The state of New Mexico is blessed with many splendors, including the amazing regional fast food chain Blake’s Lotaburger. I don’t know who Blake is, and I also don’t get burgers there, but what I do get is a breakfast burrito served Christmas style—with both red and green chile.
With a small menu featuring locally farmed Hatch chiles, I imagine Blake’s still does things pretty much the same as they did when they opened in 1952—except for one thing.
The last time I called to place an order before a road trip, I was greeted by first name by a disarmingly human computerized voice that recognized my number and suggested the exact order I planned to make.
I’ll admit I remain a little freaked out by this voice. But the compulsively antisocial part of my psyche that makes me not want to make phone calls also appreciates these shifts to using AI in customer service. Freaky or not, artificial intelligence is becoming as common as it is rapidly changing—here’s how companies like Blake’s are putting it to use.
Table of contents:
What is customer service AI?
AI can be used in customer service to help streamline workflows for agents while improving experiences for the customers themselves. Some of the more common uses of AI in this space are support ticket sorters and chatbots (like my favorite regional fast food chain’s personalized order-taker), but that’s really just the tip of the breakfast burrito.
The number of ways to implement artificial intelligence in contact centers, within eCommerce platforms, and as part of service-adjacent sales phases is practically limitless. As a whole, the AI industry is growing quickly, and implementation in the customer service space is following suit. By the time you read this, I’ll probably already be working on a piece called “AI in customer service: Even more ways to automate support that I didn’t know existed a few weeks ago.”
Here are some ways businesses can use AI in their customer service ecosystems.
Benefits of AI in customer service
Customer service AI should serve both the customer and the company employing it. Here’s what each party can gain from AI tools and practices like the ones above.
For agents
From customer service agents to the enterprises employing them, here’s what users on the back end can gain from AI.
Reduced ticket volume: Using AI to help customers help themselves means fewer tickets for agents.
Fewer low-level tasks: Since AI can help customers resolve basic issues without contacting agents directly, fewer of those repetitive tasks make it to agents. That frees them up to focus on more complex issues and higher-value tasks.
Lower costs at scale: Companies can keep expenses down by relying on software to manage growing customer needs as they grow.
More actionable insights: Machine learning analysis can synthesize massive amounts of data to forecast needs, suggest actions, maintain inventory, and much more.
For customers
Ideally, customers may not even know they’re using AI. Here’s how it can benefit them, whether they can detect it or not.
More efficient resolution: When it comes to customer service, customers really only care about getting their issues resolved fast. If a chatbot can direct them to a solution in less time than it takes to connect to an agent, that’s a win for them.
Cleaner UX: Effectively implemented AI can help support teams streamline front-end processes and address common user issues before they become issues.
More predictable UX: Customers have established paths for issue resolution and can choose the resolution methods they want with accurate ideas of response times.
Lower costs: By keeping overhead low, sellers can keep price points down for their customers.
How AI is used in customer service
Contrary to what catastrophizing media and dystopian sci-fi will have you believe, AI isn’t a one-off replacement for most customer service outfits. (If there is a sci-fi subgenre dedicated to AI customer service operations, please tell me.)
Most companies with strong customer support situations use AI to supplement their human agents, helping them save time, cut out low-level tasks, and solve problems more easily. Here are a few general ways they use AI to do that:
Customer insights: AI can be used to help customer support teams gain insights into massive amounts of customer data, helping them find actionable takeaways from both company-wide and industry-wide trends.
Customer self-support: Some AI solutions are geared toward helping customers find the answers they need themselves. This cuts down on response time for customers while cutting out repetitive queries for agents.
Agent support: Agents can use AI solutions to help them keep the supporting resources, communication channels, and data they need at the ready.
Process improvement: When integrated into workflows, AI can cut out friction points and add smart automations that move customer support responsibilities moving more efficiently.
Want to see it in action? Learn how Learn It Live reduced support tickets 40% with an AI-powered chatbot and how the nation’s largest transit ad company transformed its customer support with AI.
11 examples of AI in customer service
Still not sure what AI can do for your customer support agents, campaigns, and workflows? Here are some of my favorite customer service AI use cases.
1. Customer service chatbots for common questions
The humble chatbot is possibly the most common form of customer service AI, or at least the one the average customer probably encounters most often. When used effectively, chatbots don’t simply replace human support so much as they create a buffer for agents. Chatbots can answer common questions with canned responses, or they can crawl existing sources like manuals, webpages, or even previous interactions.
If queries like these comprise half a company’s total customer support request tickets, that’s a huge time savings for its agents. For unresolved questions, chatbots can connect customers to available agents, helping ensure that those agents are only getting the more complex or higher-value tickets.
You can build your own AI chatbot for free in a matter of minutes using Zapier Chatbots. Train the bot on your own knowledge sources, fine-tune it for your company’s tone, and then view analytics and conversation history to make your customer interactions even more seamless. Learn how to set it up.
Example use case
A recent buyer visits your website to look up the exchange policy. They connect with a chatbot, which directs them through the predetermined exchange process, helping the customer resolve their issue without involving an agent.
2. Customer self-service chatbots
Sometimes, the best way to help people is to help them help themselves.
Chatbots can do more than just answer questions; they can also use AI to suggest actions based on a customer’s browsing activities or common recent queries from across the website, identifying or even predicting friction points before the customer even tries to reach out to support. If clicks or in-site search queries are trending for a particular product type or content cluster, for example, chatbots can pop up with relevant pages to help visitors get to their likely destinations faster. This is also a great way for a business to suggest products or services to qualified leads.
Example use case
A potential customer comes to your website two weeks before the start of Hanukkah after a Google search for the term “hanukkah gift ideas.” The chatbot pops up on their screen, directing them to a curated holiday gift guide.
3. Support ticket organization
AI support ticket organization—which employs things like natural language processing (NLP) and sentiment analysis—uses rules to automatically apply tags and labels to tickets and sort them to the appropriate agent and support phase. Using AI to automate ticketing has two major benefits over manual organization: it cuts the amount of time agents spend on repetitive, low-impact tasks, and it helps companies scale their support as they grow.
AI learns from itself, so it can use analytics to adapt its processes over time. As resolution processes change, AI ticketing can change how it sorts and tags conversations, assigning tickets and keeping agents on top of issues.
Example use case
As support requests come in through your ticketing platform, they’re automatically tagged, labeled, prioritized, and assigned. Agents instantly see new critical tickets at the top of their queues and address them first.
4. Opinion mining
Through natural language processing, AI can be used to sift through what people are saying about a company to create reports that can be used to improve customer service. From private user surveys to public reviews to social media posts, AI can perform opinion mining (or sentiment analysis) in a sliver of the amount of time it would take a person to read through responses, reviews, and updates one by one.
While this process doesn’t directly address users or resolve active issues, it can still be an incredibly useful tool for identifying common friction points for customers. By using these analyses to find trends in service processes, enterprises can fix problems by changing workflows, creating new resources for self-service, or giving agents the training or tools they need to address them.
Example use case
You deploy AI to crawl recent survey results with open-ended responses to quickly identify trends in user sentiment, giving you data-driven insights into new product feature ideas.
5. Competitor review assessment
Prices are already outrageously high for fast food. What a stupid decision. https://t.co/3hLlat7vuq
— Attitude with a side of freckles (@HazeyDaisey17) February 27, 2024
What’s true for you is also likely true for your competitors—and vice versa.
Opinion mining can also be used to analyze public competitor reviews or scour social media channels for mentions or relevant hashtags. This AI sentiment analysis can determine everything from the tone of X mentions to common complaints in negative reviews to common themes in positive reviews.
Is this customer base exceptionally sensitive to wait times? Does it place a premium on human interactions? Are there complexities in the return process that are driving customers to competitors? By compiling this data en masse, businesses can see what’s driving real customers either toward or away from competitors based on customer service experiences.
Example use case
You deploy opinion mining software to monitor sentiment trends in your top competitors’ social media feeds. By collecting negative feedback, you find product gaps that help you ideate new features.