Is agentic AI ready to reshape Global Business Services?

2026-02-10 · Source: VentureBeat · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Emerging Technologies & Innovation, Robotics & Autonomous Systems · Depth: Intermediate, medium

Summary

EdgeVerve's N. Shashidar discusses the readiness of agentic AI to transform Global Business Services (GBS), noting that while rhetoric has outpaced deployment, its potential for enterprise transformation remains significant. Agentic AI, which enables goal-driven action, is distinct from generative AI (content creation), predictive AI (forecasting), and document AI (data extraction), but can integrate with them. A December 2025 VentureBeat report indicated that fundamental scaling requirements for agentic AI are still missing. Despite this, GBS and Global Capability Centers (GCCs) are well-suited for agentic AI adoption due to their existing roles in IT operations and customer service. The article outlines a five-step methodical approach for GBS organizations to move towards scaled deployment, emphasizing process and data understanding, problem identification, pilot operating models, and strategic scaling.

Key takeaway

For executives overseeing Global Business Services or Global Capability Centers, prioritizing a structured, five-step approach to agentic AI deployment is crucial. Your teams should first meticulously map existing processes and data, then pinpoint specific high-impact problems before piloting an operating model. This methodical preparation, rather than rushed pilots, will pave the way for successful, enterprise-wide scaling and significant efficiency gains.

Key insights

Agentic AI offers significant potential for Global Business Services by orchestrating complex workflows and extending human judgment.

Principles

• Agentic AI integrates with other AI types.
• Methodical preparation precedes scaled AI deployment.
• GBS is a natural fit for agentic AI ecosystems.

Method

A five-step deployment method involves understanding processes and data, identifying specific problems, piloting an operating model, and scaling up successful initiatives for enterprise-wide impact.

In practice

• Map existing business processes and data flows.
• Identify specific high-cost or high-risk problems.
• Pilot an operating model like a Center of Excellence.

Topics

• Agentic AI
• Global Business Services
• AI Deployment Strategy
• AI Ecosystems
• Process Orchestration

Best for: Executive, Director of AI/ML, AI Product Manager, Operations Professional

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Editorial summary, takeaway, and curation by AIssential. Original article published by VentureBeat.