Customers don’t hate AI. They hate self-serving AI.

Many companies are jumping into AI feet-first, hoping to save money by increasing efficiency. One of the justifications they use is that AI will increase customer satisfaction via more personalized content and faster support.

However, customers can tell when AI is implemented for the benefit of the business rather than for them. And they don’t like it.

“We invested millions in AI. Why are our customer satisfaction scores going down?” It’s a question more executives are beginning to ask, but the first question they should ask themselves before implementing AI is: Why do we need this? 

Digging deeper, they should ask:

• What problem does it solve for employees? for customers?
• What’s the friction point or the operational inefficiency this solves for?
• Is this something we could fix without AI?
• Does it improve the employee experience?  the customer experience?

Vendors sell their product on the idea that AI can make customer experiences faster, easier, and more personalized while reducing costs. The argument is that customers will appreciate the convenience, employees will become more productive, and the business will become more efficient. But, while the business became more efficient, the customer experience didn’t.

The problem isn’t AI itself. Most customers don’t object to interacting with it. They use AI every day in search engines, navigation apps, streaming services, and countless other digital experiences. When AI genuinely helps them accomplish a task more quickly or more easily, they rarely think twice about it. What customers object to is AI designed primarily to benefit the company, and that distinction changes everything.

Companies optimize for efficiency. Customers optimize for effort.

Most AI initiatives begin with a business case that addresses typical concerns: reducing call volumes, shortening handle times, eliminating or automating repetitive work, and decreasing support costs. Those are all perfectly reasonable efficiencies to introduce, and businesses should pursue them wherever it makes sense. 

The problem arises when those internal objectives become the design criteria for the customer experience. Then they may make the business more efficient, but they’ll be doing nothing to help the customer.

Customers want one thing from technology, whether it’s AI or not: Help me accomplish what I came here to do. If AI helps them do that, they’ll embrace it; if AI makes it harder, they’ll resent the brand.

When AI serves the business instead of the customer

Let’s look a little closer at AI for the benefit of the business. How do you know if that’s what’s happening? It’s not too hard to tell. Here are some examples.

• A customer asks to speak with someone and is forced to endure multiple chatbot conversations first.
• A virtual assistant responds with paragraphs of information when a simple “yes” or “no” would answer the question.
• A customer explains their issue three times because each AI interaction restarts.
• Someone with an urgent problem is presented with product recommendations before their issue is resolved.
• A customer knows exactly what they need, but can’t bypass the AI gatekeeper standing between them and a human.

These interactions might move the needle on an operational metric, but they also increase customer effort. 

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The wrong metrics produce the wrong experience

One of the biggest mistakes businesses make is assuming that operational metrics are customer experience metrics. 

• Reducing average handle time doesn’t necessarily mean customers receive better service. Oftentimes, it’s quite the opposite.
• Increasing chatbot containment doesn’t necessarily mean problems were solved. Customers may have given up.
• Deflecting calls doesn’t necessarily mean customers found answers. They may have found your competitors instead.
• Lowering support costs doesn’t necessarily mean customer value has increased.

In fact, companies can improve each metric while damaging trust, increasing frustration, and making customers less likely to return. Businesses often measure what AI prevents, while customers measure what AI enables. And those are fundamentally different perspectives.

Part of the problem is companies thinking that customers don’t want to deal with a person when they need help or information. In fact, they don’t care who or what the help is from, as long as it’s delivered quickly and accurately.

Sometimes AI is the fastest path, and sometimes a knowledgeable employee is. Customers don’t care which one solves the problem, as long as it’s solved. What they dislike is when the company prioritizes protecting its cost structure over helping them. The best AI doesn’t replace people; it knows when people are the better answer.

The best AI is close to invisible

The most successful AI experiences are often the least noticeable. Things like when AI: authenticates the customer before they ask, remembers previous interactions, understands context, routes the customer to the right expert, summarizes the conversation so nothing needs to be repeated, anticipates the next step, or removes friction instead of adding it.

Customers don’t remember those experiences because AI was involved. They remember them because everything simply worked. Easy. Just like that. And that’s exactly the point.

Leaders need to stop using cost savings as the sole criterion when investing in AI. It’s every bit as important to ask what problem AI solves for customers. Does this reduce customer effort or simply reduce our costs? Customers don’t hate AI. They hate feeling that it was implemented to help everyone except them.