Generative AI: A Leadership Guide to Organizational Structure

A lot of companies today are eyeing the potential of generative AI. The buzz is real—but so is the confusion. Who should drive these efforts? Should it sit with IT, product, or a specialized innovation team? And how do you avoid AI becoming just another experiment that never quite leaves the sandbox?

These are the right questions to ask. Getting generative AI right isn't just about the technology. It's about how you structure your teams, balance speed with control, and make decisions that position you for both short-term wins and long-term success.

Where Do You Start?

Before jumping into org charts and reporting lines, leadership needs clarity on a few fundamental questions:

What are you trying to build?

If you have clearly defined use cases (like customer support automation or compliance tools), a more distributed model can make sense sooner, as business units may already have the context to execute effectively. But if you're still in exploration mode, a centralized structure helps consolidate learnings and avoid fragmented efforts before scaling more broadly.

Is this a build or buy scenario?

If you're buying and customizing pre-built tools, a distributed model may work earlier since technical complexity is lower. However, if you're building in-house, a centralized team with the right depth of technical expertise can more effectively manage the complexity of early development and risk mitigation.

How urgent is this?

If speed is critical—perhaps due to competitive pressures—centralizing efforts under a Center of Excellence allows for faster iteration without cross-team coordination slowing things down. If the focus is on long-term capability building, moving more quickly toward a distributed model with governance might make sense after initial experiments succeed.

Why Starting Centralized Makes Sense

For most companies, the smartest move early on is to establish a centralized Center of Excellence (CoE). This focused group acts as a testbed for exploring generative AI, managing early risks, and developing foundational expertise.

Why centralized? Because generative AI development is different. The tools may feel familiar—APIs, prompts, models—but the workflows, quality control, and risk factors often catch companies off guard. Testing outputs, handling variance in results, and ensuring alignment with compliance and business goals require specialized focus early on.

A CoE helps:

• Build expertise without spreading risk too thin
• Consolidate learnings and create reusable frameworks
• Avoid disconnected, fragmented AI experiments

But here's the catch: staying centralized for too long can create bottlenecks. Teams get frustrated waiting for results, and broader adoption stalls. A centralized model works best as a launchpad, not a permanent structure.

The Critical Role of a Senior Sponsor

One often overlooked—but crucial—factor for early success is having a senior sponsor.

This isn't just about budget approvals. A strong sponsor:

• Provides organizational air cover to navigate uncertainty and experimentation
• Breaks down silos between departments (like product, engineering, and compliance)
• Ensures AI efforts stay aligned with strategic priorities rather than becoming isolated technical experiments

Without a sponsor with both influence and conviction, generative AI initiatives can quickly stall when they hit cross-functional friction or risk-averse cultures.

The Long-Term Vision: Distributed Innovation with Guardrails

Ultimately, the goal for most organizations should be distributed innovation with a shared governance framework.

Think of it like microservices in software architecture—each business unit or product team owns the AI tools most relevant to their domain but follows shared standards for:

• Data governance
• Model selection
• Performance monitoring and risk management

Why distributed? Because it scales better. Over time, teams closest to the business context can identify and act on AI opportunities faster than a centralized group. They understand the nuances of their workflows, making them better positioned to build (or buy) the right tools for the job.

However, this only works if you have strong guardrails. Without a central governance layer, you risk losing alignment, creating compliance gaps, and introducing technical fragmentation.

What Does the Journey Look Like?

The most effective companies tend to follow a phased approach:

1. Start Centralized: Launch a focused Center of Excellence to explore early use cases, manage risks, and build foundational knowledge.
2. Expand with Guardrails: As capabilities mature, shift toward a distributed model while maintaining centralized oversight for compliance, quality, and interoperability.
3. Empower Domain Teams: Enable individual business units to own their AI initiatives while staying aligned with company-wide standards.

This isn't about choosing between speed and stability. It's about planning for both—starting with focus and evolving toward scalable, responsible innovation.

Because when it comes to generative AI, the real challenge isn't just building the tools. It's about structuring your organization so those tools can deliver real, lasting impact.