AI-powered contact center software isn’t exactly new, but it has become much more sophisticated over the last few years. Most contact centers use some form of AI to automate tasks and help customers, but a comprehensive AI solution can assist with things like analyzing customer conversations, automating repetitive tasks for employees, identifying problem areas in the customer journey, and giving customers self-service opportunities to resolve their issues quickly.

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Contact Center AI: Hype vs. Reality

This guide sheds light on what the latest contact center AI software solutions can do, some of the challenges in implementing AI software, and crucial features to look for. Some of the topics we cover include:

• Ensuring customizability to improve the user experience and adapt software to your unique workflows
• How much contact center AI software has improved to reduce language barriers and streamline workflows
• Using AI software to enhance agent training
• Important considerations for implementing contact center AI software to reduce bottlenecks and agent pushback

In this article:

• What is contact center AI software?
• How is AI used in the contact center?
• 25 tips to getting the most out of contact center AI software
• Frequently asked questions

What is contact center AI software?

Contact center AI software refers to a category of technologies that leverage AI to automate or improve the operations of a call center or contact center. These tools offer a range of capabilities, such as automating various tasks, providing agents with real-time insights and guidance, and improving customer interactions.

How is AI used in the contact center?

As new AI technologies come into play, such as agentic AI and generative AI, more and more use cases for AI in contact centers have cropped up. From helping businesses understand their customers to training agents to provide top-tier customer service, AI has a place in almost every facet of contact center operations.

72% of organizations in the tech sector have embraced AI for #CX data analysis, according to our annual CX Landscape Report.

Read how tech companies that embrace new solutions are driving more business value from their customer interactions. ⬇️

— CallMiner, Inc. (@CallMiner) July 26, 2024

Here are a few common use cases for contact center AI software:

• Agent guidance: AI technology can listen in on conversations and guide agents toward helpful responses for customers
• Analytics and reporting: Contact centers use AI to collect, organize, and make sense of call data through detailed reporting, which they use to make informed decisions on training, customer needs, and more
• Customer assistance: AI tools can help customers find the answers they’re looking for without needing to contact an agent via chatbots, knowledge bases, and automated response systems
• Workload prediction: Contact center managers can improve their scheduling strategy by using AI to predict workloads and scheduling needs
• Quality control: Managers can use AI-driven data collected from calls to review agent performance and productivity

25 tips to getting the most out of contact center AI software

1. Users get personalized interfaces to streamline workflows. “AI technology can continuously learn from a user's interaction with software and adjust accordingly. For instance, our AI capabilities at Businessmap adapt to user preferences, thus ensuring a streamlined and intuitive product experience.”

- Karolina Dacheva of Businessmap, 32 examples of how AI can improve customer experience, CallMiner; X/Twitter: @CallMiner

2. AI contact center software can scale operations. “Our CX Trends Report found that 71 percent of organizations use digital channels primarily for first contact and the phone as the primary channel for resolving complex customer issues or escalations. In fact, consumers rank the phone as the top preferred channel for nuanced problems.

“AI can help support teams scale by directing customers to digital channels for quick questions and straightforward requests. This can also reduce call center overhead costs, as digital channels are typically more cost-effective than the phone.

“AI in contact centers even accelerates agent onboarding, reducing costly training time. AI-powered transcriptions enable managers to perform quality control on calls and train new agents. For example, AI-powered tools—like Klaus—automate QA by reviewing and analyzing interactions, pinpointing areas for improvement, and automatically sending personalized feedback surveys. AI can also provide agents with guidance by offering real-time suggestions on how to resolve an issue.”

- Hannah Wren, AI call center: A complete guide, Zendesk; X/Twitter: @Zendesk

3. Spend less time crafting knowledge bases for agents. “Salesforce research shows that 61% of customers prefer self-service tools for simple service issues. However, to do that, a business needs a large knowledge base that customers can search through to find a solution.

“Service agents are often tasked with publishing knowledge articles after resolving a case. But it takes time for agents to manually create, review, and publish an article, which keeps them from helping customers in need.

“Contact center AI can automatically generate a knowledge base article after a support case is closed by pulling from case notes, message history, and data from other service tools. From there, your agent just needs to review the article to ensure accuracy and add it to the queue for approval. This takes the pressure off agents to write articles from scratch.”

- What Is AI in the Contact Center? Your Complete Guide, Salesforce; X/Twitter: @Salesforce

4. AI may increase overall security in the contact center. “While security and IT risks of outdated contact center equipment are a main barrier to adoption, once deployed as part of a digital transformation initiative, AI actually makes contact centers more secure. Three in four CX professionals agree that AI tech will allow customer data to be more secure than a live agent, and four in five agree that AI will significantly help companies improve identity and authentication security in the next two years.”

- Ben Rigby of Talkdesk, Contact Center AI: An Interview with Talkdesk’s Ben Rigby, Connections Magazine; X/Twitter: @Connections_Mag

5. New AI technologies may help reduce stress in contact centers. “Japan’s SoftBank Corp., a telecommunications firm that is a subsidiary of Softbank Group (along with the Softbank Vision Fund), has unveiled an AI-driven technology that alters the voice of angry customers, giving them a calmer demeanor, which it says can help protect workers from the mental tolls of being screamed at and harassed.

“The technology is expected to be available in fiscal 2025, after additional fine-tuning, according to the company. It changes angry rants of frustrated customers to a calmer tone.”

- Chris Morris, These companies want to use AI to make call center jobs less horrible, Fast Company; X/Twitter: @FastCompany

6. Agents spend less time wrapping up calls. “AI can significantly reduce the amount of time agents spend on call wrap-ups by automating two tasks: summarizing tickets after calls and providing full transcriptions.

“AI-powered systems can listen to call recordings in real time or post call and generate accurate call summaries based on the conversation. Utilizing NLP, AI algorithms analyze the conversation content, identifying key points, topics discussed, and important details. Based on the analysis, AI generates a concise summary highlighting crucial information, such as customer complaints, resolutions, action items, and any required follow-up.

“Call transcription tools use AI to convert spoken conversations into written text, providing a full transcription of each call. AI can transcribe recorded calls or do it in real time. Both generative AI summaries and conversation transcripts are automatically added to the customer conversation, saving agents the time and effort of manually summarizing each call.”

- Hannah Wren, AI call center: A complete guide, Zendesk; X/Twitter: @Zendesk

7. Supervisors keep tabs on agents to improve performance. “AI analytics helps supervisors see the strengths and improvement areas of the call center as a whole and each agent individually to ensure the best customer experiences. For example, with CallMiner’s Eureka Analyze, supervisors can view customer scoring and agent quality scoring in an easy-to-use dashboard to learn more about what each agent does well and where they can focus on improvement.

“This drill-down allows supervisors to coach agents in key areas and identify ways for the team to enhance customer interactions.”

- How AI analytics can improve call center performance, CallMiner; X/Twitter: @CallMiner

8. Predictive analytics offers insights on future customer behaviors. “Predictive analytics has the unique ability to provide insights even before the customer voices their concerns or questions. The key lies in consolidating all relevant data onto a single platform.

“By combining various data points such as interaction histories, financial transactions, preferences, and demographics, the system becomes incredibly adept at forecasting. This not only offers real-time insights but also predictive intelligence regarding possible future customer behaviors.

“Such comprehensive and anticipatory data significantly boosts agent productivity, as they are equipped with immediate and predictive information to effectively handle customer interactions. In practice, while implementing artificial intelligence in call centers for customer feedback, it is observed that there is typically a 20% rise in customer retention rates.”

- Cassy Bayona, AI in the Call Center Industry: Benefits, Trends, and Solutions, Helpware; X/Twitter: @helpwarecom

9. AI-powered software can improve agent efficiency. “AI excels at managing repetitive, low-value tasks, which lightens the load for human agents. This allows them to concentrate on more significant aspects of customer service, enhancing overall customer and agent efficiency.”

- Top 13 AI Call Center Software for 2024, Towards AI; X/Twitter: @towards_AI

10. Route callers to the right agent using AI. “By analyzing past interactions, customer data, and even the reason for the call, AI can route inquiries to the most suitable agent, ensuring a more effective and hyper-personalized interaction. This not only saves time but also increases the likelihood of first-call resolution, enhancing customer satisfaction. HubSpot’s State of AI survey data reveals that 50% of service reps noticed an improvement in end-user experience with AI-driven routing.

“Companies worldwide continue deploying routing technologies for various purposes including but not limited to predicting call topics. As an example, Capital One’s AI-based call center uses a system that analyzes clients’ data to figure out their needs. After that it routes calls to the best-suited agents. This reduced transfer rates by 50% and improved satisfaction scores.

“One more example is Wells Fargo. They implemented an AI system that analyzes past calls and online behavior to predict users’ concerns and route them to specialized agents. This significantly reduced wait times and improved customer service.

“Bank of America utilizes predictive routing to identify high-priority calls. They implemented AI-powered sentiment analysis to identify customers with high urgency or anxiety calls, automatically prioritizing those calls for immediate attention.”

- Illia Vietrov, AI Call Center Is No Longer a Myth but a Reality: 6 Steps for Painless Implementation, Master of Code Global; X/Twitter: @master_of_code

11. AI software helps customers retain their privacy. “Customers shouldn’t need to worry about their sensitive information provided over the phone or email getting into the wrong hands. AI can listen for and redact sensitive information when it’s provided across multiple customer contact channels, allowing agents to get the information they need without having access to sensitive data.”

- 10 AI use cases for call center performance and effectiveness, CallMiner; X/Twitter: @CallMiner

12. Generative AI can improve automated email responses. “Automated email responses are a common way of handling customer inquiries in contact centres. However, they have some limitations and challenges, such as:

• They may be unable to help address complex or specific questions requiring human intervention or expertise
• They may sound impersonal, robotic or generic, affecting customer satisfaction and loyalty
• They may not be able to adapt to different contexts, situations or preferences of the customers, such as tone, language or urgency
• They may need help to provide personalized or proactive suggestions, recommendations or solutions that can enhance customer experience and value

“Gen AI is a new technology that can help overcome these issues and create more effective and engaging automated email responses. Gen AI uses natural language processing and generation to understand customer messages and generate relevant, coherent, honest responses. Gen AI can also:

• Handle a more comprehensive range of customer inquiries with higher accuracy and confidence
• Use a conversational and human-like tone that matches the customer's mood, personality and expectations
• Tailor the responses to the customer's profile, history and preferences, such as product interests, purchase behaviour or feedback”

- Dr. Jagreet Kaur Gill, Generative AI in Contact Center | The Advanced Guide, Xenonstack; X/Twitter: @xenonstack

13. A solid AI infrastructure is the key to a tech-forward, efficient contact center. “The first step towards the success of an AI transformation project is to establish an integrated call center technology infrastructure. Integrating technologies such as CRM, IVR, VoIP, Speech analytics, learning management systems, etc., resolves challenges of evolving business dynamics and providing benefits such as:

• Resilient & flexible call center operations
• Superior adeptness with smart call routing
• Improved productivity & contact center efficiency
• Personalized experiences with integrated CRM
• Reduced customer churn
• Improved agent performance
• Overall improvement in customer & agent experience

“An integrated infrastructure ensures confidentiality, integrity, and availability of critical data. Therefore, critical insights are visible across systems that empower call center agents to effectively serve their customers.”

- Jim Iyoob, Guide for using Artificial Intelligence in Call Centers, LinkedIn

14. When implementing AI tools, agent resistance can be a challenge. “Challenge: Employees may resist adopting AI due to fears of job displacement or a lack of understanding of AI benefits.

“Solution: Provide clear communication about the benefits of AI, involve staff in the implementation process, and offer training to ease the transition and foster acceptance.”

- AI Contact Center: The Ultimate Guide for Beginners, Bigly Sales AI via Medium; X/Twitter: @BiglySales

15. Use AI with care; let agents continue to use their best judgment. “Unfortunately, some contact center leaders are repeating errors made with early chatbots by viewing the current generation of AI applications as straight-on replacements for human capabilities. Predictably, this is causing a power struggle between people and machines.

“The Wall Street Journal (paywall) published an article that describes how some call centers are using machine-learning models to take decision-making responsibility away from agents. For example, they're scanning conversations and recommending what agents should say or do next based on words and sentiments expressed by customers. But agents are pushing back.

“Many agents interviewed said they value AI’s ability to access information quickly for decision making, but they object to being forced to use AI-generated scripts against their own judgment. They'd rather trust their own judgment when it comes to the inherently emotional aspect of customer service, especially since AI assistants can still make errors.

“A quick perusal of the WSJ article’s comment section makes it clear that customers aren’t fans either. They want their problems solved with the nuance and empathy that only a human can provide.”

- Jennifer Lee, How Can AI Fit Into Customer Service Call Centers Effectively?, Forbes; X/Twitter: @Forbes

16. AI software can upgrade outdated IVR systems. “With AI, the frustrations of old-fashioned clunky IVR systems are a thing of the past. AI-powered IVR systems can understand caller requests, analyze their needs, and automatically route them to the right agent or department in seconds. With AI-powered IVRs, customers can express their needs more clearly and even complete simple tasks without speaking to an agent. ”

- Rebekah Carter, 20 Contact Center AI Use Cases to Transform Agent and Customer Experiences, CX Today; X/Twitter: @cxtodaynews

17. IVR authentication could be right around the corner. “It’s already common practice to rely on knowledge based authentication methods, asking a customer to input their account, PIN, or social security number to verify their identity.

“New biometric methods use ‘voiceprint’ technology to verify a customer simply by the sound of their voice. This identifying information can be gleaned and stored after the customer repeats a series of specific phrases or in the course of casual conversation.

“The beauty of biometrics is the convenience they afford the customer. No more wasting time inputting the same numbers you’ve provided the last 10 times you called your bank or auto loan servicer.

“It’s also quite accurate, as every caller’s ‘voiceprint’ is distinct. Still, just as with facial recognition technology, your voice data can be stolen and improperly used. We aren’t likely to see the widespread adoption of biometric authentication features — at least, not without express customer consent — until certain data security and privacy concerns are addressed.”

- Corry Cummings, 5 Things Call Center AI Can Do Today and What’s on the Way, TechRepublic; X/Twitter: @TechRepublic

18. Develop a roadmap for the proper implementation of AI software. “Developing a roadmap is about setting clear objectives and timelines for AI call center integration. This structured plan should outline the steps for implementation, including testing phases and benchmarks for success. It serves as a guide, ensuring that the process of AI call center software integration is organized and progresses towards specific goals.”

- Illia Vietrov, AI Call Center Is No Longer a Myth but a Reality: 6 Steps for Painless Implementation, Master of Code Global; X/Twitter: @master_of_code

19. Speech analytics has powerful potential in contact centers. “Remember the days when quality assurance meant listening to a handful of random calls and hoping they were representative? Those days are gone. AI-powered speech analytics is like having a super-smart assistant listening to every single call, picking up on things even the most attentive human might miss.

“MVP use case: A major insurance company implemented speech analytics and discovered that a significant number of customers were calling about their renewal date. The policy renewal date was available online, but it was so far deep in the customers' accounts nobody thought of looking there. By just adding the renewal date right next to the customer policy details, they reduced call volume by 15% and improved customer satisfaction scores.

“Future use case: Use speech analytics to identify your top-performing agents based on customer sentiment and successful outcomes. Then, analyze their calls to create best practice guides and training materials.”

- Manu Dwievedi, No More Hold Music? AI in the Contact Center Is Here, CMSWire; X/Twitter: @cmswire

20. AI tools can integrate with your existing infrastructure. “Implementing artificial intelligence in call centers shouldn’t mean tearing up any existing infrastructure for the sake of new and shiny tools. Nobody wants to risk disrupting stable, functioning operations or compromising the secure connection between your QA software and customer relationship management (CRM) tools.

“The good news is that implementing contact center AI doesn’t need to be risky and doesn’t mean a total infrastructure overhaul. Thanks to these technologies' flexible and adaptive nature, integrating them into your existing workflows is relatively easy.

“Secure contact center AI tools should easily integrate into your CRM and QA software, enabling you to safely use them together to gather data and automate processes without risk.”

- David McGeough, What's the State of Call and Contact Center Generative AI?, Scorebuddy; X/Twitter: @score_buddy

21. Ethics is still a concern. “Ethical considerations regarding bias and fairness are another important challenge to deal with in deploying GenAI in contact centers. AI systems can generate biased outputs if biases are present in their training data, which may result in unfair treatment of certain customer demographics. Prioritize the ethical design of AI models during AI training and administer bias detection and mitigation strategies.”

- Liz Ticong, Generative AI in the Contact Center: Transforming Workflows, eWEEK; X/Twitter: @eWEEKNews

22. Can your software adapt to your unique workflows? “Customization is crucial when embedding artificial intelligence into the contact center. When choosing your agent-assist software, it’s important to ensure that you can adapt the technology to meet your specific needs. For instance, you should be able to create automated workflows (preferably with a low-code or no-code solution) that help guide agents through different conversations.

“When comparing your AI-powered options, consider whether you can feed your brand guidelines and other best-practice documents into the system. Also, consider how you can adjust the guidance offered to your agents to ensure you remain compliant with industry regulations and deliver exceptional customer experiences.

“It’s also worth examining the machine-learning capabilities built into these technologies. Agent assist tools that can learn from interactions over time and become more effective at coaching and supporting agents will drive better results in the long term.”

- Rebekah Carter, How to Compare AI Agent Assist Software in 2024, CX Today; X/Twitter: @cxtodaynews

23. AI can buffer language barriers. “We’ve all experienced calling customer support, and an offshore customer service agent with a heavy accent answers the call. Sometimes, it’s nearly impossible to understand the agent. New technologies are neutralizing accents. A year ago, the software sounded a little ‘digital.’ Today, it sounds almost perfect.”

- Shep Hyken, The State Of CX And The Customer Service Contact Center, Forbes; X/Twitter: @Forbes

24. Generative AI-powered voice cloning has multiple use cases. “Voice cloning creates a synthetic voice that sounds like a target speaker. It has many applications in contact centres, such as personalizing customer interactions, enhancing brand identity, and reducing agent fatigue. However, traditional ways of voice cloning have some limitations, such as requiring a large amount of high-quality data from the target speaker, being prone to errors or inconsistencies, and needing more time to update or modify. Gen AI is a new approach to voice cloning that leverages deep learning and generative models to create realistic and consistent synthetic voices with minimal data and effort. Gen AI can help contact centres achieve higher customer satisfaction, loyalty, and retention by providing flexible and scalable voice cloning solutions.”

- Dr. Jagreet Kaur Gill, Generative AI in Contact Center | The Advanced Guide, Xenonstack; X/Twitter: @xenonstack

25. AI contact center software relies on thorough agent training to succeed. “Real-time insights can greatly help your customer service team to provide better experiences – but you’ll need to train your human agents to work with your new technology. Your contact center leaders will also need to know how to interpret the insights and drive change.”

- Rosemin Anderson, Contact center AI (artificial intelligence), Qualtrics; X/Twitter: @Qualtrics

The 2024 CallMiner CX Landscape Report is here! One key takeaway? #AI continues to transform the contact center and #CX.

Findings reveal 👇
💡87% of CX leaders say genAI is key for their teams
💡91% agree AI will help optimize #CX strategies
💡and more!

Download the report ⬇️

— CallMiner, Inc. (@CallMiner) September 10, 2024

The CallMiner platform is a powerful, AI-driven conversation analytics platform that monitors 100% of customer interactions to derive actionable insights that drive results in the contact center and beyond. Request a demo today to discover how CallMiner can transform your contact center operations with the power of AI.

Frequently asked questions

How is AI software used for customer service?

AI software can be used for multiple customer service-focused tasks. One of the most common ways companies use it is through virtual assistant chatbots, which respond to customer inquiries or direct their inquiries to a person who can help. AI software can also listen to customer conversations to gather data and insights for companies to improve customer service, provide self-service solutions for customers, and offer scripts to agents to optimize their conversations with customers.

What do contact centers use AI for?

Contact centers use AI for a multitude of tasks, like training agents, providing knowledge bases to agents and customers, and assisting customers with their questions using chatbots. AI can also reduce wait times by routing calls to the right place, help companies learn about customer behavior, and transcribe calls to use for training or quality assurance purposes.

Will AI replace contact center agents?

While some experts believe AI will replace contact center agents, many others believe AI will simply continue to help agents complete their jobs efficiently and accurately. AI can increase agent productivity by automating tedious tasks and handling repetitive customer inquiries.