The Gist
What does Deloitte’s 2026 survey show about AI-mature contact centers? Businesses placing AI at the center of customer service report stronger customer experiences, improved employee experiences and higher profitability than less mature peers.How is AI’s role in the contact center changing? AI is moving beyond rule-based chatbots and single-task automation toward autonomous agents that manage multi-step requests and coordinate workflows across enterprise systems.How are businesses now measuring AI success? Leaders are shifting from automation metrics like handle time and cost per interaction toward outcomes such as first-contact resolution, CSAT, revenue contribution and customer retention.
Deloitte Digital’s 2026 Global Contact Center Survey found that businesses placing AI at the center of their customer service strategies are reporting stronger customer experiences, improved employee experiences and higher profitability than their less mature peers. Rather than treating AI as another automation tool, many of these businesses are using it to reshape how customer service operates. Those findings help explain why several AI trends are rapidly gaining momentum across the contact center industry.
Artificial intelligence has been part of the contact center for years, providing capabilities such as virtual assistants, chatbots, call routing and speech analytics. More recently, generative AI expanded those capabilities by enabling systems to summarize conversations, assist agents in real time and generate more natural customer responses. While those technologies improved efficiency, they generally remained limited to supporting individual tasks rather than managing complete customer interactions.
How AI’s Role in the Contact Center Is Evolving
Artificial intelligence is progressing beyond simple automation toward autonomous customer service and AI-assisted decision making. The table below illustrates how the role of AI is expanding across the modern contact center.
Earlier AI CapabilitiesEmerging AI CapabilitiesRule-based chatbotsAutonomous AI agentsAnswering simple FAQsManaging multi-step customer requestsBasic call routingCoordinating workflows across enterprise systemsStatic knowledge retrievalReal-time contextual recommendationsReactive customer supportProactive customer engagementAssisting individual tasksSupporting complete customer journeys
The rapid evolution of AI in customer service is reflected in Gartner’s latest research. According to Gartner, 91% of customer service leaders report growing executive pressure to implement AI in 2026, highlighting how quickly AI has become a strategic business priority rather than an experimental technology initiative.
This transition is also changing how businesses think about the role of human agents.
Ziyad Basheer, founder and CEO at Aide, told CMSWire, “The role moves up, it does not disappear. Today a lot of an agent’s day goes to the same 20 questions … the human’s job becomes the work a machine should not do alone: the complex case, the upset customer, the judgment call, the exception the AI hands off.”
Basheer also believes businesses should be careful not to allow AI to erode employee expertise. As routine work becomes automated, businesses will need to ensure agents continue developing the experience and judgment that is required to manage complex customer situations that AI cannot resolve independently.
Related Article: When Contact Center AI Starts Working Against Agents
Unified Customer Data Is Becoming a Competitive Advantage
As AI assumes a larger role in customer service, the quality of customer interactions increasingly depends on the quality of the information that is available to AI systems. Large language models (LLMs) may be capable of generating natural responses, but without access to accurate, up-to-date customer data, they cannot reliably answer questions, personalize interactions or resolve issues effectively.
That reality is driving businesses to connect customer information across CRM platforms, knowledge bases, marketing systems, commerce applications and service platforms. Rather than relying solely on what an AI model learned during training, many businesses are using retrieval-augmented generation (RAG) to provide AI with relevant enterprise knowledge and current customer information at the moment a response is generated. This approach helps improve accuracy while reducing hallucinations and ensuring responses reflect the brand’s latest products, policies and customer records.
Many brands are discovering that AI delivers the greatest business value when it has access to trusted enterprise information rather than operating in isolation. Basheer said, “They ground it in their own trusted data and connected systems … the ones who stay stuck bought a tool and hoped. The ones who succeed built trusted data and controls around it first.” He also suggested that businesses introduce AI incrementally, allowing systems to earn greater autonomy as they demonstrate reliable performance instead of attempting broad enterprise deployments all at once.
Customer context is becoming equally important. AI systems that understand previous interactions, purchase history, service cases and customer preferences are better equipped to deliver personalized assistance than those operating with only the current conversation. That persistent context enables AI to respond more naturally while avoiding repetitive questions and disconnected customer experiences that have long frustrated consumers.
For contact center leaders, this makes data quality a strategic priority rather than simply an IT concern. AI can only be as effective as the information it receives. Businesses with well-governed, connected customer data will be better positioned to deploy AI agents, support human representatives and deliver consistent experiences across every customer touchpoint. As AI capabilities continue to advance, the brands that most effectively transform unified customer data into trusted, actionable context for AI may gain a significant competitive advantage in customer service.
Success Is Being Measured by Outcomes, Not Automation
For years, contact centers measured success largely through operational metrics such as average handle time, calls per hour and cost per interaction. While those measurements remain useful, they provide only a partial picture of customer experience. As AI becomes more deeply integrated into customer service, businesses are placing greater emphasis on business outcomes rather than simply measuring how efficiently interactions are processed.
How Contact Center AI Success Metrics Are Changing
As AI matures, businesses are moving beyond measuring automation alone and placing greater emphasis on customer and business outcomes. The table below highlights how success metrics are evolving.
Traditional MetricsEmerging Business MetricsAverage handle timeFirst-contact resolutionCalls handled per hourCustomer effort scoreCost per interactionCustomer satisfaction (CSAT)Number of automated interactionsSuccessful AI containment with issue resolutionAgent productivityRevenue contribution and customer retentionAutomation rateBusiness outcomes and measurable ROI Measuring AI by Customer and Business Outcomes
First-contact resolution, customer effort, customer satisfaction (CSAT) and successful issue resolution are becoming more meaningful indicators of AI performance than speed alone. An AI agent that resolves a complex issue during a single interaction may deliver greater value than one that simply shortens call times while requiring customers to make multiple contacts. Likewise, successful containment rates matter only if customers leave the interaction with their problem solved rather than feeling frustrated or forced to find additional support.
Businesses are also beginning to evaluate AI through a broader business lens. Rather than asking how many calls AI handled, leaders are increasingly looking at whether AI improves customer retention, increases revenue opportunities, reduces employee turnover or enables agents to manage more complex interactions. AI initiatives are evolving from technology experiments into business investments that are expected to show measurable returns.
Meaningful measurement also depends on understanding business performance before AI is deployed.
Martin Taylor, co-founder and deputy CEO at Content Guru, told CMSWire, “businesses never baselined what they were starting from.” Taylor recommended establishing clear benchmarks for measures such as first-contact resolution and average handle time before introducing AI so businesses can accurately determine whether the technology is producing measurable business improvements.
Brands are also beginning to evaluate AI according to its impact on customer relationships and business performance rather than automation alone.
Latané Conant, CMO at Parloa, told CMSWire, “The smartest leaders won’t ask, ‘How many tickets did we automate?’ They’ll ask, ‘Did we make life easier for our customers and did that drive loyalty or revenue?'”
She suggested that businesses should also examine customer effort, smooth handoffs between AI and human agents, repeat contact rates and long-term customer trust when evaluating AI initiatives, rather than relying exclusively on traditional operational metrics.
This reflects a broader change in enterprise AI strategy. Businesses are becoming less interested in using AI for its own sake and more focused on applying it where it can solve meaningful business problems. As contact centers continue adopting agentic AI, the most successful implementations are likely to be those that improve both operational efficiency and the overall customer experience while delivering measurable business value.
Related Article: Contact Center AI Didn’t Plateau. It Went Operational.
Voice AI Continues to Mature
Voice AI has advanced significantly beyond the scripted interactive voice response (IVR) systems that frustrated customers for decades. Modern conversational AI can understand natural language, recognize interruptions, maintain context across extended conversations and respond with increasingly human-like speech. These improvements are making voice AI a practical option for handling a growing range of customer service interactions without sacrificing the conversational experience that customers expect.
Recent advances in LLMs, speech recognition and text-to-speech technologies have also expanded voice AI’s capabilities. Many platforms now support multilingual conversations, adapt responses based on customer intent and detect vocal cues that may indicate frustration, confusion or urgency. Combined with real-time access to customer information and enterprise knowledge, voice AI is becoming better equipped to resolve issues that previously required a human representative.
Where Voice AI Still Needs Human Expertise
Despite these advances, voice AI continues to have limitations. Complex problem solving, emotionally sensitive situations, negotiation and conversations requiring empathy or nuanced judgment often remain better suited to experienced human agents. Rather than replacing contact center employees, voice AI is increasingly serving as the first point of engagement, resolving routine requests while transferring more challenging interactions to human representatives when appropriate—without requiring customers to repeat information or restart the conversation.
As voice AI capabilities continue to improve, businesses are placing greater emphasis on designing effective collaboration between AI systems and human representatives.
Michael Hutchison, head of customer experience at eClerx, told CMSWire, “AI agents are evolving rapidly, but I see them augmenting human agents rather than replacing them.” He expects AI to assume more repetitive inquiries and administrative work while providing agents with real-time recommendations and insights, allowing employees to devote more attention to customer outcomes, critical thinking and relationship building.
As conversational AI continues to improve, businesses are likely to move beyond asking whether voice AI can replace agents and instead focus on how AI and human representatives can work together most effectively. Businesses that strike the right balance between automation and human expertise will be better positioned to improve customer experiences while maintaining the trust and empathy that remain central to customer service.
Contact center AI is moving beyond basic automation as businesses prioritize trusted data, measurable customer outcomes, human expertise and governance.Simpler Media Group Governance and Trust Become Business Priorities
As AI systems become more autonomous, AI governance is evolving from a compliance exercise into a basic requirement of doing business. Contact centers are beginning to trust AI with increasingly important responsibilities, from resolving customer issues and processing transactions to recommending next-best actions. That growing autonomy raises the stakes for ensuring AI systems operate accurately, consistently and within organizational policies.
Hallucinations remain one of the most visible risks, but they are only part of the challenge. Businesses must also ensure that AI agents have access to accurate enterprise knowledge, follow established business rules, protect sensitive customer information and generate responses that align with regulatory and compliance requirements. Strong governance helps reduce these risks while giving businesses greater confidence to expand AI into more complex customer interactions.
As AI handles a larger share of customer service activities, businesses also need better ways to evaluate AI performance rather than assuming automated systems are operating correctly.
Why Governance and Customer Trust Matter as AI Scales
Mark Hughes, co-founder and CEO at Solidroad, told CMSWire, “Almost always the difference comes down to whether the team actually has visibility into what the AI is doing. In a world where AI is handling a large volume of customer chats there has to be some third-party oversight.”
Hughes believes that successful businesses establish clear service standards before deploying AI and continuously evaluate every interaction against those standards, ensuring autonomous systems remain aligned with customer expectations instead of relying solely on high-level performance dashboards.
Governance also depends on transparency and accountability. As AI assumes greater responsibility for customer service, businesses need clear visibility into how automated decisions are made, how performance is measured and when human intervention is required. Establishing audit trails, monitoring AI performance and continuously evaluating customer outcomes help ensure that autonomous systems remain aligned with business objectives while maintaining customer trust.
Ultimately, customer trust may become one of the most important measures of AI success. Customers are often willing to engage with AI when interactions are accurate, transparent and effective, but that trust can quickly erode when AI provides incorrect information, mishandles personal data or creates unnecessary friction. As autonomous AI becomes a larger part of the customer experience, governance will increasingly determine not only whether AI operates responsibly, but whether customers continue to trust the businesses that are deploying it.
Preparing the Contact Center Workforce for AI
As AI becomes more deeply integrated into customer service, the role of the contact center agent is evolving rather than disappearing. Routine inquiries and repetitive administrative work are increasingly being handled by AI, allowing human agents to focus on complex problem solving, relationship building and situations that require judgment, empathy or negotiation. This is changing not only how contact centers operate, but also the skills that businesses need from their workforce.
How the Contact Center Workforce Is Evolving
AI is changing the work performed by contact center employees rather than eliminating the need for human expertise. As routine tasks become automated, agents are taking on higher-value responsibilities that require judgment, collaboration and customer relationship skills.
Traditional Agent ResponsibilitiesEmerging Agent ResponsibilitiesAnswer routine customer questionsResolve complex customer issuesSearch knowledge bases manuallyValidate AI recommendationsComplete post-call documentationSupervise AI-assisted workflowsFollow scripted processesHandle exceptions and escalationWork independentlyCollaborate with AI systemsFocus on efficiencyFocus on judgment, empathy and customer outcomes
Workforce priorities are evolving alongside the technology. The Gartner report revealed that 58% of customer service leaders plan to upskill agents into knowledge management specialists, recognizing that AI systems depend on accurate, well-governed information to deliver reliable customer experiences.
Contact centers are also creating new support roles around AI. Knowledge managers are becoming responsible for ensuring AI systems have access to accurate, up-to-date information. Supervisors are monitoring both AI and human performance, while AI operations teams are emerging to oversee model behavior, evaluate outcomes and continuously improve AI-assisted workflows. As autonomous systems become more common, businesses will increasingly need employees who understand both customer experience and the technology that supports it.
As AI takes over more repetitive work, businesses are also creating new roles focused on supervising, improving and optimizing AI systems. Conant explained that “An AI agent workforce should be the always-on, hyper-personalized first line of support, while humans step in where empathy and judgment really matter.” She expects service businesses to evolve rather than shrink, with employees taking on responsibilities that include managing AI agents, refining their performance and ensuring they continue delivering high-quality customer experiences alongside human teams.
Training priorities are changing accordingly. While effective communication with AI tools remains important, businesses are placing more importnace on AI literacy, critical thinking, knowledge management and data stewardship than on memorizing prompting techniques. The goal is to prepare employees to evaluate AI-generated recommendations, manage exceptions and continuously improve AI-enabled customer experiences as the technology evolves.