. It defines how GenAI is built, funded, deployed, governed, and measured at scale. Strategy provides the ambition. The Operating Model delivers the result.
Take the current enterprise landscape. A recent global survey found that 65 percent of respondents report their organization “regularly uses” GenAI in at least one function. That sounds impressive until you ask: What has actually changed? For many, these deployments are tool-level experiments with minimal impact on workflows or economics. They fail to scale because they lack the underlying model to connect ambition with architecture.
The GenAI Operating Model framework includes 6 key components:
- Devise a Component-centric GenAI Operating Model
- Define a Core GenAI Team
- Manage and Govern Data
- Select a Viable Approach to GenAI Development
- Establish Common Infrastructure for IT Teams
- Ensure Risk and Compliance Governance
Source: https://flevy.com/browse/flevypro/genai-operating-model-design-10520
What Happens to Strategy Without an Operating Model
Without an operating model, GenAI quickly becomes a sunk cost story. Organizations fund shiny tools with unclear owners, deploy copilots in silos, and forget to connect pilots to enterprise architecture. Data remains fragmented. Risk teams are pulled in late. Each use case reinvents infrastructure. Value remains elusive.
The GenAI Operating Model corrects this by structuring GenAI like any other production-grade capability. It clarifies who builds what, on which platforms, under which standards. It defines Governance without bottlenecks. It ensures business units have the tools, but not the freedom to cause duplication or drift.
This is not just about architecture. It is about accountability. A strong operating model ensures that new use cases do not collapse under the weight of legacy systems or disconnected teams. It enables reusability, platform leverage, and safe experimentation. Most importantly, it separates high-potential use cases from high-cost dead ends.
GenAI needs to be treated as an organizational capability, not an experiment. That shift in mindset only happens when the Operating Model takes center stage.
Let us examine the first two components of the model in detail.
Devise a Component-centric GenAI Operating Model
GenAI changes too fast for rigid architecture. A one-time design exercise guarantees obsolescence. Instead, organizations must build modular, component-based systems that evolve incrementally. This means designing plug-and-play capabilities—model orchestration, agent evaluation, retrieval tools—that can be swapped in and out without breaking the system.
It is easy to underestimate the complexity of extending mature components. Identity access, Cloud Pipelines, and data governance often require major upgrades to support GenAI workloads. Conversely, faster-moving elements such as model hosting and prompt tooling should be designed for rapid iteration. Prioritized use cases must be mapped to reusable components, with clear accountability across engineering, risk, and product teams.
Blueprints matter here. Define what is reusable, what is domain-specific, and what is centrally owned. Publish a release plan. Report value delivered. And most importantly, resist the urge to rebuild for each use case. Do not optimize for perfection. Optimize for reusability.
Define a Core GenAI Team
The second component is team structure. GenAI delivery slows dramatically when roles and responsibilities are unclear. Many organizations try to bolt GenAI work onto existing IT teams. This may suffice for early pilots, but once deployment accelerates, the shared team model breaks down.
A dedicated GenAI team with explicit ownership across model lifecycle, infrastructure, evaluation, and compliance will move faster and scale cleaner. This team eventually becomes a GenAI Center of Excellence. It creates guardrails, promotes reuse, and provides onboarding support to new domains. To avoid overlap, the GenAI team must work closely with central IT, which continues to own the shared platform and lifecycle infrastructure.
This split between platform ownership and use case delivery is not a governance detail—it is the foundation for scaling GenAI without generating technical debt.
Case Study
Consider a global insurance provider launching GenAI for claims processing. The central GenAI team builds the first workflow, supported by a Cloud-Based Retrieval System and an orchestration layer. It works. Other departments—underwriting, customer support, legal—want in. The question is: How to scale?
If every department builds its own stack, chaos ensues. If the central team controls everything, bottlenecks arise. The solution is a federated model. Domains own their workflows and data. The central team owns shared platforms and guardrails. This allows for speed without sacrificing structure.
This federation must be earned. It only works when teams follow shared standards and when risk governance is enforced uniformly. Move to a decentralized model only when domains have demonstrated maturity, clarity, and control.
FAQs
Why are most GenAI deployments failing to scale?
They lack an operating model. Without a structured approach to platform, ownership, and governance, pilots remain disconnected and hard to reproduce.
How is this different from a GenAI strategy?
Strategy defines the intent. The operating model defines how GenAI gets built, governed, and scaled. It provides the execution layer.
Where should GenAI teams sit?
A dedicated GenAI team should work closely with central IT. GenAI teams handle enablement and delivery. IT owns shared infrastructure and lifecycle management.
What is the best development model: centralized, federated, or decentralized?
Start centralized to build standards. Shift to federated when domains are ready. Decentralize only when guardrails and governance are consistently applied across the enterprise.
How should data be handled in the GenAI model?
Data governance must treat both structured and unstructured data. Start centralized, set tagging and access standards, then shift stewardship to domain teams after maturity.
Concluding Thoughts
Most organizations measure the wrong thing. They track pilot count instead of adoption, demo capability instead of operational impact, and model performance instead of workflow outcomes. That is a symptom of not having an operating model in place.
The real signals of progress look different. Is there a centralized prompt repository? Are risk controls embedded in the model lifecycle? Are agents version-controlled and reusable across domains? Can one team’s learning accelerate another team’s deployment? These are the questions that matter.
Technology leaders need to stop chasing GenAI tools and start designing the systems that support them. A high-performing model is not just scalable—it is visible, auditable, and manageable. GenAI maturity is not measured in use cases shipped. It is measured in platforms leveraged and risk absorbed.
Any organization can launch a GenAI pilot. Few can industrialize it. Those that do will not only see results—they will define the next decade of how work gets done.
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