AI call center coaching can help a new agent find a sound next step, but an experienced agent may need room to use judgment. If a manager measures only how quickly a conversation ends, the team can miss whether the customer’s issue was solved or the agent learned anything useful.
A 2025 study followed 5,172 customer-support agents at one large software company after the company introduced an AI assistant that suggested responses during customer chats. The researchers reported an average 15% increase in issues resolved per hour, but the effects differed by experience. Less experienced agents improved in speed and quality. The most experienced agents saw small speed gains and small quality declines. The study offers a reason to test coaching by task and experience. It does not predict results for every contact center, especially voice operations.
The study, “Generative AI at Work,” examined one firm and chat-based customer support. Agents remained responsible for replies and could edit or ignore suggestions. Keep those boundaries in mind before using the findings to plan AI call center coaching.
Define what the assistant is meant to teach
Start with one recurring request and a specific learning goal. An assistant might surface an approved policy, suggest a question to clarify the problem or offer a draft response. Those tasks differ from letting software send a reply or make a decision without agent review.
A good AI call center coaching pilot asks whether suggestions build sound habits, not whether agents accept them automatically. Write down what a useful suggestion looks like and when the agent should disregard it. For example, a billing question may need a policy reference, while a complaint about an unusual charge may require a person to review the account. Make the route to a supervisor clear when the guidance is incomplete or the customer’s situation falls outside the rule.
Because the published study used chat, a phone team should treat it as a hypothesis, not proof. Speech introduces pauses, interruptions, accents and the need to listen while talking. Test those conditions directly before assuming that chat results transfer to calls.
Use AI call center coaching to support judgment
A practical AI call center coaching plan begins with one queue or request type. Choose a small queue or request type for the first trial. Tell agents which records the assistant can use, what it cannot decide and how to flag a wrong or confusing suggestion. Keep a qualified employee accountable for decisions involving money, access, safety, complaints or sensitive information.
Review results by task and experience without turning the comparison into a ranking of employees. Ask whether newer staff can locate approved guidance sooner, whether experienced staff spend less time on routine cases, and whether either group has to correct more errors. Protect staff privacy and explain how you will use the review data.
Here is an illustrative example, not a client result. A service team tests suggested answers for a common delivery question. New agents find the right policy faster, but experienced agents report that the prompts interrupt unusual cases. The manager might narrow the prompts to routine requests and keep exceptions with a human. The test should record both the faster lookup and the disruption.
Measure learning and customer outcomes together
A useful AI call center coaching review pairs learning with customer outcomes. Before the trial, define a small set of measures that match the service. These might include time to handle a request, whether the issue was resolved, repeat contact, escalation, correction time and customer feedback. Set the definitions first so the team does not change the meaning of “resolved” after seeing the results.
For AI call center coaching, add a learning check. Can a new agent explain the policy after the assistant’s suggestion disappears? Do agents recognize when a suggestion does not fit? Does the team’s knowledge material need a correction? These checks help distinguish a useful learning aid from a faster way to copy a response.
Keep AI call center coaching focused on service learning, not speed alone. Do not treat a faster first reply as a better service result if customers must contact the company again. Compare similar request types and shifts where possible. Note other changes, such as staffing or a policy update, that may affect the comparison. If the sample is small, describe the uncertainty instead of presenting a precise effect as proven.
The International Labour Organization’s 2026 review of empirical evidence reports uneven productivity findings across methods and countries. It says worker-reported time savings have not always appeared as higher measured output, earnings or employment. That broader review is not a call-center benchmark. It is a reminder to check whether saved time improves the whole service, rather than one step.
Set a review date and a stop rule
For AI call center coaching, ask an experienced agent and a supervisor to examine a consistent sample each week. Record useful suggestions, errors, missing context and moments when an employee had to repair the interaction. Remove customer identifiers from coaching notes unless the approved process requires them. Assign an owner to update the approved guidance when the same problem appears more than once.
Before expanding AI call center coaching, agree on a review date and what would make you expand, revise or stop. Keep the trial only if customer outcomes hold, employees understand the limits and the added review work is manageable. Revise it if prompts interrupt complex cases or guidance is out of date. Stop if the team cannot maintain safe human review or if the tool uses information outside the approved process.
The decision involves work design as much as software. DBG360 can help map the service steps, clarify who owns review and align training, processes and systems. Visit our contact page to book a discovery call and discuss a measured pilot.
