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The savings are real. Early adopters are cutting enabling costs up to 20% to 50% with a health care GCC. Most health systems haven't followed, held back by myths that stopped being true years ago. Read on to learn how AI and a GCC operating model work together, what labor arbitrage delivers today, and why the myths don't hold.
Provider health systems are navigating a persistent financial squeeze: operating margins compressed to 1%–2%, rising labor and supply costs, and reimbursement rates that continue to lag. Yet a significant and underutilized lever remains largely untapped: Global Capability Centers (GCCs). Early adopters are already capturing up to 20%–50% cost savings by moving enabling functions to GCCs in the Philippines, India, Central and Eastern Europe, and Latin America. Many health systems, however, have yet to act—held back by a set of persistent myths.
While anecdotal discussions often highlight barriers to the design and adoption of these global models, they are not always rooted in truth. Are these issues that still need resolution, or are they perpetuated myths? Let's examine five truths versus myths on the provider path to GCCs and show why health care leaders cannot afford to ignore these global delivery solutions.
The arrival of advanced artificial intelligence (Generative AI/agentic AI) is reshaping how health care organizations deliver back-office, clinical support, and transactional functions—but it does not render GCCs obsolete. In reality, the path forward is not a binary AI versus GCC proposition. Instead, value leaders do both in parallel—interweaving automation, global delivery, and onshore models to create resilient and cost-effective operating structures.
Hybrid operating models: The power of ‘and’
Many health systems will achieve outsized value by combining GCC delivery and automation in a hybrid construct—selectively automating what is reliably digitized while routing tasks where human intervention remains critical to GCC teams, and keeping strategic decision-making onshore.
Regulatory uncertainty on labor arbitrage return does not eliminate the value of GCCs for health systems; rather, it necessitates smarter operating model design and targeted scope. By carefully assessing portfolios by function, location, and sourcing constructs, organizations can strategically minimize regulatory exposure while still achieving a favorable and sustainable total cost of ownership. In 2025, approximately 50% of organizations with outsourcing presence achieved more than 20% savings from labor arbitrage. Through thoughtful segmentation of processes and diligent application of compliance controls, organizations can not only maintain, but often enhance cost efficiencies and operational resilience. This approach enables health systems to adjust resource allocation, leverage global talent, and enhance financial benefits—enabling GCCs to remain a viable strategy for driving long-term value despite evolving regulatory landscapes.
Benefits of GCCs can also expand beyond cost savings, with centralization and capability expansion. Centralization enables standardization and scale, and AI-driven automation reduces full-time equivalent (FTE) intensity and reliance on external services—strengthening economics under various regulatory scenarios. With centralized capabilities, many outsourcing delivery centers are transforming to become true GCCs—expanding their scope into new strategic, value-creating, or specialized capabilities (e.g., product engineering, analytics/AI, cybersecurity, advanced finance). Scenario planning and flexible contracting can further allow health systems to pivot delivery volumes across nearshore and outsourcing hubs without compromising capability or quality.
Typical labor arbitrage ranges:
The full report takes on stand-up cost and payer contracts, then shows how modern access controls keep data privacy intact offshore. Read it for a detailed debunking of all five myths.
Figure 2. Total cost of ownership comparison: Nearshore vs. Outsourcing
Annual TCO for 200 FTEs · Health care back-office functions · Illustrative model anchored to published benchmarks