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Artificial Intelligence
CIO Bulletin, 30 April, 2026 Author: Guest
By 2028, agentic AI is expected to handle 68% of all customer service interactions for technology vendors. Early adopters across various industries are already deploying autonomous AI agents to resolve refunds, schedule appointments, update accounts, and handle phone calls without a single human keystroke in the middle.
Voice AI is at the center of this shift. The best AI contact center platforms offer AI voice agents that can authenticate callers, pull account data, navigate complex conversation flows, and close tickets without transferring to a human unless escalation is necessary. Discover the most common use cases for agentic AI in customer service and the tools that deliver them.
Faster response times across every channel, from voice to chat to email, by automating routine tasks and reducing queue times
Self-service at scale thanks to AI-powered chatbots and voice agents that handle repetitive inquiries around the clock, letting customers get immediate answers without waiting for support agents
Personalized support driven by real-time customer data and machine learning models that recognize patterns across prior interactions
Better customer experience through consistent, 24/7 AI customer service that scales without proportional headcount growth
Enhanced agent productivity through AI tools that surface knowledge base articles and suggested responses during live service interactions
AI in customer service refers to agentic systems that understand context, recognize intent, make autonomous decisions, and take action across multiple channels and business systems. Unlike a chatbot that follows a script, an agentic agent can reason through a full interaction: a customer calls about a returned item, the voice agent verifies the purchase, checks inventory and return policies, initiates the return, coordinates a pickup with logistics, processes the refund, and updates billing.
This autonomy requires three capabilities that older AI systems lacked:
ability to maintain context across interactions
reasoning capacity to evaluate options and outcomes,
compatibility with external systems (CRM, billing, ERP, logistics)
For contact centers oriented around phone support, AI voice agents are only as reliable as their speech recognition. Look for platforms that perform in noisy environments, handle accents and dialects, and filter background noise without degrading transcription accuracy. Voice quality on the AI side matters, too. Test brand voice consistency across long interactions to confirm the AI sounds like the company. Customers are more likely to hang up on voice agents that sound mechanical, regardless of how well the backend logic works.
The best AI customer service platforms expose conversation flows through a visual workflow designer that lets teams build, test, and iterate agent operating procedures without writing code. This lowers the barrier to deployment and makes continuous optimization accessible to operations teams rather than just engineers. Strong conversational capabilities distinguish AI customer service solutions built for real-world complexity from those optimized for scripted demos.
Connecting customers to the knowledge base they need to resolve routine inquiries independently is proven to reduce resolution times. Conversational AI tools guide customers through self-service portals, address customer questions using the company’s knowledge base, and deliver immediate answers to common customer queries. Evaluating the depth and quality of knowledge base and integration is essential: AI tools that draw on a rich, well-maintained knowledge base consistently provide more accurate answers and deliver better customer care outcomes.
Call recording and transcripts are foundational for service quality assurance, compliance, and voice intelligence analytics. Strong platforms offer searchable transcripts, structured call data exports, and conversation intelligence dashboards that surface customer data insights across call volume trends, customer sentiment scores, and resolution rates.
Look for agent assist tools that deliver real-time data to human agents during complex calls, not just post-call summaries. AI-powered solutions produce better outcomes than post-call coaching alone by enhancing agent productivity in the moment and surfacing tailored solutions for specific customer issues instead of generic scripts.
AI voice agents need intelligent routing capabilities to direct customer requests to the right agent or automated workflow based on intent, customer data, and real-time context. Platforms that handle routing well reduce handle time and improve first-call resolution by ensuring interactions land in the right place the first time rather than requiring transfers or re-queuing.
Clean integration with major telephony providers is a baseline requirement for any AI voice agent platform deployed in a real contact center environment. Platforms that connect natively with existing SIP-based calling infrastructure reduce implementation complexity and minimize the risk of audio quality or reliability issues that erode customer experience.
Agentic AI is not a single use case. Across industries, teams are finding the highest ROI where customer requests are high-volume, workflows are multi-step, and resolution depends on pulling records from multiple systems simultaneously.
Healthcare contact centers handle high-volume, high-stakes phone conversations: appointment scheduling, prescription refill requests, referral coordination, and pre-authorization follow-ups. AI customer service agents now handle these end-to-end.
A patient calls in. The voice agent authenticates their identity through speech recognition, checks provider availability via EHR integration, confirms insurance eligibility, books the appointment, and sends a confirmation with preparation instructions. AI in customer service for healthcare also supports automated workflows for reminders and post-visit follow-ups, improving patient satisfaction and reducing no-show rates without requiring additional customer care staff.
In retail, AI customer service tools handle the high-volume, policy-driven customer requests that consume most of a phone support team's day. A customer calls to initiate a return. The AI agent verifies the purchase, checks return eligibility, coordinates a logistics pickup, processes the refund, and updates inventory systems without a support agent reviewing the ticket. The same voice agents handle order status inquiries and proactive outreach when shipments are delayed.
Banks can use AI customer service agents for account inquiries, dispute resolution, and proactive fraud alerts. A customer calls after noticing an unfamiliar charge. The voice AI agent authenticates the caller, retrieves the transaction, cross-references fraud patterns using machine learning models, temporarily freezes the card if warranted, initiates a dispute, and sends a case number by SMS. Sentiment analysis monitors customer emotions throughout the call and escalates to human agents if the customer expresses distress. On the proactive side, voice agents identify customers at risk of overdraft and conduct outbound calls with personalized support and guidance before fees are incurred.
SaaS and tech companies deal with a wide spectrum of support complexity. Many customer queries involve repetitive questions that don't need human agents. AI customer service tools handle this first tier autonomously: searching the knowledge base, walking users through feature setup step by step, providing immediate answers to common customer questions, confirming resolution, and closing the ticket.
For renewal workflows, voice AI agents identify customers approaching contract end dates, conduct outbound calls with account-specific renewal options, and route qualified conversations to sales teams with full context pre-loaded. AI-powered automation can absorb 60–70% of incoming tier-1 tickets, improving response times while keeping customer satisfaction high across time zones.
Government agencies don't have customers in the traditional sense, but they face a massive volume of citizen inquiries that require quick action and tight security protocols. AI customer service tools can be deployed to handle benefits eligibility inquiries, application status checks, permit questions, and service requests.
For example, a citizen calls to check the status of a benefits application. The voice agent authenticates the caller, retrieves the application record from the case management system, delivers a plain-language status update, handles follow-up questions about required documentation, and sends a summary by email. Agentic AI in customer service for government extends service hours without proportional headcount growth and reduces the burden on human agents by handling routine customer requests autonomously. Voice intelligence analytics give agency managers visibility into call volume trends, common inquiry types, and customer needs across geographic regions.
RingCentral's customer service portfolio is built around RingCX, its AI-powered contact center platform. RingCX supports omnichannel operations across voice, chat, and messaging, includes workforce engagement management (WEM), and integrates natively with RingCentral's broader AI product suite.
What sets RingCX apart is that its AI capabilities aren't external. They're built from the same platform that powers RingCentral's core communications infrastructure, which means voice quality, reliability, and integrations don't degrade as AI features are layered in.
RingCX can be deployed standalone, but its full value surfaces when combined with RingCentral's AI products:
AIR (AI Receptionist): Handles inbound call routing, FAQs, and appointment scheduling before a call ever reaches a live agent. Reduces queue pressure and ensures calls land in the right place the first time.
AIR Pro: The agentic upgrade for AIR. Authenticates callers, executes multi-step transactions, and hands off to RingCX agents with full conversation context already loaded.
AVA Agent Assist: Delivers in-the-moment support by surfacing relevant knowledge, recommending responses, and guiding agents through complex or compliance-driven conversations—helping improve resolution rates and reduce handling time.
AVA Supervisor Assist: Gives supervisors real-time visibility into active conversations, highlighting interactions that may need attention and providing instant summaries and transcripts for quicker, more effective intervention.
AI Quality Management: Evaluates every interaction automatically, generating performance scores, identifying coaching opportunities, and tracking key behaviors without relying on manual sampling.
AI Interaction Analytics: Turns all customer conversations into actionable insights, predicting satisfaction, identifying trends, and enabling leaders to explore root causes and make data-driven decisions.
AI Workforce Management: Uses AI-powered forecasting and scheduling to align staffing with demand, while monitoring adherence and attendance to maintain operational efficiency.
Dialpad is a unified communications and contact center platform with AI capabilities built around real-time transcription, sentiment analysis, and in-call agent coaching. Its AI layer, called Dialpad Ai, surfaces live recommendations and flags coaching moments during active conversations without requiring supervisors to monitor calls directly. Dialpad's UCaaS and CCaaS capabilities sit in a single platform, which simplifies administration for organizations that want employee and customer communications managed together.
Nextiva is a business communications platform that combines UCaaS, CCaaS, and CRM functionality in a single interface, positioning itself around what it calls a unified customer journey. Its AI capabilities include intelligent routing, virtual agents, sentiment analysis, and automated workflows that connect communication data with customer records. Nextiva's approach emphasizes giving agents full customer context at the moment of interaction, reducing the time spent re-establishing background on returning customers and improving the consistency of service quality across touchpoints.
Zoom has expanded well beyond video conferencing into a broader communications and contact center platform through Zoom Contact Center and its AI Companion suite. Its AI capabilities include virtual agents, real-time transcription, post-call summaries, and agent assist tools that surface relevant information during live interactions. Zoom's broad enterprise adoption gives it a distribution advantage, and organizations already standardized on Zoom for internal communications may find Contact Center a natural extension that reduces the number of vendors in their stack.
NICE CXone is one of the most established enterprise contact center platforms on the market, with a comprehensive AI suite that spans virtual agents, workforce engagement management, interaction analytics, and real-time agent guidance. Its Enlighten AI layer is trained specifically on customer service interactions, giving its models domain-specific depth that general-purpose AI platforms lack. NICE has invested heavily in full-lifecycle contact center AI, covering everything from proactive outreach and self-service resolution to post-interaction quality management and forecasting.
Zendesk is a customer service platform with AI capabilities centered on ticket management, automated resolution, and agent productivity across digital channels. Its AI agents can handle common customer requests through chat, email, and messaging without human involvement, and its agent assist tools surface relevant knowledge base articles and suggested responses during live interactions. Zendesk's strength is in digital-first support environments where the primary interaction channels are text-based, and its broad ecosystem of integrations makes it a practical choice for organizations running complex support toolchains.
Zendesk is primarily a digital and ticketing-focused platform. Its voice AI capabilities are less developed than platforms built natively around telephony, making it a weaker fit for contact centers where phone-based interactions are the primary or highest-stakes channel.
AI-powered platforms address the classic contact center constraints directly, enabling autonomous resolution of routine interactions, real-time agent guidance during live calls, and conversation analysis across every recorded interaction rather than a selected few. Matching the platform to the specific bottlenecks in your operation, whether that is call volume, handle time, coaching capacity, or after-hours availability, will determine how quickly and clearly the investment pays off.
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