{"id":1934,"date":"2026-04-21T20:08:16","date_gmt":"2026-04-21T14:38:16","guid":{"rendered":"https:\/\/blogs.infosys.com\/engineering-services\/?p=1934"},"modified":"2026-04-21T20:08:16","modified_gmt":"2026-04-21T14:38:16","slug":"ai-is-live-in-your-contact-center-but-do-you-know-when-its-failing","status":"publish","type":"post","link":"https:\/\/blogs.infosys.com\/engineering-services\/contact-center\/ai-is-live-in-your-contact-center-but-do-you-know-when-its-failing.html","title":{"rendered":"AI Is Live in Your Contact Center. But Do You Know When It\u2019s Failing?"},"content":{"rendered":"<p>When AI hit the mainstream, the contact center was one of the first industries to embrace it. Articles, keynotes, conference sessions, and videos all carried the same message: AI would redefine customer service. At the time, deploying AI into the contact center was seen as an achievement. AI Bots were launched. agent copilots are enabled, natural\u2011language self\u2011service is handling real customer traffic. Dashboards showed improving metrics like more chats handled by AI bots, fewer agent calls, shorter handling times. It felt like progress.<\/p>\n<p>Today, many organizations are realizing a harder truth: going live was only the beginning. AI is now part of real customer conversations.\u00a0 And when AI doesn\u2019t behave as expected, it shows up immediately not in dashboards, but in the customer\u2019s experience. Therefore, the real question is no longer \u201c<strong>Do we have AI in the contact center?<\/strong>&#8221; but \u201c<strong>Do we actually know how AI is behaving once customers are interacting with it.?<\/strong>\u201d<\/p>\n<h2><strong>The First Phase: Deploy Fast, Automate More<\/strong><\/h2>\n<p>The initial wave of contact\u2011center AI was driven by urgency. Enterprises rushed to deploy chatbots, automate call flows, and reduce agent workload. Success was measured by tangible milestones:<\/p>\n<ul>\n<li>\u00a0AI chatbots going live<\/li>\n<li>Call Containment rates where customer issues are fully resolved within an AI automated system, without needing to speak to a human agent<\/li>\n<li>Average Handle Time (AHT) reductions<\/li>\n<li>Cost savings from call deflection, where calls are redirected to self\u2011service options instead of live agents, reducing call volume and agent demand.<\/li>\n<\/ul>\n<p>This phase delivered real value in contact\u2011center operations, but it also exposed gaps in the customer experience. For example, an AI chatbot could contain an interaction yet still leave the customer frustrated. An IVR might route calls efficiently but still force customers to call back. An agent copilot could retrieve information quickly but at the wrong moment.<\/p>\n<p>For customers, the technology used behind the scenes doesn\u2019t matter. What matters is whether their issue is resolved<\/p>\n<h2><strong>The Reality Check of AI: What Happens in Live Customer Interactions<\/strong><\/h2>\n<p>What changes everything is the first visible failure a confidently incorrect response, a loop a customer can\u2019t escape where the AI keeps repeating the same question or response and forces the customer to disconnect the call, or an AI system that waits too long to hand the interaction over to a human agent. These are not edge cases; they are production realities.<\/p>\n<p><strong>When that happens, customers don\u2019t blame the AI. They blame the brand.<\/strong><\/p>\n<p>This is the point where many contact centers realize they lack basic operational answers:<\/p>\n<ul>\n<li>How do we know when AI is underperforming?<\/li>\n<li>Which customers are most affected?<\/li>\n<li>Under what conditions does failure spike?<\/li>\n<li>Who owns the decision to intervene?<\/li>\n<\/ul>\n<p>At this stage, AI problems are no longer technical. They are customer experience problems.<\/p>\n<h2><strong>The Second Phase: Taking Ownership of AI in the Contact Center<\/strong><\/h2>\n<p>The contact\u2011center industry is now entering a second phase one defined not by how much interaction AI handles, but by how well it is controlled in real customer interactions. This is where below three capabilities become important:<\/p>\n<ol>\n<li><strong>Observability<\/strong>: Contact centers need to clearly see how AI behaves in real customer conversations not just how fast or accurate it is. What matters is whether AI actually solves the customer\u2019s problem, reduces customer effort, and prevents customers from calling again.\u00a0 If you can\u2019t see how AI behaves across different customer scenarios, you can\u2019t manage it.<\/li>\n<li><strong>Continuous Testing<\/strong>: AI can\u2019t be tested once and left alone. Real customers introduce new scenarios every day. Testing has to continue after launch, using real interactions, not just lab examples.<\/li>\n<li><strong>Governance with Human Oversight<\/strong>: AI needs clear boundaries. It must know when to hand off, when to pause, and when a human should take over. Governance isn\u2019t about slowing things down it\u2019s about knowing when intervention is needed.<\/li>\n<\/ol>\n<p>Without these, AI feels like a black box. And black boxes don\u2019t build trust.<\/p>\n<h2><strong>Why \u201cProduction\u2011Ready\u201d Now Means \u201cOutcome\u2011Ready\u201d<\/strong><\/h2>\n<p>For years, \u201cproduction\u2011ready\u201d meant stability and performance. In AI\u2011driven contact centers, that definition is taking a new form. Today, production\u2011ready means:<\/p>\n<ul>\n<li>Does the AI actually resolve customer needs?<\/li>\n<li>Does it reduce failure demand?<\/li>\n<li>Does it improve journey completion?<\/li>\n<li>Does it know when to step aside?<\/li>\n<\/ul>\n<p>Metrics like call containment and Average Handle Time (AHT) reductions still matter but most meaningful indicators are now resolution rate, repeat contacts which highlights unresolved issues that force customers to reach out again, customer effort, and AI\u2011specific CSAT. These metrics map AI performance directly to customer trust.<\/p>\n<h2><strong>Who Owns AI When It Goes Wrong?<\/strong><\/h2>\n<p>One of the biggest challenges enterprises faces is organizational, not technical. Accountability for AI in production often falls between teams:<\/p>\n<ul>\n<li>IT owns platforms<\/li>\n<li>Digital teams own AI initiatives<\/li>\n<li>Operations own service levels<\/li>\n<li>Contact centers own outcomes<\/li>\n<\/ul>\n<p>When something breaks, it\u2019s often unclear who owns the problem. Customers, however, experience the system as one interaction not a series of handoffs between teams. What contact\u2011center operations now need is a shared definition of ownership, where AI performance is governed continuously and someone clearly owns the customer outcome.<\/p>\n<h2><strong>The Next Phase of Contact Center AI<\/strong><\/h2>\n<p>The future of contact\u2011center AI isn\u2019t about replacing humans or automating everything. It\u2019s about running AI responsibly in live environments, with the same discipline we apply to any mission\u2011critical system.<\/p>\n<p>That means:<\/p>\n<ul>\n<li>Treating AI as part of operations, not a one\u2011time project<\/li>\n<li>Designing for visibility and control from day one<\/li>\n<li>Measuring success through customer outcomes, not just automation rates<\/li>\n<\/ul>\n<h2><strong>Closing Thoughts<\/strong><\/h2>\n<p>AI has already entered the contact center. The question isn\u2019t whether to deploy it anymore. The real question is whether organizations are prepared to operate, govern, and own AI when real customers are on the line. Because in the end, customers don\u2019t experience your AI strategy. They experience one interaction and that interaction defines your brand.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>When AI hit the mainstream, the contact center was one of the first industries [&hellip;]<\/p>\n","protected":false},"author":759,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"categories":[99],"tags":[],"coauthors":[96],"class_list":["post-1934","post","type-post","status-publish","format-standard","hentry","category-contact-center"],"acf":[],"_links":{"self":[{"href":"https:\/\/blogs.infosys.com\/engineering-services\/wp-json\/wp\/v2\/posts\/1934","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/blogs.infosys.com\/engineering-services\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blogs.infosys.com\/engineering-services\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blogs.infosys.com\/engineering-services\/wp-json\/wp\/v2\/users\/759"}],"replies":[{"embeddable":true,"href":"https:\/\/blogs.infosys.com\/engineering-services\/wp-json\/wp\/v2\/comments?post=1934"}],"version-history":[{"count":10,"href":"https:\/\/blogs.infosys.com\/engineering-services\/wp-json\/wp\/v2\/posts\/1934\/revisions"}],"predecessor-version":[{"id":1981,"href":"https:\/\/blogs.infosys.com\/engineering-services\/wp-json\/wp\/v2\/posts\/1934\/revisions\/1981"}],"wp:attachment":[{"href":"https:\/\/blogs.infosys.com\/engineering-services\/wp-json\/wp\/v2\/media?parent=1934"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blogs.infosys.com\/engineering-services\/wp-json\/wp\/v2\/categories?post=1934"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blogs.infosys.com\/engineering-services\/wp-json\/wp\/v2\/tags?post=1934"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/blogs.infosys.com\/engineering-services\/wp-json\/wp\/v2\/coauthors?post=1934"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}