If customer success were just about sending friendly check-in emails and hosting the occasional training webinar, life would be easy. But in reality? Customer success managers are juggling a hundred things at once: tracking customer interactions, trying to predict customer behavior, and somehow preventing churn before it happens (without mind-reading powers).
Enter AI for customer success. No, it’s not here to replace human relationships with dull creepy chatbots. And no, it won’t magically make every customer love your product overnight. But what it can do is take the repetitive, time-consuming parts of customer success operations and make them faster, smarter, and less of a headache.
Think of it like this: instead of manually digging through customer data to spot churn risks, customer success AI can surface early warning signs automatically. Instead of guessing which accounts need attention, AI tools can help customer success teams focus on the right customers at the right time. And instead of staring at a long-winded customer email trying to craft the perfect response, AI can suggest one before you even open it.
In this article, we’ll break down the real, practical ways AI is actually useful in customer success. From the well-known AI-powered chatbots and predictive analytics to the weird and wonderful (yes, we’re talking about AI-generated deepfake customers and lie detection for feedback calls). Whether you’re a customer success manager looking to streamline your workflow or just curious about how AI is shaking things up, this guide will show you what’s hype, what’s real, and how to actually make AI work for you.
AI in Customer Success Management: Beyond the Hype
We're past the "AI is a buzzword" phase, by a lot! In fact 52% of customer success teams are already using AI tools in some form. And no, it’s not just massive enterprises with bottomless budgets. Startups and large corporations are leading the charge, proving that AI is just as valuable for small, scrappy teams as it is for global giants.
AI is currently mostly being used for productivity boosts (e.g. drafting emails, summarizing meetings, and automating routine tasks). But that’s changing. More companies are realizing that AI can do more than just save time, it can help predict customer behavior, identify customers at risk of churn, and surface valuable insights before it’s too late. Chief customer officers who recognize AI’s potential aren’t just using it to increase customer satisfaction. They’re embedding it into their customer success strategy to make sure no valuable account slips through the cracks.
One of the biggest areas where AI is making a difference is onboarding and customer engagement. The State of AI in Customer Success 2024 report found that AI is particularly effective at analyzing customer behavior in real time, ensuring that new customers actually get the value they signed up for. Instead of manually checking in with every new account, AI-powered tools can flag who’s struggling, who’s thriving, and who might need extra attention. This improves the overall customer experience by providing proactive support at exactly the right time in the customer journey.
But there's still some low-hanging AI fruit to pick! According to Gainsight's report, customer success teams see predictive churn analytics as one of the biggest untapped opportunities, as shown in the graph above. This is followed by logging and analyzing CSM interactions with customers.
So if you’re still wondering whether AI is worth it, take a look around. More than half of CS teams are already using it. The companies that figure out how to leverage AI for customer retention and expansion, rather than just basic automation, will be the ones actually revolutionizing customer success. So, where do you get started with AI in your operations? Let's walk through some use cases!
How to Use AI for Customer Success
There aren't many elements of customer success operations that can't be improved using artificial intelligence. You've probably already heard about a handful (at least) of use cases for AI in CS. Here's the deal, we'll run you through the obvious ones first. After which you'll learn about some more "out there" use cases. We hope these will inspire you to discover new application domains or think outside the box when it comes to applying artificial intelligence to your CS operations.
Common AI Use Cases for Customer Success Managers
As you can see from the chart, there's a number of clearly common use cases to use AI for as a CSM. So don't hesitate to skip this section if you're already ticking all the use case boxes!
Predictive Analytics and AI-Driven Customer Insights
Every customer success manager has had that moment: an important customer churns out of nowhere, and no one saw it coming. But the truth is, there were probably signals all along. The problem is that customer success teams don’t always have the time to connect the dots. That’s where predictive analytics and AI-powered insights come in.
AI takes customer usage data (e.g. support history, customer meetings, email response rates, and even purchase history) and turns it into something useful. Instead of skimming through endless reports, CSMs get clear, data-driven insights on which accounts need attention and why. If an account’s customer health score is slipping, AI can flag it before it becomes a churn risk. If AI notices that a customer’s engagement is dropping or that they’ve started submitting more support tickets, it can nudge the CSM to step in with a proactive check-in.
At the end of the day, AI doesn’t replace a good customer success strategy. It just gives customer success professionals a clearer picture of what’s happening, so they can act before it’s too late.
Want to learn how to use AI-powered predictive analytics effectively? We wrote a detailed guide on training AI models in CS platforms for better and more accurate insights.
Good places to start: Gainsight, ChurnZero, Pecan AI
AI-Powered Chatbots and Virtual Assistants
Let’s be honest, no one enjoys waiting in a support queue, and most customer success teams don’t have time to answer the same three questions over and over again. That’s why AI-powered chatbots are taking over the repetitive stuff.
Modern AI-powered tools aren’t just glorified FAQ pages. They can actually understand customer interactions and respond in a way that feels human. Customers ask a question, and instead of a generic response, the chatbot pulls from customer data to give an answer that makes sense for their specific customer journey. If the issue is too complex, the chatbot can pass it off to a real person, along with a quick summary so the customer success manager doesn’t have to dig through a long conversation history.
Beyond support, chatbots are getting smarter at engaging customers in proactive ways. If AI notices a new user is struggling during customer onboarding, it can step in with a quick tip or suggest a help article. Instead of waiting for a frustrated email, you’re preventing the problem before it happens.
Good places to start: Intercom, Zendesk
Personalized Customer Engagement
Mass emails don’t work. Nobody wants another generic “We value your feedback!” message sitting unread in their inbox. AI is making customer success operations more personal by helping CS teams send the right message to the right person at the right time.
For example, if AI notices that a customer has been using the same feature every day but hasn’t explored other areas of the product, it can nudge them with a relevant tip. Maybe they don’t even realize that there’s a tool that could make their workflow easier. Instead of pushing upgrades, AI helps customer success managers drive actual customer value by making sure people get the most out of what they’ve already paid for.
The same goes for retention. If AI detects that a customer’s engagement is slipping, it can recommend a well-timed check-in, complete with insights into their customer sentiment. Are they frustrated with the product? Have they stopped responding to outreach? AI helps customer success teams prioritize the right conversations before it’s too late.
Good places to start: Salesforce's Einstein Bots, Yellow.ai
Automated Customer Support
Most customer support teams don’t have the luxury of handling every request personally. AI is stepping in to support customers in a way that actually makes their lives easier. So we're definitely not talking about replacing humans with bots.
Take AI-powered ticketing. Instead of making customers explain their issue from scratch every time they reach out, AI scans past customer interactions, understands the context, and suggests the most relevant solution before a human even gets involved. It can even analyze vast amounts of past support tickets to find trends. For example, a particular feature might cause repeated confusion. The AI can then flag it for improvement.
On top of that, AI can generate account summaries for customer success managers, so they don’t have to dig through old emails before a meeting. Knowing what a customer has struggled with in the past makes it easier to provide excellent customer service without wasting time on back-and-forth explanations.
Good places to start: Aisera, Chatbot.com
AI-Powered Feedback Analysis
Customer feedback is gold ... if you can actually make sense of it. But when you’re staring at a wall of survey responses, chat logs, and email threads, it’s hard to extract anything useful. Nobody has time to manually sift through every comment! And let’s be real, by the time someone finally does, the problem has probably already gotten worse.
AI changes this by making feedback analysis instant. Using natural language processing, AI can scan through thousands of open-ended responses, categorize them, and pull out the most common pain points. It can even do sentiment analysis, so instead of just counting how many people mentioned a feature, you also know whether they love it or hate it.
Good places to start: SentiSum, Thematic
Frontier AI Use Cases for Customer Success Managers
We figured you might have heard of aforementioned use cases, and might even implement some of them already. So here's a couple of wackier AI powered applications which might yet prove relevant for you as a CSM. If anything they'll definitely take you off the beaten path on your ai journey.
AI-Generated Deepfake Customer Avatars for Empathy Training
Every customer success manager has handled that call: the one where a customer is frustrated, overwhelmed, and just done with the product. The problem? There’s no way to practice these tough conversations before they happen.
That’s where AI-powered deepfake avatars come in. Imagine an AI that generates a realistic, emotion-filled customer avatar, pulling from actual customer interactions, past support tickets, and even customer sentiment data. Instead of awkwardly role-playing with a teammate (yikes), CSMs can practice with an AI-driven “customer” who gets frustrated, interrupts, or reacts based on the conversation.
The AI could even simulate different customer behaviors: a passive-aggressive enterprise client, a confused new user, or a power user who thinks they know everything but is using the product completely wrong. It’s one thing to read a guide on customer success principles, but it’s another to practice de-escalating a heated conversation with a lifelike AI who actually pushes back.
Good places to start: HeyGen, Hume.ai
AI Doppelgänger CSM That Customers Can Chat With Anytime
Even CSMs need sleep, crazy we know. But customers expect instant answers, even when their customer success manager is off the clock. AI is starting to bridge that gap. Not just with generic chatbots, but with AI-powered tools that mimic real human interactions.
Imagine a tool that clones a CSM’s voice, tone, and communication style, pulling from past customer interactions and customer data. Instead of a robotic chatbot, customers get responses that actually sound like the person they usually talk to. The AI doesn’t just pull canned responses. It understands context, remembers previous conversations, and can even keep up with ongoing support issues.
The customer knows it’s an AI, but it still feels like a natural, thoughtful response. Customer success teams get to scale their efforts without losing that personal touch, and customers get excellent customer service without waiting for an email reply.
Good places to start: HeyGen, Akool
AI That Reads Customer Emails and Prepares a Response Before You Even Open Them
Inbox anxiety is real. Every customer success manager has opened their email in the morning only to find a pile of messages ranging from simple questions to very long-winded complaints. On don't get us talking on opening your inbox after a holiday.
AI is starting to take some of that workload off the table. There's a wide range of tools available for you to rapidly decrease the size of that email mountain. These range from AI email organisers, to tools . Recently we've even soon the emergency of solutions that will fully automatically draft replies to incoming mail and wait for you to review them and hit send!
Good places to start: HyperWrite, Zapier
AI Lie Detector for Customer Feedback Calls
As in all relationships, customer relationships aren't immune to the occasional lie. We're guessing you'll be familiar with the problem: customers don’t always tell the truth in feedback calls. They’ll say everything’s fine when it’s definitely not, or they’ll downplay frustration until they suddenly cancel.
It's rather unwieldy to ask your customers to strap a lie detector across their chest before every call. Luckily, there's a number of SaaS companies leveraging AI to detect "falsehoods" (which is just a fancy word for lie, let's face it). Just upload the audio, video or transcript of your conversation and these tools will tell you whether your customer is a liar liar, pants on fire! You might be wondering whether these are accurate at all, and even if it's scary ... they are!
The goal isn't to rub your customer's nose in their lies, but to avoid taking feedback at face value. Instead of waiting for a churn notice, customer success teams get a chance to address issues before they escalate.
Good places to start: Prodigy Intelligence, Arche AI
Real-World Examples of AI in Customer Success
So how do you improve customer success using AI in practice? Here's two examples of companies that have taken AI beyond the buzzwords and actually made it work for them
Comcast’s “Ask Me Anything” AI Assistant: Guiding Customer Interactions
Being a customer service agent at Comcast means handling a constant flow of customer inquiries, each with its own unique challenge. Keeping track of everything: past conversations, product details, and common issues. To streamline this process, Comcast introduced the “Ask Me Anything” (AMA) AI assistant. Basically a tool designed to be the Watson to their customer success' Sherlock.
This AI marvel allows agents to tap into a vast reservoir of knowledge in real-time, providing accurate responses without the need to sift through endless manuals. By analyzing customer data and employing machine learning, AMA offers valuable insights that help agents understand and address customer issues more efficiently. This resulted in a smoother customer journey, increased customer satisfaction, and agents who can finally focus on what they do best: solving problems.
In practice, agents using AMA spent approximately 10% less time per conversation that required a search, leading to significant annual savings. Moreover, nearly 80% of agents gave positive feedback about the tool, highlighting its role in revolutionizing customer success at Comcast.
Travis Perkins’ Predictive Churn Model: A Deep Dive into Customer Retention
Travis Perkins is a leading supplier in the building and construction industry. As is the case for us all, they're facing the challenge of customer churn. Customers were drifting away, and the task was to identify who was likely to leave before they actually did. Like predicting which brick in a wall might come loose. That's the only construction joke we'll make ... pinky swear!
Enter RedEye’s predictive churn model, an AI solution that analyzes customer behavior, from past purchases to recent website clicks. By diving deep into this data, the model identifies customers exhibiting signs of potential churn. Armed with these valuable insights, Travis Perkins can proactively engage these at-risk customers with personalized communications, effectively nipping churn in the bud.
The results were impressive: a 54% reduction in customer churn and a 34% increase in customer lifetime value over a 12-month period. Obviously these numbers are self reported by Travis Perkins and RedEye so they might be slightly exaggerated. Still, it’s a testament to how analyzing customer data with AI solutions can lead to a deeper understanding of customer behavior and significantly boost customer retention.
How to Implement AI in Your Customer Success Strategy
We're firm believers in learning from the successes of others when it comes to implementing strategic changes. In early 2024, GitHub’s customer success team faced a challenge familiar to many growing organizations: how to efficiently handle a surge in routine support inquiries without compromising the quality of assistance. It's 2024, so everyone's reflex was to look towards AI for a solution.
AI Customer Success at Github
GitHub implemented an AI-powered conversational assistant, seamlessly integrated into their support portal. This assistant was designed to automatically respond to routine inquiries by providing access to known information and solutions as best as possible. It leveraged GitHub's product documentation as the AI's knowledge base to ensure accuracy and relevance. This approach not only reduced the steps and friction customers often faced but also allowed the support team to focus on more complex cases requiring human ingenuity.
The Return on Investment
Since its general availability in February 2024, the AI assistant has resolved 60% of the cases presented to it, with the majority being solved in under 7 minutes. This equates to over 20,000 customers having their problems solved through generative AI. By handling these routine queries, the AI assistant enabled GitHub’s support team to dedicate their expertise to more complex customer questions, thereby enhancing overall customer satisfaction. Although they don't explicitly mention this we would also assume that their customer success teams are happier as well!
General Advice for Implementing AI in CS
Drawing from their experience, GitHub offers the following steps for organizations looking to enhance their customer success with AI:
- Identify a Customer Pain Point: Pinpoint specific areas in your customer success process where AI could alleviate challenges or improve efficiency.
- Define Desired Benefits and Avoid Disbenefits: Clearly outline the advantages you aim to achieve with AI integration and be mindful of potential drawbacks to avoid.
- Evaluate AI Solutions: Assess various AI tools and platforms to determine which aligns best with your identified needs and objectives.
- Plan for Tracking Customer Feedback: Establish mechanisms to gather and analyze customer data post-implementation to ensure the AI solution meets their needs and to identify areas for improvement.
By following these steps, organizations can effectively integrate AI in customer success strategies, leading to improved efficiency and enhanced customer experience. Top AI Tools for CSMsThe list of tools you can use is getting too long to write down in a single section (or maybe even article). We've curated a shortlist of the most well-known tools in various categories, which we consider good starting point to further explore the market of SaaS companies offering AI-powered CS tools.
Customer Success Management Platforms
ChurnZero is built to help CSMs track customer health, automate engagement, and reduce churn before it happens. It’s got powerful analytics, great automation, and a super helpful support team. But, setting it up can be a bit of a headache, and some integrations don’t go as deep as you might hope.
ClientSuccess is designed to help CSMs track client interactions, centralize communication, and manage customer relationships effectively. Its clean UI and seamless Gmail integration make it a favorite among teams that want a lightweight, easy-to-use solution. Yet, some users feel that its task management features could be more refined, and the abundance of tracking options can feel overwhelming at times.
Churn Prevention & Predictive Analytics
Totango is focused on providing you real-time health monitoring and customizable reporting, making it easy to track client engagement. While it’s great for keeping tabs on accounts, new feature releases can feel rough, and health scores aren’t always reliable, sometimes flagging disengaged customers as highly active.
Custify offers comprehensive integrations and customizable health scores, making it a valuable tool for managing client relationships. However, some users find that its extensive capabilities can be overwhelming, and the user experience could be more intuitive. In summary, while Custify provides robust features and strong support, its complexity and user interface may require some adjustment.
AI Avatars
HeyGen is an AI-powered video creation tool that makes it easy to produce professional-looking videos quickly. However, some advanced features are only available with the premium plan, and the template library could be expanded.
Vidyard’s AI Avatars feature allows users to create personalized videos using hyper-realistic avatars that look and sound like themselves, streamlining the video creation process. However, some users have noted that the analytics could be more intuitive, and the user interface may benefit from improvements.
Customer Engagement and Chatbot Platforms
Intercom is a comprehensive customer communication platform that has integrated advanced AI features to enhance support efficiency. Its AI-powered tools, such as Fin AI Copilot, assist support agents by providing instant, accurate answers sourced from various content repositories, thereby streamlining customer interactions. However, its complex pricing structure and overwhelming feature set can make it challenging for smaller teams to navigate
Drift (part of the Salesloft platform) is an AI-powered conversational marketing platform designed to enhance customer engagement by transforming website visitors into loyal customers. Its patented AI capabilities listen, understand, and learn from site visitors, enabling personalized interactions that elevate the buyer experience. However, it comes with a steep learning curve and a high price tag, making it less accessible for smaller businesses.
Turning Customer Data into Action: AI Without the Overkill
AI for customer success isn't about replacing human connection with robotic efficiency; it's about making life easier on the CSMs while actually improving customer relationships. The best AI tools don't just automate busywork; they surface insights you'd otherwise miss, help you engage with customers at the right moment, and make sure no one falls through the cracks.
At this point, AI isn’t a futuristic nice-to-have. It’s something over half of customer success teams are already using. The question isn’t if AI can help, but how you’ll use it in a way that makes sense for your team. Whether that's starting small with predictive churn analytics, integrating AI-powered customer engagement, or going full sci-fi with deepfake customer avatars (if you do that, please reach out, we'd love to know how it goes) the point is to use AI in order to amplify your customer success strategy, not drown in an ocean of unnecessary automation.
So, what's next? Tweaking customer journeys based on AI insights? Scaling truly personalized engagement with AI-generated responses? Or just reveling in the fact that you can finally have AI write those emails you were procrastinating over? Whatever the case may be, customer success AI is about not overcomplicating your job but making it easier for you and the customer to feel happy. And that? That's an AI revolution we can all get behind.
