50 Contact Center AI Use Cases for 2026

The contact center is on the cusp of a major AI revolution, but with so many potential use cases, knowing where to innovate next can feel overwhelming.

From automating customer interactions to assisting agents, optimizing operations, and beyond, the possibilities seem almost limitless.

Meanwhile, pressure is mounting. Indeed, 77% of customer service leaders say that senior executives are urging them to accelerate AI adoption, according to a 2025 Gartner survey.

Yet, before hitting the gas pedal, it’s crucial to understand the full spectrum of contact center AI use cases. Only then can leaders feel confident in making informed decisions that align with their requirements and drive real impact.

With that in mind, here are 50 contact center AI use cases for 2026 and beyond.

1. Automating Customer Conversations

Example: Google Conversational Agents (inside its Customer Engagement Suite)

Contact centers have long deployed chatbots to automate customer conversations via self-service. Historically, they used natural language understanding (NLU) to gauge the customer contact reason and then relayed scripted answers.

However, over recent years, they have improved significantly, especially since the advent of generative AI (GenAI).

Now, they can follow intent as it changes throughout a conversation, scouring knowledge articles, best practice guides, and other trusted sources to answer customer queries in an appropriate tone of voice.

The next step is AI agents collaborating with other AI agents embedded in various business systems, pulling data, triggering actions, and automating long-tail resolution workflows.

That future is creeping closer, with Google sharing the following demonstration of a multi-modal conversational AI agent for customer service.

2. Testing Self-Service Systems

Example: Cresta Automated AI Agent Testing

As customer self-service advances, testing suites are emerging that help customer operations and IT teams isolate issues before customers.

The Cresta Automated AI Agent Testing suite is an excellent example. It comprises LLM (large language model) judges, simulated customers, and in-product feedback.

LLM judges act like expert reviewers at scale, checking not just what the virtual agent says, but whether it follows required workflows, security steps, and approved responses, catching the subtle compliance and accuracy issues that humans often miss.

Meanwhile, simulated customers behave like their human counterparts, generating hundreds of scenarios and personas to expose edge cases, breakdowns, and risky behavior before the virtual agent reaches production.

Finally, the suite identifies mistakes in real conversations, turns them into test cases with a single click, runs them continuously, and reinforces the correct behavior over time.

3. Analyzing AI Escalations

Example: CallMiner Eureka

Conversational intelligence solutions allow contact center leaders to group queries that escalate from self-service to a live representative.

From there, leaders may spot escalation patterns by running an analytics initiative on a conversational intelligence platform, like CallMiner Eureka.

In doing so, they can isolate opportunities to update knowledge content and bolster AI guidance, improving containment rates.

Often, the self-service system will work as intended, but the customer still requests human assistance. Contact centers may log this preference and alter the routing algorithm to ensure a more personalized experience in future interactions.

4. Gathering Information Before Interactions

Example: babelforce Voicebot AI

Traditional IVR systems are fading fast, replaced by AI that understands customer intent in natural language and automatically routes them to the best-placed representative.

However, as the customer waits in the call queue, many contact centers are also utilizing conversational AI there to extract pertinent information to their query.

In doing so, they’re not just cutting down the wait time, but they’re also unpacking helpful information to pop up on the agent’s screen at the start of the interaction.

As a result, reps can lead the call with authority, instead of spending time combing through the CRM and battling multiple UIs.

Almost all contact center conversational AI providers can offer this use case. Yet, babelforce has long heralded this use case, promising to shave 45 seconds from every call.

5. Generating Customer Cases

Example: Case Management Agent for Microsoft Dynamics 365 Customer Service

AI agents work within enterprise systems, reasoning, adapting, and acting to automate workflows. Now, they’re starting to augment contact center CRM systems.

For instance, Microsoft has a native agent within Microsoft Dynamics 365 Customer Service that automatically creates a case whenever a customer contacts support.

In doing so, it pulls in relevant context from the customer’s history and conversations, populating the necessary fields.

Additionally, the agent provides service reps with summaries and key insights to help them quickly get up to speed and can initiate collaboration with a subject matter expert (SME) in Microsoft Teams if necessary.

Here’s a closer look at how AI can transform contact center case management.

6. Drafting Suggested Customer Replies

Example: Zendesk Agent Copilot

Many CCaaS and CRM platforms now offer virtual assistants to support reps. These can be trained on company knowledge bases, resolved tickets, and internal procedures.

Leverage that training, these assistants draft responses to customer queries across digital channels, such as email, live chat, and messaging apps.

From there, the human agent may review, edit, and send their response, saving them from writing it from scratch.  

Zendesk Copilot is one of many AI tools that can assist agents in this way, but what makes it stand out is its flexibility.

For instance, agents can change the tone of the drafted response with a single click, making it more friendly or formal. They can also ask the AI to expand or simplify their replies. There’s even an option for the AI to mimic the rep’s individual tone of voice, offering a glimpse into the future of personalized AI assistants.

7. Recommending Next Best Actions

Example: Genesys Agent Copilot

Contact center virtual assistants can do much more than auto-draft replies… On the voice channel, they can also offer guidance when the agent appears to be stuck.

Such guidance may include pulling up a snippet from a relevant knowledge article or suggesting a solution that worked previously for a customer in a similar predicament.

In the future, the AI assistant may also offer to perform the next step on the rep’s behalf.

For instance, it could pull in data to complete a customer form, schedule a field service visit in an adjacent system, or send a confirmation email on command. That’s where this technology is heading.

8. Spotlighting Upsell & Cross-Sell Opportunities

Example: Creovai Conversation Intelligence

Service conversations sometimes evolve into sales interactions when a support rep realizes that their customer’s product or subscription isn’t meeting their current needs.

Alternatively, the customer may have ended the call in an excellent mood and could be open to expanding their purchase.

Contact centers can spot such signals during a conversational intelligence exercise and train their virtual assistants to prompt agents when such a signal appears during a live contact.

Of course, they must be careful to ensure agents don’t attempt upsells at inappropriate moments. However, with proper training, this approach can work well for contact centers shifting from a traditional service model toward more of a customer-success–focused strategy.

9. Summarizing Customer Conversations

Example: Five9 AI Summaries

After every customer interaction, contact center agents load a conversation summary to the CRM, documenting the issue and the agreed resolution.

Generative AI has automated this process and automatically tags each ticket with a disposition code, further streamlining post-contact processing.
Nearly every CCaaS and CRM provider supports this use case, but Five9 stands out as an early innovator. Indeed, it was the first vendor to let contact centers customize the LLM prompt behind summaries, ensuring they follow a consistent format that aligns with brand best practices.

10. Warming Agent Handovers

Example: Verint Smart Transfer Bot

For decades, customers have been frustrated by having to repeat information, especially when their issue passes between agents or escalates from a self-service application.

Today, AI solutions can summarize the initial interaction and pass that critical context to the next agent, allowing them to pick up right where the conversation left off.

These handovers can also include additional insights, like customer sentiment, so agents are better prepared before the conversation begins.

11. Monitoring Human & AI Collaboration

Example: KYP.ai

Service leaders often struggle to understand how their teams are using assistive AI tools.

For example, when a contact center deploys AI-generated summaries, how much time do agents spend editing them before uploading them to the CRM, and does that effort justify the investment?

Alternatively, when AI is suggesting next-best actions, are agents following their advice, or are they ignoring it altogether?

These insights are crucial to improving AI-assisted service. However, vendor reporting is still immature when it comes to monitoring human and AI collaboration.

For this reason, other AI solutions, like KYP.ai, are emerging to track how agents use AI to complete tasks and monitor differences between individual users.

12. Role Playing with Customer Simulations

Example: Centrical AI Role-Play

Contact center coaches have often role-played customer conversations with new agents in a safe environment to help them prepare for life on the phones.

Now, AI can take their place, simulating real customers, engaging with agents across common customer contact reasons, and quickly bringing them up to speed.

The solution can also support more experienced agents. Perhaps they struggled with a difficult customer conversation earlier in the week. AI can replicate elements of that conversation, allowing the agent to try another approach in a controlled learning environment, banishing their demons.

Alternatively, perhaps a coach has isolated a skills gap. In this situation, they could include simulations designed to bring out that skill as part of a personalized development plan.

Beyond that, contact centers can model hypothetical scenarios ahead of new product launches or major events, so agents stay ahead of the curve.

13. Note-Taking Assistance

Example: NiCE AI Agent Notetaking

Historically, contact centers have deployed live transcriptions across the voice channel to help agents converse with customers who have distinct accents. AI note takers are the next evolution.

Rather than recording a verbatim transcript, these note-takers surface the most critical points as the conversation unfolds, which agents may refer back to when solving the issue.

Meanwhile, the agent can stay focused on active listening as the customer describes their issue, without worrying about losing track of key points or manually writing out notes.

In time, these note-takers may also capture conversational data and auto-fill CRM fields, further lightening the load on agents.

14. Crafting New Knowledge Content

Example: Zendesk Knowledge Builder

Generative AI can scour customer conversation transcripts, case notes, and comments pertinent to a particular customer contact reason and generate new knowledge articles.

Additionally, specialized tools can update existing content by identifying new resolutions and organizing knowledge into a uniform format that’s easier for human and AI agents to read.

Zendesk Knowledge Builder is one example of such a solution. It can also categorize knowledge content based on topics and themes, as showcased in the video below.

15. Transforming Knowledge Search

Example: Talkdesk Knowledge Management

Nowadays, many contact center agents interact with the knowledge base like a GPT.

Instead of applying filters, navigating categories, and scouring individual articles for critical pieces of information, agents can ask questions of all their knowledge content.

While virtual assistants may deliver knowledge directly into the agent’s workflow, this is a helpful backstop that enables agents to find answers with a single search query.

Many CCaaS providers now offer this capability within their knowledge bases, with Talkdesk a prime example.

16. Mechanizing Quality Assurance

Example: MaestroQA

For years, service teams have used natural language processing (NLP) models to auto-fill their quality assurance (QA) scorecards and track all customer conversations.

However, generative AI has made this capability mainstream, allowing quality analysts insight into every call instead of the 1-2% they have historically monitored.

The trouble with this use case is that there is little value in constantly evaluating the same criteria. As such, contact centers must continually evolve their scorecards, tracking new agent skills and behaviors.

That’s why leading providers of automated quality assurance (Auto-QA) systems are augmenting their platforms with deeper conversational analytics, which is just one of many contact center trends to watch in 2026.  

17. Automating Performance Summaries

Example: Scorebuddy AI Agent Buddy

Some contact centers provide agents with live sentiment analysis and coaching tips during calls. However, agents often find this unhelpful, as they don’t need AI to tell them whether a conversation is going well; they can tell for themselves.

Instead, it’s better to share feedback later that helps reps reflect and learn, with some systems delivering daily AI-generated coaching packets to agents.

Yet, there are alternative approaches. For example, Auto-QA provider Scorebuddy places an AI summary button on the desktop, which automatically reviews the agent’s last 200 scores and generates a personalized performance summary based on that data.

In doing so, it offers a more comprehensive view of their performance, rather than honing in on one or two particularly good or bad interactions.

18. Spotlighting Coaching Opportunities

Example: Balto Contact Center Coaching

Modern Auto-QA systems pinpoint outlier conversations in sentiment and handling time, where the ripest learnings are likely to arise.

Some then utilize AI to identify key moments that will help coaches more quickly isolate where the conversation broke down.

Balto offers one such solution, packaging these moments into oven-ready coaching sessions, tailored to specific agents, with automated training ideas for coaches to review.

The system can also spot issues outside of the agent’s control, whether that’s an internal knowledge gap or a broken process, for supervisors and managers to review.

19. Tracking the Impact of Coaching

Example: amplifAI Coaching Effectiveness Score

Contact centers track the impact of coaching to ensure it's adding value for the agent, customer, and broader business.

Recognizing this, some performance management systems have added a coaching effectiveness score to track how each coach is affecting critical customer service outcomes.

One example is amplifAI, which uses AI to offer supervisors personalized guidance on how to improve coaching quality alongside the score in itself.

The system also sends AI-generated alerts to supervisors, prompting them to deliver personalized recognition to high-performing agents, helping them keep morale high.

20. Gamifying Agent Work

Example: amplifAI Gamification, Recognition, and Incentive Management

Contact centers have long ranked agents on leaderboards based on their performance across key metrics, such as first-contact resolution.

However, the bottom 70% on these leaderboards quickly realize they have little to no chance of ranking first, turning the initiative into more of a hindrance than an enabler of agent engagement.

Thankfully, AI is helping contact centers create cohorts of agents performing at a similar level and developing data-driven challenges specific to the individual.

With a solution like amplifAI, agents can track their progress against these challenges, monitor their individualized goals, and understand how close they are to unlocking rewards and other performance incentives.

21. Modeling High Performers

Example: amplifAI High Performer Persona Modeling

A contact center’s best learnings often come from tracking the actions and behaviors of its best agents. AI can now support this process.

For instance, with amplifAI High Performer Persona Modeling, brands can create a blueprint for navigating specific contacts.

Crucially, that insight can inform coaching activities to help scale the skills of a contact center’s top performers.

However, as customer-facing virtual agents begin to develop their own personas, this modeling can also guide their design to enhance customer outcomes.

22. Monitoring Case Complexity

Example: Relevance AI Customer Support Ticket Categorization AI Agents

As AI automates routine contacts, human agents face a fastening stream of complex issues, with fewer simple interactions to reset between them.

Considering how 59% of contact center agents are already at risk of burnout, that’s an issue.

As such, some service teams are taking action by tagging tickets with a predicted complexity score - i.e., high-, medium-, or low- - and actively managing the mix of contacts they receive.

Relevance AI offers AI agents specifically built for this use case, considering factors beyond the customer’s stated contact reason to inform the complexity score.

23. Routing Contacts Intelligently

Example: Genesys Predictive Routing

Contact centers can route queries by complexity, per the example above. Yet, there are many more ways service teams can harness AI to triage customers.

Genesys kickstarted this trend in 2021, launching Predictive Routing. The solution uses contact center data to match each customer with the agent most likely to deliver the best outcomes.

Service leaders can determine what those outcomes are, whether it's sales conversions, retention, handling times... Yet, the chosen outcome may vary by contact reason.

The future of AI routing is also fascinating to consider. For instance, perhaps contact centers could pull workforce management (WFM) data to match a contact that’s likely to be short with an agent whose shift is almost over, boosting schedule adherence. The possibilities are exciting!

24. Predicting Staffing Requirements

Example: Aspect Workforce Forecasting Software

One of the first applications of contact center AI was using machine learning to forecast contact volumes, enabling more accurate staffing decisions.

Over recent years, some WFM teams have trialed more advanced forms of AI, like neural networks. Yet, as every service operation is different, there’s no one-size-fits-all solution.

As such, more contact centers are pulling in forecasting models outside of what is available within their workforce management (WFM) solution, testing them on old data to spot which align best with their demand.

Services like CCMath, which host hundreds of these models, are worth exploring for contact centers aspiring to improve forecast accuracy.

25. Automating Scheduling Processes

Example: Calabrio Forecasting & Scheduling Software

Many contact center WFM solutions use AI to automatically build schedules that align with forecasted demand, taking employee contract and preference data into account.

In doing so, the AI ensures compliance with local labor laws, balances agent skillsets, and incorporates various shift patterns.

While AI handles the heavy lifting, these solutions should still allow manual adjustments, enabling resource planners to apply their own knowledge and experience to optimize staffing coverage.

26. Adding Flexibility to Agent Shifts

Example: Verint TimeFlex Bot

Contact center planners have always struggled to add flexibility levers to schedules, beyond enabling shift-swaps. Yet, AI innovations are helping.

The Verint TimeFlex Bot is an excellent example. It breaks down the schedule into 15-minute increments, giving each a specific value in terms of credits.

Agents can spend credits to take those periods off and earn them back by working overtime when the contact center needs extra support.

As a result, planners may offer more flexibility without uprooting their tried-and-tested scheduling practices.

The video below provides a closer look at how the Verint TimeFlex Bot works.

27. Mechanizing Time Off Processes

Example: Peopleware Time Off Management

Managing time off can be a time drain. Yet, AI is supporting planners by auto-approving and auto-rejecting vacation requests based on their specific criteria.

Consider Peopleware Time Off Management as one such AI solution. It also automatically accounts for time-off balances, based on contracts, scheduled hours, and custom leave policies.

Additionally, when an agent cancels time off, AI automatically updates their vacation balance, sparing the planner additional work.

28. Predicting Customer Behaviors

Example: Amazon Connect AI-Powered Predictive Insights

Speech analytics has long enabled contact centers to flag keywords indicating customers are considering leaving the business and take mitigating actions. However, this technology is becoming more sophisticated.

AWS is leading the charge. First, through its Customer Profiles solution embedded into Amazon Connect, its CCaaS platform. It allows contact centers to pull information on individual customers from across the enterprise into a single view.

Now, it’s augmenting this solution with AI-powered predictive insights to weigh up all that data and anticipate what customers are likely to want next.

In doing so, it can predict not only which customers are likely to churn but also those who may need assistance with other issues.

29. Creating New Outbound Support Cases

Example: Sprinklr Case Management

CCaaS platforms are expanding beyond omnichannel, routing, and workforce optimization. Now, they’re incorporating new tools, like social media listening.

Sprinklr is one vendor that does this, scanning online brand mentions to identify those in need of support, linking them to a customer profile, and using AI to generate new support cases.

These cases then route through to an agent, who can reach out to the customer and proactively try to turn around their experience.

AI may also help to prioritize cases based on customer sentiment.

As AI promises to reduce customer support volumes, this use case can help reinvent the agent role, ensuring support teams add increasing business value.

30. Monitoring Trends in Customer Sentiment

Example: Dialpad Contact Center Sentiment Analysis

Sentiment analysis uses AI to monitor customer emotions. In the past, conversational intelligence systems relied on keyword analysis to assign a score to call post-interaction that reflected the customer’s overall mood.

However, the technology has advanced significantly, with AI monitoring the customer’s tone, pauses, and behavior to generate a real-time score.

That insight is of little use to agents. However, it can help supervisors monitor which reps are struggling with difficult conversations in real time. From there, they may use the “whisper” and “barge” functions with the typical supervisor desktop to offer live support.

Additionally, QA teams can analyze historical trends in negative sentiment to identify the types of contacts that individual agents, or entire teams, struggle with, and use those insights to refine their coaching programs.

31. Supporting Agents When They Become Overburdened

Example: Thrive for Webex Contact Center

Alongside sentiment, modern AI solutions can monitor stress signals amongst the agent population. Webex’s AI-powered burnout tool is an excellent example.

The tool spotlights moments of “peak stress” and, via the corresponding Thrive for Webex Contact Center solution, offers agents a 60-second reset break to “recharge and bring themselves back to center” ahead of their next conversation.

Per Webex, a financial services company recovered four times the time invested through reduced call durations and saw an increase in customer satisfaction post-deployment.

32. Ensuring Reps Keep Their Customer Promises

Example: CallMiner Eureka

Contact centers can use conversational intelligence solutions to monitor situations where agents promise a callback. From there, it verifies whether that commitment is fulfilled.

British Gas has implemented this use case, as the video below showcases, as part of an effort to identify service problems with analytics and extract insights to guide mitigating actions.

33. Identifying Vulnerable Customers

Example: Serene

Many organizations prioritize delivering excellent vulnerable customer support to foster trust, deepen loyalty, and strengthen their reputation.

However, identifying vulnerability remains a major challenge. Indeed, 2025 research shows that 58% of vulnerable customers do not disclose their circumstances to financial services providers.

Thankfully, AI solutions can help identify the warning signs. For example, Serene profiles and segments customers based on the Financial Conduct Authority’s key drivers of vulnerability, uncovering hidden risks and potential support needs.

The next step is delivering AI support experiences tailored to vulnerable customers. An example here could be an AI avatar that converses with a deaf customer through sign language.

34. Automating Outbound Voice Calls

Example: NiCE Cognigy Voice Agents

In the future, contact centers will go further to track real-time customer issues, solve them behind the scenes, and update customers pre-emptively. AI voice agents can help relay the message.

Outbound voice agents may also engage in other activities, such as courtesy calling at the start of a customer relationship to welcome the customer and proactively resolve common queries.

A final example of where they may add value is in notifying customers ahead of a subscription’s expiration and enabling a simple, AI-guided renewal process.

35. Suggesting AI Optimization Opportunities

Example: Zendesk Copilot Recommendations

Contact center admin dashboards are increasingly being augmented with AI. For example, Zendesk provides AI-driven recommendations within its admin panel to help teams optimize how they use AI to resolve specific tickets.

For instance, the AI may recommend that admins take actions such as setting up an auto-reply, decreasing the priority of a specific ticket, or routing a call type to a special group of agents.

In doing so, it predicts the potential impact of each change on key outcomes such as contact volume, resolution time, and customer satisfaction.

Salesforce has made similar moves, with its My Service Journey tool, which proactively recommends features admins should explore next and explains why.

36. Synthesizing Survey Data

Example: evaluagent xNPS (Expected Net Promoter Score)

As survey fatigue grows and response rates decline, contact centers that depend on post-call questionnaires to calculate North-Star metrics like NPS face increasing difficulties.

In response, some contact centers are turning to predictive metrics, using AI to analyze customer conversations, synthesize data, and extract insight.

For instance, in 2024, evaluagent launched an Expected Net Promoter Score (xNPS), which leverages generative AI to tag each customer contact with an xNPS score.

The vendor claims the metric is up to 90% accurate when validated against traditionally captured NPS data and offers a more comprehensive overview of how likely customers are to recommend the business.

37. Analyzing Survey Commentary

Example: Qualtrics XM for Customer Experience

While survey fatigue is real, surveys are far from dead. Today’s contact centers can use AI-infused voice of the customer (VoC) tools to transform open-text feedback into structured, actionable insights.

Indeed, the AI can help to detect themes and root causes behind negative feedback to drive focused improvements across the contact center and beyond.

Sharing this insight across the business can also inspire actions to fix broken processes that ultimately result in failure demand, especially if tied to other data points.  

38. Generating Business-Specific Transcriptions

Example: Verint Exact Transcription Bot

Speech-to-text transcriptions form the bedrock of many NLP and generative AI contact center use cases. Yet, generic models aren’t always the best fit for transcribing jargon-filled conversations.

As such, industry-specific models and innovations to improve transcriptions are emerging, helping contact centers maximize the value of their AI investments.

The Verint Exact Transcription Bot exemplifies such an innovation. It learns a business’s unique terminology and phrases to deliver transcription accuracy that improves over time.

39. Tracking Customer Contact Reasons

Example: Microsoft Customer Intent Agent

In the past, contact centers required agents to tag tickets with a disposition code that signaled why they had reached out.

Ultimately, that resulted in wildly inaccurate data, as agents often attached the wrong code when rushing to wrap up or struggled to identify the most appropriate tag.

Generative AI (and now agentic AI) is helping to automate that process, allowing contact centers to more accurately track why customers are contacting them.

In this space, such intent data is critical, as it informs contact center automation, performance management, and knowledge management strategies.

Interestingly, Microsoft has an AI agent that detects intent and collaborates with another agent to draft knowledge content. See how this works in the video below.

40. Running Root Cause Analysis

Example: Talkdesk Utterance Analysis

Some conversational intelligence solutions clip key moments in conversations where the customer is describing their issue.

Then, AI looks for patterns in what is causing the problem, saving manual analysis where teams scour through entire transcriptions to spot common pain points.

The utterance audio player tool inside Talkdesk Interaction Analytics is an example.

Using this feature after segmenting contacts by intent (as described in the previous use case) helps isolate the root causes behind the contact center’s most pressing demand drivers.

41. Benchmarking Performance

Example: NiCE Enlighten Actions Industry Benchmarks

Through its Enlighten AI suite, NICE provides an AI-powered solution that allows contact centers to benchmark performance against anonymized industry data across metrics like call reasons, customer satisfaction, and wait times.

Then, service leaders may drill down via AI search to isolate opportunities that businesses within their sector are utilizing to optimize against the outcomes.

Similarly, Genesys is using AI to transform contact center reporting, creating an Experience Index with a scoring engine at its core.

The scoring engine compares unique company data to anonymized industry data, generating a metric to benchmark over time and guidance on how to improve it.

42. Simulating Customer Journeys

Example: Dovetail

When deploying AI-augmented experiences, contact centers must ensure they not only work effectively and but improve on what came before.

Traditionally, service teams created customer personas to put themselves in the customer’s shoes, run through the new journeys, and spot opportunities for improvement.

Yet, AI agents are enhancing this process. Through solutions like Dovetail, brands are developing AI customers, trained on real customer data, which simulate customer journeys and provide feedback.

In doing so, they spotlight friction points and recommend improvements, allowing teams to continually optimize experiences.

43. Removing Background Noise

Example: Krisp Noise Cancellation

A decade ago, contact centers would install carpets, curtains, and even acoustic roof tiles to lower background noise. However, AI noise cancellation has helped change the game.

Innovators like Krisp have developed desktop apps for agents that detect background noise and use AI to generate counter sound waves that cancel it out.

Krisp calls this “anti-noise,” and it works in both directions, so neither the agent nor the customer hears background noise, even if only the agent has the app installed.

44. Altering Agent Accents

Example: Tomato.ai Accent Softening

Some voice AI innovators sell accent softening software to global contact centers, finding particular success in the outsourcing space.

Indeed, Teleperformance, Alorica, and Everise are prominent examples of business process outsourcers (BPOs) that have deployed the technology worldwide.

It works by leveraging speech-to-speech neural networks to modify real-time audio, altering the agent’s accent and simplifying communication.

Tomato.ai has launched an API for all communications platforms, including CCaaS solutions, to integrate accent softening and noise reduction.

Below is an example of the company’s AI softening a Filipino accent.

45. Translating Customer Conversations

Example: Sanas Language Translation

The latest example of contact center voice AI is near-real-time language translation, which allows service agents and customers speaking different languages to interact with each other.

Previous applications of this technology relied on a chain of speech-to-text, translation engines and text-to-speech, resulting in awkward pauses between speakers.

However, the next generation of this technology utilizes LLMs to capture context, tone, and intent within an architecture that integrates neural speech models, which preserve the speaker’s voice characteristics during translation. That accelerates the process.

Sanas delivers live language translation through an app that also integrates real-time noise cancellation, enabling seamless communication across voice and video.

46. Automating Desktop Processes

Example: UiPath Desktop Automation

While the enterprise technology industry swoons over the prospect of AI agents automating more customer experience workflows, old-fashioned robotic process automation (RPA) still does much of the drudge work in contact centers.

For instance, RPA embedded into the agent desktop is still commonplace, helping to copy and paste information between systems, auto-fill forms, and send confirmation emails.

Traditional vendors of this technology, like UiPath, are quickly pivoting to embrace agentic AI. However, they still have a deep portfolio of more basic RPA solutions that can help simplify contact center agent experiences.

47. Authenticating Customers

Example: Pindrop Fraud & Deepfake Detection

Voice biometrics systems, which use the unique characteristics of a customer’s voice as a passkey similar to a fingerprint, are a longstanding contact center AI use case.

However, with the rise in voice deepfakes, some AI leaders have called this technology into disrepute. OpenAI CEO Sam Altman is a prime example, warning that an overreliance on voice biometrics could lead to an AI ‘fraud crisis’.

However, it can still deliver value as part of a multi-layered customer authentication strategy. Meanwhile, leading providers, like Pindrop, are also releasing tools to better detect deepfakes.

Other AI customer authentication tactics are also emerging. For instance, Vonage uses AI agents and Network APIs from Ericsson (its parent company) to verify whether the caller recently changed their phone number. That offers another layer of protection against SIM swap attacks.

48. Extending the Customer Support Function

Example: Agentforce for Field Service

Delivering proactive customer service shouldn’t fall solely on contact centers; everyone who interacts with customers should share information that helps create a friction-free experience.

As such, when contact centers engage in root cause analysis and pinpoint common customer knowledge gaps that lead to increased demand, digital transformation, sales, and marketing teams should help proactively fill these.

Field service teams are another key stakeholder, and AI can ensure they pass on useful information when they interact with customers.

Consider a solution like Agentforce for Field Service, which automates their pre-work briefs. Contact centers can ensure these briefs incorporate key information to pass onto the customer, related to the job, which helps avoid follow-up.

49. Redacting PII

Example: Verint PII Redaction Bot

As contact centers feed more data into external AI models, they risk violating international privacy regulations, from Europe’s General Data Protection Regulation (GDPR) to the U.S. Freedom of Information Act (FOIA).

To mitigate these risks, it’s essential to redact personally identifiable information (PII) from conversation transcripts before processing them with AI.

AI-powered PII redaction solutions are increasingly stepping into this role. For example, the Verint PII Redaction Bot automatically removes sensitive information, such as credit card and Social Security numbers, from voice recordings, helping organizations stay compliant.

50. Tracking Compliance

Example: NiCE Compliance Center

With a conversational intelligence solution, contact centers can monitor all customer conversations, extracting insights into compliance breaches, consent management, and script adherence.

Some contact center providers package up these capabilities as part of a specialized compliance offering. For instance, NiCE presents a Compliance Center.

The Compliance Center uses AI to identify relevant customer records and call recordings that fall under specific compliance rules, such as GDPR’s right to be forgotten. This reduces manual effort and the risk of human error.

Additionally, users may set up policies to automatically delete both media and metadata according to defined rules, time ranges, and customer requests. AI ensures that these deletions are precise and complete.

Lastly, the system uses AI to retrieve, review, and process policies efficiently. Once a policy is applied, it generates summaries and reports that serve as compliance evidence, streamlining audits.

Which AI Use Cases Should My Contact Center Deploy?

There are many ways contact centers are using AI in 2026. Yet, ultimately, the most valuable AI use cases are those that address real, measurable challenges.

By focusing on the areas that will deliver the greatest impact, whether it’s boosting sales conversion, enhancing agent training, or automating customer contact, organizations can ensure contact center AI investments drive meaningful results.

The key is clarity. Contact center leaders should understand the problem they wish to solve and define the business outcomes they aim to achieve.

With that focus, AI becomes not just a tool but a powerful driver of efficiency, performance, and customer satisfaction.