An AI Centre of Excellence Is Not an Innovation Lab
An organisation establishes an AI Centre of Excellence, hires a team of machine learning engineers, launches several pilots and presents a promising innovation roadmap.
Twelve months later, leadership asks a difficult question: Which of these initiatives has changed how the business actually operates?
That question exposes the difference between experimenting with AI and building an enterprise AI capability.
India’s Global Capability Centres are increasingly positioned to bridge that gap. According to the Government of India’s 2026 GCC overview, nearly half of GCCs established since 2021 were AI-focused from inception.
However, an AI mandate alone does not guarantee operational impact. The Centre of Excellence must have the authority, infrastructure and business ownership necessary to move solutions into production.
Who Is Building These Capabilities?
The development is visible across industries, particularly healthcare, retail and enterprise technology.
Evernorth Health Services India has publicly identified an India AI CoE leadership role and highlighted enterprise AI enablement through its Hyderabad operations.
Meanwhile, Target’s Bengaluru technology organisation describes an Advanced AI team responsible for building production-grade AI and machine learning systems across business applications.
These examples illustrate two related approaches: building coordinated enterprise AI expertise and embedding advanced AI engineering directly into business-focused technology teams.
They are not necessarily identical organisational structures. What matters is that AI capability becomes connected to enterprise operations rather than remaining isolated from them.
The Operating Model That Makes an AI CoE Useful
A functional AI CoE needs three connected layers.
The central capability layer establishes reusable technology foundations, evaluation methods, security standards, engineering practices and access controls.
The domain layer places AI specialists alongside finance, supply chain, technology, customer operations or other business functions. Domain owners define actual problems and validate whether solutions improve performance.
The governance layer determines which applications can be deployed, who accepts operational risk and how systems are monitored after release.
For a GCC, this often points toward a hub-and-spoke structure: shared technical standards centrally, with execution responsibilities embedded within specific enterprise functions.
A purely centralised team can become disconnected from business priorities. A completely decentralised model risks fragmented infrastructure, inconsistent controls and duplicated investment.
What Companies Commonly Get Wrong
The first mistake is measuring success by the number of pilots launched.
Ten proofs of concept may demonstrate experimentation without demonstrating sustainable value. Production adoption, reliability and business outcomes provide a more meaningful assessment.
The second mistake is treating AI as an exclusively technical responsibility.
A forecasting application requires finance leadership. A customer-service agent needs operational ownership. A compliance application needs risk and legal involvement.
The ISACA framework for AI governance reinforces the need for executive accountability, cross-functional involvement and alignment with business objectives.
The third mistake is ignoring the transition from development to production. Data pipelines, monitoring, documentation, cybersecurity testing, integration and user training are not secondary activities.
Without them, even technically promising models struggle to become dependable business systems.
A Practical Framework for Building the CoE
Companies planning an AI CoE inside an India GCC should begin with a defined enterprise mandate rather than a broad instruction to innovate.
First, select two or three priority workflows. Focus on problems with measurable baselines, accountable business sponsors and sufficient data availability.
Second, design the team around delivery. Combine AI engineering and data expertise with product ownership, domain professionals, security specialists and change-management responsibility.
Third, define production governance. Establish model inventories, testing standards, approval authorities, monitoring arrangements and escalation protocols.
Fourth, build reusable capabilities. Common data connectors, evaluation tools and controlled deployment patterns allow subsequent projects to benefit from earlier investments.
Finally, measure business adoption. Track production use, process performance, reliability, business-owner satisfaction and verified financial impact.
This approach makes the CoE accountable for outcomes without forcing every business function into the same technology solution.
The Real Advantage Is Enterprise Ownership
An AI CoE creates lasting value when the GCC can influence enterprise workflows rather than merely deliver AI components requested by headquarters.
That requires global sponsorship and meaningful local decision-making authority.
India’s talent ecosystem provides a foundation, but sustainable results depend on aligning mandate, engineering, governance and business accountability.
For companies considering an AI Centre of Excellence within an existing or planned GCC, Enorbe can help structure the capability mandate, leadership responsibilities and execution model. The goal is to build an AI function that the business can rely on—not simply one that produces impressive demonstrations.
