{"id":3568,"date":"2026-09-11T05:22:44","date_gmt":"2026-09-11T05:22:44","guid":{"rendered":"https:\/\/www.plura.ai\/articles\/contact-center-ai-performance-metrics"},"modified":"2026-09-11T05:23:12","modified_gmt":"2026-09-11T05:23:12","slug":"contact-center-ai-performance-metrics","status":"publish","type":"post","link":"https:\/\/www.plura.ai\/articles\/contact-center-ai-performance-metrics","title":{"rendered":"AI Contact Center Metrics: KPIs That Survive Scrutiny"},"content":{"rendered":"<p><em>Written by: Matt Beucler, CEO, Plura AI<\/em><\/p>\n<h2 id=\"key-takeaways\">Key Takeaways<\/h2>\n<ul>\n<li>Contact center AI performance metrics must separate containment from true resolution. Containment without resolution becomes a vanity metric that fails executive scrutiny.<\/li>\n<li>The AI performance hierarchy spans seven layers: interaction, understanding, correct answer, resolution, satisfaction, repeat contact, and cost per resolution.<\/li>\n<li>Legacy KPIs like AHT, CSAT, and FCR need segmented, outcome-based versions that distinguish AI-only, AI-assisted, escalated, and human-only interactions.<\/li>\n<li>Re-contact rate acts as the critical check on false containment, and cost per resolution rewards completed outcomes.<\/li>\n<li>Plura AI unifies voice, SMS, RCS, and <a href=\"https:\/\/www.plura.ai\/plura-webchat\" target=\"_blank\">AI webchat<\/a> on a single Stateful Conversation Database so every layer of the performance hierarchy becomes measurable and defensible from one platform.<\/li>\n<\/ul>\n<h2>The AI Performance Hierarchy: From Interaction To Cost Per Resolution<\/h2>\n<p>The seven-layer AI performance hierarchy tracks the journey from interaction initiated through intent understood, question answered correctly, issue resolved, customer satisfied, no repeat contact, and lower cost per resolution. Each layer has its own metrics and failure modes. A dashboard that scores well at the top of the hierarchy and poorly at the bottom flatters the deployment instead of describing it.<\/p>\n<p>Most vendor dashboards stop at the interaction and understanding layers. They report containment, handle time, and intent recognition without tying those signals to resolution, satisfaction, or repeat-contact behavior. <a href=\"https:\/\/zendesk.com\/blog\/ai\/workflow-automation\/ai-service-quality-metrics\" target=\"_blank\" rel=\"noindex nofollow\">Zendesk notes that legacy KPIs like deflection, containment, and average handle time were built for a world where humans handled one conversation at a time and do not translate cleanly to measuring AI in customer service.<\/a><sup data-disclaimer-id=\"25\" data-disclaimer-index=\"4\">4<\/sup><\/p>\n<p>Plura\u2019s Stateful Conversation Database runs voice, SMS, RCS, and AI webchat on a single data layer. Leaders can measure every layer of this hierarchy from one platform instead of stitching reports together from multiple point tools.<\/p>\n<figure style=\"text-align: center\"><img decoding=\"async\" src=\"https:\/\/cdn.aigrowthmarketer.co\/1779338680098-bf2bbd201647.png\" alt=\"Plura Unified Inbox interface showing centralized AI Voice, SMS, RCS, and Webchat conversations in one omnichannel workspace.\" style=\"max-height: 500px\" loading=\"lazy\"><figcaption><em>Plura Unified Inbox centralizes AI Voice, SMS, RCS, and Webchat conversations into one streamlined omnichannel communication workspace.<\/em><\/figcaption><\/figure>\n<p><a href=\"https:\/\/plura.ai\/calculator\" target=\"_blank\"><strong>Run your numbers through Plura\u2019s ROI calculator to check your cost per resolution in real time.<\/strong><\/a><\/p>\n<p><sup data-disclaimer-id=\"24\" data-disclaimer-index=\"3\">3<\/sup> <\/p>\n<h2>AI Containment Rate vs. True Resolution Rate: The Extractable Comparison<\/h2>\n<p>Containment rate and true resolution rate often get blended together in reporting. They measure different outcomes and produce different numbers on the same deployment. The table below shows how each metric is defined, where it can be inflated, and which sources support the definition so you can see where the numbers diverge.<\/p>\n<table>\n<thead>\n<tr>\n<th>Metric<\/th>\n<th>Formula<\/th>\n<th>Trap<\/th>\n<th>Source<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Containment rate<\/td>\n<td>AI-contained interactions \u00f7 AI interactions, where the denominator (engaged, total, or in-scope interactions) varies by definition and materially changes the result<\/td>\n<td>Counts absence of transfer, not resolution<\/td>\n<td><a href=\"https:\/\/inkeep.com\/glossary\/chatbot-containment-rate\" target=\"_blank\" rel=\"noindex nofollow\">Inkeep<\/a><\/td>\n<\/tr>\n<tr>\n<td>True resolution rate<\/td>\n<td>The percentage of customer issues fully and permanently resolved, calculated as issues resolved without repeat contact divided by total issues or interactions handled, where resolution is confirmed by tracking whether the customer contacts the organization again about the same issue within a defined window (commonly 7\u201314 days)<\/td>\n<td>Requires defined re-contact window<\/td>\n<td>NiCE<\/td>\n<\/tr>\n<tr>\n<td>Re-contact rate<\/td>\n<td>Case-based Repeat Contact Rate = Index cases followed by a repeat within the window \u00f7 Total eligible cases (distinct from the customer-based formula, which divides customers who re-contacted by total customers who contacted)<\/td>\n<td>Window definition changes the number<\/td>\n<td><a href=\"https:\/\/umbrex.com\/resources\/company-analysis\/customer-service-support\/repeat-contact-rate\/\" target=\"_blank\" rel=\"noindex nofollow\">Umbrex<\/a><\/td>\n<\/tr>\n<tr>\n<td>Automated resolution rate<\/td>\n<td><a href=\"https:\/\/zendesk.com\/blog\/ai\/workflow-automation\/automated-resolution-rate\" target=\"_blank\" rel=\"noindex nofollow\">Issues fully resolved by AI \u00f7 total issues handled by AI<\/a><\/td>\n<td>Excludes abandoned, partial, escalated<\/td>\n<td><a href=\"https:\/\/zendesk.com\/blog\/ai\/workflow-automation\/automated-resolution-rate\" target=\"_blank\" rel=\"noindex nofollow\">Zendesk<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>A contained interaction is one that ends without transfer to a human agent. <a href=\"https:\/\/usefini.com\/glossary\/what-is-resolution-rate\" target=\"_blank\" rel=\"noindex nofollow\">A resolved interaction is one where the customer\u2019s issue was addressed completely with no repeat contact within a defined window.<\/a> <a href=\"https:\/\/lewiscrook.com\/glossary\/containment-rate\" target=\"_blank\" rel=\"noindex nofollow\">A call contained today that returns tomorrow for the same intent has not been resolved, and defensible measurement subtracts 7-day re-contact for the same intent from the numerator.<\/a><\/p>\n<p><a href=\"https:\/\/lewiscrook.com\/glossary\/containment-rate\" target=\"_blank\" rel=\"noindex nofollow\">A contact center taking 100,000 calls a month where the voice AI engages on 60,000 and 30,000 finish without escalation produces three different numbers.<\/a> The vendor quotes 50% containment (30,000 \u00f7 60,000 engaged). Operations measures 30% (30,000 \u00f7 100,000 total). Finance reports 24% resolved after subtracting the 6,000 callers who rang back within 7 days about the same issue. Three numbers, one deployment. Re-contact rate is the check on false containment.<\/p>\n<p><a href=\"https:\/\/lewiscrook.com\/glossary\/containment-rate\" target=\"_blank\" rel=\"noindex nofollow\">Defensible containment targets vary by intent complexity: transactional intents 60-80%, mixed service intents 35-55%, complex intents 10-30%, blended enterprise call mix 25-45%.<\/a> Figures materially above those bands deserve scrutiny of the denominator and measurement method.<\/p>\n<h2>Intent Recognition Accuracy and Knowledge Coverage: The Understanding Layer<\/h2>\n<p>Containment and resolution describe what happened at the end of an interaction. The understanding layer shows whether the AI correctly identified the customer\u2019s need before responding.<\/p>\n<p><strong>Intent recognition accuracy<\/strong> = (correctly classified intents \u00f7 total intents) \u00d7 100, expressed as a percentage. This rate shows how often the AI correctly understands what a caller or chatter wants before attempting an answer. <a href=\"https:\/\/zendesk.com\/blog\/ai\/workflow-automation\/ai-service-quality-metrics\" target=\"_blank\" rel=\"noindex nofollow\">First contact resolution rates below roughly 60% suggest responses without resolutions.<\/a><\/p>\n<p><strong>Knowledge coverage<\/strong> = AI coverage rate = (Number of contact categories the AI can handle autonomously \u00f7 Total distinct contact categories) \u00d7 100. This is a capability measure of the share of contact types the AI can handle. A volume-weighted alternative = (Volume of contacts in AI-capable categories \u00f7 Total contact volume) \u00d7 100. A deployment can show high intent recognition on a narrow configured intent set while the long tail routes to humans or fails silently.<\/p>\n<p><a href=\"https:\/\/zendesk.com\/blog\/ai\/workflow-automation\/ai-service-quality-metrics\" target=\"_blank\" rel=\"noindex nofollow\">Zendesk recommends categorizing handoff reasons into policy exception, authentication requirement, missing data, low confidence, negative sentiment, VIP or high-risk account, compliance or privacy concern, and repeated failed resolution attempt so escalation data becomes an improvement roadmap<\/a> instead of a number to minimize without context.<\/p>\n<p>Plura\u2019s <a href=\"https:\/\/plura.ai\/business-intelligence\" target=\"_blank\" rel=\"noindex nofollow\">conversation intelligence<\/a> layer surfaces which intents are failing and why. That signal feeds directly into workflow tuning instead of leaving operators to diagnose failures manually.<\/p>\n<figure style=\"text-align: center\"><img decoding=\"async\" src=\"https:\/\/cdn.aigrowthmarketer.co\/1779338480670-5b2fbc1c92ba.png\" alt=\"Plura Conversation Intelligence dashboard displaying AI-powered call analytics, transfer tracking, and customer conversation insights.\" style=\"max-height: 500px\" loading=\"lazy\"><figcaption><em>Plura Conversation Intelligence gives businesses AI-powered analytics, call transfer tracking, and customer interaction insights across every conversation.<\/em><\/figcaption><\/figure>\n<h2>AI Handle Time Segmentation: Why Blending Lets AI Take Credit<\/h2>\n<p>Once you know whether the AI understood the intent, the next question is how long each type of interaction takes.<\/p>\n<p><strong>AI handle time<\/strong> = AI Handling Time = Total Duration of AI Interactions \u00f7 Number of AI Handled Queries, segmented by interaction type. The four segments that matter are AI-only resolutions, human-only resolutions, AI-assisted human resolutions, and AI escalations requiring agent follow-up.<\/p>\n<p><a href=\"https:\/\/zendesk.com\/blog\/ai\/workflow-automation\/ai-service-quality-metrics\" target=\"_blank\" rel=\"noindex nofollow\">Without AHT segmentation, AI may reduce the time agents spend on routine requests while increasing the complexity of conversations that reach humans.<\/a> A higher human AHT can therefore mean the AI is filtering routine work correctly. A blended AHT that falls after AI deployment may simply reflect that easy interactions left the human queue, not that the AI improved efficiency.<\/p>\n<p><a href=\"https:\/\/ml6.eu\/en\/blog\/why-containment-metric-for-voice-ai-in-customer-service\" target=\"_blank\" rel=\"noindex nofollow\">ML6 recommends measuring AI-assisted AHT reduction against a baseline of human-only calls.<\/a> A realistic target is 25% to 35% reduction, with roughly 30% AHT reduction typical in ML6\u2019s voice AI deployments.<sup data-disclaimer-id=\"24\" data-disclaimer-index=\"3\">3<\/sup> The reduction comes from the AI handling the first two minutes of intake, authentication, and intent capture before the agent picks up.<\/p>\n<p><a href=\"https:\/\/messagemind.ai\/blog\/ai-customer-service-metrics\" target=\"_blank\" rel=\"noindex nofollow\">MessageMind recommends reporting CSAT as three separate lines, AI-only, AI-assisted human, and escalated interactions, rather than one blended number, because the split reveals whether AI is lifting or dragging satisfaction.<\/a> The same segmentation logic applies to handle time.<\/p>\n<h2>Automated Resolution Rate: Measuring Agentic AI Outcomes<\/h2>\n<p><strong>Automated resolution rate<\/strong> = issues fully resolved by AI \u00f7 total issues handled by AI. This metric applies specifically to agentic AI connected to backend systems that complete transactions end-to-end without human involvement.<\/p>\n<p><a href=\"https:\/\/zendesk.com\/blog\/ai\/workflow-automation\/automated-resolution-rate\" target=\"_blank\" rel=\"noindex nofollow\">A true automated resolution requires the AI to deliver an accurate answer, complete any required action, and not require follow-up. Automated resolution rate should exclude abandoned conversations, partial answers, escalations, generic replies, unresolved closures, contained interactions where the user gives up, and repeat contacts for the same issue.<\/a><\/p>\n<p>The trap here is counting the end of a conversation as an outcome. A session can end because the answer landed, the customer gave up, the browser closed, or the timeout fired. Only the first of those four outcomes is a resolution. <a href=\"https:\/\/zendesk.com\/blog\/ai\/workflow-automation\/automated-resolution-rate\" target=\"_blank\" rel=\"noindex nofollow\">Unlike response-based metrics that measure activity, automated resolution rate measures outcomes: how often automation actually resolves the problem.<\/a><\/p>\n<p>Plura\u2019s <a href=\"https:\/\/plura.ai\/managed-workflows\" target=\"_blank\" rel=\"noindex nofollow\">no-code workflow builder<\/a> connects AI agents to backend systems so resolution requires an action completed, not just an answer delivered.<\/p>\n<figure style=\"text-align: center\"><img decoding=\"async\" src=\"https:\/\/cdn.aigrowthmarketer.co\/1779339007666-229aec148cdb.png\" alt=\"Plura Managed Workflows interface showing AI conversation workflows, automation logic, scripts, and operational process management.\" style=\"max-height: 500px\" loading=\"lazy\"><figcaption><em>Plura Managed Workflows gives businesses fully built AI conversation workflows designed to automate customer engagement and operational tasks.<\/em><\/figcaption><\/figure>\n<h2>AI Quality and Hallucination Measurement: Sampling and Groundedness<\/h2>\n<p><strong>Hallucination rate<\/strong> = unsupported verifiable claims \u00f7 all verifiable claims, evaluated under a declared evidence contract that specifies which sources the application was permitted to use.<\/p>\n<p>The sampling methodology: <a href=\"https:\/\/alicelabs.ai\/en\/insights\/llm-hallucination-enterprise\" target=\"_blank\" rel=\"noindex nofollow\">randomly sample 2-5% of live AI outputs and route them to human reviewers for factual verification. At 10,000 queries per day with 2% sampling, this yields 200 reviewed outputs per day, enough to produce a statistically meaningful weekly hallucination rate.<\/a><\/p>\n<p><a href=\"https:\/\/gaas.co.com\/reliability\/measuring-hallucination-rates-in-agentic-workflows\" target=\"_blank\" rel=\"noindex nofollow\">Groundedness checks use NLI (natural-language inference) models to check entailment between each claim and its supporting passage, flagging claims that are neutral or contradicted.<\/a> <a href=\"https:\/\/precisionaiacademy.com\/insights\/measuring-llm-hallucination-rates\" target=\"_blank\" rel=\"noindex nofollow\">A hallucination rate belongs to a full configuration, including model version, prompt, decoding temperature, chunk size, retrieval depth, and reranker, not to a model name.<\/a><\/p>\n<p>The denominator trap: <a href=\"https:\/\/gaas.co.com\/reliability\/measuring-hallucination-rates-in-agentic-workflows\" target=\"_blank\" rel=\"noindex nofollow\">a single hallucination percentage is misleading because the denominator is ambiguous. A 3% per-claim rate on an agent making 40 claims per task means almost no task is clean, while a 3% per-task rate means 97 of 100 jobs are trustworthy.<\/a> Because the two rates tell different stories, report both alongside the average number of claims per response.<\/p>\n<h2>Inferred CSAT vs. Survey CSAT: Reconciling Two Signals<\/h2>\n<p><strong>Inferred CSAT<\/strong> is an AI-generated customer satisfaction score from 1 (least satisfied) to 5 (most satisfied), predicted entirely from conversation transcripts in real time without any user input.<\/p>\n<p><strong>Survey CSAT<\/strong> is customer-reported satisfaction collected via post-interaction survey. These surveys typically capture responses from roughly 5\u201320% of interactions, with post-interaction support surveys sometimes reaching 10\u201325%.<\/p>\n<p>Sentiment modeling can misread neutral or transactional interactions as positive and can miss dissatisfaction that customers do not express in the conversation. <a href=\"https:\/\/zendesk.com\/blog\/ai\/workflow-automation\/ai-service-quality-metrics\" target=\"_blank\" rel=\"noindex nofollow\">Zendesk recommends evaluating customer experience quality beyond surveys using AI-inferred CSAT, sentiment analysis, conversation reviews, QA scoring, and repeat contact data, because surveys only capture the slice of customers who respond.<\/a><\/p>\n<p>The reconciliation approach reports inferred CSAT and survey CSAT side by side and investigates divergence. <a href=\"https:\/\/messagemind.ai\/blog\/ai-customer-service-metrics\" target=\"_blank\" rel=\"noindex nofollow\">Reporting CSAT as three separate lines, AI-only, AI-assisted human, and escalated interactions, rather than one blended number, reveals whether AI is lifting or dragging satisfaction.<\/a><\/p>\n<p><a href=\"https:\/\/plura.ai\/plura-webchat\" target=\"_blank\" rel=\"noindex nofollow\"><strong>Book a live demo with Plura to see how contact center AI performance metrics surface natively across every channel.<\/strong><\/a><\/p>\n<figure style=\"text-align: center\"><img decoding=\"async\" src=\"https:\/\/cdn.aigrowthmarketer.co\/1779338970100-7644e3233eb9.png\" alt=\"Plura Webchat interface showing AI-powered customer messaging, automated responses, and real-time conversational engagement.\" style=\"max-height: 500px\" loading=\"lazy\"><figcaption><em>Plura Webchat delivers AI-powered customer conversations with real-time engagement, automated responses, and seamless appointment scheduling.<\/em><\/figcaption><\/figure>\n<h2>The Executive Scorecard: Customer, AI, Operations, and Financial Layers<\/h2>\n<p>A VP of Contact Center Operations needs a scorecard organized by four layers, not a single blended dashboard number. The customer and AI layers show whether the deployment works for customers. The operations and financial layers show what it costs to make that performance sustainable. Read together, these layers prevent a strong AI layer from masking a weak financial outcome.<\/p>\n<p><strong>Customer layer:<\/strong><\/p>\n<ul>\n<li>CSAT segmented by AI-only, AI-assisted, escalated, and human-only<\/li>\n<li>Customer effort score<\/li>\n<li>Repeat contact rate<\/li>\n<\/ul>\n<p><strong>AI layer:<\/strong><\/p>\n<ul>\n<li>Containment rate<\/li>\n<li>True resolution rate<\/li>\n<li>Automated resolution rate<\/li>\n<li>Intent recognition accuracy<\/li>\n<li>Knowledge coverage<\/li>\n<li>Hallucination rate<\/li>\n<\/ul>\n<p><strong>Operations layer:<\/strong><\/p>\n<ul>\n<li>AI handle time segmented by interaction type<\/li>\n<li>Escalation rate<\/li>\n<li>Agent assist adoption<\/li>\n<li>Human minutes per resolved contact<\/li>\n<\/ul>\n<p><strong>Financial layer:<\/strong><\/p>\n<ul>\n<li>Cost per resolution<\/li>\n<\/ul>\n<p>Cost per resolution is the financial metric that survives scrutiny because it rewards completed outcomes. <a href=\"https:\/\/zendesk.com\/blog\/ai\/workflow-automation\/ai-service-quality-metrics\" target=\"_blank\" rel=\"noindex nofollow\">Zendesk defines it as total support cost divided by successfully resolved inquiries, which is stronger than cost per contact.<\/a> The reason is that <a href=\"https:\/\/joulica.io\/blog\/roi-agentic-ai-contact-center\" target=\"_blank\" rel=\"noindex nofollow\">a high-deflection AI system can look cheap on a per-contact basis while quietly generating many repeat contacts.<\/a><\/p>\n<p>Plura\u2019s <a href=\"https:\/\/plura.ai\/business-intelligence\" target=\"_blank\" rel=\"noindex nofollow\">AI Conversation Intelligence<\/a> surfaces outcome-based metrics such as conversion lift, contact rates, and cost per completed action instead of dashboard summaries. Operators get the data layer this scorecard requires.<\/p>\n<h2>Legacy KPIs in the AI Era: A Translation Guide<\/h2>\n<p>Human-era KPIs break when an AI handles the interaction. Each one needs a direct translation into an outcome-based metric.<\/p>\n<ul>\n<li><strong>AHT<\/strong> becomes segmented AI handle time across AI-only, AI-assisted, escalated, and human-only interactions. Blending them lets AI take credit for removing easy interactions from the human queue.<\/li>\n<li><strong>CSAT<\/strong> becomes inferred CSAT plus survey CSAT, segmented by interaction type. Blended CSAT misses non-respondents and hides whether AI is lifting or dragging satisfaction.<\/li>\n<li><strong>FCR<\/strong> becomes true resolution rate with a re-contact check. FCR counts premature closures; true resolution rate tracks completed outcomes.<\/li>\n<li><strong>SLA<\/strong> becomes time to first AI-powered contact. Traditional SLA measures human queue time and ignores AI response time.<\/li>\n<li><strong>80\/20 rule<\/strong> becomes intent coverage and knowledge coverage, the same coverage concepts defined in the understanding layer. The long tail of unconfigured intents is where AI deployments fail silently.<\/li>\n<li><strong>Productivity measures<\/strong> becomes cost per resolution instead of cost per contact. Cost per resolution aligns spend with completed outcomes.<\/li>\n<\/ul>\n<h2>Frequently Asked Questions<\/h2>\n<h3>What Metrics Are Used to Measure AI Performance?<\/h3>\n<p>The core contact center AI performance metrics are containment rate, true resolution rate, automated resolution rate, intent recognition accuracy, knowledge coverage, segmented AI handle time, hallucination rate, inferred CSAT, re-contact rate, and cost per resolution. These metrics sit across a hierarchy from interaction through understanding, resolution, satisfaction, repeat contact, and cost. The hierarchy, not any single metric, provides the full picture.<\/p>\n<h3>How Do You Calculate Containment Rate and True Resolution Rate?<\/h3>\n<p>Containment rate is AI-contained interactions divided by AI interactions, as defined in the comparison table above. True resolution rate is the share of issues resolved without repeat contact within a defined window. The gap between these two numbers on the same deployment represents the difference between what vendors report and what finance approves.<\/p>\n<h3>What Is a Good AI Containment Rate?<\/h3>\n<p>There is no universal benchmark. The intent-complexity bands in the comparison section above are the defensible ranges, and figures materially above them deserve scrutiny of the denominator. The only defensible headline number subtracts 7-day re-contact for the same intent from the numerator.<\/p>\n<h3>How Is AI Handle Time Different from Human AHT?<\/h3>\n<p>AI handle time must be segmented across the four interaction types described above. The blended figure is misleading for the reasons already covered: it can fall simply because the mix of interactions reaching humans changed. Segmentation is the only way to read the signal correctly.<\/p>\n<h3>What Is Inferred CSAT and Can You Trust It?<\/h3>\n<p>Inferred CSAT is the transcript-based score defined above. It should be reported alongside survey CSAT, not as a replacement, because sentiment modeling can misread neutral interactions and miss unexpressed dissatisfaction. When the two signals diverge, that divergence becomes a diagnostic input, especially when CSAT is segmented by AI-only, AI-assisted, and escalated interactions.<\/p>\n<h2>Conclusion: Measure What Survives Scrutiny<\/h2>\n<p>The AI performance hierarchy, from interaction through understanding, resolution, satisfaction, repeat contact, and cost, provides the organizing logic that separates defensible measurement from flattering dashboards. Containment without true resolution becomes theater. The metrics that survive executive scrutiny are tied to resolved outcomes and repeat-contact behavior. Re-contact rate checks false containment, and cost per resolution rewards completed outcomes.<\/p>\n<p>Plura AI is built for this measurement standard. Plura owns its FCC-licensed carrier, runs voice, SMS, RCS, and <a href=\"https:\/\/plura.ai\/plura-webchat\" target=\"_blank\" rel=\"noindex nofollow\">AI webchat<\/a> on one Stateful Conversation Database, and supports TCPA compliance, DNC compliance, HIPAA, SOC 2, ISO certification, GDPR, and SHAKEN\/STIR caller ID verification, with compliance features enforced inside the platform on outbound contacts.<sup data-disclaimer-ids=\"22,23\" data-disclaimer-indexes=\"1,2\">1,2<\/sup> Every layer of the AI performance hierarchy is measurable from one platform instead of assembled from disconnected point tools.<\/p>\n<p><a href=\"https:\/\/plura.ai\/calculator\" target=\"_blank\"><strong>See how your cost per resolution compares with Plura\u2019s ROI calculator.<\/strong><\/a> <a href=\"https:\/\/plura.ai\/pricing\" target=\"_blank\"><strong>Compare contact center AI plans and rates side by side.<\/strong><\/a><\/p>\n<hr data-disclaimer-divider=\"true\">\n<div data-disclaimer-footer=\"true\">\n<p data-disclaimer-id=\"22\" data-disclaimer-type=\"content_based\"><sup data-disclaimer-index=\"1\">1<\/sup> Plura AI maintains SOC 2, HIPAA, ISO, and GDPR posture as part of its platform infrastructure. References to compliance frameworks in this article describe Plura\u2019s platform capabilities and do not constitute a guarantee that any customer using Plura will themselves be compliant with applicable laws or standards. Customers remain solely responsible for their own regulatory obligations, certifications, consent management, recordkeeping, and the claims they make to their own end users. Consult qualified legal counsel for guidance specific to your use case.<\/p>\n<p data-disclaimer-id=\"23\" data-disclaimer-type=\"content_based\"><sup data-disclaimer-index=\"2\">2<\/sup> This article describes regulatory frameworks at a general level and does not constitute legal advice. Laws and regulations vary by jurisdiction, change over time, and apply differently depending on facts and circumstances. Readers should consult qualified legal counsel before making compliance decisions.<\/p>\n<p data-disclaimer-id=\"24\" data-disclaimer-type=\"content_based\"><sup data-disclaimer-index=\"3\">3<\/sup> Performance figures, customer outcomes, and industry statistics referenced in this article are drawn from cited third-party sources or Plura customer case studies. Individual results vary based on implementation, use case, industry, audience, and execution. Past or aggregate performance is not a guarantee of future results.<\/p>\n<p data-disclaimer-id=\"25\" data-disclaimer-type=\"content_based\"><sup data-disclaimer-index=\"4\">4<\/sup> References to third-party products, services, companies, or research are made for informational and comparative purposes only. Plura AI is not affiliated with, endorsed by, or sponsored by any third party named in this article unless explicitly stated. Trademarks and product names referenced remain the property of their respective owners.<\/p>\n<p data-disclaimer-id=\"21\" data-disclaimer-type=\"fixed\">This article is provided for informational purposes only and reflects Plura AI\u2019s understanding at the time of publication. Product capabilities, integrations, and specifications are subject to change. For the most current information, visit plura.ai.<\/p>\n<p data-disclaimer-id=\"27\" data-disclaimer-type=\"fixed\">This article was produced with the assistance of AI tools and reviewed by Plura AI prior to publication.<\/p>\n<\/div>\n<section data-read-next=\"true\">\n<h2>Read Next<\/h2>\n<ul>\n<li><a href=\"https:\/\/www.plura.ai\/articles\/contact-center-ai-kpis\" target=\"_blank\">Contact Center AI KPIs: Key Metrics for 2026<\/a><\/li>\n<li><a href=\"https:\/\/www.plura.ai\/articles\/best-contact-center-metrics-2026\" target=\"_blank\">Best Contact Center Efficiency Metrics for Enterprise Teams<\/a><\/li>\n<li><a href=\"https:\/\/www.plura.ai\/articles\/ai-call-center-roi-metrics\" target=\"_blank\">AI Call Center ROI: 6 Metrics That Prove Real Value<\/a><\/li>\n<li><a href=\"https:\/\/www.plura.ai\/articles\/measure-roi-ai-contact-centers\" target=\"_blank\">How to Measure the ROI of AI Agents in Contact Centers<\/a><\/li>\n<li><a href=\"https:\/\/www.plura.ai\/articles\/contact-center-ai-quality-assurance\" target=\"_blank\">AI Quality Assurance for Contact Centers: A Complete Guide<\/a><\/li>\n<\/ul>\n<\/section>\n","protected":false},"excerpt":{"rendered":"<p>Move beyond containment rate. Plura AI breaks down the AI contact center KPIs that reveal true resolution, cost, and quality.<\/p>\n","protected":false},"author":106,"featured_media":3567,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[2],"tags":[],"class_list":["post-3568","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-contact-centers"],"_links":{"self":[{"href":"https:\/\/www.plura.ai\/articles\/wp-json\/wp\/v2\/posts\/3568","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.plura.ai\/articles\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.plura.ai\/articles\/wp-json\/wp\/v2\/types\/post"}],"replies":[{"embeddable":true,"href":"https:\/\/www.plura.ai\/articles\/wp-json\/wp\/v2\/comments?post=3568"}],"version-history":[{"count":1,"href":"https:\/\/www.plura.ai\/articles\/wp-json\/wp\/v2\/posts\/3568\/revisions"}],"predecessor-version":[{"id":3572,"href":"https:\/\/www.plura.ai\/articles\/wp-json\/wp\/v2\/posts\/3568\/revisions\/3572"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.plura.ai\/articles\/wp-json\/wp\/v2\/media\/3567"}],"wp:attachment":[{"href":"https:\/\/www.plura.ai\/articles\/wp-json\/wp\/v2\/media?parent=3568"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.plura.ai\/articles\/wp-json\/wp\/v2\/categories?post=3568"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.plura.ai\/articles\/wp-json\/wp\/v2\/tags?post=3568"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}