CX and artificial intelligence: a promising but difficult relationship
Your company, like many others, is probably already exploring the use of conversational agents, virtual assistants, or artificial intelligence bots to improve customer experience (CX). It sounds logical: automation, cost reduction, 24/7 attention, and improved response times.
But the reality is different: most of these projects do not deliver what they promise.
According to multiple studies, more than 70% of AI agent implementations in CX fail to achieve their goals. It’s not because the technology is bad, but because the approach is often wrong from the start. Companies fall into strategic, design, expectation or execution errors that compromise the experience that customers receive.
So if AI is so powerful, why does it keep failing at something as critical as customer service?
When AI doesn’t understand your business (or your customers)
One of the most common mistakes is deploying an AI agent without prior context. The system is expected to solve problems without having been trained in the particularities of your product, your processes, your language, your internal flows, your customers.
The result: generic interactions, empty or outright wrong answers. This doesn’t just frustrate the customer. It also damages your brand.
Large companies have fallen into this error. One well-known case was that of a global airline that launched a chatbot to handle claims. The problem: the bot didn’t understand the differences between domestic and international flights, nor did it distinguish between lost and delayed baggage. The result was a collapse in service, a lawsuit, since the airline was responsible for the information delivered by its virtual assistant and thousands of complaints on social networks.
Artificial intelligence is not magic. It needs data, rules, training, context, and human oversight. If your agent doesn’t have a solid foundation of knowledge relevant to your business, they’ll just be a useless assistant with a robot voice.
Design to automate, not to converse
Another frequent problem is assuming that an AI agent must look like a human. You invest in giving it a “personality,” but not in actually solving the customer’s problems.
This leads to confusing experiences: users who feel like they are “talking to someone”, but who do not actually get solutions. An assistant who does few things well is better than one who tries to do everything without success.
Most customers don’t want to chat. They want to solve their problem fast.
Companies that do it well prioritize functionality over form. For example, some digital banks design AI agents that do not simulate a natural conversation, but do successfully automate specific actions such as blocking a card, checking balances, or changing a password. In those cases, the customer doesn’t need a lengthy dialogue: they need efficiency.
The focus should be on solving tasks, not entertaining users.
Unrealistic expectations from day one
Many implementations fail because they are launched with very high expectations. The agent is expected to answer all questions, handle all exceptions, and integrate with all systems… from day one.
That doesn’t end up being realistic.
Even the most advanced AI models need progressive training, iterations, and constant human feedback. Implementing a conversational agent is not a “turnkey project,” but an ongoing process. It’s a learning curve for technology and for your team.
For example, companies like Shopify or Uber didn’t launch their customer service systems with hundreds of features from the start. They started with simple use cases, tested them with real users, refined, and then expanded their reach. That is the winning strategy.
If you launch an AI agent without a clear roadmap, without defined metrics or continuous improvement processes, you are building an announced failure.
Lack of integration with key processes
A technical but common mistake is to implement the AI agent as an isolated solution. If it doesn’t connect to your databases, CRM, ticketing system, or interaction history, its usefulness will be very limited.
AI needs access to context to be useful. What did the customer buy? What was your last interaction? Do you have an open case? If the agent does not have this information, he will only be able to answer generalities.
For example, imagine that a customer wants to know the status of an order. If your assistant can’t see the order in real time, all they can do is “redirect” or say “I don’t have that information.” That’s not automation, that just creates one more frustration.
Companies like Amazon have managed to integrate their assistants with their entire ecosystem: logistics, payments, browsing history, preferences. That’s why they offer a seamless experience. It’s not just about AI. It is an AI that is well integrated into business processes.
If you want your agent to have an impact, you need them to be connected to the key systems of your operation.
Lack of human monitoring and supervision
An AI agent is not a solution that is turned on and forgotten. It is a system that needs continuous learning. And that can only be achieved with human monitoring.
Many projects fail because there is no team responsible for reviewing interactions, detecting errors, adjusting responses, or improving flows. The agent is launched and is expected to “work on its own”.
But even the most advanced models make mistakes. If no one detects them, the customer experience deteriorates over time.
As a leader in automated CX, you have your team actively monitoring AI performance at all times. You check if the answers are truly helpful, verify that your clients’ problems get solved, and identify new topics to incorporate into the model. Your hands-on supervision drives continuous improvement every step of the way.
To make your conversational assistant evolve, you must establish a clear, ongoing process to analyze, correct, and advance the model.
What does work: A customer-centric strategy (not AI)
Finally, what makes the difference isn’t how “advanced” your AI agent is. It’s how well it solves your customers’ real problems.
Companies that successfully implement AI in CX follow a user-centric approach. They start with concrete problems, simple use cases, and then scale based on real data. They evaluate success not by “automation rate,” but by metrics such as customer satisfaction, resolution time, and avoided contact rate.
It’s also clear to them that the best virtual agents work alongside the support team, not instead of them. They automate repetitive tasks so people can focus on solving complex cases and planning strategies.
For example, a company in the telecommunications sector reduced the volume of human tickets by 40% by automating only five key flows (invoice query, plan change, SIM activation, fault report, and line unlocking). The rest was kept under human management. Result? Better CX and greater efficiency.
The key is not to use more AI. It’s about using it better.
Conclusion: AI in CX does work… if used intelligently
AI agents have enormous potential to transform the customer experience. But that potential only materializes if you approach implementation with strategy, realism, and a focus on real value for the user.
It’s not about having a “pretty” chatbot. It is about having a useful, functional, integrated system, and with the capacity to learn.
The secret: Don’t start with technology. Start with the customer’s problem.
Design simple solutions. Measure results. Continuously improve. And yes, he supervises. Always.
Here’s how leading CX companies are making artificial intelligence deliver on its promise. And in your company, they can achieve the same.
