Top 6 use cases for AI in contact centers

AI-powered solutions and tools are shaping the way organizations engage with and manage their workforce. Generative AI chatbots in call centers excel at providing efficient troubleshooting and technical support to customers. They have the ability to guide customers through basic troubleshooting steps for common technical issues. By offering step-by-step instructions or interactive tutorials, chatbots empower customers to resolve problems independently, without requiring assistance from a live agent.

Listen to Spotify long enough and soon the recommendations on your home screen will surprise you with their accuracy. The same goes for YouTube, which is constantly adjusting recommended content based on your interactions with the system. In the context of the customer journey, AI is behind many of the conveniences that were once science fiction to the modern biped.

This  provides a deep data source for the algorithm to consider, further enriching the overall customer and agent experience. It follows that 80% of customer service agents agree that having quick access to knowledge has an impact on key contact center metrics, such as case resolution time. By now, you’ve likely encountered some form of generative AI (GenAI), such as Gemini or ChatGPT.

Augment agents with AI

The most significant impact is the ability to deliver personalization at scale, closely followed by the opportunity to leverage call data for business insights. By tailoring interactions based on a deep understanding of the customer’s emotional state, AI enables a more empathetic and personalized customer experience. This evolution marks a significant leap towards humanizing artificial intelligence in contact centers, promising a future where technology and emotional insight converge to redefine customer engagement. Emphasizing that AI is designed to handle routine inquiries and data analysis allows agents to focus on more complex and rewarding customer conversations, thereby improving job satisfaction. Being transparent about the planned use of artificial intelligence in call centers is key to building employee trust. Beyond embedding AI across self-service, routing, and the agent experience—as well as into chat and voice bots—enterprises can take AI for customer experience a step further.

Such automation also ensures accuracy in data management and provides a streamlined process for tracking customer interactions, enhancing the overall operational efficiency of the contact center. You can foun additiona information about ai customer service and artificial intelligence and NLP. Workflow automation streamlines various operational processes within the contact center. It includes tasks like assigning agents to queues, managing customer interactions, and ensuring compliance with service level agreements (SLAs).

To do this, you’ll need to dive into reviews and testimonials to gauge user experiences and the overall usefulness of their tools. Additionally, AI’s ability to analyze customer history and preferences paves the way for hyper-personalized experiences. Each interaction can be tailored to the individual, offering solutions and recommendations that resonate on a personal level. Providing comprehensive training on using artificial intelligence in call centers can help demystify the technology and highlight how it can enhance job performance instead of diminishing the value of human workers. Selecting reliable vendors for contact center AI solutions is one of the best ways to help fortify your data privacy. Vendors with a proven track record of compliance and robust data protection can significantly mitigate the risk of a breach.

Start by identifying goals like improving customer satisfaction, reducing costs from automation, or boosting teams’ productivity. An AI chatbot can instantly retrieve relevant knowledge to answer your customer queries. This not only accelerates response times but also ensures customers receive accurate and consistent information 24/7. Advanced analytics on call data to uncover insights to improve customer satisfaction and increase efficiency. The path of least resistance would be to simply reduce agent headcount, but that will only be effective if AI is also deployed in other ways to keep service levels high with fewer agents. As such, cost reduction should be a core use case, but not in isolation from everything else needed to provide great CX.

Forecasting tools in contact centers predict incoming call volumes and schedule agent shifts accordingly. By analyzing historical data and current trends, these tools ensure optimal staffing, reducing both understaffing and overstaffing scenarios. As communication preferences change, traditional phone support is no longer the only choice for customers. This knack for understanding and responding suits interactions better, easing customer frustration and boosting satisfaction and loyalty levels.

Leveraging AI to Transform Customer Service

And AI is poised to improve efficiency and productivity within the contact center. According to our research, 95% of decision makers at organizations with AI report cost and time savings, and 92% say generative AI helps them deliver better customer service. Every day, I speak with service leaders who are excited about the potential for generative contact center AI. 66% say that their employees don’t have the right skills to successfully put generative AI to use. So let’s look at what AI in the contact center is, its benefits, and four ways you can use contact center AI, along with example use cases and tips to get started.

With AI for customer service, you get AI-powered knowledge management, intelligent routing and triage, and sentiment analysis—everything you need to level up your call center. But sometimes, they get passed around, or agents don’t know the context of the situation, causing customers to repeat the same information. To fix this, CX leaders plan to combine phone systems with AI tools, enhancing the original communication channel with conversational AI without sacrificing experiences with live agents. AI in call centers can detect customer sentiment through NLP algorithms that analyze verbal cues, tone, and language patterns in real-time or recorded conversations. It can determine positive, negative, or neutral sentiments, enabling agents to gauge situations, respond effectively, adjust their tone, or escalate interactions when dealing with angry customers.

AI provides the opportunity to implement self-service, improve agent efficiency, and automate routine tasks, allowing your call center to become more efficient and focus on providing a better experience to customers. Tools like Google Cloud AI and IBM Watson can be utilized to power many key tools that CX providers can utilize to deliver a better experience. This includes functionality like agent assistance, speech analytics for conversational IVRs, suggested responses during CX interactions, sentiment analytics, and much more. Implementing a self-service system can help reduce wait times, reduce call abandonment, and improve average handle time by lowering the amount of incoming customer calls that go into the call queue. Interactive voice response IVR delivers options through the phone for callers, allowing them to choose where to go with their personal voice.

AI contact center tools make it easy for your business to handle more conversations and provide exceptional customer experiences without adding staff. With features like generative AI, AI-powered chatbots, call summaries and transcriptions, and data-driven insights, you can take your CX to a whole new level. AI tools allow employees to reduce repetitive, routine tasks and focus on more strategic, value-added work, boosting productivity and job satisfaction.

Google Cloud’s Generative FAQ for CCAI Insights allows contact centers to upload redacted transcripts to unlock this capability. The tool may also generate conversation highlights, summaries, and a customer satisfaction score to store in the CRM. These case studies illustrate some of the key benefits of using artificial intelligence in the call center environment.

It’s the best way to keep on top of fluctuating customer expectations and market changes, as it constantly evaluates data in real-time. IVR systems are automated solutions that guide customers to the right department using pre-recorded messages and speech recognition. This technology manages customer calls effectively, keeping them engaged and reducing the likelihood of repeated calls.

Maybe one day far in the future, but as of right now, artificial intelligence is best used as a supplementary tool for agents and supervisors. There will always be those very niche, specific questions that are best answered by a human. AI can help surface useful documentation and other answers for a live agent, but may not always be able to answer every single “edge case” question. The second point is that the accuracy and relevance of the information is critical to agent performance.

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. Thankfully, implementing AI technology with call center software makes delivering an exceptional customer experience easy. Our guide details the benefits, best practices, trends, and ways to use AI call center tools for voice and digital channels. An artificial intelligence (AI) call center is a customer service operation that uses AI technologies to manage customer inquiries, interactions, and tasks across voice and digital channels.

• Additionally, businesses can take advantage of improved contact center visibility through AI-derived analytics, metrics and KPIs.
• These analytics play a crucial role in shaping customer service strategies, ensuring they are data-driven and customer-centric.
• The merging of AI and call centers signifies a shift towards more efficient, responsive, and intelligent customer service operations.
• You can use Topic Analysis to organize calls by topics such as products, competitor mentions, and more.
• For example, MiaRec is a Conversation Intelligence platform that provides Voice Analytics and Generative AI-powered Automated Quality Management solutions.
• Generative AI automates responses to customer inquiries on social media platforms, enhancing engagement and maintaining an active presence.

Paired with personalization features brought about by integrating with customer relationship managers (CRMs), customers can have a unique, personal experience that’s memorable and effective. In addition, AI-driven routing systems can help manage the overall workload within the call center by distributing calls based on agent availability and skill level. This prevents individual agents from becoming overwhelmed or burnt out, helping boost customer satisfaction and agent morale. Generative AI has already disrupted many industries, including call and contact centers.

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. AI and automation can handle routine, time-consuming tasks—such as call routing—freeing agents to focus on more complex and high-value interactions.

The use cases are vast and transformative, from sentiment analysis and virtual agents to automated summaries and personalized training materials. Using intelligent routing in a call center greatly reduces hold times by efficiently directing customers where they need to go — including across multiple call centers and branches if ai use cases in contact center needed. It works by using data about the caller’s digital journey, such as the webpages they visited, to route them according to their intent. Agents are also presented automatically with pertinent information about callers and their intent. That helps to drive higher agent productivity and a better overall customer experience.

According to a recent Gartner survey, 62% of millennials and 75% of Gen-Z customers prefer self-service almost all the time, even when they have an option of contacting customer support. This compares to 19% of baby boomers and 43% of Gen X customers who report they would do the same. Lastly, multi-mode GenAI-powered features – for instance, interpreting images sent as part of a customer service interaction – will become more common. Generative AI will allow us to democratize our AI applications for customer service, bringing them to many more customers.

Will AI replace customer service roles?

AIMultiple informs hundreds of thousands of businesses (as per Similarweb) including 60% of Fortune 500 every month. The decision to deploy AI should be viewed holistically, as there will be benefits that extend beyond the contact center that will impact the overall organization. Equally important is the fact that AI is constantly evolving, and while it won’t be perfect from the start, the benefits will accrue as usage increases. Data is the oxygen that drives AI, and as the data sets grow, the outcomes will be more accurate and more precise.

This provides enterprises with true VOC in the digital channel, not just the words the customer thinks the company would use, but their own description of the pain points as queries. ML models like recommendation models improve the CX by making it feel more personalized https://chat.openai.com/ and intuitive, demonstrating that you know your customers. Coveo’s own ML models include Advanced Relevance Tuning, Query Suggestion, and Event/Product Recommendation. Start slowly and build your contact center AI program out as your business skills-up on AI.

Ready to transform your contact center with conversational AI, automated sentiment analysis, GPT auto-scoring, and more? Explore Scorebuddy’s quality assurance solution to harness the full potential of artificial intelligence in your operations. Contact center AI can work alongside agents in real-time, giving them powerful tools to improve the customer experience. It can offer prompts and suggestions from your internal knowledge base, analyze interactions in real-time, and even help translate text conversations to other languages to enable multilingual support. When customers type a question, NLP helps the system understand the query’s intent and context.

AI-powered self-service is most effective when used to handle narrowly defined, predictable tasks such as authenticating callers or providing account balances. As more of these routine tasks are handled by self-service, the mix of interactions handled by agents will shift and become more complex. Many agents will find it more engaging and satisfying to spend their time on value-added problem solving rather than on mundane, repetitive tasks. Early uses of AI and machine learning (ML) showed up in call centers years ago in intelligent routing solutions and interactive voice response (IVR) systems. These “cutting-edge” technologies at the time helped managers better distribute and manage voice calls more efficiently.

Best practices, code samples, and inspiration to build communications and digital engagement experiences. But to get the most out of your AI integrations, you need to give your AI tools high-quality data. Now that you have a better understanding of basic AI functionality, let’s look at the top six use cases for contact centers. However, if you have a contact centre with hundred of agents and thousands of phone calls, knocking 30 seconds per call of the back will add up to some serious productivity gains. After the call, you can use AI to summarise calls, write these summaries in to systems of record, and generate post-call disposition codes.

AI use case #4: Chatbots and virtual agents

We always start with the challenges first, and never the solutions, which is why we’re so successful at helping our customers get every drop of value from their investments. Not only do customers expect access to their preferred choice of contacting you, but they expect accurate and timely resolution no matter which path they take. For the contact center leaders interested in using AI to improve the customer experience, the possibilities are many. Its no-code environment allows businesses to create and deploy AI chatbots without needing deep technical expertise.

It leads to a scenario where employees can concentrate on enhancing customer satisfaction while the technological aspect of the operation focuses on efficiency and speed. Automation in contact centers leads to a significant boost in efficiency and productivity. Tasks that are prone to human error, such as data entry, can be reliably managed by automated systems, minimizing mistakes and streamlining processes. Additionally, automating routine post-call administration allows for meticulous data handling without the time constraints that human operators might face. This increase in accuracy and efficiency directly translates to improved overall productivity of the contact center.

The focus extends to various automation tools – from AI chatbots and automated forecasting to proactive messaging – illustrating how they contribute to superior customer satisfaction. One reason contact centers might embrace AI is to enable self-service, which can decrease wait times and offer 24/7 access to support. Companies that implement self-service capabilities can deliver on these customer demands and improve customer experience (CX).

Rick’s Custom Fencing & Decking has five retail locations where sales agents take calls and schedule appointments. Coaching based on such a small sample of calls was prone to human error and didn’t give a full picture of agent performance. CHRISTUS Health Plan, an international faith-based, not-for-profit headquartered in Irving, Texas, deployed Invoca’s platform to automate QA in its call center and better train its call center agents.

Data, analytics and software engineering

Beyond this, leveraging the compliance features of quality assurance software provides an additional layer of security, helping to align with best practices and regulatory requirements. The sudden surge in the popularity of artificial intelligence in call centers also brings significant data privacy concerns. Even companies like OpenAI aren’t immune to data breaches, having reported a data breach in 2023 that exposed personal customer information.

Call center AI plays an important role in ensuring industry or regulatory compliance by monitoring customer conversations in real time. AI can ensure that mandatory disclosures are communicated to callers effectively by analyzing dialogue and detecting specific keywords or phrases. Companies that recognize the potential of Chat GPT AI-powered contact center solutions are gaining a significant competitive edge. As LLMs continue to evolve and their capabilities expand, their role in contact centers will become even more indispensable. Customers no longer accept call center experiences where they are put on hold or passed between different departments.

In the rapidly evolving landscape of customer service, call centers are embracing cutting-edge technologies to elevate their operations and meet the growing expectations of today’s consumers. Among these transformative technologies, Generative AI chatbots have emerged as a game-changer. These intelligent virtual assistants are modifying the way call centers engage with customers, streamline processes, and deliver exceptional experiences. In this article, we delve into the diverse use cases of Generative AI chatbots in call centers, uncovering their potential to optimize customer support, improve efficiency, and drive business success. We explore the transformative impact of Generative AI chatbots in enhancing customer experiences within call center environments.

Empower agents with real-time response suggestions

Customers are now looking for more ways to self-service for faster issue resolution. AI with natural language processing powers best-in-class virtual agents with emotion and sentiment analysis built in. Your chatbots and interactive voice responses will feel more intelligent and useful, rather than frustratingly impersonal. Customer service is provided faster, with conversational AI able to handle rote queries without needing live agents.

• The adoption of AI allows agents to easily fill in gaps of knowledge between training sessions or during calls, leading to more informed and accurate service.
• Artificial intelligence makes forecasts more accurate, and since agent schedules are based on forecasts, the scheduling process receives some downstream benefits from AI.
• Putting AI to work on forecasting is like throwing a juicy steak to a hungry dog – AI will be “happy” and finish the task in no time.
• Such an approach involves combining data, such as images, text, and speech, with advanced algorithms to make predictions and generate outcomes.

The integration of the AI applications is seamless, can pick up where humans left off, and bring context within their “social” interactions with customers. Tools like AI chatbots and voice assistants are available to help take care of long call queues or a hectic spike in call traffic. They can lighten customer anxieties and effectively resolve a wide variety of issues without any human intervention.

Generative AI handles billing inquiries and assists with payment processes, ensuring a smooth and hassle-free customer experience. Generative AI-powered simulations replicate real-life customer scenarios, providing immersive training experiences for customer service representatives. Ask most contact center supervisors how they spend their days and they would probably say they do too much firefighting and not enough agent coaching or proactive operational analysis. We’ve already discussed how artificial intelligence can help supplement supervisor coaching, now let’s review how AI analytics can provide actionable insights about performance. Artificial intelligence can be particularly helpful when identifying the best algorithm to use. As you may know, there are several forecasting algorithms to choose from, each with their own strengths and weaknesses and each more effective in specific situations than the others.

These virtual agents can engage in natural language conversations and provide 24/7 support, reducing the workload on human agents. AI tools such as NLP (natural language processing), machine learning, and data analytics have emerged as crucial players in elevating contact center performance. These technologies empower centers to understand and predict customer behavior, streamline operations, and deliver personalized service at scale. The contact center industry is rapidly changing as communication technology evolves. AI as a fundamental part of contact center operations is fast becoming the main driver of customer satisfaction, because it can enable the frontline to do their best work in powerful new ways. It improves agent productivity, giving them the tools for quicker and more efficient decision-making, and creating more time by reducing or eliminating repetitive tasks.

For contact center leaders, the important point here is that AI is not itself a solution. Due diligence is needed to understand what the underlying components are for each vendor. Chatbots can be used as a first-line for customers who self-service first before seeking the help of a live agent. By integrating chatbots with an effective CRM system, chatbots can provide self-service and allow customers to resolve their resolutions without having to speak to an agent. Real-time automation in the call center is achievable through the use of contact center AI, or CCAI. CCAI can help automate repetitive tasks in the contact center to reduce the need for human resources in your CX operations.

This article covers strategies for data breach and ransomware protection and highlights how a Dallas BPO provider can enhance cybersecurity. This strategy involves outsourcing customer service functions to a reliable third-party provider. The tool offers these employees real-time AI-powered recommendations from troubleshooting source material – including product manuals – to support them in solving issues remotely. They often engage with customers to snuff out any potentially simple fixes before making a site visit. Such metrics include customer sentiment, call reasons, automation maturity, and more.

With developments like Google Cloud AI, ChatGPT, Microsoft AI, and IBM Watson, it seems as though AI could be taking over. One of the best use cases for artificial intelligence in your CX is through implementing self-service. Self-service is great for improving operational efficiency by allowing callers to communicate with virtual assistants. Automatically generate summaries of customer-agent phone calls, making it easier to review and extract insights from these interactions. These summaries are valuable for long-term improvements in call quality and identifying positive and negative communication patterns.

87% of customers are receptive to conversational AI interactions, showing that AI contact centers adopting these new interfaces won’t scare off customers. Put simply, an AI call center uses the power of artificial intelligence to support and expand its customer service offering and manage a greater volume of interactions across more channels. As a result, you and your agents will be empowered to deliver a superior, AI-enhanced customer experience at every digital touchpoint. Thanks to the power of AI and natural language processing, contact centers can convert audio recordings of customer conversations into written transcripts.