6 use cases for generative AI in customer service

They can not only automate routine tasks but also understand nuances, adapt to diverse needs, and even learn from past interactions, enabling powerful AI-powered contact center solutions. Implementing AI in call centers can help businesses provide tools that make agents’ jobs easier. Software features like call transcriptions and call summarization boost productivity and efficiency, enabling teams to handle more inquiries without increasing headcount. This helps businesses keep staffing needs balanced, driving down operational costs. Imagine a buyer calls customer support and asks a question about a previously purchased product.

Generative AI analyzes customer preferences and browsing history to generate personalized product recommendations, enhancing cross-selling and upselling opportunities. Generative AI dynamically generates and updates FAQ sections on websites or apps, ensuring customers can access relevant information. Let’s explore 21 use cases showcasing the versatility and effectiveness of Generative AI in customer service. Verify each customer’s identity automatically using AI-based authentication to request personal information at the start of a conversation. From email to birthday to two-factor authentication, Parloa provides extra layers of security.

They can even be a great tool for analytics, allowing agents to search for specific words/phrases and identify trends in customer behaviors. This enables your agents to communicate effectively and efficiently with customers, whatever language they speak – creating a seamless experience for all your overseas clients. Real-time translation technology enables contact centers to communicate with customers in their native tongue. https://chat.openai.com/ This not only saves time for your agents but also guarantees that your customers receive the best service for their specific situation – without having to repeat themselves to multiple representatives. Here, we’ll cover five applications of contact center AI and how each one can be used to supercharge customer service. Thanks to these technologies, customers can have access to instant support and digital self-service.

Meanwhile, the contact center team saw a 47% increase in customer appointments made, and a 129% increase in agents correctly assessing callers’ needs. Another benefit of using AI solutions in the contact center is gaining access to intelligent call routing. While it is not AI-powered itself, many leading AI platforms for call centers, including Invoca, offer intelligent routing as a companion feature that complements AI capabilities. Conversational IVRs interact with callers in a natural, human-like way by allowing them to respond via voice instead of keypresses. IVR systems like Invoca’s can be set up quickly (i.e., in minutes), without any coding or help from IT. And because IVRs from Invoca work with every phone system, they can be deployed immediately without any worry about business disruption.

Intelligent automation stands as a beacon of efficiency and innovation in contact centers. This technology revolution is not just about replacing human effort with machines; it’s about enhancing and streamlining the interaction between customers and service providers. Imagine if you had a magical assistant who could handle a lot of the routine work, answering customer questions with a personal touch. This lets the human customer service folks spend more time on important stuff and connecting with customers. It is like having a secret weapon to save time and money and make everyone happy – customers and the support team. AI algorithms can also provide intelligent product recommendations during customer interactions.

Learning Opportunities

For example, the assistant could create an entire IVR script, along with the necessary configuration. Indeed, it is the only option for contact centers to responsibly and effectively use AI for their CX. When people were first introduced to GenAI tools such as ChatGPT, they unknowingly gave personal information, such as their name or date of birth. That digital footprint is permanently etched into the fabric of the AI and used to inform later generations of GenAI models.

A global consumer goods manufacturer has deployed eGain’s virtual assistant to engage thousands of its own sales reps and answer their questions on products, sales, and customer service. The potential applications of generative AI are numerous, from enabling content producers and marketers to effortlessly create new content, to assisting scientists and researchers in generating simulations and models. By using generative AI, brands can automate tasks that previously required human intervention, ultimately saving time and increasing efficiency. Clearly defining the results your business seeks through AI for customer service is key.

Expect Much More Generative AI Innovation In Customer Service

In financial services, AI is helping customers understand complex financial products, providing information on account balances, transaction history and investment options. Additionally, it can help detect and prevent fraudulent activities by monitoring customer interactions and account activities. Generative AI is being used in contact centers across various industries, with each having its own challenges and customer expectations. Unless you’ve been living under a rock for the past few months, you’ve likely heard about generative AI, if not tried it yourself.

They deploy phone menus and intelligent routing to mitigate the daily onslaught of calls. At the very least, these tactics serve as a frontline for call volume that helps make the process more efficient. By automating the communication loop between customer service and other departments, insight automations contribute to a more cohesive and responsive organizational approach to customer feedback. Proactive outbound messaging involves sending automated messages to customers based on specific triggers or events. Continuous innovation will also mark this AI-led, customer-focused future, enhancing support and creating a dynamic landscape where staying ahead in customer service means staying one step ahead of the competition. Managing this data, ensuring its quality, and complying with data protection regulations like GDPR and CCPA is complex.

When it comes to contact centers, attackers may attempt to manipulate voice recordings or generate synthetic voices to mimic legitimate customers and gain unauthorized access to systems protected by voice biometrics. GenAI can “listen” during the call and populate the agent’s screen with relevant information from approved sources. This prevents the agent from needing to look up the data manually, which could otherwise form percent of the interaction, and reduces the time required to train agents. Such data is critical for contact centers to spot their demand drivers and take targeted actions – either via process fixes or conversational AI – to lower contact volumes and customer effort.

Keeping abreast of AI advancements is crucial for ensuring chosen solutions remain up-to-date and effective in addressing evolving market trends and consumer expectations. For example, in financial call centers, AI can verify that an agent made a disclosure to a customer before pulling their credit report. This ensures adherence to regulatory requirements and mitigating compliance risks. Autonomous AI agents will be the new front line in CX—instantly deployable and capable of resolving the vast majority of customer issues. You can foun additiona information about ai customer service and artificial intelligence and NLP. CX leaders often look for new ways to predict staffing needs and accelerate training now and in the future.

Improve replies with AI assistance for support reps

Looking ahead, the integration of advanced AI technologies in call centers will help large and small companies optimize operations and redefine the entire customer experience. Nextiva’s AI-based tools, including our advanced interactive voice response (IVR) system, offer unparalleled efficiency for phone systems. They automate tasks and facilitate seamless support across channels, allowing your team to assist customers without interruptions.

DRUID is an Enterprise conversational AI platform, with a proprietary NLP engine, powerful API and RPA connectors, and full on-premise, cloud, or hybrid deployments. Finally, in the telecommunications industry, AI is being used to troubleshoot technical issues, assist with plan upgrades or changes, and answer questions about billing, coverage and device compatibility. You can also look at other public Chat GPT sources, but be careful about including anything you didn’t create yourself; accuracy and trustworthiness should never be left behind. Adjusting the tone of the message to each customer is also possible with just a click, modifying your response to be friendlier, or more formal. With the expand option, you can simply write short notes or bullet points, and then enhance into full responses.

What’s more, the same report found that the #1 CX pain point leaders hope to solve through digital channels is long wait times to reach agents (75%). Nike’s routing system can identify and prioritize customer inquiries based on a range of factors, such as urgency, customer history, and the availability of agents. Your customers don’t always know exactly how to articulate a problem, which can be an issue at scale even for empathetic human agents. AI can track every interaction across a multitude of touchpoints – including voice and text, on owned and third-party platforms – and discern effort and emotion. AI can then route calls to agents and flag that full, holistic history, letting you know who’s most in need of assistance, and what their issue’s been about. By speeding up response times and directing customers to the most relevant resources, first contact resolution (FCR) scores and other critical metrics see substantial improvement.

• Bots will listen in on agents’ calls suggesting best practice answers to improve customer satisfaction and standardize customer experience.
• Conversational AI tools are incredibly beneficial in guaranteeing the best customer experience.
• We’re in the face of a customer service revolution, where humans will leverage the power of AI and automation to meet – and exceed – customers’ expectations.
• AI-powered interaction analytics can be the data analyst every supervisor and manager needs, on steroids.
• AI can track every interaction across a multitude of touchpoints – including voice and text, on owned and third-party platforms – and discern effort and emotion.
• The correlation between customer satisfaction and a company’s financial performance is undeniable.

In only months, it has expanded contact center agent-assist portfolios, shaken up knowledge management, and transformed conversational AI applications. With over 300 locations, ai use cases in contact center AutoNation is America’s largest and most admired auto retailer. AutoNation uses Invoca to train its sales team to close more deals and better serve customers.

Contact center leaders are increasingly coming to understand the direct connection between agent experience and CX. There is a multitude of AI-driven applications that make for a better agent experience, such as noise suppression, automated call summaries and sentiment analysis. For contact centers that view human interaction as paramount to CX, the agent experience could be the best use case for AI — not just for customer service, but for maintaining a strong team of agents.

AI-Powered Contact Center Solutions: Use Cases Of Llms In Contact Center

CCaaS Magic Quadrant leader Genesys is one vendor to offer such a solution – automating these post-call processes for agents to review, tweak, and publish in the CRM after each conversation. When a service agent ends a customer interaction, they must complete post-call processing. That typically involves uploading a contact summary and disposition code to the CRM system. In trawling these, GenAI automates a relevant customer response, which the agent can evaluate, edit, and forward to customers. That capability sits at the core of many new customer service use cases for the technology – such as auto-generating customer replies.

The difference between contact center chatbots using AI vs. traditional methods is that human agents aren’t required. While recent surveys show that contact center users still prefer to work with a human agent, this preference is quickly trending downward. A self-learning, conversational AI chatbot can swiftly provide relevant answers and content suggestions without the need for time-intensive rule adjustments. With the help of conversational AI, chatbots can deliver seamless customer experiences by leveraging various question-answering and intelligent search capabilities to index and deliver content across platforms. They can also capture customer interaction data and integrate it with other channels, deepening the context that agents have for each and every case.

Following these best practices enables call centers to effectively implement AI solutions, maximize their benefits, and drive improvements in customer service, operational efficiency, and overall business performance. For example, call center teams can deploy a chatbot to support customers over digital channels while call center representatives are off the clock. AI can also generate a summary after each call, saving agents valuable time from manually summarizing calls so they can help the next customer faster. To adapt to the changing landscape, many companies have shifted to remote and hybrid workplace models, leading to investments in new contact center technologies. Contact center artificial intelligence (AI) has emerged as a powerful tool to ensure high-quality and personalized customer service experiences.

The agent can then choose the response best suited to the customer’s inquiry and send it seconds later. You can add predicitive analytics to anticipate call volume and also type of seasonal call (e.g. Mortgage calls when interest rates change, or tax certificate requests when its end of Tax year etc etc). Call Centers are still viewed as cost centers (rather than revenue gen) and companies are doing their level best to deflect the call in the first place. This preparation enables agents to address customer needs more efficiently, improving resolution times and reducing the overall burden on customer support staff. This run through should help any contact centre or CX leader understand where and how AI can help you improve customer experience and increase operational efficiencies. Deciding on what AI tools your contact center needs can be difficult, especially when different contact center solutions offer different tools and services.

• This increased revenue is achieved through the adoption of innovative AI technologies, which allows businesses to get a step ahead both financially and against competitors.
• It can help deflect cases too, as people get AI-powered, personalized help directly in their own workflows.
• With the Talkative platform, this capability is powered by our OpenAI integration – allowing automatic summaries of every chat, voice, and video interaction.
• She asks Austin to do a full-system reboot through the Nation-Wide Web mobile app.

The next wave of contact center AI innovations will feature tools capable of understanding customer emotions through tone, language, and visual cues from video calls. This advancement will enable AI to interpret the subtleties of human communication, allowing for responses that are contextually appropriate and emotionally resonant. For contact centers, data analytics and reporting are crucial for measuring performance, understanding customer behavior, and improving service delivery. These tools can track key performance indicators (KPIs) such as first call resolution rate, customer satisfaction scores, and service level agreement (SLA) compliance.

AI is there to Improve Contact Center Quality Monitoring, Not Just Automate It

AI-powered contact centers will be at the forefront of driving better customer satisfaction and experiences through a thorough understanding of their needs and desires. The need of the moment, therefore, is to find the right partner who can enable successful AI-transformation suited to the scale, operations, and specific requirements of your contact centers. By leveraging Yellow.ai’s advanced AI chatbot technology, businesses can transform their customer service operations into efficient, responsive, and highly personalized customer engagement centers. With experience in handling over 12 billion conversations, Yellow.ai’s chatbots are enriched with a depth of conversational intelligence that is unmatched. This vast database of interactions ensures that the AI is well-equipped to handle a wide range of customer queries with nuanced understanding and precision.

Automating routine tasks, using natural language processing (NLP) to understand human speech, and generating human-like responses virtually via text or voice are primarily what drive the main benefits of call center AI. Call center managers can create a response ecosystem encompassing live agents and virtual agents to streamline workflow, speed up routine tasks, and allow live agents to focus on complex and more serious customer issues. It can power chatbots, interactive voice response (IVR), and virtual agents to handle routine queries that don’t need human interaction or assist self-service tools. Large language models, such as GPT-3 and GPT-4, have demonstrated their capabilities in understanding and generating humanlike text, making generative AI an attractive option for online businesses of all sizes. RPA, or Robotic Process Automation, in the context of a contact center, involves using software robots to automate mundane and repetitive tasks. These tasks can range from data entry and form filling to more complex actions like customer query categorization and routing.

As LLMs become more sophisticated, expect further waves of customer service use cases for generative AI to rise up. At its heart, the solution contains a wealth of anonymized contact center conversation data that NICE has pulled together and used to develop sector-specific benchmarks for many metrics. Like Nuance and Google, Cognigy has pushed the boundaries of generative AI innovation in customer service, as its “Conversation Simulation” tool exemplifies. Another advantage of these auto-generated articles is that they’re in the same format, allowing agents to quickly comprehend and action them. That makes it easier for future agents – handling follow-ups – to get to grips with what happened on the previous call.

RingCentral’s new contact center-as-a-service offering, RingCX, is poised to shake up the CCaaS market. As per an article by CX Today – 76% of contact centers have experienced higher contact volumes over the past year. Convin records, transcribes and analyzes all your sales calls to give insights on what’s working on calls and what’s not. Additionally, we’ve curated a collection of AI resources specially designed for contact center professionals. Visit our AI resource page to learn more about the power of artificial intelligence.

The implementation of AI is highly beneficial to customer satisfaction, team member productivity, and overall business effectiveness. Chatbots and voice bots are vital to a brand’s customer engagement strategy to deliver on the self-service customer experience. The intelligence behind the AI tools can understand human speech, language, and emotions. The tools can improve customer service and customer support experience in all aspects of a business. Naturally, contact center platforms that integrate generative AI often include AI-powered chatbots that can handle routine inquiries and simple customer service requests. These chatbots are trained to understand and respond to natural language, empathize with customers and learn from previous interactions to improve their responses over time.

Other uses involve workflow automation for internal processes, such as scheduling and performance analytics, to enhance operational efficiency. Imagine a bustling contact center, traditionally powered by the tireless efforts of countless personnel. Now, infuse this image with intelligent systems capable of handling routine inquiries and tasks – that’s contact center automation. These automated systems deftly handle the repetitive aspects of the job, freeing up agents to focus on more complex, nuanced customer interactions. The implementation of intelligent automation revolves around thoughtful integration. It’s about deploying AI-powered virtual assistants for simple, repetitive tasks, thereby freeing human agents to handle complex, customer-centric interactions.

By providing real-time translation services, businesses can reach a wider audience and provide support to customers around the globe. Understanding sentiment is critical because it provides a measure of both customer satisfaction and agent performance. With AI taking the marketplace by storm and shaping the future of digital customer service, it’s important that brands are ready to embrace these innovative solutions – or they may get left behind. The output could be the final decision or be one of the decisions in a larger customer engagement and contact center management process.

In line with our discoveries, companies are currently seeing the impact of AI for customer service. According to our report The State of AI in Customer Service 2023, 69% of support leaders intend to increase their investments in AI-powered solutions over the next year¹. For contact center leaders, this will require different expectations from investing in legacy systems. And it will be doubly important to work with technology partners who understand those expectations — and know how to effectively support them.

This type of AI was built specifically for contact centers, including all proper guardrails. Still, this saves a lot of time for agents, thus producing a great ROI, but also minimizes the risk of hallucinations by involving human intelligence. Indeed, GenAI can make ‘wait time’ productive, gathering information before a customer interacts with an agent – such as who is calling and the nature of their query – to segment and prioritize calls.

Alternatively, check out the rest of our blogs to learn more about AI use cases, Voice Analytics best practices, and more. In this section, you will learn how AI can improve customer experiences while decreasing agent workloads. Discover AI-driven tools that will support agents before and during their customer calls. It also enhances the granularity of reporting by segmenting data based on different demographics, agent performance, and the type of interactions. This helps businesses learn about their agents and their workflow just as much as they learn from their customers, which can then be used to create more effective training. AI tools can also offer real-time suggestions and access to relevant information during calls, further reducing the time agents need to spend on calls.

Customer Contact Week: CX Leaders Dish on AI Breakthroughs and Blunders

Brands that wish to stick with their current platforms should evaluate their current systems and processes to determine where AI can be useful and more easily integrated. This may involve updating software platforms, implementing APIs or working with third-party providers to ensure a smooth integration. In the travel and hospitality industry, generative AI can assist customers with booking flights, accommodations and tours, as well as provide real-time updates on travel advisories, delays and cancellations. By analyzing customer data, it can also offer personalized travel recommendations based on customer preferences and travel history.

Artificial Intelligence (AI) is rapidly transforming call and contact center operations, making them more efficient and cost-effective and helping to reduce work-related stress for human agents. Cloud-based technologies enabled the expansion of AI for contact centers, and the need to support customers effectively during the COVID-19 pandemic prompted many businesses to speed up the adoption of these solutions. Contact Centers are enthusiastic about the future of  AI; Gartner predicts that Conversational AI tools could reduce agent labor costs by $80 billion in 2026.

Increasingly, AI and customer service automation can drastically speed up the process of determining which agent to assign to a call. Automatic summary allows agents to spend less time on administrative tasks and more time delivering exceptional customer service. Enabling agents and customers to self-serve the answers they need, an AI-powered search function needs a rich index of content to work with.

Automated customer service interactions sometimes break down when customers change their intent halfway through a conversation – confusing the virtual agent. MoneySolver, a financial services company, provides customized student loan, tax, business, and credit solutions. Before deploying Invoca’s AI-driven platform, MoneySolver tracked only a small percentage of calls into its call center where over 100 agents handle customer inquiries. AI tools like ChatGPT are effective in generating text responses to customer inquiries and speeding up the time to resolution for contact center agents. As we embrace these technologies, the role of contact centers evolves from mere problem resolution to delivering a seamless, engaging customer experience, marking a new era in customer service excellence.

Then, the platform spits out a bot, which the business can adapt and deploy in its contact center. It’s allowing users to build applications using natural language alone instead of drag-and-drop tooling. Alongside sentiment, contact centers may harness GenAI to alert supervisors when an agent demonstrates a specific behavior and jot down customer complaints. Alongside this, the solution provides a rationale for the automated answer in case quality analysts, supervisors, or coaches wish to delve deeper or an agent wants to challenge it.

With AI handling repetitive tasks, agents can focus on more complex and meaningful interactions, reducing burnout and churn rates. This shift results in significant cost savings for organizations, with each agent turnover costing around $20,000 annually in replacement costs alone. These 24/7 capabilities allow companies to strategically staff shifts for 24/7 service, taking a majority of the weight off of individuals expected to be on-call throughout all hours of the night. Utilizing AI for 24 hour contact service assists companies in allowing less irregular work hours for employees, yet allowing customers to still receive help whenever they need. The movement to self-service also allows businesses increased service capacity and improves existing employee workloads.

Generative AI chatbots can be highly effective in handling frequently asked questions (FAQs) in call centers. AI can’t replace everything that a human agent can do, but it is often sufficient to reach a satisfactory resolution for simple requests. You can leave routine, day-to-day questions, and other fundamental interactions that might fall under the banner of “self-service” to AI. Help your callers complete simple tasks like placing an order, checking a balance, or paying a bill on their own, so your human agents are free to respond to more complex calls.

For the customers that do submit cases, the case information and subsequent recommendations are far more dialed in, helping to reduce average handle time. But contact center AI can take the most complex email and chat conversations and generate a proposed wrap-up summary. Your agent just needs to review these summaries before they’re saved to the case log. Leveraging AI in customer service is easy when you have an experienced BPO partner that makes the implementation seamless.

Labor is the biggest cost component for contact centers, so this use case will resonate not just with contact center leaders, but also senior management. Important as this objective might be, you must carefully think through the specific applications of AI. Contact center leaders don’t buy “AI;” rather, they invest in a family of “smart” technologies that leverage today’s digital technologies. In this context, AI is more of an umbrella term for a family of technologies that enable smart solutions. This year has been widely viewed as the “Year of AI,” and that certainly seems to be the case for contact centers.

With Talkative, for example, you can opt to have every video chat automatically transcribed into a highly accurate, text-based format. This technology allows brands to revisit any video call with greater ease, convenience, and clarity. It’s why Talkative’s live chat comes with automatic translation into over a hundred languages as standard.

These and other generative AI algorithms rely on large language models (LLMs) to generate text and create fluent conversations. Within the contact center, GenAI has the potential to provide human-like service interactions in a number of key channels. Tips in this article help Chicago startups maintain empathy in customer interactions, ensuring a human touch in call center services. OpenAI’s development of ChatGPT-3 has opened the door for businesses to easily provide self-service options to their customers, which can dramatically reduce hold and resolution times in customer service. And OpenAI recently released a new language model, GTP-3, which has the capacity to produce far more realistic text than prior models. AI has long been viewed as a technology destined to streamline and enhance call center operations.

The integration of AI into contact centres promises a future where customer interactions are more efficient, personalised, and satisfying. This all not only streamlines administrative tasks but also offers actionable insights into customer behaviour or and service quality, enabling continuous improvement. This not only simplifies the process, eliminating the need for multiple phone numbers, but also significantly reduces call transfers, enhancing customer satisfaction and operational efficiency. However, Conversational IVRs, or AI-based IVRs, provide a more personalized and helpful experience.

However, even that can impede an agent’s ability to engage in active listening as they multi-task, resulting in increased resolution times. Again, the contact center must plug the solution into various knowledge sources for this to happen – as is the case across many other use cases – and an agent stays in the loop. Indeed, GenAI applications – like Service GPT by Salesforce – can do this by first understanding the customer query and sieving through various knowledge sources looking for the answer.

It doesn’t just churn out generic responses but uses the information in the review to generate a personalized response. Talkdesk Virtual Agent handles common customer queries like orders, returns, and billing. If complex cases require empathy and expertise, the virtual agent seamlessly redirects customers to a human agent. It leverages generative AI to capture, transcribe, and analyze every customer interaction. It identifies key conversation moments, topics, and sentiments helping businesses understand customer intent more clearly.