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AI Team Org Structures: Centralized, Embedded, Hub-and-Spoke

The four ways to structure an AI team, and how to pick the one that fits your maturity.

Elena Voss·Head of AI Delivery, Aiporate··7 min read·Share on XLinkedIn

Key takeaways

  • Four models: centralized, embedded, hub-and-spoke, vertical squads.
  • Centralized concentrates scarce expertise early.
  • Embedded drives adoption and domain fit at scale.
  • Most mature orgs land on hub-and-spoke.

How you structure an AI team shapes its output as much as who's on it. There are four common models, and the right one depends on your AI maturity and business shape.

The four models

ModelStrengthRisk
CentralizedCritical mass, standardsDistance from business, bottleneck
EmbeddedDomain fit, adoptionSilos, duplication
Hub-and-spokeConsistency + domain fitNeeds strong coordination
Vertical squadsDeep domain focusHarder to share learnings
AI team structures

How to choose

  • Early / scarce expertise: centralized.
  • Mature / many AI-fluent teams: embedded.
  • Scaling with governance needs: hub-and-spoke.
  • Highly domain-specific use cases: vertical squads.

Frequently asked questions

What's the best AI team structure?

It depends on maturity. Centralized works early; embedded scales adoption; most maturing organizations converge on hub-and-spoke, central standards with embedded execution.

How do I avoid silos with embedded teams?

Centralize governance and standards, run knowledge-sharing, and use a hub-and-spoke model so embedded members still share tooling and learnings.

When should governance be centralized?

Almost always, model review, ethics, data usage and standards belong in a central hub even when development is distributed.

Elena Voss

Head of AI Delivery, Aiporate

Elena has spent 12 years building and embedding AI and data teams inside B2B SaaS companies, from first pilot to enterprise-wide platform. At Aiporate she leads how forward-deployed talent is matched, onboarded and shipped to production.

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