The gap between AI leaders and laggards is widening fast. Real-world evidence now proves the payoff is massive when you get this right:
Employee AI adoption at work doubled from 20% to 40% in two years, but how organizations structure AI deployment determines whether they unlock its full potential or limit it to basic automation (Anthropic)
High performing organizations are nearly 3× more likely to fundamentally redesign workflows and pursue transformative change rather than incremental gains (McKinsey)
74% of the most scaled GenAI initiatives meet or exceed ROI expectations (Deloitte)
When we talk about organizational structure for AI, we mean the operating model: how companies distribute AI responsibilities across governance, platform ownership, tool selection, and business execution.
The models are ready, but most companies are still in the early stages of AI maturity. Understanding where you are on the maturity spectrum and choosing the right organizational model will determine if your employees can actually use AI to get work done.
The AI Maturity Spectrum
Despite rapid investment, the majority of organizations remain in the early stages of AI adoption. According to McKinsey's latest global survey, roughly two-thirds are still only experimenting or running pilots, while true enterprise-wide scaling remains the exception (McKinsey).
Gartner's classic five-level AI Maturity Model gives us a useful framework to visualize that journey (Gartner):
| Level | Name | Traits |
|---|---|---|
| 1 | Awareness | Individual experimentation, no coordination or governance, ad hoc tool adoption |
| 2 | Active | Pilot projects in silos, departmental initiatives, early bottlenecks or sprawl emerging |
| 3 | Operational | Centralized governance forming, cross-functional pilots, emerging standards and best practices |
| 4 | Systemic | Enterprise-wide deployment, mature governance and workflows, intentional cross-functional collaboration |
| 5 | Transformational | AI embedded in all operations, self-service capabilities, autonomous optimization, continuous innovation |
From my observations, the vast majority of enterprises still live in Levels 1 or 2, which is exactly why choosing the right organizational model is the unlock.
Most companies level up not by waiting for better AI models, but by deliberately evolving how they govern, share, and collaborate around AI. Here are the four models worth understanding for AI adoption:
The Core Problem: Finding the Right Balance
Too Centralized (Pure Hub)
- • Central team becomes a bottleneck
- • Disconnected solutions → low adoption
- • Slow to respond to business needs
- • High control → low velocity
Too Distributed (Pure Spokes)
- • Shadow AI and tool sprawl explode
- • Duplicate spend and redundant contracts
- • Inconsistent security, data quality, compliance
- • High velocity → high chaos
This isn't theoretical. When organizations fail to balance centralization and distribution, governance collapses: 74% of organizations cite data security as their top priority when choosing AI platforms, yet only 24% have established governance frameworks (Box). That 50-point gap reveals the cost of poor structure.
Neither extreme works long-term. The right model depends on your organization's maturity, culture, and regulatory environment. Let's examine the approaches and how to choose the one that fits.
Four Organizational Models to Consider
Pure Hub (Fully Centralized)
Single central team owns everything: strategy, tool selection, deployment, and support. All AI requests flow through this team.
Common for: Early stages (Level 1-2) or highly regulated industries (finance, healthcare, government)
Common pattern: Central AI teams of 5-10 people managing hundreds of requests create 3+ month backlogs, causing business units to give up or work around the system.
Typical fate: Outgrown in 12-18 months as demand surges and the central team becomes a bottleneck
Pure Distributed (Spokes Only)
Every department runs its own AI stack with minimal coordination. Marketing picks their tools, Finance picks theirs, Product picks theirs.
Common for: Startups or holding companies with separate business units
Common pattern: Organizations where different departments independently purchase AI licenses, creating duplicate spend and confusion about approved tools for different data types. Marketing has one ChatGPT contract, Product has another, and IT discovers both only during security reviews.
Regular outcome: Shadow AI crisis leading to forced recentralization of governance after security incidents or compliance violations
Hub-and-Spoke (Hybrid): Increasingly Common
Central CoE (Center of Excellence) sets guardrails and platforms; business units own delivery and adoption. The Hub provides standards, security, and shared services. The Spokes own use cases, ROI tracking, and change management.
Hub Responsibilities
- • Standards, security, platforms
- • Broad education and enablement
- • Vendor management
- • Communities of practice (CoPs) framework
Spoke Responsibilities
- • Use cases and ROI tracking
- • Adoption and change management
- • Workflow redesign
- • Facilitating communities of practice (CoPs)
- • Continuous improvement
Why it works: Balances governance with speed while avoiding the extremes of pure centralization or pure distribution.
Hub-and-Spoke + Cross-Functional Connectors: Next-Stage Model
Deliberate bridges between related functions (Marketing → Product → Data, IT → Operations, etc.). Traditional hub-and-spoke assumes spokes will naturally collaborate. They don't. Connectors create intentional bridges.
Impact: Organizations that embed liaisons between functions and create shared KPIs typically see substantial reductions in deployment times.
Connector Mechanisms
- • Shared KPIs (e.g., Marketing + Product co-own retention)
- • Joint project squads for cross-functional initiatives
- • Embedded liaisons (IT rep in Marketing's planning)
- • Cross-functional communities of practice
Ideal for: End-to-end or agentic initiatives and Level 4+ transformation. Organizations moving from Operational to Systemic maturity.
Getting Started: Five Concrete Steps
- Diagnose your pain points. Bottlenecks & low adoption → too centralized. Sprawl & security incidents → too distributed.
- Plot yourself on the maturity spectrum. Use the AI Maturity Spectrum table above to identify where you are today (most large enterprises are at Level 2).
- Pick your target model. Match your maturity level to the organizational models described above. Most enterprises benefit from Hub-and-Spoke. If you're aiming for Hub-and-Spoke + Connectors, start with Hub-and-Spoke first, then graduate to the Connectors model as clear collaboration needs emerge.
- Address data security and compliance concerns early. Establish basic guidelines for which AI platforms are appropriate for sensitive information and how teams should evaluate risks before sharing proprietary data. Start simple rather than waiting for comprehensive policies.
- Bake evolution into your rhythm. Make the AI operating model a mandatory topic in annual planning and quarterly business reviews. This isn't a one-time project.
What Success Looks Like
The right organizational model creates something more valuable than any single AI deployment: it embeds AI into your culture. When structure enables rather than constrains, AI shifts from being a tool people occasionally use to being how work gets done.
What separates the leaders from the laggards comes down to two questions:
Employee Success
Can your people use AI every day to get more done, think bigger, and pursue problems that were previously out of reach?
Competitive Advantage
Can your organization move faster than the competition because AI is baked into how you operate, not an afterthought?
Companies that answer "yes" to both questions will have success both with their employees and against their competitors. The right organizational model makes this possible.