Agentic AI connects that gap and plugs the holes in traditional GBS systems that need to be filled by humans

Instead of relying on monolithic models and many fragmented points of automation, agentic AI assigns specialized roles to networks of intelligent agents. These work in cohesion to mirror real-world functions. Imagine a network of agents that coordinate and share context across different tasks to fulfil the duties of a compliance auditor or a procurement analyst.

By deploying this modular, multi-agent structure the technology enables goal-driven orchestration. Agents aren’t bound to linear workflows like traditional bots—they dynamically negotiate roles, delegate tasks, and respond to changing inputs. This makes agentic systems vastly more resilient and adaptive in complex, multi-step business processes like invoice reconciliation, onboarding, or policy compliance.

And this doesn’t mean that humans have effectively been replaced.

Agentic AI, in effect, offers a more intelligent execution layer that knows exactly when to escalate decisions. Human-in-the-loop design is selectively integrated at different points within a given workflow to make certain regulatory thresholds are never crossed, ethical judgments are navigated, and for highly complex exceptions requiring oversight. This retains a great deal of trust and accountability, while enabling entire workflows to be intelligently automated. What’s more, GBS teams experience reduced redundancy, faster decision cycles, and smarter cross-functional coordination.

From Data-Rich to Decision-Rich: Closing the Last-Mile Gap

Today, GBS enterprises certainly aren’t starved of data—in fact, they’re drowning in it. Dashboards are everywhere, yet decisions often stall because insight isn’t coordinated across people, processes, and platforms.

Agentic AI addresses this by embedding contextual reasoning and high-levels of inter-agent coordination into the execution layer. A distributed network of agents continuously ingests and learns from structured and unstructured data—think ERPs, helpdesk tickets, audit logs, internal and external comms, and much more—while maintaining a persistent memory of interactions across the enterprise.

In turn, this allows for agents to correlate events, better recognize intent, and coordinate action in ways that traditional automation tools simply can’t.

Take a real-world case from a major global software firm’s internal IT support: performance issues were repeatedly flagged via tickets, but resolution lagged due to siloed awareness—support teams were unaware of a concurrent product test causing the degradation. An agentic system, with shared context and inter-agent communication, would quickly identify the link between the two streams, trigger root-cause analysis, and escalate to the appropriate decision-maker with clear insight into how to solve the issue.

Maintaining memory of past interactions and using structured context to track patterns and dependencies, allows AI agents to spot issues like these across disconnected systems and trigger the right response automatically. The resulting shift from passive reportage to proactive, multi-tiered decision support is what will transform GBS operations from merely data-rich to decision-rich.

How Microsoft’s Agentic AI Platform Enables Enterprise-Scale AI

For operational agentic AI deployments, the choice of platform becomes critical and Microsoft, with its expanding agentic stack, is a powerful contender. At the core of Microsoft’s agentic AI framework is a series of tools that allow for faster, more effective agentic deployment and operations.

The foundation for this ecosystem is Azure AI Foundry, a library of over 11000 models. Foundry also allows enterprises to quickly operationalize LLMs and multi-agent systems across both cloud and edge computing environments. This sort of flexibility is particularly useful for GBS use cases that stretch across global ops, geo-specific compliance zones, or locations that require consistently high latency. And, with native integration into Azure services like Microsoft Fabric, enterprises can unify structured and unstructured data across finance, procurement, HR, and IT to drive highly contextual agent performance at scale.

To better benchmark agent performance, Microsoft offers Azure AI Evaluation Library—a Python library designed to assess agentic AI systems and workflows. It uses both traditional Natural Language Process (NLP) metrics like Bilingual Evaluation Understudy (BLEU) and Recall-Oriented Understudy for Gisting Evaluation (ROUGE), and AI-assisted evaluators such as relevance, coherence, and compliance risk. When combining these capabilities with Azure Foundry, enterprises can acquire greater visibility into agent behaviour, while business continuity is maintained via automated rollback mechanisms that kick in when a certain regression or risk threshold is crossed.

Microsoft’s stack also offers core security, compliance and governance with enterprise-grade controls including role-based access, identity management, secure data enclaves, and integrated governance via Microsoft Purview. This makes the stack especially suited to more sensitive GBS domains like finance and HR, where oversight and auditability are non-negotiable.

And human oversight is deliberately built into every layer. Azure’s Responsible AI toolkits and governance frameworks allow teams to configure escalation logic, embed review checkpoints, and control autonomy levels. Other core features of the stack include,

Microsoft serves as “Customer Zero” for agentic AI, having deployed custom and prebuilt agents across core business functions—from HR and IT support to finance and legal operations.

The learnings from these deployments—including the value of well-defined processes, curated knowledge bases, and tight human-in-loop controls—continue to shape Microsoft’s agentic AI frameworks and tools, creating a feedback loop that improves not only its own operations but also the offerings available to enterprise customers worldwide.

Recently, Dow, a global chemical company, used Microsoft Copilot Studio to build low-code agent workflows that automatically handle exceptions in invoice processing—detecting mismatches, querying vendors, and escalating discrepancies only when needed. What once required manual triage now runs with agent-led efficiency, driving millions in cost savings.

Let’s take a quick look at how other GBS enterprises are engaging with agentic AI.

Real Use Cases Across GBS: Internal, External, and Industry-Level

We can already see how agentic AI a performance catalyst in some of the most complex global enterprises is. A leading IT company has implemented an agentic framework for its internal employee systems, where a master AI agent directs employee queries to specialized sub-agents in HR, finance, and IT services. The master agent can take action, trigger workflows, and escalate issues to humans only when necessary—such as when queries fall outside predefined rules or are highly ambiguous. This approach reduces ticket loops, speeds up query resolution, and enhances the overall employee experience.

In the airline industry, agentic orchestration is being tested for use in real-time crew and passenger coordination during flight disruptions. When a cancellation occurs, agents reassess pilot schedules, reallocate flight attendants, and assign passenger re-accommodation with minimal human input—a high-stakes example of multi-agent collaboration under real-world constraints.

As AI interoperability and tech collaborations grow in scope, agents are able to better communicate across different systems. Microsoft is piloting agent-to-agent analytics in finance, where autonomous agents reconcile accounts and flag anomalies across systems like SAP and SharePoint. This agent-level communication removes bottlenecks typically caused by manual integration workflows or limited API bridges. *

The takeaway: Agentic AI is flexible enough to support both high-scale operational ecosystems and targeted process automations. Its value lies in its ability to coordinate, adapt, and persist intelligence across silos, regardless of the complexity or size of the domain.

Challenges in Implementation: What GBS Leaders Need to Know

The potential of agentic AI is massive—but so is the complexity of getting it right. For GBS leaders harnessing the potential of agentic AI, the transition requires rethinking architecture, culture, and governance.

Multi-agent systems typically demand new layers of architectural complexity. Companies that get it right invest in modular design and build clear boundaries around agent deployments. Ideally, orchestration frameworks that support sandboxed execution, versioning and role hierarchies should be the pillars around which initial deployments are built.

Data quality is another ongoing struggle. GBS data is often fragmented, but AI agents thrive best on high-quality, normalized data with clear ownership. Setting up the right data infrastructure for agentic AI can vastly improve time-to-value and make it easier to drive executive buy-in for AI deployment at scale.

Upskilling and change management are also essential parts of the agentic transition. Operational teams will now need hybrid skillsets that combine process knowledge, data engineering, and a rich understanding of AI practices, especially when overseeing high stakes workflows.

All of these challenges may seem solvable, but they demand intentional, strategic design choices from day one.

Agentic AI thrives where deep domain knowledge meets robust technical infrastructure. The recent EdgeVerve–Microsoft collaboration, for instance, is a blueprint for how domain-led co-innovation can accelerate transformation in GBS. EdgeVerve’s AI Next platform features a built-in agentic layer that orchestrates workflows across finance, procurement, HR, and customer operations. This layer is powered by Microsoft’s foundational technologies, which provide the scalability, security, and modularity needed to operationalize multi-agent systems at scale.

Tech partnerships like this are just one part of a growing suite of platforms and tools that will power the GBS industry’s leap toward agentic AI.