A moment of organizational reckoning
AI also raises a deeply human tension — one that surfaces repeatedly across industries. Employees worry what automation means for them. Some fear that AI will shrink their roles before they understand how to evolve. Others see workforce reductions associated with automation as an immediate and tangible risk. For example, an organization with 6,000 shared services employees projects that digital and AI-enabled transformation could reduce that number to 500.
This kind of structural change cannot be hidden. It must be navigated.
“If you get the end-to-end process right and enable it with technology and AI, humans aren’t doing throughput work anymore — they’re managing exceptions,” Maria Saggese, EY Global and Europe West GBS Leader says. “But that requires trust in the data, trust in the process and time for people to understand what their future actually looks like.”
Organizations handling this well tend to do three things:
- Educate early and often: Help teams understand what AI can do — where it excels and where human oversight remains essential.
- Create space to practice: Let employees experiment with AI tools so familiarity replaces fear.
- Articulate a vision for new roles: Reinforce that future GBS teams will manage exceptions, interpret insights and drive decision-making — not execute repetitive tasks.
From an EY US perspective, this marks a shift toward a skills-based GBS workforce — one that combines AI literacy, analytical judgment and human-centric capabilities such as collaboration, ethics and change leadership. Organizations need to address the skill set mismatch from processing transactions to troubleshooting how exceptions are handled.
Leaders also emphasize cross-functional awareness. Sales teams must see how decisions impact receivables. Procurement teams need to understand downstream impacts on accounts payable. AI reveals the interdependence of end-to-end workflows more clearly than any tool before it — and GBS becomes the orchestrator of that interconnected enterprise, becoming an even greater advocate for global process ownership.
Lessons from the rise of RPA: a familiar transformation pattern
The enterprise shift toward AI echoes an earlier wave of change — robotic process automation (RPA). The RPA era revealed that the organizations achieving the greatest gains were those that built on globally standardized workflows and strong governance structures. These models created the consistency and clarity needed for automation to scale across processes, geographies and business units.
The organizations that struggled were typically those that pursued narrow, quick‑hit automations — small wins applied to fragmented processes. These efforts delivered isolated relief but not meaningful transformation. Many of these organizations found themselves forced to pause and rethink their entire operating landscape. Ultimately, they recognized that automation needed end‑to‑end process discipline, not point solutions. That realization is directly relevant today as AI raises the stakes and magnifies the consequences of fragmented foundations.
RPA also exposed the danger of automating broken or inconsistent processes. When policies, workflows or exception paths varied widely across regions or teams, automation often increased complexity rather than reducing it. AI makes this lesson even more urgent. Because AI depends on high‑quality, well‑structured data and stable workflows, inconsistent processes can quickly undermine model accuracy, reliability and trust.
Importantly, RPA surfaced the emotional and organizational realities of transformation. Many employees feared job loss, questioned whether automation outputs were trustworthy or struggled to envision their future roles. The same concerns are resurfacing today, only amplified by AI’s broader reach and greater capability. The organizations that navigated RPA most effectively were those that invested early in communication, education and transparency — helping employees understand how their roles would evolve and where new opportunities would emerge.
If RPA was a catalyst for incremental improvement, AI is a catalyst for structural change. The core lessons remain the same: strong governance, standardized workflows, consistent policies, clear accountability and a commitment to capability‑building are not optional — they are prerequisites. These foundations determine whether AI becomes a sustained enterprise capability or a series of disconnected experiments.
GBS as the first scalable home for AI
When examining where AI can take hold most effectively, GBS emerges as a logical nucleus. It is where global processes converge, where data flows aggregate and where governance is already embedded into operating models.
The next wave of AI — particularly generative AI (GenAI) and agentic AI — pushes this even further. These models are not just automating tasks; they are beginning to manage steps within end-to-end processes, triage exceptions and ultimately execute recommendations. This is why organizations are reimagining GBS as an intelligence hub rather than a transactional one.
But this evolution requires capability building:
- AI centers of excellence that codify governance and training.
- Talent pathways that reward analytical acumen, process fluency and technical literacy designed to accommodate both a digital and human workforce.
- Upskilling programs designed not for specialists alone but for large segments of the workforce.
- Integrated operating models where experienced AI users sit beside process owners rather than in a separate technical silo.
It is no surprise that 85% of organizations say they are prioritizing AI, automation and analytics skills within GBS, according to the SSON R&A report. And 34% cite upskilling as their top workforce priority. GBS is transforming not only what work gets done but who does it and how they do it.
