How AI In Customer Support Is Changing The Role of Customer Success Teams

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Businepromises 24/7 customer support at a very reasonable rateb. Even if you don’t use chatbots, you can use it to automate a lot of simple tasks. You can even have an AI agent support your  team.

But many companies are using it to help shift their focus. They’re using AI to shift from reactive support toward proactive engagement, better customer relationships, and strategic problem-solving.

In this post, we’ll discuss how AI is changing the role of customer success teams. We’ll also look at a potential stumbling block, hallucinations in generative AI, and what you can do about it.

Why Customer Success and Artificial Intelligence?

Traditionally, customer success teams focused on ensuring customers could use a product effectively, reducing churn, and driving long-term value. While these responsibilities remain, AI is changing how teams approach them.

From Problem-Solvers to Strategic Advisors

AI-powered chatbots and virtual assistants now handle common support queries, account updates, and troubleshooting. This automation reduces the volume of routine support tickets, freeing up customer success teams for higher-value tasks.

Instead of spending time answering repetitive questions, teams can:

• Focus on long-term customer success strategies
• Identify upsell and expansion opportunities
• Offer more personalized guidance and consulting for clients

Proactive Engagement Instead of Reactive Support

With AI analyzing customer behavior in real-time, companies can predict when a user is likely to encounter an issue or disengage from the product. AI-driven analytics allow customer success teams to act before problems escalate.

For example:

• If AI detects a drop in feature usage, customer success can reach out with training resources.
• If AI flags a high-risk account based on sentiment analysis, a customer success manager (CSM) can proactively check in.
• If AI predicts potential churn based on historical patterns, teams can intervene with personalized incentives or support.

This shift from reactive problem-solving to proactive relationship-building is one of the most significant changes AI brings to customer success.

Enhanced Customer Onboarding and Education

AI enables hyper-personalized onboarding experiences. Instead of a one-size-fits-all approach, AI-driven systems can:

• Guide new users through product setup with tailored tutorials
• Provide in-app recommendations based on user behavior
• Automate follow-ups to ensure customers stay engaged

This automation ensures customers get the information they need without overburdening customer success teams, allowing them to focus on complex cases.

Scaling Customer Success Without Losing the Human Touch

For growing companies, scaling customer success can be a challenge. AI helps by providing:

• Automated responses to FAQs
• Smart ticket routing to the right expert
• AI-driven knowledge bases that surface relevant content

These tools allow smaller customer success teams to support a larger customer base without sacrificing quality. However, the key is to use AI as an enhancement—not a replacement—for human interaction.

The Risk of Hallucinations in Generative AI

While AI brings efficiency and scale to customer support, it’s not without its challenges. One major concern is AI hallucinations—when generative AI produces false or misleading information.

What Are AI Hallucinations?

AI models generate responses based on learned patterns, but they don’t truly “understand” information. Sometimes, they create convincing but entirely incorrect answers. In customer support, this can lead to:

• Misinformation about product features
• Incorrect troubleshooting advice
• False claims about policies or pricing

For example, if a chatbot confidently tells a customer that a refund policy includes free returns when it doesn’t, it can lead to frustration, lost trust, and potential legal issues.

Why Does AI Hallucinate?

The whole purpose behind training large language models is that they can give people answers. In some cases, the AI doesn’t know what the answer is. In these cases it can either admit it needs help or make something up. Sometimes it might make incorrect assumptions based on faulty logic.

AI doesn’t reason in the same way we do. It can make jumps that don’t quite make sense. How accurate the answers are depends on how well you train the model initially. If you use outdated information, the AI won’t be very useful. If you don’t use enough relevant training data, it won’t be able to deal with edge cases.

How to Minimize Generative AI Hallucinations

Most people think they can let artificial intelligence do its own thing. We’re not at that stage yet. You’ll need to oversee any bots you use to make sure they’re giving the right information and avoiding hallucinations in generative AI. Fortunately, there’s a lot you can do to overcome this issue:

• Look for a Hallucination-free AI solution: Working with a company with specific experience in this area is essential. They’ll help you develop a highly accurate bots.
• Human review for critical interactions: You should clearly explain to the bot what high-stakes queries it should escalate to human agents.
• AI confidence scoring: AI systems can flag responses with low confidence for human verification.
• Training AI with verified knowledge bases: Ensuring AI pulls information from up-to-date and approved sources reduces the risk of misinformation. Make sure you use the best quality, relevant data.
• Giving customers clear AI disclaimers: Customers should know when they’re interacting with AI and how to escalate to a human if needed.

Generative AI hallucinations highlight the importance of keeping human oversight in customer interactions. Customer success teams need to act as quality control, ensuring AI-driven support remains accurate and helpful.

Looking Ahead

AI’s changing the way we look at customer support, but it won’t replace human-led teams completely. Instead, it reinvents the role, focusing on strategic guidance, proactive engagement, and building trust.

As AI continues to evolve, customer success teams will play a vital role in:

• Fine-tuning AI systems for better accuracy and personalization
• Developing deeper, long-term relationships with customers
• Providing human insight where AI falls short

Naturally, there are some issues to overcome first. Generative AI hallucinations can cause you some embarrassment, so you must find a solution that deals with these. And that’s also why you need a balanced approach when it comes to AI.

When you get the balance right, your customers get the best of both worlds.