Global Capability Centers (GCCs) have been evaluated through the lens of cost, scale, and operational efficiency for the past two decades that model served its purpose and now with AI, this fundamental equation has changed.

The questions that are raised by healthcare organizations have shifted from, “How do we build a lower-cost engineering center?” to, “How do we build an organization that continuously creates AI-powered products, accelerates innovation while protecting our data?”

This is why the next generation of Healthcare GCCs will not be Engineering Centers.

They will become AI Innovation Hubs.

The organizations that recognize this shift early will create a lasting competitive advantage. Those that continue to view GCCs primarily as delivery organizations risk competing on cost and scale in a market increasingly defined by intelligence, innovation, and speed.

Over the past several years, I have observed Healthcare GCCs progressing through four distinct stages of maturity.

Stage 1 – Delivery Center

The first generation of GCCs focused on execution and efficiency was the key objective. the success was measured by utilization, ticket closure, SLAs, and cost optimization. The teams primarily supported headquarters by executing predefined work.

Stage 2 – Engineering Center

As organizations matured, ownership replaced execution. GCCs began owning engineering functions. Teams became responsible for application development, cloud engineering, DevOps, testing, and platform modernization.

Stage 3 – Product Ownership Center

The next evolution shifted accountability from projects to products. Business outcomes became more important than project completion. Cross-functional teams owned digital health platforms, patient engagement solutions, clinical workflows, and enterprise applications from concept through production.

Stage 4 – AI Innovation Hub

Today, The Healthcare GCCs are becoming an organization’s strategic growth and innovation engines rather than operational support centers. These teams don’t just develop software, they build intelligent systems, deploy AI agents, engineer enterprise data platforms, modernize legacy applications, and create reusable AI capabilities that accelerate innovation across the business.

In our experience working with global technology organizations, successful AI-first GCCs consistently invest in five foundational capabilities.

1. AI-Native Engineering

Modern software teams are evolving into AI engineering teams where the developers are increasingly collaborating with AI agents throughout the software lifecycle. From requirement analysis and modernization to coding, testing, documentation, and operations. This is significantly creating more productive engineers rather than reducing the number.

2. Enterprise Data as a strategic asset

Artificial Intelligence is only as effective as the quality of enterprise data behind it. Healthcare organizations must move beyond fragmented operational data toward governed, unified data estates that support clinical, operational, and business intelligence. Without trustworthy data, even the most advanced AI models would fail to deliver meaningful outcomes.

3. Product Ownership over project delivery

High-performing GCCs no longer think in terms of project completion. They own business capabilities. Whether it is patient engagement, connected care, revenue cycle optimization, clinical decision support, or provider experience, teams are measured by business value rather than development velocity alone.

4. Agentic AI as the new workforce multiplier

The next wave of productivity will not come from additional automation scripts. It will come from intelligent AI agents working alongside engineering, operations, and business teams. These agents can assist developers, automate testing, accelerate modernization, improve clinical workflows, support customer service, and enable knowledge workers across the enterprise.

The organizations that successfully integrate human expertise with AI agents will scale innovation dramatically faster than those relying solely on traditional delivery models.

5. Trust by design

Healthcare has always demanded trust and with AI innovation in the conversation, the bar is raised even higher. Every AI Innovation Hub must embed governance into its operating model through security, observability, responsible AI practices, regulatory compliance, and enterprise-grade controls. In this evolving model, trust cannot be an afterthought, it must become a design principle.

Treating AI adoption as a standalone initiative is one of the biggest mistake organizations are making.

Successful Healthcare GCCs integrate AI, Data Engineering, Platform Engineering, and Digital Product Engineering into a single operating model.

This convergence enables organizations to modernize legacy platforms, create reusable AI capabilities, and continuously deliver intelligent digital products.

At OptiSol, we have built our engineering approach around this integrated model to help healthcare organizations modernize with our proprietary platform – iBEAM , operationalize AI with elsai, and build digital foundations with our specialised AI engineers and domain expertise.

Technology alone, however, is never the differentiator. Operating models are.

Traditional GCC metrics are becoming increasingly irrelevant. Counting of billable hours, utilization percentages, or team size tells us very little about an organization’s ability to innovate.

The new scorecard should answer different questions like:

• How quickly can we move from an idea to production?
• How much faster can we modernize legacy platforms?
• How many AI-enabled capabilities are being delivered every quarter?
• How much engineering effort is being augmented through AI?
• How effectively are digital products improving patient, provider, and operational outcomes?

Innovation has become the new benchmark for success.

The most valuable assets of an Healthcare organization such as the, clinical knowledge, patient data, proprietary algorithms, and operational intelligence cannot simply be exposed to public AI ecosystems at any cost.

And for this reason, enterprises are looking to build Sovereign AI environments where models, agents, enterprise data, and intellectual property remain securely governed within their own infrastructure. This shift is transforming Healthcare GCCs into custodians not only of engineering excellence but also of enterprise intelligence.

The future belongs to those organizations that can innovate rapidly without compromising governance, compliance, or data sovereignty.

Five years from now, Healthcare GCCs will no longer be compared by the number of engineers they employ or the cost savings they generate. They will be measured by how quickly they create new digital capabilities, how effectively they operationalize AI, and how consistently they transform data into better patient and business outcomes.

The most successful GCCs will resemble AI product companies operating within the enterprise, combining engineering excellence, governed intelligence, and product ownership into a single innovation engine.

At OptiSol, this is the future we are helping our customers build: Smart, Skilled, and Scalable AI-first GCCs that accelerate innovation, protect enterprise knowledge, and create sustainable competitive advantage.

The conversation has moved beyond building larger GCCs. The real opportunity now is to build smarter ones.