Contact center AI now helps most with routine, high-volume work such as agent assist, summaries, routing and self-service, but human agents still matter in more complex situations.
Contact center AI is not just an experiment anymore. The real question now is where it actually helps and where it just creates more friction.
For most contact centers, the best place to start is not full automation. It is narrow, repeatable work where AI can save time, reduce busywork and help agents respond more consistently.
Here are some of the most useful ways to use contact center AI today:
Agent assist. AI can listen to or analyze live conversations and surface knowledge articles, CRM history, next-best actions and draft responses. This can help agents move faster and stay more consistent.
Conversation summaries and after-call work. Newer generative AI tools can turn calls and chats into summaries, notes and follow-up suggestions.
Self-service for routine requests. AI can help with basic, high-volume issues that do not always need an agent. It tends to work best when the request is simple and the underlying information is up to date.
Routing and triage. AI can help identify intent, sort incoming requests and move customers to the right queue or workflow faster.
Quality monitoring, coaching and language support. AI can review large numbers of interactions, spot patterns and help supervisors coach agents. It can also help with translation and multilingual support, though that does not remove the need for human review in more sensitive cases.
Human agents still matter most when the customer is angry, the case is unusual or the answer is not straightforward. In those situations, AI should support the interaction, not run it on its own.
If the knowledge is weak or the workflow is messy, AI is not going to fix that.
A lot of this comes down to the basics. If the knowledge is weak or the workflow is messy, AI won't fix it. In some cases, it can make the problem worse by giving fast but unhelpful answers. That is one reason teams still need guardrails around accuracy, escalation and sensitive customer data.
It usually makes more sense to start with one or two narrow use cases and see what improves before expanding. Early signs and metrics to watch include handle time, after-call work, containment on routine issues, agent adoption and customer satisfaction.
Editor's note:This article was originally published in 2019 and updated in April 2026 to reflect current contact center AI use cases, rollout approaches and customer service priorities.
James Alan Miller is a veteran technology editor and writer who leads Informa TechTarget's Enterprise Software group. He oversees coverage of ERP & Supply Chain, HR Software, Customer Experience, Communications & Collaboration and End-User Computing topics.
Scott Sachs is president and founder of SJS Solutions, a consultancy that specializes in contact center strategy assessments and technology selection.
Customer-centric contact center processes need better journey visibility, stronger feedback loops and solutions that extend beyond the service team.
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