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Published On: 6th Aug 2026 11:00
Phyllis Fang at Level AI covers practical call centre use cases, explains how agentic AI differs from traditional AI, explores how AI agents are deployed across industries, and includes real world examples.
The most practical AI agents in call centres were built to handle one task at a time. A bot answers FAQs. A separate tool logs tickets. A QA team manually reviews a fraction of calls.
No single tool is the failure point. The failure is that none of them connect, so agents burn time on hand-offs and managers make decisions from an incomplete picture.
An IBM study of over 3,500 senior executives found that 92% of leaders expect agentic AI to deliver measurable ROI. contact centres are one of the clearest places to see this impact.
AI agents can interpret customer intent, connect multiple decisions, and take action automatically. For example, they can process refunds, update account details, and pull records from connected systems without waiting for human sign off at every step.
However, these systems are designed to work alongside customer support reps, not replace them. Every deployment should also include a clear escalation path to a human agent for judgment calls, sensitive conversations, or situations that fall outside the defined scope.
Most AI tools in contact centers fall into two categories: rule-based chatbots that follow decision trees, and generative AI tools or virtual agents that produce responses but leave a human to decide what to do with them.
Both stop short of taking action, which means a rep still has to read the output, switch to another system, and complete the request manually.
Agentic AI closes that gap by combining intent detection, multi step planning, and direct execution within connected tools.
A single customer request, say a billing dispute tied to a recent order, can require pulling data from a CRM, checking a payment system, and updating a ticket, and an agentic AI handles that whole chain without waiting for human sign-off at each step.
AI agents are being deployed at specific points in the contact center workflow where volume is high, tasks are repeatable, and speed matters.
AI agents handle inbound voice and chat by identifying what a customer needs, pulling relevant data from connected systems CRM, order management, knowledge bases and resolving the request without transferring to a live rep.
Common examples include order status checks, appointment scheduling, returns processing, product questions, account verification, and billing updates.
This reduces queue wait times on high volume, routine interactions, though it requires accurate speech recognition and well-defined handoff protocols for cases the agent cannot resolve.
Some AI agents do not interact with customers at all. They work in the background during live conversations, identifying intent and emotional cues, searching internal systems, and surfacing relevant knowledge articles, policy information, and suggested responses directly to the rep.
This improves handle time and first call resolution without the customer knowing AI is involved. The rep stays in control of the conversation while the AI handles the information retrieval that would otherwise mean putting the customer on hold.
AI agents evaluate every customer interaction against quality standards, identify coaching opportunities, and flag compliance violations as they occur.
Traditional QA processes review only 1-2% of calls. AI scoring closes that gap by covering 100% of interactions, though it requires custom scorecards built around actual business standards rather than generic criteria.
AI agents infer customer satisfaction by analyzing tone, word choice, and conversation patterns across every interaction without waiting for a customer to complete a post call survey.
This works when models are trained on contact center conversation data rather than general sentiment data, which tends to miss domain specific language and context.
During implementation, results should be validated against actual survey responses to confirm that the model is calibrated correctly.
After a call ends, AI agents generate summaries, populate CRM fields, create follow-up tasks, and log interaction outcomes by removing the manual data entry that extends handle time and delays reporting.
This requires the model to understand company specific terminology and the structure of the CRM schema it is writing to; otherwise, outputs need significant correction before they are usable.
AI agents are being deployed in industries where customer interactions are high-volume, time-sensitive, and tied to backend systems that a human rep would otherwise have to navigate manually.
The examples below cover those industries, with the specific tasks AI agents handle in each one.
Retail contact centers deal with two distinct problem types: pre-purchase questions and post-purchase issues.
An AI agent can handle both without transferring the customer, pulling product data, order status, and return policy details from connected systems in the same interaction.
Healthcare organizations use AI agents to improve patient experiences, take administrative workload off front-desk and billing staff.
The agents handle the repeatable, rulesdriven tasks that consume time without requiring clinical judgment, and escalate when the situation calls for a human.
Financial services firms operate under strict compliance requirements and high interaction volumes, two conditions that make manual oversight impractical at scale.
AI agents address both by monitoring transactions and conversations continuously rather than in periodic spot checks.
Contact centers generate large volumes of repetitive work that sits outside the conversation itself, documentation, scoring, and coaching. AI agents handle that work automatically, which reduces handle time and gives managers better data to act on.
Telecom providers handle some of the highest contact volumes of any industry, with a large share of those contacts covering billing disputes, service outages, and plan changes.
Most of those requests follow predictable patterns, which makes them well-suited for AI agents that can pull account data, check network status, and make changes without routing to a live rep.
A. Unlike rule based chatbots in contact centers that follow fixed scripts, AI agents understand customer intent, maintain context, and take action across backend systems without handing off to a human.
A. AI won’t fully replace human call center agents but will automate repetitive tasks like FAQs and call routing.
Human agents are still needed for complex, emotional, and high-value interactions.Most call centers will adopt a hybrid model where AI supports agents, not replaces them.
A. By accurately understanding customer intent, accessing data across systems, and taking action in real time, agentic AI minimizes follow-ups and escalations. It can resolve requests like order updates, refunds, or scheduling within a single interaction.
A. Agentic AI delivers ROI by reducing support costs, increasing automation rates, and improving agent productivity. It also boosts customer satisfaction by resolving queries faster and reducing repeat contacts.
Reviewed by: Robyn Coppell
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