Understanding AI for customer support: How AI is transforming customer service

AI is used in customer support to create smoother, more personalized interactions while lightening the load for human customer service reps. Most commonly, AI is used in chatbots that use machine learning (ML) and in natural language processing (NLP) to mimic human speech and resolve customer issues. But AI in customer support doesn’t begin and end with chatbots. From virtual assistants to AI-powered search, companies can integrate AI into almost every facet of the user experience to support their customer service teams. The goal isn’t to replace customer support but to enhance it.

When used to its fullest potential, AI can help customer support teams boost productivity, increase customer satisfaction, and bolster their bottom line. Long-term customers are more profitable and cheaper to retain, and they talk up their favorite brands to peers. Good customer service can turn tentative customers into brand loyalists. But customer expectations are also rising. Users expect frictionless experiences that resolve their issues faster and better than before. And 80% of customers claim that the experience that a company provides is as important as its product and services.⁵ So, being behind the curve might mean losing them. Therefore, it’s no surprise that businesses are scrambling to implement AI tools for customer service. 

Here’s how to do it right to satisfy customers and streamline customer success operations.

AI has significantly expanded what can be automated in customer service. While chatbots are still the most common and most accessible AI tool for customer support, they might not be the best fit for your business. Familiarizing yourself with the landscape can help you decide which AI tool will best serve your teams and your customers.

Chatbots are automated systems that can tackle basic questions and routine tasks, giving customers quick answers and easing the load on human agents. They’re built to handle tons of interactions at once and are a staple for any high-traffic support system.

Virtual assistants can manage more sophisticated queries than chatbots and give customer interactions a more personalized touch. They’re a better option than chatbots if a user needs to be guided through a complex process.

When customers are searching for something but aren’t using the exact keywords, vector search can help. Through embeddings — numerical representations of data that capture its context — vector search can identify information that’s conceptually like the search term even when exact matches of keywords aren’t being used.

Hybrid search combines different search techniques like vector and keyword search to deliver better relevance. It blends dense and sparse embeddings to fine-tune the balance between understanding context and matching specific terms.

NLP is used to interpret and respond to human language with nuanced understanding. It lets chatbots and virtual assistants grasp what a customer is saying and respond in a way that makes sense and feels like a natural conversation.

Machine learning algorithms are at the heart of AI’s continuous improvement. By analyzing vast amounts of data from previous interactions, machine learning helps AI systems refine and improve their future responses.

These systems are designed to handle large volumes of customer queries. They automate responses to common questions and issues so that human agents don’t get overwhelmed and can focus on more complex customer issues instead.

With AI-powered analytics, you can dive deep into customer data and find insights that help you predict what customers want. It also helps you make informed decisions about the best ways to tailor your support strategies in the future.

A roadblock for many businesses that want to experiment with AI is the implementation process. Integrating AI into your existing systems doesn’t have to be intimidating. The right tools and the right partners make adding AI integrations intuitive. But to find the right tools, the first step is to establish exactly what your goals are. Only by knowing what your business — and your customers — really need can you make significant improvements to your customer support systems. Here’s how to implement AI in customer support.

What are their demographics and interests? What are their pain points? Do they respond more positively to voice or digital interactions? By understanding the customer, you can tailor your solutions to proactively address their needs.

Self-service provides your customers with tools that’ll help them serve themselves. Virtual agents or chatbots are usually a good choice here. Normally this process requires analyzing customer queries, understanding their intent, and then having a customer service expert create dialogue flows to help the customer get where they need to be. These flows were often time-consuming to create — a Choose Your Own Adventure-style of writing that could easily go off course if the customer sends it a curveball. Fortunately, generative AI has made the process a lot simpler. Not only does it make building the flows easier, but it’s also more resilient to digressions and variations during customer interactions. 

Supporting your human agents with AI is another approach you can take. A new call center agent might be spending a lot of time searching knowledge bases, documentation, and case histories to get the right answer for a customer. GenAI can retrieve information faster and summarize it quickly, cutting down customer wait times in the process. If you’ve ever been on hold for a long time, you know how much this can improve the customer experience. Another way GenAI can help is by automatically drafting responses to customer emails based on what they’re asking and any context available. The customer service rep can then review the email before it’s sent to make sure it makes sense and appropriately handles the query.

You know your audience, and you have a general idea of the ways you’d like to serve them. It’s time to map out your end-to-end customer journeys (chances are you’ll have at least a few) and then look at the best tools to support them. Don’t limit yourself to budget concerns as you brainstorm these journeys — go big. You can rein things in during the next step.

First, calculate the cost of different AI tools and technologies — and remember to factor in both your upfront investment and ongoing expenses, maintenance, and updates.  When you have all that assessed, consider your ROI. Using the in-house data you have, think about the different ways your plan will improve customer satisfaction and how that will affect your bottom line. 

It’s time to look back at Step 3 with Step 4’s budget in mind. Choose your tools and design strategies that serve both your customers and agents.

Can your customer service reps seamlessly pick up a conversation if a chatbot passes one to them? Do they understand when and where to intervene if a customer is having a poor experience that the AI tools can’t solve? Do they have a basic understanding of how to use the technology correctly during interactions and gather insights from it later? By training your customer service team, you can still succeed in giving customers a personalized experience when AI struggles to provide answers.

AI makes this process easier, too. For example, a big chunk of a call center’s job is documenting conversations. GenAI can make transcripts of every call, which gives time back to the operators. It can also use these transcripts to come up with insights as to why certain calls are taking longer or if certain products or services are having issues that need to be reported to the product and marketing departments. 

Implementing a new technology comes with its challenges. And despite the potential of AI, some customers — and some teams — might be skeptical. With the right AI tool and the right implementation process, you can get past most of the common obstacles. Here are some of the potential challenges of AI in customer support and how to solve them.

According to a recent Gartner study, some customers may worry that GenAI will just add another barrier between them and a real agent. Others are concerned it’ll provide the wrong answers or possibly even be biased against certain customers.⁶ 

Solution: It will be up to your business to communicate to customers how AI can make the service experience better. Be upfront: Let customers know when they’re interacting with AI. And never hesitate to escalate complex cases to human agents when AI falls short.

Any system that handles customer data is a prime target for hackers, and AI is no exception. These systems often need to process large amounts of personal information, so privacy and security can’t just be an afterthought. 

Solution: You’ll need to stay on top of data protection laws and regularly update your security measures to keep up with new threats — it’s crucial for maintaining customer trust.

Many companies still rely on older systems that weren’t built with AI in mind. This can mean upgrades — often expensive and time-consuming ones. 

Solution: Start with a thorough assessment of your current systems, looking for areas where AI can fit in smoothly and where upgrades are necessary. Phased rollouts can help minimize disruptions, allowing you to iron out issues before scaling up.

Support teams might push back against AI integration because they’re resistant to a new way of doing things, or they’re worried that automation will replace them. 

Solution: The key here is to involve the team from the start, offer plenty of training, and show them how AI can make their jobs easier, not take them away.

AI systems need regular updates to keep up with changing customer needs and new tech developments. If companies don’t keep up, they risk their AI falling behind and becoming more of a liability than an asset.

Solution: You’ll want to establish a schedule for regular system reviews and updates as well as invest in ongoing learning for your AI systems by feeding them fresh data and refining their algorithms.

Once you’ve pushed through the challenges, you get to reap the benefits of AI in customer support. Here are some of the ways AI in customer support can transform your business:

• 24/7 availability: Customers will have access to assistance around the clock, regardless of time zones or business hours.

• Instant response: AI delivers immediate answers and solutions and keeps the customer experience smooth and frustration-free.

• Personalized service: Customer data helps AI tailor its responses and recommendations. This helps it make each customer experience feel personable.

• Enhanced efficiency: AI streamlines support operations by automating routine tasks and inquiries. This lets human agents focus on more complex and nuanced issues.

• Reduced costs: Automating processes means less money spent on manual tasks and more streamlined operations.

• Improved data collection and analysis: AI digs into every customer interaction to gather valuable insights, helping you understand trends and improve your support strategy.

• Scalability of support operations: AI handles increasing volumes easily so that your business can expand without growing pains.

Founded in 1984, Cisco is the backbone of the global network economy, serving more than 87% of Fortune 500 companies. But with millions of service requests and countless documents to sift through, it faced a real challenge: how to deliver quick and accurate support with such substantial volume.

The solution was an overhaul of Cisco’s search capabilities powered by AI. To do this, Cisco partnered with Elastic to revamp its customer support system. Elasticsearch, running on Elastic Cloud on Kubernetes, is now the engine at the center of Cisco’s new enterprise search architecture. This new tool, called Re-imagined Topic Search, saves Cisco’s support engineers 5,000 hours a month. Now, engineers can quickly pull up relevant documents and similar cases whether they're helping customers over the phone or online.

“Feedback from our engineers is extremely positive,” says Sujith Joseph, principal enterprise search and cloud architect at Cisco Systems. “They now use Topic Search to solve 90% of service requests. They can deliver a better customer experience by easily finding on-target information and fixing issues much faster than before.”

Cisco.com also got a major AI upgrade. It’s now powered by the Re-imagined Search Platform, an AI search solution built on Google Cloud services and Elasticsearch. This overhaul has slashed search response times by 73% and boosted user engagement while reducing operational costs. 

Since rolling out Re-imagined Search across customer support and Cisco.com, Cisco’s search team has also integrated it into more than 50 internal and external apps, including the Cisco intranet.

“Today, people expect instant search access to the information they need,” Joseph adds. “Keeping customers and potential customers aligned with relevant content about our solutions and services is fundamental to these relationships.”

Elasticsearch combines generative AI with powerful search technology to boost self-service support and streamline agent workflows. It taps into your organization’s own data, knowledge base, and process docs to deliver precise answers and smart recommendations, all while keeping document security tight and costs low.