How artificial intelligence is reshaping the Global Business Services model, from centralizing finance and HR to using data analytics to elevate customer experience across every function.
Key Highlights
- The GBS model is being redefined by AI, moving from a cost-centralization play into a multi-functional, analytics-led value engine.
- A multi-functional GBS integrates finance, HR, and customer experience under one governed operating model instead of isolated functional towers.
- GBS data analytics is emerging as the differentiator, turning centralized data into forecasting, decisioning, and CX intelligence.
- Deloitte’s 2025 research shows roughly half of GBS organizations already achieve 20%+ savings, with GenAI now the top change agent.
- Customer experience and next-gen capability development are now top priorities for over 50% of GBS organizations, not cost alone.
- GCCs are becoming the practical execution layer that operationalizes AI across the modern GBS model at scale.
What is the GBS model in the AI age?
The GBS model in the AI age is a multi-functional operating model that centralizes finance, HR, and customer experience under unified governance, and uses AI and data analytics to shift service delivery from cost savings toward measurable enterprise value. In short, AI is turning the GBS model from a consolidation engine into an intelligence engine.
For years, service delivery centralization was judged on efficiency. That lens is now too narrow. Deloitte’s 2025 Global Business Services Survey points to a clear change in why organizations run these models. Low-cost talent still matters, but leaders are now layering experience and transformation on top of it. Deloitte’s own analysts frame cost as a weakening advantage, one that GBS organizations must move beyond by diversifying the value they create.
Traditional Global Business Services connected functions to improve consistency and scale. The modern GBS model goes further by embedding AI, automation, and analytics directly into workflows. The service delivery function has become an operational backbone, running support work alongside back- and middle-office operations under steady pressure to be both more efficient and more effective.
The mandate has widened and the GBS model has evolved beyond traditional shared services. The center is no longer measured only on how cheaply it runs, but on how intelligently it operates. This article breaks down how the AI-era GBS model works: how it is evolving, why a multi-functional GBS matters, how it centralizes finance and HR, the role of GBS data analytics, and how it improves customer experience.
How is GBS evolving in the AI age?
In short: GBS is evolving from an efficiency-focused consolidation model into an AI-enabled, analytics-driven value model. GenAI has become the primary catalyst of that shift.
The evidence is clear on direction. Deloitte’s 2025 survey found that of the 66% of organizations planning to invest in GenAI in the next three years, the top expectations for business impact are improved employee performance and productivity, reduced manual work, and increased innovation. Adoption is already underway across service functions, and the automation foundations are mature. The automation groundwork is already laid: Deloitte’s Global Shared Services Survey reports that close to 60% of organizations have deployed RPA and similar tools to lift both efficiency and process reliability.
That maturity matters because it makes AI adoption faster. Crucially, most GBS leaders begin their AI journey with a head start. A decade of hyperautomation has already built the experimentation habits and change-readiness that successful AI adoption depends on.
The next stage is agentic. Gartner expects agentic AI to scale fast, appearing in roughly a third of enterprise software applications by 2028, a dramatic jump from under 1% in 2024. By then, it estimates that about 15% of routine work decisions could be made autonomously. For the GBS model, that means whole processes, not just tasks, will increasingly run with AI in the loop, enabled by intelligent automation and AI-powered transformation at scale.
Three shifts define the AI-era GBS model:
- From tasks to outcomes: measured on business impact, not activity.
- From silos to integration: functions connected around end-to-end workflows.
- From reporting to real-time: decisions informed by live analytics.
What are the benefits of a multi-functional GBS model?
A multi-functional GBS brings finance, HR, IT, and customer operations together under one framework instead of running them as isolated towers. This integration is where AI-era value is created, from discrete functions playing support roles, to standardized business-unit outcomes, and finally to innovation-led value creation.
Benefits of a multi-functional GBS model:
Benefit
What It Delivers
Why It Matters in the AI Age
How does the GBS model centralize finance and HR?
The AI-era GBS model centralizes finance and HR by consolidating high-volume, rules-based work into a shared environment, then layering AI on top to automate, forecast, and self-serve.
Finance is where the automation runway is longest. McKinsey estimates that over 40% of finance roles are partially or fully automatable within a decade. And at roughly 80% of Fortune 500 firms, much of that automatable work, general accounting is a common example, already sits inside a GBS model. That existing footprint is exactly why finance centralization compounds so well under AI.
In a centralized finance function, the GBS model uses AI for planning and forecasting, intelligent document processing, faster month-end close, and variance analysis. In HR, it centralizes hiring, onboarding, and employee self-service, with AI reducing manual effort and improving response times. Aeries’ consulting and transformation services help enterprises centralize finance and HR functions end to end.
The market is reinforcing this multi-functional consolidation. Industry reporting notes that GCCs are becoming AI-driven business hubs, integrating Finance, HR, R&D, and Supply Chain for greater agility and efficiency, with AI-led GCCs expected to be 30–40% more efficient by 2026.
The practical enabler is a governed centralization environment. Enterprises increasingly stand this up through a Global Capability Center, which acts as the execution layer for the GBS model across functions.
What role does data analytics play in GBS?
GBS data analytics is the differentiator of the AI-era model. Once finance, HR, and CX data sit under one roof, the GBS model can convert that data into forecasting, decision intelligence, and continuous improvement.
Data is no longer optional infrastructure, it is the core asset. As these models mature, agility increasingly depends on real-time, data-driven decisions, with optimization coming from aligning people, process, and technology.
But analytics only creates value when it is scaled responsibly. The gap between adoption and impact remains the hard part. GBS teams are already absorbing enormous data volumes, from digital processes, connected devices, and AI-driven inputs, and converting that into insight takes governance, not just tooling.
Where GBS data analytics creates value:
- Finance: demand forecasting, cash-flow prediction, anomaly detection
- HR: attrition modeling, workforce planning, skills-gap analysis
- CX: sentiment analysis, journey analytics, proactive resolution
- Cross-functional: unified dashboards that connect cost, quality, and experience
A centralized model prevents fragmented, unreusable pilots, the most common failure mode when every function builds analytics independently.
How does the GBS model improve customer experience?
The GBS model improves customer experience by connecting service delivery end to end, removing handoffs, resolving issues faster, and applying AI and analytics to personalize interactions.
Customer experience is now a top-tier priority, not a byproduct. Deloitte’s 2025 survey found that over 50% of responding GBS organizations consider next-gen capability development and customer experience as top priorities, with the strongest CX value coming from a durable brand and the ability to reliably deliver standout results.
AI reshapes how those interactions are handled. Modern language models now handle customer interactions with growing nuance and context, and agentic systems can coordinate across multiple platforms to run complex journeys with little human involvement.
The result is a virtuous loop: centralized CX operations generate data, analytics turn that data into insight, and AI applies the insight back into faster, more consistent, more personalized service.
Building an AI-era GBS model: a practical framework
Modernizing the GBS model is an operating-model decision, not a headcount exercise. Use this five-step framework:
- Assess maturity. Map which finance, HR, and CX processes are standardized versus fragmented.
- Integrate around outcomes. Break functional towers; organize teams around end-to-end workflows.
- Centralize data first. Build governed, reusable data foundations before scaling AI use cases.
- Embed AI with governance. Deploy GenAI and agentic AI with Responsible AI controls and human-in-the-loop review.
- Measure beyond cost. Track CX, productivity, forecast accuracy, and decision quality, not just savings.
This is where a purpose-built Global Capability Center becomes the practical engine of the GBS model, providing the talent, governance, and analytics depth to operationalize AI at scale.
Ready to turn your GBS model into an intelligence engine? Partner with Aeries to centralize finance, HR, and CX under an AI-enabled operating model built for scale. Book a strategy session with our experts to design a GBS operating model built around your functions, governance, and AI readiness
Sources
• Deloitte — 2025 Global Business Services Survey
• McKinsey & Company — The future of global business services / State of AI 2025
• Gartner — Agentic AI enterprise adoption forecast
FAQ Section
The GBS model is an integrated service delivery framework that centralizes multiple business functions, such as finance, HR, IT, and customer operations, under unified governance. In the AI age, it also embeds AI, automation, and data analytics to shift focus from cost savings toward enterprise value.
It is moving from an efficiency-focused consolidation model to an AI-enabled, analytics-driven value model. GenAI and agentic AI are automating end-to-end processes, improving forecasting, and elevating both employee and customer experience.
A multi-functional GBS integrates finance, HR, customer experience, and other functions under one operating model and governance layer, rather than running them as isolated towers. This enables end-to-end workflows and shared AI and data foundations.
It consolidates high-volume, rules-based finance and HR work into a shared environment, then applies AI for forecasting, document processing, faster close cycles, and employee self-service, reducing manual effort and improving accuracy.
GBS data analytics turns centralized data into forecasting, decision intelligence, and CX insight. Real-time analytics helps leaders act on live signals across cost, quality, and experience instead of lagging reports.
By connecting service delivery end to end, the GBS model reduces handoffs, resolves issues faster, and uses AI and analytics to personalize interactions, making CX a differentiator rather than a cost line.
