Global capability centers in life sciences are undergoing a fundamental transformation, shifting from cost-reduction engines to AI-enabled enterprise platforms that own business outcomes end-to-end. Historically designed to move transactional work to lower-cost locations, these centers now face pressure to deliver real-time decision-making, cross-functional agility and automation at scale. The challenge isn't talent or funding; it's a structural design problem that legacy operating models can't solve.

What's Wrong With the Old Global Capability Center Model?

For decades, global capability centers (GCCs) worked as cost arbitrage machines. Organizations would centralize routine, lower-value activities in geographies with cheaper labor, creating straightforward economics: more work equals more people, more people equals lower per-unit costs. That model made sense in an era of stable processes and linear scaling.

But today's business environment has shifted. Life sciences companies now need real-time insights, rapid decision-making and the ability to scale operations without proportionally adding headcount. The old GCC design creates structural barriers to achieving this:

• Fragmented Accountability: Work gets siloed across functional teams, slowing execution and decision-making while limiting enterprise integration.
• Control-Heavy Governance: Layered approvals and static review mechanisms create operational latency instead of enabling speed.
• People-Dependent Scaling: Growth still requires hiring more staff, driving disproportionate cost increases that undermine the original cost-arbitrage advantage.
• Disconnected AI Adoption: Companies deploy AI tools without organizational context, adding complexity rather than enabling transformation.

The result: despite sustained investment, only a minority of GCCs have evolved into true enterprise value creators.

How Are Leading Companies Redesigning Global Capability Centers?

Forward-thinking life sciences organizations are reimagining GCCs as integrated, AI-enabled execution engines that combine human expertise, standardized products, and cross-functional teams organized around business outcomes rather than functional silos.

This redesign rests on three pillars: productized execution, decision-centric governance, and context-tuned AI. Instead of managing activities and outputs, GCC leaders now orchestrate enterprise outcomes and show how work creates real business value across teams, tools and processes. This leadership shift requires corresponding cultural changes, including outcome-oriented accountability, cross-functional collaboration, continuous learning, and data-driven decision-making.

Execution structures are also changing fundamentally. Rather than relying on disconnected functional handoffs, future-ready GCCs operate through a products-and-platforms paradigm. Persistent cross-functional teams own business outcomes from start to finish, integrating domain expertise, analytics, engineering, AI and operational excellence within a single execution unit. Standardized platforms and shared services reduce duplication, improve interoperability and enable consistent execution across business units and geographies.

Performance measurement is shifting too. Instead of traditional activity or utilization-based metrics, GCC success is increasingly evaluated through speed-to-value, automation maturity, decision quality, customer impact, innovation velocity and business outcomes.

What Role Does AI Play in the Future-Ready Global Capability Center?

AI and agentic workflows are embedded directly into the operating model of future-ready GCCs, not bolted on as an afterthought. This enables organizations to automate repeatable tasks, generate real-time insights, adjust priorities dynamically and deliver work faster. However, generic AI deployed without organizational context is often less effective than AI grounded in an organization's specific data, processes, domain expertise and business context.

Governance is also being transformed by AI-enabled capabilities. Organizations are replacing traditional governance models with decision-centric frameworks that align governance structures to decision types, business criticality, product and platform life cycles, risk thresholds and compliance requirements. AI further enhances governance by embedding intelligence directly into execution workflows, enabling real-time operational visibility, predictive risk and compliance monitoring, automated controls and policy enforcement, and exception-based governance mechanisms.

"Increasingly, an organization's true competitive differentiator is how effectively it operationalizes AI. Generic AI deployed without organizational context is often less effective than AI that's grounded in an organization's specific data, processes, domain expertise and business context," according to the research.

ZS Consulting Analysis

Steps to Transform a Global Capability Center for the AI Era

• Stabilize Operations First: Before scaling automation and AI, successful GCC transformation requires stabilizing core operations to ensure a solid foundation for change.
• Redesign Around Outcomes: Shift from activity-based accountability to outcome-oriented leadership, organizing persistent cross-functional teams around end-to-end business results rather than functional silos.
• Implement Decision-Centric Governance: Replace layered approval processes with governance frameworks aligned to decision types and business criticality, reducing execution friction while maintaining control and compliance.
• Embed Context-Tuned AI: Deploy AI systems grounded in your organization's specific data, processes and domain expertise rather than generic tools, ensuring real competitive advantage.
• Adopt Products-and-Platforms Execution: Build reusable enterprise capabilities, standardized data products and interoperable technology components that form the foundation of scalable execution.

Why This Shift Matters for Life Sciences Companies

The economics of this transformation are compelling. When the right activities are centralized, standardized and automated, organizations can do more with fewer people while still improving consistency, quality, speed and control. Growth no longer needs to mean proportionally adding more headcount. Instead, productivity gains come from smarter work design, better decision-making and AI-enabled execution.

This shift also addresses a critical competitive reality: in an AI-driven economy, the ability to operationalize AI effectively is becoming a defining differentiator. Companies that successfully redesign their GCCs around AI-enabled outcomes will pull ahead of those clinging to legacy cost-arbitrage models. The transformation requires rethinking leadership, culture, governance and technology ecosystems simultaneously, but the payoff is substantial: enterprise value creation at scale, with the flexibility to adapt as business needs evolve.