Key takeaways

• Life sciences global capability centers must shift from cost arbitrage to enterprise value creation.
• Future-ready GCC operating models combine productized execution, decision-centric governance and context-tuned AI.
• Successful GCC transformation stabilizes operations before scaling automation, AI and agentic workflows.

Global capability centers (GCCs) in life sciences are at an inflection point. Historically engineered as engines of cost efficiency and functional support, most GCCs remain constrained by fragmented accountability, siloed execution and governance models designed for control rather than speed or innovation.

Despite sustained investment, only a minority of GCCs have successfully evolved into true enterprise value creators. At the same time, AI-orchestrated workflows are rapidly redefining productivity frontiers and exposing structural limitations embedded in traditional operating models. GCCs don’t face talent or funding challenges, but a design problem.

To remain relevant, GCCs must transition from execution arms to integrated enterprise capability platforms—structures that own outcomes end-to-end, accelerate decision-making and orchestrate AI-enabled execution at scale.

The future-ready GCC: Why legacy models are failing

Most GCCs were designed for an era of stable processes, linear scaling and labor arbitrage. But that model is increasingly misaligned with what life sciences companies need: real-time decision-making, cross-functional agility, automation and AI-enabled execution at scale.

Today, organizations are reconsidering how and why they centralize operations with a GCC. A decade ago, organizations used GCCs primarily to shift transactional, lower-value activities to lower-cost locations. Today, a GCC’s greatest value comes from centralizing activities with the highest potential for standardization, automation, data integration and AI enablement—not simply those activities with the lowest labor cost.

This centralization fundamentally changes the economics of scale for life sciences companies. Growth no longer needs to mean proportionally adding more people, because when the right activities are centralized, standardized and automated, organizations can do more with fewer people—while still improving consistency, quality, speed and control.

But for many companies, maximizing the potential of their GCC remains easier said than done. Some key structural challenges holding them back include:

• Fragmented accountability slowing execution and decision-making
• Siloed functional delivery limiting enterprise integration and AI value realization
• Control-heavy governance creating operational latency and complexity
• People-dependent scaling models driving disproportionate cost increases
• Disconnected AI adoption adding complexity instead of enabling transformation

These challenges slow execution, limit AI’s potential and make it difficult to scale with enterprise demand. Thankfully, there’s a better way forward.

An integrated, AI-orchestrated operating model for the future-ready GCC

Leading life sciences organizations are fundamentally reimagining GCCs as integrated, AI-enabled enterprise execution engines. In this new model, leadership, operating structures, governance and technology ecosystems align around business outcomes, enterprise agility and continuous value creation.

Future-ready GCCs operate as strategic capability platforms that combine human expertise, AI-driven intelligence, standardized products and platforms and cross-functional execution models. These GCCs accelerate innovation, improve decision-making and scale enterprise impact. But what do they mean for today’s GCC leaders?

Leadership and culture: From activity management to enterprise accountability

In a future-ready GCC, instead of managing activities and outputs, leaders focus on orchestrating enterprise outcomes. They’re increasingly expected to own work from end to end and show how it creates real business value across teams, tools and processes.

GCC leaders are also expected not only to oversee delivery execution, but to align talent, data, technology, AI systems and governance mechanisms to drive business impact.

Of course, this leadership evolution requires a corresponding cultural transformation. High-performing GCCs foster:

• Outcome-oriented accountability rather than task-based execution
• Cross-functional collaboration over siloed operations
• Continuous learning and experimentation
• Data-driven and AI-enabled decision-making
• Shared ownership of enterprise goals

How GCCs measure performance is also changing. Rather than using traditional activity or utilization-based metrics, GCC success is increasingly evaluated through metrics such as speed-to-value, automation maturity, decision quality, customer impact, innovation velocity and business outcomes.

Productized, AI-orchestrated execution

At the GCC of the future, fragmented, functional delivery is out. Integrated, productized execution structures enabled by AI and intelligent automation are in.

In this model, organizations increasingly operate through a products and platforms paradigm, in which reusable enterprise capabilities, standardized data products, interoperable technology components and scalable automation layers form the foundation of execution.

Rather than relying on disconnected functional handoffs, execution is driven by persistent cross-functional teams responsible for delivering business outcomes from start to finish. These teams are organized around end-to-end business outcomes, and they integrate domain expertise, analytics, engineering, AI and operational excellence within a single execution unit.

Why enterprise context matters for GCCs

AI and agentic workflows are embedded directly into the operating model of future-ready GCCs. This helps GCCs automate repeatable tasks, generate real-time insights, adjust priorities dynamically and deliver work faster.

Standardized platforms and shared services further enhance scalability by reducing duplication, improving interoperability and enabling consistent execution across business units and geographies.

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.

But adopting AI alone isn’t a competitive advantage. It has to be context-tuned to your enterprise to deliver real value.

Decision-centric governance

Governance in future-ready GCCs must be redesigned to enable execution speed while maintaining enterprise control, transparency and compliance. Organizations are increasingly replacing traditional governance models—often characterized by layered approvals, fragmented oversight and static review mechanisms—with decision-centric governance frameworks. These frameworks align governance structures to:

• Decision types and business criticality
• Product and platform life cycles
• Risk thresholds and compliance requirements
• Operational and strategic priorities

This shift helps organizations reduce execution friction while strengthening accountability and control. AI-enabled capabilities further transform governance by embedding intelligence directly into execution workflows. Organizations increasingly leverage:

• Real-time operational visibility
• Predictive risk and compliance monitoring
• Automated controls and policy enforcement
• Exception-based governance mechanisms
• Continuous performance and KPI tracking

Governance is becoming an integrated execution enabler rather than a bottleneck, supporting both agility and risk management simultaneously.

The AI-enabled execution backbone in the GCC operating model

At the core of the future-ready GCC’s operating model is an integrated AI-enabled execution backbone that connects data, workflows, platforms, governance and decision systems across the enterprise.

This AI execution layer serves as the orchestration engine for enterprise operations and enables continuous optimization at scale through four key capabilities:

• Decision intelligence: Advanced analytics, AI models and real-time insights support proactive, data-driven decision-making across business and operational processes.
• Intelligent automation and agentic workflows: AI agents and intelligent automation systems execute repeatable and rules-based activities autonomously, reducing manual effort while improving speed, consistency and scalability.
• Embedded governance and risk monitoring: Compliance, controls and risk management capabilities are integrated directly into operational workflows, enabling real-time governance without slowing execution.
• Enterprise connectivity: Unified data platforms, interoperable systems and connected workflows create seamless coordination across functions, products, geographies and ecosystems.

Together, these four capabilities transform GCCs into intelligent, self-improving systems capable of continuously learning, optimizing and scaling enterprise value creation.

An AI-enabled execution backbone stems from this expanded mandate for GCCs: They shouldn’t simply be early adopters of AI. Today, GCCs are expected to be engines that make AI context-ready for the organization by embedding proprietary data, domain expertise, business rules and organizational knowledge directly into AI models and workflows. It is this contextualization of AI, more than adoption alone, that unlocks and compounds value across the enterprise.

Strategic levers for a future-ready GCC operating model

Transforming your GCCs’ operating model requires deliberate choices, disciplined sequencing and clear intent. Organizations must make decisions about the model that align with their strategic ambition, enterprise context and capability maturity—because no single operating model will fit all organizations or business environments.

From there, life sciences organizations must activate a focused set of strategic transformation levers to simplify, scale and modernize execution while balancing efficiency, agility and ownership across the enterprise.

These levers—centralization, regionalization, automation, internalization and rationalization—shouldn’t be applied uniformly. Instead, they must function as strategic design dials that can be dynamically orchestrated based on business priorities, capability maturity, regulatory complexity and the desired operating model outcomes. Future-ready GCCs continuously recalibrate these levers to optimize enterprise value creation, operational resilience, execution speed and AI-enabled scalability.

Move from operational stability to AI-enabled execution in 3 phases

Execution must progress through a deliberate, phased path to value.

The first phase focuses on establishing operational stability through standardized processes, clear accountability, aligned governance, harmonized data foundations and foundational automation. Without this layer of stability, transformation efforts often struggle to scale.

The second phase shifts the organization toward productized and platform-based execution models. Capabilities become organized around reusable enterprise assets, shared platforms, integrated workflows and outcome-oriented delivery structures that enable consistency, scalability and faster innovation across functions.

The final phase embeds AI deeply into operational and decision-making flows, enabling intelligent automation, predictive insights, autonomous workflows and exception-based governance. At this stage, the GCC evolves from a service delivery organization into an adaptive, self-optimizing enterprise execution engine.

Organizations that attempt to bypass foundational stabilization frequently encounter fragmented execution, weak adoption, governance breakdowns and unsustainable automation outcomes.

GCCs as the engine of enterprise value at life sciences firms

The role of the GCC has fundamentally changed. Future-ready GCCs aren’t support organizations focused on cost efficiency. They’re AI-enabled, productized enterprise capability platforms designed to orchestrate execution, accelerate innovation and drive enterprise value creation at scale.

Organizations that successfully integrate outcome-oriented leadership, product-centric execution, decision-centric governance and AI-enabled operational backbones will achieve:

• Faster innovation cycles
• Greater scalability and resilience
• Better decision-making
• Higher workforce productivity
• Sustained enterprise value creation

As AI continues to redefine enterprise operations, GCCs will increasingly emerge as strategic nerve centers for intelligent execution across the life sciences global enterprise ecosystem. The next generation of GCCs won’t be defined only by labor arbitrage, but by their ability to seamlessly integrate human and AI capabilities, accelerate decisions at scale and continuously self-optimize.

Building a future-ready GCC today sets your firm up for competitive advantage tomorrow.