Getting started with AI: Transformative use cases for contact center customer experience

We’ve all experienced reaching out to customer support, hoping for a quick answer, only to find ourselves stuck in an endless loop of automated responses. It’s frustrating, right? Now, imagine a different scenario: you ask a question, and within seconds, you get the perfect answer, tailored just for you. Or even better, the company reaches out before you even realize you have an issue, solving it before it becomes a headache. That’s what AI does, bringing seamless, next-generation customer experience, that anticipates needs, delivers instant solutions, and creates personalized interactions.

It transforms how businesses interact with customers, from reducing the time it takes to resolve issues to predicting needs before they arise. To fully harness the power of these digital technologies, organizations must adopt a new mindset—one that embraces innovation and is willing to experiment with AI-driven solutions.

In this article, we’ll explore transformative AI use cases that can significantly enhance both agent efficiency and customer experience, helping businesses stay competitive in an increasingly digital world.

An AI contact center changes the equation for time to value (TTV).

When consumers have a question, they want the answer to be right—and fast. TTV refers to how quickly customers receive satisfactory solutions to their inquiries, and as such provides a useful glimpse into a contact center’s operational efficiency.

To stay competitive on TTV, companies need AI capabilities that reduce the duration of interactions while ensuring quality and customer satisfaction.

Real-time insights keep agents a step ahead.

Without AI, agents might only have basic information like the customer’s name and account number when a call or message comes in. AI, however, can quickly analyze customer data and provide real-time insights into their intent, enabling agents to offer more personalized and efficient service. This not only reduces handling time but also ensures that agents are well-informed and can take appropriate actions promptly.

Automatic summaries ensure accuracy and reduce workload.

AI can automatically summarize customer interactions, taking that task off agents’ plates and providing them with clear information about what happened during the conversation. Humans are great at a lot of things, but iAI is proving to be better at listening, interpreting, and turning conversations into action items. Humans forget, get distracted, or get tired—that doesn’t happen with AI.

Strategic mood shift analysis.

AI can also analyze the sentiment of customer interactions, tracking shifts in emotions during a call. If a customer starts a call feeling frustrated and ends it feeling relieved, AI can pinpoint what caused the change. Was it the agent’s response? A particular offer? Understanding these dynamics allows businesses to refine their strategies in the future to reduce customer annoyance and increase satisfaction.

Powered by AI, virtual agents are becoming flexible and friendly.

Virtual agents powered by AI are not just a step forward in customer service; they are a leap. They don’t just follow a “Press 1 to talk to X” script; they engage in real conversations, understanding and responding to customer needs almost like a human would.

One of the most impressive advancements is the level of personalization these virtual agents offer. Imagine calling a customer service line, and instead of the generic “Hi, how are you?” the bot greets you with, “Hi Bob! The last time you contacted us, you called about this problem—is that still an issue, or are you calling about something new?”

This level of personalization makes each individual feel seen and valued as a customer. The virtual agent remembers past interactions and gets straight to the point, saving time and making the experience much more pleasant.

Virtual agents are also getting incredibly good at handling complex, multimodal requests. Think about how people naturally converse—it’s normal to ask for several things in one sentence. For example, “I’m calling about a ticket for my flight tomorrow from Dallas Fort Worth to Florida, and I want to change my seat; by the way, I’d like to upgrade to the premium meal.”

A human might struggle to process all these requests at once, but a well-trained virtual agent can handle them seamlessly. This capability allows the virtual agent to capture multiple elements of a request in one go, making the interaction much more efficient.

AI is breaking down data silos to enable hyper-personalized customer service.

Data is fuel for the customer experience. Many companies have a lot of data, but it’s often unstructured, difficult to manage, and challenging to glean insights from. It might live in various internal systems, CRMs, documents, and so on.

With large language models (LLMs), it’s possible to break down data silos and make that data useful. AI can request and pull the right information, finding the “needle in the haystack” and identifying connections and patterns in customer data. It can uncover hidden themes, transcending the limitations of traditional structured SQL queries.

With this level of data insight, businesses can start to see patterns and probabilities. For instance, if customers perform one action, they might be more likely to perform another. This propensity modeling is a powerful force for hyper-personalizing customer service, enabling businesses to determine the most effective way to cater to each customer in real time. It’s similar to the targeted approach of outbound marketing but with a focus on inbound customer service.

The future of customer service is predictive.

The old, reactive approach to customer service is giving way to a more proactive model. Today, AI can suggest answers to customer inquiries, but tomorrow, it will go a step further by predicting what customers need before they even ask.

For instance, by analyzing usage data, AI might determine that a customer’s phone is likely to fail in the next few years. Instead of waiting for a problem to arise, the contact center could proactively reach out after three years to suggest an upgrade.

Predictive analytics enable businesses to stay ahead of issues, identifying trends early and taking action to resolve them before they escalate. For example, if AI detects a surge in calls about a specific product, it can recommend proactive measures like offering replacements or issuing a recall. This approach mitigates potential negative impacts and strengthens customer loyalty by demonstrating a commitment to quality and care.

In real-time scenarios, AI’s predictive power can be even more impactful. During a utility outage caused by bad weather, AI can analyze call trends and suggest immediate actions such as increasing agent capacity, launching additional chatbots, or sending automated SMS notifications to preempt customer inquiries. This kind of proactive engagement not only resolves issues swiftly but also enhances the overall customer experience.

Ready to transform customer experience with AI?

AI enables businesses to build out new services quickly and with minimal effort. What once took months to change, modify, and implement can now be done in a day—or even minutes.

The journey can start with something as simple as building a bot to handle a common inquiry, like “What’s your return policy?” Automating this frequent question frees up human agents for more complex tasks. It’s a straightforward way to see the immediate benefits of AI without a huge initial investment.

Yes, generative AI is still new and mistakes can happen, but this highlights the importance of partnering with experts. Choose partners who know the practical use cases and can help identify the low-hanging fruit that delivers high value with minimal effort.

Points to keep in mind when considering AI for a contact center:

Identify a specific, solvable issue and focus on that. Start by listening to contact center agents—they know the pain points and repetitive tasks that feel robotic. These are prime opportunities for automation.

Address industry-specific challenges. Some industries, like healthcare, face additional challenges due to compliance requirements such as HIPAA. Patients now expect healthcare interactions to resemble their retail experiences, raising the bar for customer experience (CX). It’s crucial to identify which processes can be automated and which still require human intervention.
Ensure responsible AI safeguards are in place. With generative AI and LLMs, it’s important to safeguard data, implement strong data management standards, and ensure protections are in place for how data is used in customer interactions.

Start your AI journey with us today—explore our innovative tools and see how easy it is to deliver exceptional, personalized customer experiences at scale.