Unlocking the Potential of Agentic AI in Service Operations

Introduction

Imagine two financial institutions. The first manages its loan origination process through a patchwork of task-level automation and predictive models. It uses historical credit scores, rigid underwriting rules, and batch processing to move applications through a series of sequential steps. While some parts of the process have been digitized, many decisions still require human intervention—whether due to exception handling, regulatory compliance checks, or risk flagging. This results in slower decision-making, inconsistent applicant experiences, and rising operational costs as application volumes climb.

Now, consider the second institution. Here, the loan origination journey is orchestrated by a network of agentic AI systems—autonomous, reasoning agents capable of executing and adapting entire workflows end to end. They don’t just execute predefined workflows, as traditional AI could, or transform unstructured data into new insights or media, as gen AI can. Instead, these agents ingest real-time data across dozens of sources, from macroeconomic indicators and applicant digital behavior to regulatory changes and even sentiment analysis, all to make complex decisions. They not only assess creditworthiness but also adjust pricing, recommend optimal product bundles, and proactively flag anomalies for human review.

Agentic AI: The potential and price of entry

Agentic AI marks a sharp departure from traditional systems built on deterministic, rule-based architectures. In the past, enterprise decision-making relied on hard-coded logic and static workflows—think customer service scripts, underwriting checklists, or supply chain triggers. While useful in predictable environments, these approaches fall short when facing today’s dynamic, high-volume, and context-rich realities.

The potential of agentic AI lies in these systems’ ability to fundamentally reshape how organizations operate. They can unlock exponential gains in speed, scale, and precision, enabling companies to reduce decision latency, eliminate handoffs, and continuously improve outcomes. Imagine underwriting decisions that can be generated in seconds, compliance reporting that updates itself in real time, or customer experiences that feel human—at machine speed and cost. In short: higher productivity, better decisions, and a more adaptive enterprise.

AI agents as corporate citizens—who need management

To fully realize the value of agentic AI, organizations should focus less on treating these systems as experimental tools and more on managing them like they manage people. In this future-ready enterprise, AI agents become corporate citizens: accountable, governed, and expected to deliver measurable value. That means rethinking how they are funded, evaluated, and integrated. Just like human employees, AI agents require the following infrastructure:

• A full cost structure
• Defined objectives
• Performance management
• Governance and oversight
• Cross-functional enablement

Rethinking decision-making with ‘smart ops’

To unlock the full potential of AI in service operations, organizations need to rearchitect how decisions are made and how work is done—by building a “smart ops” structure where humans and AI agents operate in coordinated, complementary roles.

Redesigning processes: Not what to automate, but which decisions

While agentic AI offers potential across nearly any function, service operations remain the sharpest proving ground. These environments are rich with high-volume, repetitive tasks and data trapped in silos, making them ideal for intelligent automation. But the question is no longer what companies can automate. It’s which decisions they should automate—and where human judgment still matters.

That’s where a decision-making framework based on risk and complexity becomes essential. Rather than chasing automation for automation’s sake, organizations should classify decisions based on their inherent risk and the degree of judgment required. Low-risk, low-complexity decisions, such as verifying account details or checking claim status, are prime for full automation. High-risk, high-judgment scenarios, such as fraud resolution or complex policy exceptions, may still require human oversight, supported by AI copilots.

Getting started

As AI agents take on more operational tasks, organizations need to rethink how work is designed, how people are supported, and how value is measured. Deploying agentic AI at scale isn’t just a technical shift—it’s an organizational one. To get there, companies can start by making the following moves:

• Bridge the tech–business gap—with leadership accountability
• Redesign roles and invest in reskilling
• Elevate culture and change management
• Strengthen data and architecture foundations

FAQ

What is agentic AI?

Agentic AI refers to autonomous, reasoning agents capable of executing and adapting entire workflows end to end. They can make complex decisions, adjust pricing, recommend optimal product bundles, and proactively flag anomalies for human review.

How can organizations leverage agentic AI?

Organizations can leverage agentic AI by treating AI agents as corporate citizens, investing in infrastructure, defining objectives, measuring performance, implementing governance and oversight, and enabling cross-functional collaboration.

Conclusion

Unlocking the potential of agentic AI in service operations requires a strategic approach that focuses on decision-making, role redesign, and organizational alignment. By treating AI agents as corporate citizens, organizations can harness the power of AI to improve efficiency, accuracy, and customer experience. The future belongs to those who can make the smartest decisions about how AI and humans work together in a coordinated and complementary manner.