AI-first GCC

AI GCC Setup & Operations.

Design the team topology, workflow, platform architecture, governance, and run model for an AI-first Global Capability Center.

An AI GCC needs a different operating model. Team design, tooling, governance, and delivery cadence all change when AI is a core capability not an experiment.

Deliverables

What we deliver

Team topology

Define roles, reporting lines, and collaboration models for AI, data, and engineering teams.

Workflow design

Establish delivery cadences, sprint structures, and handoff processes tailored for AI workloads.

Platform architecture

Design the compute, storage, and tooling stack for ML development and deployment.

Governance model

Create decision rights, review gates, and escalation paths for AI delivery and operations.

Run model

Define how AI systems are monitored, maintained, and improved post-deployment.

Common use cases

Building a new AI center of excellence

Transitioning a data team into an AI-first function

Scaling ML from proof-of-concept to production

Setting up MLOps infrastructure and practices

Establishing AI governance alongside delivery

Frequently asked questions

How is an AI GCC different from a traditional GCC?

An AI GCC requires specialised roles, experimentation workflows, GPU infrastructure, and governance models that traditional GCCs don't need.

How long does it take to set up an AI GCC?

Initial setup typically takes 3–6 months, with full operational maturity achieved over 12–18 months.

Do you help with hiring and team building?

We design the team topology and role definitions. We can also support hiring strategy and interview frameworks.

What platforms do you typically recommend?

We are platform-agnostic and recommend based on your existing stack, scale requirements, and budget.

Can you help transition an existing team?

Yes. We frequently help organisations evolve existing data or analytics teams into AI-first functions.