Not so long ago, consumers used to approach chatbots with big skepticism and readiness to ask for a real agent two seconds into the exchange — and rightfully so. Those bots were often rigid and rarely informative, and they had trouble understanding free-form input. Everything has changed with the expansion of conversational AI.
Today’s AI-powered solutions can understand context, interpret natural language with nuance, and respond in a way that feels genuinely helpful — often solving issues end-to-end without human escalation. They are available around the clock and capable of learning from previous interactions, which makes them a valuable addition to any customer success team.
In this post, we’ll take a closer look at the trending customer success AI tools and their use cases, which go far beyond text-based chats. You’ll find out how Natural Language Processing (NLP) models can be used to gain a deeper understanding of how your customers feel about your brand and what their current needs are. Moreover, you’ll see how these customer success AI tools can help you uncover and close customer service gaps based on relevant data gathered in recent customer interactions.
Content:
- Use case #1. 24/7 customer support and personalized recommendations
- Use case #2. Proactively detecting recurring issues
- Use case #3. Driving successful onboarding and product adoption
- Use case #4. Responding with empathy and preventing customer frustration in the long run
- Use case #5. Educating and empowering customers at every step
- Use case #6. Efficiently distributing tickets and preserving customer context
- Use case #7. Measuring customer health and highlighting the underlying tendencies
- Use case #8. Uncovering and utilizing upsell opportunities
- Use case #9. Automating and personalizing follow-ups
- Use case #10. Analyzing customer feedback at scale
- Ready to take the next step?
Use case #1. 24/7 customer support and personalized recommendations
Customer success has a lot to do with high customer satisfaction, and AI helps achieve exactly that, providing instant, consistent, around-the-clock support, reducing wait times, and scaling support channels.
Not only do AI agents and chatbots understand free-text queries, but they also retain context and recognize deeper intent behind the user’s questions and gently lead them toward the right solution — and they do so in multiple languages.
In this example, the fashion-forward AI shopping assistant named ISA offers a highly personalized buying experience. Rather than asking users to search manually, ISA presents curated options, allowing users to instantly jump to the relevant apparel category.
But this is just scratching the surface — customer success AI apps are capable of addressing issues down the funnel as well. For instance, they often assist users in diagnosing and resolving product or technical issues, often without human intervention.
Conversational flows guide users step-by-step through troubleshooting, while contextual memory allows the AI to remember previous steps and adapt. In addition, visual recognition with image or video input can help the bot spot physical issues.
The example above demonstrates how AI can streamline real-time customer support by securely handling admin tasks like updating a billing address. In this interaction, the AI requests the last four digits of the user’s account number, retrieves the current billing information, and guides the user through the whole process. There’s zero waiting on hold, back-and-forth with different agents, or navigating through bulky help pages — as a result, the customer is more likely to leave with a positive impression.
With SendPulse, you can create an AI-powered support bot just like the one above with ease and give it your tone of voice and brand personality. By integrating a SendPulse-powered chatbot with OpenAI, you can entrust your bot to handle repetitive and surface-level inquiries and set up a seamless handoff to human agents when matters get more complex.
AI chatbots you create with SendPulse can even generate images, which is great for helping customers visualize their needs.
You can test our chatbot builder with the free plan — or choose one of the more advanced plans starting at $11 a month, billed monthly.
Use case #2. Proactively detecting recurring issues
Another way to use AI for customer success is by having it monitor customer behavior and product usage patterns to predict potential churn or frustration before things go haywire.
For instance, AI tools like Attention AI continuously analyze calls, messages, tickets, and CRM notes to surface insights. These systems detect patterns across vast volumes of customer interactions. Instead of waiting for support teams to manually tag or escalate common problems, AI recognizes frequently mentioned complaints, confusing workflows, or product gaps in real time.
For example, if dozens of customers mention a checkout bug in slightly different ways (“I can’t recover my account,” “I can’t log in,” or “The activation link isn’t working”), the AI clusters these conversations using semantic similarity models. It can then flag this as a high-frequency issue, alerting product or support teams.
This way, you can easily register repeated feature requests, possible confusion around onboarding steps, or indicators of frustration or churn.
AI tools like this surface actionable insights like “15% of your users are getting stuck in onboarding step 3” or “multiple accounts in healthcare are confused about X feature.” The information is followed by a call to action allowing teams to jump in, adjust the related flows, rewrite documentation, or personally reach out to at-risk accounts.
Use case #3. Driving successful onboarding and product adoption
Artificial intelligence can be a powerful tool for tailoring onboarding flows based on a customer’s profile, industry, or behavior. Let’s take Stonly and Helpbar AI as an example: these AI-powered customer success tools are specifically designed to drive proactive onboarding and product adoption by delivering personalized, timely, and contextual guidance to users.
Instead of waiting for users to reach out with questions or get stuck, AI-powered solutions “anticipate” when a user needs help — and deliver it in the right format, at the right time. For instance, Stonly uses no-code, step-by-step guides that can be embedded directly into your product. What makes it powerful is its interactivity and customization logic. It detects what users are doing — or not doing — and triggers guides that explain features or troubleshoot friction points.
Moreover, with Stonly or its alternatives, you can enhance your onboarding process with AI answers — a go-to place for your users to clarify any product-related questions in seconds.
Helpbar AI serves a similar purpose, providing instant, AI-generated answers based on your documentation, help center, and product context. Users can type open-ended questions in natural language, and the AI part gives a contextual answer pulled from your internal docs.
The beauty of this solution is that it’s convenient both for the end users and for your team, as it saves time and reduces the need to answer repetitive questions. With it, text-heavy and unapproachable knowledge bases suddenly transform into engaging learning hubs.
💡 Find out more about building a knowledge base chatbot.
Use case #4. Responding with empathy and preventing customer frustration in the long run
As you probably know, AI tools have become pretty good at detecting customer sentiment and emotional tone. Solutions like Synthesia, in particular, brought AI conversations to a new level — they rely on realistic AI avatars to respond with empathy and make customers feel valued and understood.
Generating human-like video messages from text is a powerful way to humanize digital interactions. This can be useful for:
- sending onboarding videos that feel personal (“Hi Sarah, welcome to [Product]”);
- responding to complaints with empathetic face-to-camera videos, showing care and attentiveness;
- delivering renewal or feedback requests in a way that feels direct yet gentle and considerate.
Replacing dry text emails with friendly, custom videos will help you retain and nurture your relationships with customers, especially during critical or emotionally charged moments.
Another way to approach this topic is by tracking customer mood in the long run. ChurnZero, a multi-purpose AI customer success platform, focuses on reducing churn by assessing and visualizing customer risk, making it easier for teams to win back disgruntled customers.
Tools like ChurnZero rely on AI to monitor real-time product usage and detect drop-offs or red flags like user inactivity, skipped onboarding steps, or negative NPS feedback. This enables customer service reps to easily distinguish at-risk accounts and follow up with tailored messaging.
If you consider using a conversational AI chatbot powered by SendPulse, we have good news for you — our bots can now understand and transcribe customers’ voice messages, thanks to the integration with Whisper, OpenAI’s automated speech recognition system. This empowers you to better capture user sentiment toward your brand and sparks more spontaneous interactions with your bot.
Use case #5. Educating and empowering customers at every step
When it comes to educating users, written documentation is ubiquitous. However, while effective, static articles can be overwhelming, impersonal, or difficult to follow — especially for visual or auditory learners.
The aforementioned video generation tool Synthesia addresses this by using AI to convert written instructions into short, dynamic videos, narrated by realistic virtual presenters. These videos simulate a human trainer walking the user through steps or concepts, making the content more approachable and memorable.
💡 Find out more about deploying AI avatars for online courses.
For example, instead of sending a user a link to a 1,000-word help article on setting up integrations, you can provide them with a 60-second AI-narrated video that explains the same process step-by-step, with intuitive visuals, animations, and captions. The same can be done for “dull” reports, presentations, studies, and so on.
The best part is that you don’t have to ditch your existing assets or rewrite them — it’s possible to upload them straight to the video generation app, define the desired length and structure of the video, kick back, and relax.
AI-generated customer success videos are perfect for getting new users familiar with your product faster and with less confusion. Thanks to them, your users will be able to solve problems independently, while feeling like they’re being guided by a real person — and in their preferred language. For you, this approach will result in drastically reduced production costs and delivery times.
Use case #6. Efficiently distributing tickets and preserving customer context
AI in customer success doesn’t mean total automation and a complete lack of the human element — in fact, it’s quite the opposite. You can rely on it to make sure every complex inquiry gets proper attention from the best-equipped experts on your team.
Many AI customer success tools now come with the smart ticket routing feature, allowing them to direct customer messages to the right support or success agent based on issue type, urgency, or customer tier.
AI customer success solutions like Forethought, in particular, are designed to dramatically improve the efficiency and intelligence of support ticket workflows — this is achieved by preserving customer context across the entire support journey.
Instead of relying on manual routing or rigid rule-based systems, Forethought uses natural language understanding to not only analyze the incoming ticket’s content but also to understand the customer’s intent, urgency, and sentiment.
This AI-driven process often outperforms manual tagging or keyword filters because it interprets the meaning of a request, not just surface-level terms.
Forethought also helps maintain customer context throughout the ticket lifecycle, which is essential for empathetic and effective support. It does this by pulling relevant historical data from CRMs like Salesforce, past tickets, and conversations and surfacing that information directly to agents when they receive the ticket.
As a result, every handoff goes smoothly, and agents don’t have to ask customers to repeat themselves, which can be a common source of frustration, and can jump straight into meaningful problem-solving.
Use case #7. Measuring customer health and highlighting the underlying tendencies
Another integral part of customer success is relationship management. But, building trust and rapport is impossible without a deep understanding of each customer’s needs, goals, and behaviors over time.
This means maintaining visibility into ever-changing data points, such as product usage trends, support interactions, feedback sentiment, engagement frequency, contract milestones, and more. What sounds like a tedious task can become an exciting analysis when outsourced to AI customer success apps.
This is where tools like Staircase AI come into play. These platforms offer dynamic customer health scores by combining engagement data, product usage, support history, and feedback — in other words, everything you need to make surgically precise and perfectly timed interventions.
These tools aggregate data from multiple sources to create a comprehensive, real-time picture of each customer’s health status. Instead of relying solely on static metrics like NPS or renewal dates, Staircase uses AI models to identify subtle signals indicating customer satisfaction, engagement — or risk. The dynamic health scores reflect how likely a customer is to succeed or churn.
What makes Staircase AI especially valuable is its ability to uncover the root causes behind the health scores through pattern recognition and anomaly detection. For example, it can highlight if a decline in product usage coincides with:
- increasing support tickets about a particular feature;
- negative sentiment in customer communications;
- delays in payment or contract renewal discussions.
By knowing the underlying tendencies, your success managers will be able to tailor their approaches to each customer’s unique challenges. This contextual insight will also help you learn from each case and work toward a more future-proof customer success strategy.
Use case #8. Uncovering and utilizing upsell opportunities
Maximizing customer lifetime value (CLTV) is a core objective of customer success, and it’s done through proactive renewal management, upselling, and cross-selling based on customer needs and readiness.
As you may suspect by now, AI can be a fantastic solution when it comes to analyzing usage patterns and “sniffing out” those subtle indicators signifying the customer’s readiness to upgrade.
Trendskout can serve as a good example of how leveraging advanced data analytics and AI algorithms is used to continuously monitor customer interactions, product usage patterns, and purchasing history.
This particular platform specializes in detecting shifts in customer needs, increased engagement with premium features, or signs of growing business demands. In short, it identifies customers who are prime candidates for upselling.
For example, if a customer suddenly increases their usage of a core feature or frequently accesses advanced functionalities, Trendskout can flag this behavior as a signal that they may benefit from a higher-tier plan or add-on products, triggering personalized outreach. This is a reliable, data-driven way to match offers to genuine needs and focus efforts where they’re most likely to pay off.
It’s no secret that natural and customer-centric upselling should be focused on delivering added value rather than aggressive selling. AI comes in handy even here by providing playbooks and workflows that guide your success managers on the best timing and approach to propose upgrades or add-ons.
To give an example, here’s how ChurnZero empowers customer success agents to meet users exactly where they are in their product journeys.
ChurnZero’s proprietary AI engine makes it absurdly easy to draft content, research information, and develop ideas for stellar outreach, upselling, and beyond. It is trained by customer success experts on industry best practices and can be a lifesaver when it comes to crafting personalized emails or feedback to high-value clients.
Use case #9. Automating and personalizing follow-ups
The average customer success representative is likely to have a lot on their plate at any given moment. It’s only logical to employ AI to help your busy agents manage their schedule and day-to-day processes. For instance, with Attention AI’s integration with Slack, any customer success agent can kick off their day with confidence, thanks to the automated daily agenda feature.
When it comes to nuanced and time-consuming exchanges with customers, you can rely on customer success AI tools to generate conversation-specific follow-up emails in seconds. Unlike generic AI chatbots, these tools thoroughly analyze customer interactions such as support tickets, emails, chat conversations, and behavior patterns to determine the optimal timing and content for follow-ups.
Attention AI as a tool stands out exactly because of its ability to customize follow-up content based on customer context. It leverages historical data and crafts dynamic messages to address the customer’s specific needs, concerns, or milestones, making communication feel undoubtedly human-like, relevant, and thoughtful.
What makes this feature even better is the fact that it gives you fine control over the AI-generated conversations. You can prepare on-brand templates for each customer success AI use case and let the software adjust them to the conversation as necessary.
As if that wasn’t enough, there’s also the AI email overview feature, which can spare you literal hours spent on reading lengthy formal messages and squeezing crucial bits of information out of them. This way, AI can handle every aspect of your customer interactions without depriving you of the essential context, tone, or personal touch that makes those relationships meaningful. Instead of skimming through threads or chasing updates, you get a clear summary — empowering you to respond thoughtfully and focus your energy where it matters most.
Use case #10. Analyzing customer feedback at scale
Last but not least, AI is irreplaceable when it comes to collecting and processing big volumes of customer feedback. Not only can it aggregate and synthesize countless survey responses, but it is also useful for identifying trends, feature requests, and pain points — and doing so faster than any manual analysis.
For starters, AI chatbots can engage users via chat at key moments, for example, after a purchase, support resolution, or onboarding, to ask for quick feedback. They can tailor the questions dynamically, asking about what the customer liked, what could be improved, and whether they’d recommend the product based on their journey.
Then, AI can summarize longer customer feedback into a concise, readable quote or adapt the testimonial to different formats: short web quote, voice message, social media blurb, or detailed case study snippet. The bot can ask for consent to publish the testimonial and, if allowed, personalize it with the user’s name, job title, or company.
Because the AI-powered feedback collection takes place in real time, in the form of a natural conversation, the user is less likely to postpone or skip it, which is often the case with conventional surveys.
One example of such tools is Chameleon AI. The platform’s main purpose is to automatically ingest and interpret feedback from multiple sources such as surveys, reviews, support tickets, social media, and chat logs. Instead of manually sifting through thousands of comments, Chameleon AI rapidly categorizes and summarizes key themes, sentiments, and emerging issues.
Beyond simple sentiment analysis, this AI customer success solution is good at detecting:
- common pain points or feature requests;
- trends over time in customer satisfaction;
- emotional nuances like frustration or delight;
- segmented feedback based on customer demographics or behavior.
Armed with specialized customer success AI tools like this one, your team will be able to spot recurring problems before they escalate, validate or challenge assumptions about product-market fit, and track the effectiveness of your changes.
Another way to reach out to your customers when they’re most likely to engage is by using smart conditional pop-ups powered by SendPulse. This way, instead of dull surveys, you can serve them with intuitive conversational feedback collection forms strictly related to the content they’re currently interested in. It’s possible to customize the format and appearance of your widget to have it match your branding and CTA. Our plans start at just $11 a month, billed monthly.
Ready to take the next step?
Hopefully, our use cases for AI customer success tools have shown you how multifaceted and powerful these solutions can be. However, to achieve such stellar results, you need a robust set of tools for conducting your day-to-day outreach and storing customer data.
Explore the full capabilities of our platform — from our AI chatbot builder, CRM, and email campaign automation to pop-ups, SMS campaigns, and online course builder. SendPulse is an all-in-one solution for managing all of your marketing and sales communications from a single tab. Our comprehensive help center and 24/7 support with human agents will get you up to speed in no time.
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