AI in Customer Service: How To Get It Right
Prior to working on the content team here at Help Scout, I spent several years working in customer support. With a career focused on customer service and creative content, I have had mixed feelings about the release of ChatGPT.
On one hand, I think using artificial intelligence (AI) in customer service is pretty exciting. There are so many opportunities for AI to elevate the work support teams are doing and to make a positive impact on the customer service field in general.
On the other hand, with technology capable of writing, speaking, troubleshooting, and creating original content, it’s hard not to feel a bit insecure.
In this book, we take a closer look at AI in customer service. We’ll cover what it is, how it works, and how it can be used as part of your support strategy.
What is AI in customer service?
AI in customer service is the practice of using artificial intelligence technologies like chatbots, AI assistants, and automated workflows to improve the support experience for customers by helping teams provide faster responses.
How does AI in customer service work?
While most people have probably (much to my chagrin) asked Alexa a question before, the technology behind Alexa’s answers — conversational AI — is likely still a mystery.
What is conversational AI?
Conversational AI is the technology that enables humans to have realistic text or speech-based conversations with machines and applications such as chatbots, smart devices, wearables, and virtual assistants.
Conversational AI is built on two major branches of artificial intelligence: natural language processing (NLP) and machine learning (ML).
Natural language processing, or NLP, is the AI subfield that facilitates natural conversation between humans and computers. It’s a complicated task, as computers need to not only understand what words and phrases people are saying or writing, but they also need to understand the context and sentiment behind that language.
While NLP is impressive on its own, the real benefits start to emerge when it is combined with machine learning.
What is machine learning?
Machine learning is another branch of AI that uses algorithms and data to continuously “learn” and improve its output over time with minimal human involvement.
When it comes to conversational AI, machine learning makes it possible for a computer application to take the results of all previous conversations it has had with a human, as well as any additional data provided, and use it to deliver better responses in the future without additional programming.
Historically, conversational AI hasn’t always provided the best customer experiences. However, since the recent advances in the field of generative AI, namely the aforementioned release of Open AI’s GPT-3.5, more people have looked for ways to incorporate the technology into their lives, both personally and professionally in fields like customer support.
What is generative AI?
Generative AI is technology that is capable of facilitating natural conversation; however, it is also capable of producing other types of content like images and music, and its outputs are not necessarily tied to structured data.
Generative AI relies on large language models (LLMs) — deep learning algorithms trained on large quantities of data. Using that information, the technology can create original responses to user prompts.
Conversational AI in customer support has been around for a long time (any time a chatbot and customer have a conversation using natural human language — even when it’s a rule-based experience — it's considered conversational AI.) However, it's the use of generative AI that now allows businesses to create more personalized, unscripted customer experiences.
Can you trust AI in customer service?
How reliable the computer’s response might be depends on the specific LLM the computer is using to process and generate language.
LLMs like Open AI’s GPT-3.5 , GPT-4, and GPT-4o have been at the heart of the recent AI surge. However, not all LLMs and AI products are created equal. And even with high-performing models, hiccups can occur. For instance, sometimes AI can suffer what is known as a hallucination — an incorrect response that is presented confidently as fact.
This means that while there is substantial promise in the field of AI and accuracy is constantly improving, we’re not yet at the point where you would want AI flying solo with anything that could have a serious impact on your business, such as customer service.
However, as a co-pilot, there is potential.
The benefits of using AI in customer service
Though AI isn’t ready to handle every customer conversation, it’s still a powerful tool that your staff can tap into to improve the support your team provides and the experience your customers receive.
There are the obvious benefits like increased support coverage, faster response times, and potentially lower support costs, but there are also less obvious advantages to incorporating AI into your support processes.
For instance, AI copilots can provide a better onboarding experience for new team members by giving them the information they need to gain confidence in the queue more quickly. AI can also provide space for support team members to take on more interesting assignments, acquire new skills, and make progress toward individual career goals.
Want to learn more?
We’ve got you covered! Skip ahead to our chapter on the benefits of AI.
The types of AI used by customer service teams
AI shows up in customer service in a number of places, though it’s predominantly encountered in self-service and agent-assist experiences like:
Chatbots and virtual agents: AI-powered chatbots — sometimes referred to as virtual agents — provide a much improved CX over their rule-based predecessors. Where users may have previously reached a dead end when the interaction strayed from the assumed support flow, AI-powered chatbots can better handle unexpected situations and provide more accurate, human-like responses.
Interactive voice response (IVR) systems: While traditional IVR systems are rule-based and can sometimes struggle to understand spoken language when it is complicated by accents, slang, or a bad phone connection, conversational IVR systems rely on AI technologies like automatic speech recognition (ASR), NLP, natural language understanding (NLU), and ML to deliver better support.
Knowledge bases: Even with good architecture and a solid search engine, it can still be tough for customers to find the information they’re looking for in a standard knowledge base. When a knowledge base is enhanced with conversational AI, people can locate the right resource fast by simply asking the knowledge base a question in plain language and having it “understand” and respond in kind.
Copilots and AI assistants: While people often think of AI as technology designed to replace humans, one of its greatest strengths is its ability to help people do their jobs better. AI copilots and assistants can help agents crack tough cases by suggesting relevant saved replies or drafting new responses based on internal and historical data. They’re also great for helping with simple writing tasks like spelling and grammar checks or bridging language gaps via automatic translation services.
AI can also help with “housekeeping” and analytical tasks:
Inbox management: AI is great at helping triage incoming messages, assigning tasks to the correct people or teams, and tagging conversations to help categorize issues. It’s also helpful for completing more complex tasks, like directly replying to messages based on predefined conditions and available data.
Sentiment analysis tools: For businesses with a high contact volume, sentiment analysis tools can be handy. The AI can locate conversations where customers weren’t happy and flag them for follow up, or, in some cases, AI can even intervene when a case is in progress to proactively prevent a negative outcome.
Analytics platforms: AI reporting software can analyze the huge amount of customer, product, and performance data located in your help desk, allowing you to better understand the state of your customer experience and business in general. They also frequently have features that allow for real-time analysis and can even present their findings in natural language, helping demystify results for those less mathematically inclined.
Ways to use AI in customer service
AI can perform a number of customer support tasks that can improve CX and lighten the load for your support team.
A few of the main tasks you may choose to streamline using AI include:
Responding to frequently asked questions (FAQs): Answering customer requests, even FAQs, can be time consuming. AI can give your team back their time by taking care of common questions.
Providing order updates: AI is great for providing order information such as shipping statuses and tracking numbers.
Returns and exchanges: In a similar vein, AI-enabled chatbots and IVR systems can process simple returns and exchanges.
Product or content recommendations: Conversational AI can take prior communications, browsing behavior, and known personal preferences into consideration to provide customers with suggestions on products to buy or content to consume.
Collecting data and customer information: Conversational AI chatbots and IVR systems excel at asking customers questions and gathering data such as contact information and user feedback.
Triaging support requests: AI tools are capable of assessing an incoming support query and routing it to the appropriate agent or team.
Troubleshooting simple technical issues: AI can run through simple troubleshooting steps like performing resets and checking device settings.
Providing multilingual support: AI technology can instantly translate customer requests and provide support across multiple languages.
Appointment scheduling and reminders: Conversational AI support tools can create service appointments or schedule support callbacks as well as remind customers of upcoming service visits.
Suggesting responses: AI can analyze the content in an incoming support request and provide a suggested response. Agents can then review and modify the response as needed before hitting send.
Flagging at risk customer interactions: Sentiment analysis software can tell when a conversation may be going south and alert not only the agent, but also someone on the escalation team who may be able to help turn the situation around in real time.
Customer service tasks that you shouldn't use AI for
While AI shines in the scenarios above, there is still a lot that it can’t (and you shouldn’t want it to) do.
Here are a few tasks best left to members of your customer support team:
Crisis management: AI should never handle situations where the stakes are high for either the customer or your business.
Complex troubleshooting: When troubleshooting moves beyond the basics, it’s best to escalate to a higher tier of support. Even simple troubleshooting can be frustrating for a customer, and humans are uniquely qualified for creating a connection and balancing information with patience and empathy.
Public support requests: While conversational AI has come a long way, it’s still best to avoid letting it provide responses in public forums like your brand’s Facebook page or Instagram comments without human supervision.
Requests that involve legal, security, or privacy issues: Whenever you’re dealing with a legal, security, or privacy issue, your company is exposed to potential liability. While conversational AI may have the skills necessary to complete the request, it’s best that these types of requests are managed by a human, just in case.
Issues that involve emotions, ethics, or judgment calls: While AI tools can detect sentiment and even mimic tone, they lack the ability to empathize. When emotions are running high or a situation requires your team to operate outside of protocol, it’s best for the case to be handled by someone on your team.
Best practices for using customer service AI responsibly
There is a lot of (justified) excitement around all of the different ways that AI can improve the way your customer service organization operates. At Help Scout, we have changed our view on using AI in customer service and have even launched some AI features that we think are pretty great.
That said, when you’re in the business of helping people, the stakes for getting AI right are high. Even just one poor customer support experience with your brand can cost you a customer, so it’s important to balance excitement over new technology with the responsibility of maintaining a good experience for people — both your customers and employees.
Here are some things to keep in mind when looking to responsibly offer AI experiences.
Be transparent and set expectations
For a customer, it can be hard to tell the difference between a chat reply crafted by a customer support agent and one confidently generated by AI. Your experience should make it obvious who (or what) your customer is interacting with at all times.
In addition to ensuring that customers know who they’re talking to, be honest with them about the customer experience your AI solution offers. If it can only handle specific types of queries like returns and exchanges, then be upfront with that information so that customers aren’t disappointed if your experience doesn’t live up to their expectations.
Always provide a pathway to a human
There are lots of reasons why a customer might prefer to speak to a human over a chatbot or virtual agent. Whatever their reason, customers who are communicating with AI should always be provided with a way to speak to a real person if they would prefer it.
As part of that transition, let customers know what the expected response time for the new channel will be. For instance, if the customer wants to speak to someone via email or phone, let them know when they should expect to receive a message from your team. If they’re requesting a live chat, let them know what the wait time to speak to a person is.
This will allow customers to choose the option that will not only make them feel most comfortable but will also align with the urgency of their request.
Train your tech with relevant data
When setting up your AI experience, be sure that the data used to train your solution — this can be anything from knowledge base articles to CRM information to past support interactions — is well-written, comprehensive, and accurate. Also, ensure that it doesn’t include anything that you wouldn’t want it to consider (e.g., proprietary data) when formulating a customer-facing answer.
If you’re building the experience in-house, you’ll have more control than if you’re using third-party software. If you are using a third-party option, ask their team what data is used when formulating responses and whether the technology utilizes machine learning to improve responses over time.
Test and monitor your CX
Once the AI is trained, be sure to thoroughly test the results internally before rolling it out to your customers. This will help ensure that the customer experience is good, and if there are shortcomings, you’ll be able to set customer expectations.
Remember: Your involvement isn’t over once your AI solution is live. Post rollout, your AI experiences will require regular monitoring to spot issues and identify opportunities for improvement.
Collect customer feedback
Part of the monitoring process is creating a feedback loop with your customers to gauge satisfaction with your AI-powered features.
Closely monitor CSAT and NPS scores and consider other feedback options such as targeted in-app surveys or video calls with key customers to learn what is working and what needs attention.
Keep privacy in mind
While there are plenty of great use cases for AI in fields like law or medicine, companies with strict security requirements need to explore how the AI tools they use work, who has access to client data, and what kind of security precautions are used to keep data safe.
Before adding AI solutions to your customer experience, double-check that your chosen AI tools are following all security and privacy practices that your business requires.
Can AI replace your support team?
One of the biggest draws for companies to implement AI solutions is cost savings; however, I’m just going to cut to the chase with this one:
AI cannot replace your support team.
Customer support is more than just providing the right answer as fast as possible. It’s about creating positive brand experiences and customer connections, improving your product or service, creating a feedback loop between executives, engineers, and end users, humanizing data, and many other things that a computer just isn’t capable of.
As you approach integrating AI into your customer service processes, remember that AI is not an alternative to having a customer support team, but rather a tool that empowers them to do their best work.
What to look for in a customer service AI platform
Given AI’s popularity, there are obviously many choices out there for support teams to consider when deciding on which AI software to add to their tech stack. While many factors like cost or technical lift can impact your decision, here are three things to consider when evaluating tools:
Conversation volume: If your business’ volume is high, then you may benefit most from chatbots and self-service tools to keep simple questions out of the queue and analytics to keep an eye on important metrics. If your volume is low, these features may not be as necessary.
Issue complexity: If your team mostly handles complicated or highly technical issues, then you should look for AI features that can help your team find the information they need quickly, such as AI assistants and copilots. For easier queries, chatbots and AI-powered knowledge base software might be the better bet.
Customer experience goals: If you’re trying to improve response times, then you’ll want features that provide direct support to your customers. If the goal is to offer a personalized, high-touch customer experience, then it's better to provide tools that empower your team like automated workflows, conversation summaries, and AI assistants.
If you’re looking for more tips on choosing a platform or maybe even a few recommendations, visit our chapter on AI customer support software.
How to introduce AI into your customer support experience
Ready to introduce AI into your customer support experience? Here’s how to get started.
Set goals: Do you want to reduce first response time? Lower contact volume? Give your team time to focus on more complex tasks? Knowing why AI is a good fit for your support strategy is an important first step.
Get your team on board: The introduction of AI may make your team uneasy, so it is important to get their buy-in early. Meet with the team, listen to and work through any concerns, and get their opinions on where in the support experience AI would be most helpful.
Determine resources: Implementing AI may involve technical resources or additional budget. Figure out what you have to work with so that you can seek out the right solution for your team.
Consider tools: Many help desks offer AI features, or they can be added on through an integration. Alternatively, you may prefer to build your own solution. If you’re going with a third-party option, you may want to try them out to see which works best for you.
Seek feedback: Check in with your team and customers regularly to figure out what is and isn’t working, and make changes accordingly.
Customer service is changing
AI is introducing new ways for customers to receive faster, more personalized support, and customer service teams are able to leverage that technology to ease the burden of busy work and grow their own roles beyond the queue.
However, despite all of that good stuff, it’s okay if you or your team are feeling nervous.
This space is new and evolving quickly, and feeling uneasy around change is completely natural. The important thing to remember is that as good as AI is, it is no match for the creativity, experience, and heart that support teams bring to the table each day. Support roles are changing, but they aren’t going away.
