25 Use Cases for Generative AI In Customer Service

3 Ways to Build Better Relationships with AI in Customer Experience

This model is more human-like, listening to the customer’s real intentions and using massive data pools to optimize their journey with T-Mobile. This allows us to tap into millions of success and failure cases generated by both AI and humans. To achieve this target, T-Mobile hopes to use the intent data to understand how problems start in the first instance and stop them before escalation. With this solution, we aim to maintain good relationships with customers through sound communication while ensuring the psychological welfare of our workers. Unfortunately, widespread issues such as short staffing, long wait times, and misfiring self-service deployments only exasperate the problem. From there, Couture introduced how Zoom has infused AI across all of its CCaaS packages, allowing customers to pick and choose the best use cases for their specific requirements.

During a weather emergency, the AI proactively communicates updates, reschedules bookings, and ensures the customer is aware of safety protocols. The AI rebooks connecting flights, updates the itinerary, and arranges hotel stays or transportation, notifying the customer immediately. The AI Agent identifies the behavior, sends a reminder with a discount offer, and even allows the customer to complete the purchase via a single click in the email. Most of us have found ourselves wanting to scream at the bot because it doesn’t understand what we need or want, and we end up in an endless loop. We often figure out the workaround so that we don’t even have to be disappointed and get ourselves straight to an agent. Additionally, they are based on advanced AI with autonomy, reasoning, and planning.

Talkdesk Launches a One-Click “AI Rewriter” Solution for Contact Center Agents

These are great in theory, but how many companies truly love their Salesforce data? This process involves more than just pushing out new capabilities every quarter; it requires ongoing support and engagement. If CCaaS vendors don’t adapt to this approach, they risk becoming marginalized as voice plug-in providers to CRM or CEC systems.

A popular item is out of stock, and the AI proactively informs customers on the waitlist when it’s back, providing pre-order options. After a customer buys a smart device, the AI sends setup instructions, schedules a tutorial call, or offers accessories for their purchase. Today’s consumers expect organizations to be able to serve them across every channel with the same level of professionalism, context, and speed.

With the right conversational AI solutions, companies can build humanized chatbots that delight consumers with fast, relevant interactions. This will allow business leaders to craft customer experiences based on a deep understanding of a customer’s emotional state, leading to more empathetic, personalized interactions. The ability for AI solutions to optimize self-service experiences is one of the biggest benefits of embracing AI in the contact center today.

It Supports the Convergence of Service and Sales

After all, these first-gen CCaaS solutions offered little more than monolithic stacks of software and did little to change the architecture of the contact center. But, CCaaS can’t just be about delivering new software capabilities, especially for contact center managers who aren’t accustomed to constant updates. Pureplay CCaaS providers face increasing competition from CRM and Customer Engagement Center (CEC) vendors, which can provide a deep and rich experience across all kinds of use cases. CCaaS is a crowded market, with vendors announcing massive release waves, moving into adjacent markets, and rolling up competitors to differentiate and grow.

Through AI-based analytics, managers gain real-time insights into key metrics such as response times, resolution rates, and customer satisfaction scores, regardless of the agents’ physical locations. AI-based management is a must for any contact center that wants to maintain agents working from home. These less-than-stellar interactions typically happen because contact centers are loaded with too much data – so much so that agents cannot process information fast enough to meet customer demands. Over the years, contact centers have added more and more channels (chat, email, apps, knowledge bases, etc.), which has compounded the problem.

Transparency is crucial in the ethical development of generative AI systems for contact centers. Customers need to be made aware when interactions are mediated or augmented by artificial intelligence. This means companies will need to ensure they’re informing customers when they’re interacting with virtual agents and chatbots.

Today, PBR is evolving as a key tool in delivering personalized customer experiences by turning what would otherwise be random call assignments into tailored interactions. By analyzing the natural predispositions and communication habits of both customers and agents, PBR ensures more seamless and positive exchanges. Modern PBR solutions integrate with CRM software, IVR systems and skills-based routing to provide agents with the most relevant data in real time, enabling faster resolutions and greater customer satisfaction. For brands’ call center agents, conversational AI allows them to focus their time and energy on more interesting, complex issues while automation takes care of repetitive tasks.

Five9 Introduces Genius AI to Simplify Contact Center AI Adoption – CX Today

Five9 Introduces Genius AI to Simplify Contact Center AI Adoption.

Posted: Thu, 10 Oct 2024 07:00:00 GMT [source]

These tools can make agents more efficient and productive, reduce operational costs, and unlock new opportunities for enhanced customer experiences. However, some leaders in the contact center and customer service landscape are also building new workflow automation opportunities into their AI solutions. For instance, tools now exist that can automate conversation scoring and quality analysis and alert supervisors when issues arise.

AI is enhancing the customer experience while improving the lives of call center employees. As mentioned above, AI agent-assist solutions are evolving to offer agents support across a range of channels. They’re also helping companies unlock new ways to serve and support customers through integrations with emerging tech. The AI solutions you use for data analysis should make it easy to surface valuable insights from a range of conversations. Look for a solution that allows you to tap into real-time monitoring options, and create custom reports based on the metrics that are most important to your business.

Historically, contact centers review only one or two percent of calls, which is not a statistically valid sample. By iterating and refining in this “sandbox,” the contact center can perfect its solutions before rolling them out directly to customers. That dual benefit of improving agent efficiency and gaining business insights is where AI shines. For example, by analyzing unstructured contact center data with generative AI, companies can optimize FAQs, update product manuals, or even refine product labels to reduce customer confusion. For example, consider a customer who has endured ongoing service issues with their internet provider.

AI tools integrated into a comprehensive contact center solution can improve user authentication processes, using biometrics to identify callers in an instant. This ensures companies can keep their customers engaged and informed automatically, without compromising on a highly relevant and personalized experience. Investing in proactive support doesn’t just boost customer satisfaction, it has the potential to increase sales. NLP (Natural Language Processing) is one of the most valuable components of AI in the contact center. Already, NLP solutions are revolutionizing self-service, turning Interactive Voice Response (IVR) systems into convenient tools customers can navigate with just their voice. Sometimes, based on a customer’s history, the contact center should route them directly to a human for better service.

As an added benefit, it can infer CSAT for every interaction, not just the ones where customers opt-in. Additionally, AI can analyze data to understand performance at an individual agent level. This can lead to personalized training and development insights, helping agents continuously improve their skills and service quality.

Copilots & Virtual Assistants: Emerging Use Cases, Trends, & Strategies

Customer experience isn’t one-size-fits-all, so we must deliver AI innovation that adapts to how customers want to engage with agents while helping agents provide a more efficient yet personalized service. Either way, Talkdesk believes that the solution will help improve customer satisfaction (CSAT) rates and average handling time (AHT). Plus, companies can leverage AI assistant tools to automate the creation of work schedules and enhance resource allocation based on historical data.

Its “expanding agent replies” solution allows agents to type the bare bones of their response and then fleshes it out for them, saving them time in responding to customers across digital channels. Embracing the advent of large language models (LLMs), Zendesk built a customer service version of this – on steroids. That capability sits at the core of many new customer service use cases for the technology – such as auto-generating customer replies. Indeed, only software development and marketing teams have experienced greater GenAI investment than customer service – according to Gartner research. Achieving this future state will require a concerted effort to build up the right knowledge, data and content to feed these advanced AI systems. It will also demand a cultural shift, as organizations navigate the emotional curve of AI adoption and get their customers comfortable with increasingly intelligent bots.

For example, generative AI can create relevant, customized content during interactions, from suggesting products based on past behaviors to remembering customer preferences for more tailored support. This level of personalization will improve customer satisfaction, which leads to greater loyalty. Personalization is often done at a demographic level, such as where the person lives, gender, or age range, but generative AI can personalize down to the individual and continually update as required. Using generative AI, contact centers are now about to deliver hyper-personalized services. By analyzing customer data—such as past interactions, purchase history, and preferences—AI can craft personalized experiences tailored to individual customers.

• They can also operate across multiple channels, accompanying your contact center IVR system, chat apps, social media service strategies, and more.
• As such, it takes a lot of effort to get them set up, and – other than for super simple workflows – they end up frustrating customers.
• For instance, the Flow Modelling system by Cresta can determine “troubleshooting paths” for specific challenges based on the impact steps will have on business outcomes.
• Conversational AI has become the backbone of many advances in the customer experience and contact center landscapes.
• The broader lesson here is the importance of realistic, strategic partnerships that complement rather than compete.
• As such, some virtual assistants can automatically take notes when a customer talks for the agent, so they can keep track of critical topics throughout a discussion.

In most cases, the agents like playing a more important role and view it as expanding their skill set. Businesses can build such a customer-facing AI Agent for the Dynamics 365 Contact Center via the Copilot Studio. For voice, they may also leverage the native conversational IVR, powered by the Studio. The first is its Customer Intent Agent, which discovers new intents by listening in on customer conversations across all channels.

Pinpointing Broken Processes

Additionally, the Copilot may soon plug into non-Microsoft CRM platforms – like ServiceNow or Zendesk – to send Copilot-generated prompts into these systems. Again, this will help better equip human agents, this time with real-time, relevant knowledge from the CRM. Collectively, these agents are trained to autonomously learn to address new and emerging issues via self-service, improve the quality of issue resolution across channels, and help drive time and cost savings. For instance, it might find that customers regularly ask about return policies and that a return policy document isn’t available for team members.

While this is also possible through NLP, GenAI is augmenting these systems and also helping to surface performance improvement and agent recognition opportunities. Next, develop a robust knowledge base to ensure the virtual assistant provides accurate, efficient responses. As contact centers grow increasingly complex, the conversational knowledge curator will coordinate with teams to update and optimize insights, bridging any knowledge gaps that arise.

Make sure the AI tools you use for self-service can also transfer important information from customer interactions to agents too. This will help to retain context throughout the customer’s journey, and prevent consumers from having to repeat themselves. As AI solutions grow more advanced, with new algorithms and frameworks to explore, the use cases for AI in customer support are evolving. Today’s companies can leverage AI for everything from increasing conversions with proactive outreach, to generating responses for customer queries.

By optimizing such a flow – in one specific place or channel – the contact center not only innovates but also reduces complexity. After all, they can place less emphasis on developing different journeys across various channels to answer the same queries. According to Kohli, the future of customer contact handling will fixate on intent-level customer journey orchestration. As a result, the agent knows the customer’s query from the start of the conversation and has critical information at hand to solve it. Companies can even use AI tools throughout the ecosystem to track crucial information related to data security and compliance, minimizing risks and regulatory issues.

An AI agent will increasingly understand when to handover to a human agent, such as recognizing via sophisticated sentiment analysis when a customer is angry or upset. If a customer asks a question that the AI cannot handle, it will flag the interaction for human input. “The AI can help them [human agents] by preparing data and providing context before the agent even speaks to the customer. This helps the customer feel heard and understood, improving both customer satisfaction and agent experience,” emphasizes Glock. Agentic is a type of artificial intelligence that is designed to act autonomously, making decisions and taking actions towards a specific goal.

Don’t Wait Around for the Perfect Moment to Introduce AI Agents

Here we dive into some of the primary risks with the technology and whether there’s alternatives for the enterprise looking to upskill and upscale UC without AI. There, many of its customers will take to the stage and discuss their deployments of Five9 AI, dissect their strategies, and share the results they’ve achieved. Now, Five9 is educating its partners on Genius AI via an early-learning program, ensuring they are well-equipped to deliver that to customers. “Unlike competitors relying on third-party integrations or bolt-ons, Five9’s unified solution provides an end-to-end AI experience with a cohesive user interface,” said John. AI Expert Assist also stands out by pairing with the Zoom Contact Center, which supports a robust suite of channels, including voice and video. Zoom’s vision to empower agents and achieve a new standard in the industry with AI-driven tools is brought to life by Zoom AI Expert Assist.

He writes about a broad range of technologies and issues facing corporate IT professionals. “Many remote people in contact centers have a number of responsibilities, including help desk functions,” said Frank Dzubeck, president of Communications Network Architects. “So, after an agent spends time solving a technical problem, they are going to launch into a sales pitch? Well, an agent customarily is not looking for that, in fact they could get a little pissed off about that.” As a result, these platform providers cannot easily bake AI across various layers of the stack. CRM users may then devise an automated proactive outreach strategy, which differs depending on the customer’s “health status”. Nevertheless, with IntentCX, T-Mobile has a much more comprehensive strategy and is actively testing the platform to incorporate it into its business plans.

Those channels could include phone, email, texts and a variety of social media platforms. Customers typically use multiple channels over the course of one transaction and demand that the experience look and feel the same. Therefore, contact centers must have an automatic call distributor that intelligently routes contacts from multiple channels. AI has transformed call centers into hubs of personalized, efficient customer interactions. From conversational AI resolving simple queries to predictive behavioral routing and AI-enhanced IVR systems streamlining complex issues, these technologies empower agents and enhance experiences. As AI continues to evolve, call centers are becoming central to proactive, hyper-personalized customer engagement.

As organizations scale their use of copilots, this role becomes a linchpin, ensuring virtual assistants remain relevant, compliant, and consistently aligned with business objectives. With low-latency responses and natural, humanlike voices, these interactions feel smooth and personal, increasing customer trust and acceptance of AI-driven service. Below, each participant shares emerging use cases for copilots and virtual assistants, trends for 2025, and best practices for deploying them. Look for a voice AI solution that supports a range of use cases, so you can scale your AI strategy over time, and unlock additional benefits as your business grows. For an outstanding solution, you’ll need to find a solution that can adapt and change to handle different languages, voice channels, call flows, and requirements simultaneously. You should be able to create multiple versions of your voice solution, to suit various needs.

These tools can even pass critical data from previous conversations to new agents, ensuring customers don’t have to repeat themselves during calls. Leading vendors like XCally give companies access to flexible AI systems that can power everything from chat and voice self-service strategies, to sentiment analysis and predictive insights. With these tools, you can improve efficiency, productivity, and customer satisfaction, without having to compromise on ethical standards, or compliance. AI solutions can also support and guide employees throughout customer support tasks. They can use sentiment analysis to detect positive and negative feelings, and provide real-time insights to employees on how to de-escalate or improve a situation. They can even help with ensuring conversations are routed to the right employee, based on sentiment and intent analysis.

It’s clear that using an LLM for sentiment analysis provides the most clear and precise view into each of your calls, helping you to create an excellent contact center experience for both your customers and your agents. Classifying calls into these categories gives businesses a quantifiable way to measure customer interactions and gain valuable insights into the CX. This helps them identify trends, improve agent performance, and make informed business decisions to provide callers with the best support possible.

Below are what I consider the top five use cases for contact centers today, all of which are good starting points. By bringing agentic AI into the platform so early, it will also expand its market awareness. Yet, most additional capabilities are brick-in-the-wall additions, such as the availability of key operational metrics, messaging APIs, and broader regional voice coverage. Other headline soon-to-be-released features include proficiency-based routing, a proactive outreach solution, and a fraud prevention tool. By May, Microsoft also plans to use its native Copilot to retrieve customer feedback.

• It’s allowing users to build applications using natural language alone instead of drag-and-drop tooling.
• Around 71% of customers now expect a personalized experience from every contact center.
• However, perhaps most critically, JetBlue’s human agents – or “crew members” – are onboard.

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. 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. In trawling these, GenAI automates a relevant customer response, which the agent can evaluate, edit, and forward to customers.