Agentic AI is moving enterprise AI from individual prompts and pilots toward systems that can support multi-step work. For Global Capability Centers (GCCs), this creates an opportunity to combine AI, enterprise technology, and operational expertise in one environment. The priority is not maximum autonomy. It is controlled, measurable AI that improves how work gets done.
Key Highlights
- Agentic AI can extend AI beyond content generation into workflow coordination, operational follow-through, and decision support.
- Aeries has publicly described agentic AI as part of its GCC operating strategy, including A1 GCC and its AI-Centered GCC initiative.
- The strongest starting point is a defined business workflow with a measurable outcome and clear controls.
- Agentic AI requires governance around data, system access, human approvals, monitoring, and operational accountability.
- The business case should be measured through workflow outcomes such as cycle time, productivity, quality, cost, and exception handling.
What Is Agentic AI?
Agentic AI refers to AI systems designed to do more than generate a response. Depending on how they are built, AI agents can support a defined objective by coordinating steps, interacting with approved tools or systems, and supporting actions within specified boundaries.
For enterprises, the distinction is important. A generative AI application may summarize information or produce a draft. An agentic workflow can be designed around a business process, where AI supports multiple stages of the work and hands exceptions or higher-risk decisions to people.
Aeries’ public description of A1 GCC provides a concrete example of this direction. The platform brings together planning, workforce visibility, governance, coordination, and embedded AI capabilities for GCC operations. Aeries says it is introducing function-specific AI agents to support coordination, operational follow-through, governance, and decision support.
Why Are GCCs Becoming Important to Agentic AI Adoption?
GCCs increasingly bring together technology, engineering, analytics, finance, IT, cybersecurity, and other enterprise capabilities. That combination gives a GCC visibility into both the technology environment and the business processes that AI is expected to improve.
Aeries’ 2025 AI-Centered GCC announcement described a framework that embeds Agentic AI, automation, and data-driven decision systems into core GCC functions including finance, IT, HR, and customer operations. The announcement positioned the GCC as an environment for applying AI within operating processes rather than treating AI as a standalone technology initiative.
Aeries’ 2026 A1 GCC announcement extends that approach through a platform designed to sit above existing enterprise systems, including ERP, CRM, HRMS, ATS, and project management tools.
Which GCC Workflows Can Benefit From Agentic AI?
The right starting point is not the most sophisticated AI capability. It is the workflow where the organization can clearly define the objective, inputs, actions, controls, and expected outcome.
GCC Function 
Potential Agentic AI Role
What Should Remain Controlled
How Should Enterprises Select Agentic AI Use Cases?
A practical use-case assessment should start with five questions:
- Is the business problem clearly defined?
- Is there a repeatable workflow that can be mapped?
- Does the workflow have measurable business outcomes?
- Can the required data and systems be accessed under controlled permissions?
- Can higher-risk decisions be routed to a human?
This approach also helps separate a useful production opportunity from an AI experiment. If the baseline cannot be measured, the organization will struggle to demonstrate whether the agent has created business value.
What Changes in the GCC Operating Model?
Agentic AI should be treated as an operating capability, not simply as another software tool. Once AI is connected to business workflows, ownership needs to extend across technology, process, data, security, and business leadership.
- Business ownership: A business leader should own the outcome the AI workflow is intended to improve.
- AI and engineering ownership: Teams need responsibility for design, testing, deployment, monitoring, and improvement.
- Governance: Permissions, approvals, data access, auditability, and escalation paths need to be defined.
- Operations: The workflow needs clear procedures for exceptions, outages, changes, and continuous improvement.
How Should Agentic AI Be Governed?
The more authority an AI system has to interact with enterprise systems, the more important governance becomes. Controls should be proportionate to the risk of the workflow.
- Define what the agent is allowed to access and what actions it can take.
- Use approval points for decisions or actions with material business, financial, security, or people impact.
- Maintain appropriate logs so actions and outcomes can be reviewed.
- Monitor performance and exceptions after deployment, not only during testing.
- Define how an agent is stopped, changed, or escalated when it behaves unexpectedly.
A1 GCC reflects this principle by combining AI capabilities with governance, operational insights, workforce visibility, and coordination rather than positioning AI as a standalone layer.
How Should Enterprises Measure Agentic AI ROI?
The business case should be tied to the workflow being changed. Useful measures can include:
Value Area
Possible Measures
The objective is to establish a baseline before implementation and compare it with post-deployment performance. That makes AI investment easier to evaluate and creates a clearer path from pilot to scale.
What Is Aeries’ Approach to Agentic AI?
Aeries has publicly described two related parts of its AI strategy. A1 GCC is its AI-driven platform for GCC operations, bringing planning, workforce visibility, governance, coordination, and AI-enabled capabilities into a connected environment.
In August 2026, Aeries launched AxAI as an agentic AI offering designed to help enterprises build, deploy, and scale production-ready AI applications. Aeries describes AxAI as covering the enterprise AI lifecycle from identifying high-value opportunities and rapid prototyping through production engineering, enterprise integration, and managed operations.
Together, these public announcements show a clear direction: applying AI to enterprise workflows while connecting engineering, integration, governance, and ongoing operations.
What Should Enterprises Do Next?
- Identify one or two workflows where the business outcome is clear.
- Document the current process and establish a baseline.
- Assess data, system access, security, and governance requirements.
- Build a controlled proof of value with defined human oversight.
- Measure the outcome before expanding to additional workflows.
This creates a practical path from AI experimentation to operational adoption. It also keeps the focus on business value rather than deploying agents simply because the technology is available.
Agentic AI in GCCs: From Experimentation to Operations
Agentic AI has the potential to change how GCCs support enterprise operations, but the value will come from disciplined application rather than autonomy for its own sake. GCCs that combine domain knowledge, technology capabilities, governance, and measurable workflows can create a stronger foundation for scaling AI.
For enterprises, the practical question is where agentic AI can improve a specific workflow, what level of autonomy is appropriate, and how the result will be measured. Those decisions should shape the technology, operating model, and governance together.
Sources
• Aeries Technology, “Aeries Technology Launches A1 GCC, an Agentic AI-Driven Operations Platform for GCCs,” May 18, 2026.
• Aeries Technology, “Aeries Technology (AERT) Launches AI-Driven GCC Framework to Accelerate Digital Innovation for Private Equity Portfolio Companies,” June 23, 2025.
• Aeries Technology, “Aeries Technology Launches AxAI to Accelerate Enterprise Adoption of Agentic AI,” August 4, 2026.
FAQs
Agentic AI in GCCs refers to applying AI agents to defined enterprise workflows, where the agents can support coordination, operational follow-through, decision support, or other approved actions within defined controls.
Generative AI primarily creates content or responses. Agentic AI can be incorporated into workflows that involve multiple steps, system interactions, and actions within defined boundaries.
Potential use cases include finance operations, IT operations, customer operations, HR workflows, and technology and engineering processes. The right use case depends on the workflow, data, risk, and measurable business outcome.
Governance should address data access, system permissions, human approvals, monitoring, auditability, escalation, and controls for changing or stopping an agent.
Measure the workflow before and after deployment using relevant metrics such as cycle time, productivity, quality, cost, exceptions, controls, and experience.
