AI in Customer Success: Possibilities and Limitations

Recently, I joined a workshop as an observer for Customer Success (CS) professionals to explore how AI can be used in their work.

Most attendees shared that their companies give them access to a chat-based AI tool (likely ChatGPT), and sometimes they are given a library of prompts to support the usage. But beyond that, very few had seen any top-down changes in their operational flow. Instead, they’ve largely been figuring it out themselves, experimenting with ways AI could ease their already overloaded daily tasks.

They were the people closest to customers, and they were all genuinely curious about how to make their work more efficient, hopefully with AI.

Opportunities with AI in Customer Success

From the conversation, here are the use cases where they use (or think they can use) AI tools to improve the way they do their operations:

• Shortening time per case: Using AI to draft responses or suggest solutions could help reduce the time spent per customer problem.

  
  

• Collecting data for product/service improvement: Summarizing conversations and surfacing recurring pain points could give CS teams powerful insights to share with Product or R&D.

  
  

• Internal reporting: Automatically generating reports of conversations could help track customer health, flag churn risks, and highlight when proactive follow-ups are needed.

Limitations and Concerns

But the group was also clear about the challenges:

• Vague requests are tricky: Customers often reach out without giving enough context. CS professionals spend the first interactions verifying what the customers really mean. AI might suggest possible reasons, but participants found these guesses were usually “off.” Humans still do better at asking clarifying follow-ups to get to the point.

  
  

• Human preference: Many customers still prefer a real human on the other end, and can tell quickly when something feels AI-generated. At least from their point of view, efficiency gain through AI adoption cannot be justified from customer satisfaction perspective.

  
  

• No time to step back: CS teams are buried in execution. Few have the breathing room to rethink workflows and decide how AI could fit in strategically. Even if they come up with ideas, they all thought their managers won’t understand how AI adoption could improve the overall operation, especially at scale.

  
  

• Trust but verify: Perhaps the most important point: most of the professional I spoke with said they never trust AI outputs blindly. Their expertise tells them when something feels “off,” and they see AI as a tool to speed things up, not as a replacement for judgment.

My Takeaway

AI can absolutely help CS teams save time, generate insights, and support customers better, but only if it’s paired with human judgment and experience. The core of CS still is empathy and understanding and AI, at its best, is a supportive assistant, and not the driver.

I’m an AI enthusiast, but I completely agree with them: AI should never replace humans to complete the entire CS operations. AI can help companies to streamline some parts of it, but not all - at least, not yet.

Getting things done with AI does not mean doing everything with AI. The win is in crafting excellent customer services that balances the human touch, professionalism, and automation with AI that makes financial sense.

Are you a Customer Success professional or a SME owner who wants to see if AI can improve the way your Customer Success team operates? Book a call with me to discuss what’s best for you!