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Over the past decade, businesses have automated tasks, introduced bots, and digitized workflows. But even with all that, most operations still rely heavily on people manually executing processes. That model is reaching its limits.
Enter Agentic Operations, a new way of working where AI agents take the lead, and humans step into higher-value roles as supervisors, exception solvers, decision-makers, and improvers. It's not automation-as-usual but a fundamental redesign of how work gets done.
Why the old operating model can't deliver on AI's promise
Most legacy operations scale only one way: by adding more people. But it's not a scalable model. It's slow, expensive, inconsistent, and too dependent on manual effort.
And now there's an even bigger issue: AI is everywhere, but it's not delivering its full value. Enterprises are investing heavily, yet only 35% say their AI applications are actually very effective at delivering measurable business outcomes. That gap exists because AI is being plugged into operating models that were never designed to supervise it, train it, or control it in real time. Organizations today need speed, scalability, and resilience. But traditional ops models can't do any of this. That's why the operating model itself must evolve.
What are Agentic Operations?
Think of agentic operations as a hybrid workforce model where AI agents do the bulk of transactional work, while humans focus on:
Reviewing and validating AI outputs
Handling exceptions and judgment-based scenarios
Improving the AI (through prompts, training data, feedback loops)
Governing risk, compliance, and ethics
In this model, humans aren't replaced – they're elevated.
They move from repetitive tasks to the kind of work people are uniquely good at: solving problems, improving systems, managing risk, and innovating.
From doing the work to driving the value
In traditional operations, people are the process. In Agentic Operations, AI agents execute, and humans supervise, guide, and continuously improve the system.
That shift doesn't reduce human importance – it elevates it. People move from repetitive task execution to roles that require judgment, coaching, exception handling, and system tuning.
The evolution enables:
Faster cycle times, because AI handles repetitive, high-volume work instantly
Higher accuracy through real-time human oversight and continuous tuning
Lower cost by scaling AI capacity instead of hiring more head count
Better compliance and auditability with humans reviewing exceptions, validating AI outputs, and maintaining a complete audit trail
Sustained value realization because humans actively improve the AI, not just watch it
And maybe the most important change: it unlocks a cultural shift – from task-following to continuous improvement. AI drives the execution, and humans drive the value.
Introducing AORA: The system that makes agentic ops real
Transforming an entire operating model isn't something organizations can do overnight. The first step is deploying agentic solutions themselves: domain-tuned AI agents, last-mile decision logic, and the data foundations needed to generate real value.
Genpact's maturity model helps leaders understand where they are on the agentic journey. It provides a quick view of readiness across data, AI adoption, governance, and skills – so organizations can take the right steps to scale.
Once those agentic solutions are live, the Genpact Agentic Operations Readiness Accelerator (AORA) provides the playbook to scale them. AORA is built around four key elements:
1. Role evolution: Redesigning the human side of work
Agentic Operations change the role of humans form executing work to ensuring outcomes. As AI agents handle high-volume transactions, people focus on orchestration, innovation, improvement, and governance – where judgment and accountability matter most.
This shift enables scale without loss of control. Human leadership keeps AI aligned to business priorities and value targets. Ongoing refinement of prompts and workflows allow performance to improve as conditions change, and embedded ethical oversight ensures compliance and trust are built into daily operations.
The result is a more resilient model where AI delivers speed and consistency, and humans ensure accuracy, and sustained value over time.
2. Operating model reinvention: Pods, not pyramids
AORA replaces traditional hierarchical teams with integrated pods, supported by a modern governance layer designed for Agentic Operations:
Processing: AI executes tasks; humans validate outputs, handle exceptions, and ensure safe decision-making
Value realization: Continuously improves AI performance by tuning prompts, retraining models, refining workflows, and resolving root cause issues
Support: Keeps the AI tech stack healthy through integrated L1/L2/L3 support, ensuring stability and rapid issue resolution
Together, the pods create a closed-loop system that gets smarter and more effective over time.
3. Capability building: Upskilling the workforce for the new model
Shifting to an Agentic Operations model isn't easy. It requires significant investment in upskilling the workforce. Employees need to acquire new competencies and a new mindset for collaborating with AI.
The Genpact Pathfinder Academy is a tiered learning program that trains employees to thrive in Agentic Operations. It starts with shifting the foundational mindset to "human + AI co-intelligence," then moves to multilevel certification paths.
Pathfinder Academy doesn't just build skills – it also smooths the transition to Agentic Operations giving employees a clear growth path, hands-on practice, and confidence in their evolving roles, The result is a workforce that is supported, energized, and ready to step into higher-value roles to accelerate the business.
4. Responsible AI governance: Safety built into the system
AI brings incredible potential but also needs strong oversight. That's why, Responsible AI (RAI) sits at the heart of the Agentic Operations model. RAI is a delivery capability, not a compliance requirement. The real difference is being able to see everything at the transaction level – the ability to demonstrate not just the outcomes are right, but every decision is explainable, traceable and defensible.
In an agentic world, where AI systems act across processes, not just assist, this matters more than ever. Safeguards that are bolted on after the fact cannot keep pace with agents that act at machine speed. By building RAI principles directly into the architecture and control layers of every agentic solution, safeguards become built-in rather than an audit exercise applied after the fact.
AORA embeds responsible AI through a model with three lines of defense:
First line – Operational Control: AI agents and delivery teams embed controls within the solution architecture to ensure security, traceability, robustness, and responsible output generation, while owning day-to-day execution and evidence creation
Second line – RAI Governance and Oversight: A centralized function sets standards, monitors compliance across deployments, arbitrates risk decisions, and maintains the enterprise RAI standard
Third line – Independent Audit: External assurance validates that the governance model is effective; and that controls and evidence meet regulatory and client obligations, including EU AI Act, Sarbanes-Oxley Act of 2002 (SOX), National Institute of Standards and Technology AI Risk Management Framework (NIST AI RMF), and the Open Worldwide Application Security Project (OWASP) Top 10 for LLMs
This three lines ensure that trust is not claimed, but demonstrated at every transaction, across every deployment. The result is confidence in the quality of decisions, the integrity of the outputs and the value in the results.
What this looks like in the real world
In a Fortune 500 accounts payable (AP) transformation, Agentic Operations went beyond automating tasks to rebuild the entire way AP worked. The client had nine ERP systems, low optical character recognition (OCR) accuracy (about 40%), fragmented workflows, high exception rates, and slow cycle times.
The results speak for themselves:
Touchless processing increased dramatically from 7% to 65% or more
Auto-posting to ERP reached up to 97%, enabled by AI agents performing end-to-end posting with human oversight only for exceptions
P&L impact delivered more than $5 million in savings through productivity, improved match rates, and error reduction
Working capital improved by more than $250 million, enabled by faster cycle times and more captured discounts
Duplicate payments prevented (more than $400 million) by using agents to detect and block high-risk invoices before processing
This is what Agentic Operations look like in action: a process that becomes faster, smarter, and more resilient – unlocking levels of accuracy, control, and business value that simply weren't possible before.
The bottom line
Agentic Operations represent the next evolution of business process delivery – not incremental change, but a step-change transformation.
The future of operations isn't a choice between all human or all AI. It's the hybrid model where both do what they do best – together.
To help leaders and practitioners go deeper, look out for our blog series that unpacks each pillar of the AORA model in detail – from role evolution to pod design to responsible AI governance – offering practical guidance for organizations ready to take the next step.
