The Rise of Agentic AI: Beyond Automation in Global Business Services
The promise of agentic AI – AI capable of goal-driven action – has been a major topic of discussion, but widespread deployment hasn’t yet matched the hype. While 2025 was anticipated to be a breakthrough year, the fundamentals needed for scalable implementation were still lacking, according to VentureBeat contributing editor Taryn Plumb.
From Generative AI to Orchestration: A Natural Evolution
Agentic AI unlocks new capabilities in software workflow orchestration, often leveraging techniques like Large Language Models (LLMs), but not always requiring them. The experience with generative AI (GenAI) provides a cautionary tale. A February 2025 survey at the Shared Services & Outsourcing Network (SSON) summit revealed that 65% of Global Business Services (GBS) organizations hadn’t even completed a GenAI project. This suggests agentic AI adoption is still in its early stages.
Why GBS is Primed for Agentic AI
GBS and Global Capability Centers (GCCs) are already evolving from back-office support to strategic enterprise partners. Agentic AI is a natural fit, as its use cases often align with existing GBS and GCC functions, such as IT operations and customer service. The potential for transformation is significant, but a methodical approach is crucial.
Five Steps to Successful Agentic AI Deployment
Successful agentic AI implementation isn’t isolated; it builds upon existing AI foundations. GenAI, predictive AI, and document AI are mutually supportive and can be stacked within modern systems. Learning from the GenAI hype cycle, industry leaders should prioritize careful preparation before launching pilots.
1. Know Thy Processes
Complex business operations require thorough understanding. Organizations must map existing processes and workflows, recognizing variations and manual intensiveness. Only then can they effectively rethink or rework them.
2. Know Thy Data
Data is the lifeblood of AI. Understanding data flows, APIs, structure (or lack thereof), and existing data platforms – including systems of record and vector databases – is essential. Consider data governance and security implications.
3. Identify the Problem
Clearly defining a problem translates into a focused use case with measurable objectives. For example, a shipping and logistics firm might target cost reduction, SLA improvements, or risk mitigation through workflow optimization.
4. Pilot an Operating Model
Choose a deployment model – Center of Excellence (COE), citizen-led development, or Build-Operate-Transform-Transfer (BOTT) – and ensure structural clarity. Agentic AI often involves multiple agents working in coordination, requiring careful consideration of environment, complexity, risks, and governance.
5. Scale Up
Successful pilots pave the way for enterprise-wide initiatives. A multinational bank, after automating non-core processes, used a software platform to complete over 100 discovery projects in under 14 months, demonstrating the potential for rapid scaling.
Agentic AI at Scale: Beyond Task Automation
Real impact comes from scale. A global shipping provider leveraged agentic AI to build data pipelines, digitize documents, apply rule-based reasoning, and orchestrate work across teams, resulting in significant efficiency gains across 16 initiatives. Agentic AI turbo-charges operations by enabling contextual perception, cross-domain collaboration, and autonomous action within defined governance parameters.
Consider procurement: while document AI extracts data from purchase orders, an agent can evaluate vendor risk, verify compliance, check budget availability, and even initiate negotiation, all while maintaining audit logs. Similarly, in financial advisory, an agent can assist professionals with targeted strategic investments based on predictive AI analysis.
Building Agentic Ecosystems in GBS
GBS is uniquely positioned to lead the charge into the agentic AI era. Its central role across finance, HR, supply chain, and IT provides a natural launchpad for creating interconnected agentic AI ecosystems. These ecosystems differ from standalone automation; agents share insights, learn from each other, and coordinate to optimize outcomes at the enterprise level. This enables GBS to leapfrog incremental automation and achieve end-to-end process orchestration.
Did you know?
Mastercard’s Decision Intelligence Pro uses recurrent neural networks to analyze 160 billion yearly transactions in under 50 milliseconds, delivering precise fraud risk scores at 70,000 transactions per second during peak periods.
Frequently Asked Questions
What is agentic AI? Agentic AI is a type of artificial intelligence capable of taking goal-driven action, rather than simply responding to prompts.
Is agentic AI replacing human workers? No, agentic AI is designed to extend human capabilities, enabling faster, more consistent, and scalable decision-making.
What are the key prerequisites for deploying agentic AI? Understanding existing processes, data infrastructure, and a clearly defined problem are crucial first steps.
What is the difference between agentic AI and generative AI? Generative AI primarily focuses on content creation, while agentic AI focuses on orchestrating actions and workflows.
How can GBS benefit from agentic AI? GBS can leverage agentic AI to automate complex tasks, improve efficiency, and enhance strategic decision-making.
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