How AI Is Changing Customer Support

Exploring How AI Agents Will Impact The Customer Support Experience

May 06, 2025

Across industries, the relationship between companies and their customers is evolving. Customers today are more informed, more connected, and more vocal than ever before. When things go wrong, they don’t just want solutions, they want to know that companies care. Great customer support experiences can be a competitive differentiator. Businesses with consistently smooth, personalized, and responsive support teams are far more likely to retain and attract customers. AI agents now promise to make the support process seamless. They can address customer concerns far more effectively, which benefits both customers and customer support teams.

Why Do Customer Support Teams Need AI Agents

AI can help them meet rising customer expectations and increasingly demanding workloads.

Today’s customers want their experiences to be fast, simple, and easy. Both when using products and engaging customer support. Research indicates that 80% of customers believe that their experience interacting with the company is as important as its products and services. This creates pressure on customer support teams to adapt their support processes to meet changing customer expectations. Customer support agents report that 82% of customers ask for more than they used to. As a result, 56% of agents report experiencing burnout because of their workload.

AI agents in customer service reduce the burden customer support teams face, while simultaneously making support experiences fast, efficient, and personalized. They enable support automation at scale, saving support teams time and money. When flooded with requests, AI can help human agents distinguish requests that can be easily and quickly resolved from those that require more time and effort. AI can also help human agents review all the customer information (across support channels, CRMs, customer profiles, etc.) and determine the right solution for them.

With AI purpose-built for customer service, you can resolve more issues through automation, enhance agent productivity, and provide support with confidence. It all adds up to exceptional service that’s more accurate, personalized, and empathetic for every human that you touch.
— Tom Eggemeier, Zendesk CEO

What’s the difference between customer support, customer success, and customer service?

Companies often use the terms customer support, customer success, and customer service interchangeably. Each term relates to a slightly different purpose. Customer support is about helping customers resolve issues and problems → reactive assistance. Customer success is about helping customers benefit from the product → proactive assistance. Customer service is about maximizing customer satisfaction and strengthening customer relationships. The main differences lie in the timing (reactive vs. proactive), scope (issue-specific vs. relationship-based), and goals (resolution vs. satisfaction vs. long-term success). While these can be distinct functions, they all share the same purpose of improving the customer experience, building loyalty, and maximizing customer value.

What are the types of customer support?

There are five tiers of customer support. These tiers indicate the level of support a customer requires based on the nature of their request.

Tier 0: Issues that can be solved with tools like FAQs, knowledge bases, etc.
Tier 1: Minor, routine issues that self-service tools couldn’t resolve.
Tier 2: Technically complex issues requiring technical knowledge.
Tier 3: Highly complex issues requiring specialized expertise.
Tier 4: Issues requiring assistance from external vendors or providers.

Note: There is no standard definition of tier 1 support. It can mean different things at different companies, so even seemingly complex problems can sometimes be considered Tier 1.

How Can AI Agents Help

AI agents can help support teams handle routine issues more efficiently.

Tier 1 requests are the most common type of customer support request. Often customers are looking for answers to basic, straightforward questions. Many organizations already automate Tier 1 support even without AI. For example, when you call a support number and you are asked to “please press 1 for this, 2 for that” this is a simpler type of automation. However, the downside to this approach is that it can take a while for customers to find what (or who) they need to address their problem. This leaves them frustrated and annoyed, even if they manage to find a solution.

The real value of AI agents is their ability to handle most Tier 1 support requests with far greater efficiency. They do not rely on rigid flows. Instead, they efficiently resolve or escalate issues to ensure customer issues are resolved as seamlessly as possible. They can engage in multi-step conversations, ask follow-up questions, and adapt responses based on user input. For example, Klarna’s AI agent was able to handle two-thirds of their customer service chats, equivalent to the work of 700 full-time agents, within its first month.

(Tier 1) this is the easiest place for your AI agent to start. All the answers are in your knowledge base somewhere or should be or need to be for the AI agent to be able to answer it, so it’s just having that support content in place.
— Brian Donahue, Intercom’s VP of Product

What can AI agents do for customer support?

24/7 Support: They offer a dependable, always-available resource for all basic support needs across multiple channels, enhancing accessibility and convenience.
Customer FAQs. They can handle many basic customer queries and direct them to relevant support resources (tutorials, product pages, etc.).
Multilingual Support: They can provide support in multiple languages, which can be especially helpful for companies spread across different regions globally.
Intelligent Routing: They can direct customer queries to the right support agents based on context (from support tickets, conversation history, etc.).
Improved Support Intelligence: They provide vital context to human agents by providing insights (on customer history, problems, potential actions, etc.).

What are the benefits of using AI agents in customer support?

Faster Resolutions: They reduce the average customer query resolution time by providing relevant, context-aware responses, giving customers answers faster.
Increased Efficiency: They automate routine, repetitive customer query responses, freeing up human agents to handle complex customer queries.
Improved Scalability: They help customer support teams to scale support capability without compromising on the quality of service.
Improved Consistency: They help support teams provide consistent responses to customer queries, which reduces confusion, increases clarity, and builds trust.

Integrate AI In Customer Support The Right Way

Solve the right problems.

Support requests are a symptom of problems within the product (poor design, lack of guidance, etc.). Fixing a limitation in a product that causes frequent customer support requests is often more effective than getting better at responding to these requests. Creating great customer experiences is about minimizing the need for help, but providing excellent support when customers do need it. Companies often end up with oversized and overburdened customer support teams because they fail to address the real root causes of support issues. Since the arrival of Open AI’s ChatGPT, many companies have rushed to add AI bots to their workflows. However, these additions are meaningless if they do not create any value for customers. Research indicates that despite the increased availability of AI chatbots, adoption remains flat.

While AI agents can certainly increase the cost-effectiveness of delivering support if customers don’t actually them, customer support teams don’t get to reap the benefits. Low adoption is often a sign of a misalignment in priorities — what the customers care about vs. what the team thinks the customer cares about. Support requests can give companies excellent feedback on what needs to be improved. They can reveal a great deal about what the customer experience is really like. Solutions need to help customers solve real problems, making their lives easier and better. Therefore, creating a better support experience starts with first building a better product.

Minimize the risk of hallucination.

AI agents can hallucinate — there have been several instances of AI chatbots providing false or misleading information. This is especially damaging to customer relationships. When users contact customer support, they are looking for answers and assurance. Their emotional state can range from uncertain (in the best case) to enraged (in the worst case). Getting the wrong information from an AI agent will likely only worsen their mood and their impression of the company.

A hallucination occurs when AI agents encounter topics they are not trained to handle. Therefore, support teams need to thoroughly test AI agents to ensure they work as expected. They must put safeguards in place to ensure that AI agents handle out-of-bounds topics appropriately. They must clearly define escalation paths for potentially out-of-bounds requests. When non-permitted topics come up in conversations, AI agents must be directed to refer customers to a human agent/ limiting any potential damage from “bad” responses.

Ensure efficient escalation to human agents.

When AI encounters issues that are too complex to handle, it eventually has to hand off the customer to a human agent. However, AI agents must escalate support requests efficiently. The longer it takes for a customer to get to a solution, the more frustrated they get. Sometimes the right solution is escalating a support request directly to a human agent rather than trying to resolve it autonomously. AI agents are only helpful if using them takes customers less effort than relying on traditional customer support. When AI agents misdiagnose customer problems they give customers wrong answers, directing them to irrelevant solutions. Customers get frustrated, making them less likely to use these tools again.

While AI cannot replicate human empathy (at least not yet), it needs to be trained to detect frustration with answers. It should direct frustrated customers to the right human agents. Sometimes, customers just want to talk to someone who will understand why the problem matters to them. They are looking for real empathy. If they feel like they are getting nowhere talking to an AI agent, they will feel like the company does not care about them and look for alternatives. Customer loyalty is hard won but easily lost. So escalating support requests to human agents at the right time can make a big difference in how customers feel about the entire experience.

Conclusion

AI can transform the customer support experience, but only if it’s thoughtfully implemented.

Great customer support is an essential part of great customer experiences. While AI agents can certainly help create more efficient support processes, the business should be evaluating whether these tools truly better support experiences for their customers. In a world where customer experience is a strategic differentiator, every support interaction is an opportunity to strengthen customer relationships. Thoughtfully designed support processes don’t just offer answers, they show customers that companies care about them. This is what builds customer loyalty. Therefore, AI agents must be adopted in a way that reinforces trust rather than eroding it.

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