The Chatbots Trap — Why Your AI Isn’t Working

Author(s): Louis Dupont

Originally published on Towards AI.

Everyone builds chatbots to “leverage AI” — but they’re the reason most projects fail.

When most companies think of AI, they think of chatbots.

It’s intuitive — chatbots let us “talk” to AI, like we’re chatting with a colleague or a friend. It feels magical, like we’re facing some super intelligence.

The truth? Chatbots are not the solution. They’re just an interface — and most of the time, they’re the wrong one.

If your team is building a chatbot because “we can benefit from AI,” let me tell you — you’re falling into the chatbot trap. I’ve seen it happen over and over again. The result? AI projects that frustrate users, fail to deliver real value, and are impossible to measure or improve.

Here’s a typical example

• You’ve got many documents. You’ve got people working with it. You want AI to make your team smarter and faster.
• Someone says, “Let’s build a chatbot. It’ll help us get answers quickly.”
• A prototype gets built, a few team members try it out, but it just doesn’t stick. It’s nice, but for some reason it’s not adopted.
• After countless iterations, you give up, convinced the technology isn’t mature enough yet

Let’s break down why this happens and, more importantly, how to avoid it.

What’s Really Going Wrong?

1. You’re Starting with the Tool, Not the Problem

Going back to that typical chatbot example, the issue is that no one asks the critical question:“What problem are we actually solving here?”.

At the end of the day, your users don’t care about chatbots. They care about outcomes.

Take this example:

If consultants are navigating through a massive library of documents, they don’t want to “chat” about those documents. They don’t care about asking questions, and honestly, they don’t care about answers either. What they actually want is specific, structured pieces of information that help them do their job faster.

Now imagine giving them a structured report — answers delivered upfront, no questions needed.

• No back-and-forth.
• No frustration about phrasing questions correctly.
• Just the answers.

Suddenly, the AI is solving the real problem. And as you will see, it’s also a much easier to develop!

2. Chatbots Are Open Systems — and That’s a Measurement Nightmare

Here’s something that’s often overlooked: a chatbot lets you ask anything.

• What will users ask?
• How will they phrase it?
• What edge cases will they uncover?

This creates an open system — and open systems are a nightmare to measure or improve. How do you evaluate success when the scope is infinite?

You can’t.

Now compare that to a closed system — like automatically generating a report or extracting specific, predefined pieces of information. In a closed system:

• You know exactly what you’re delivering.
• You can measure accuracy, recall, and completeness.
• And because you can measure it, you can improve it.

This is where most AI projects fail: teams jump straight into chatbots because they feel magical. But from a product perspective, it’s chaos.

3. Chatbots Set the Wrong Expectations

Here’s the user experience problem: when you give someone a chatbot, you’ve implicitly promised, “You can ask me anything.”

But the reality? They’ll get an answer like:

“I’m sorry, I can’t help with that.”

That’s incredibly frustrating. It destroys trust.

On the other hand, a clear, simple solution — like clicking a button that says “Generate Report” — sets expectations. Users know exactly what they’re going to get, and they don’t feel let down.

Here’s the rule: The simpler the solution, the clearer the expectations — and the better the user experience.

Rethink AI

This is the mindset shift teams need to make: AI isn’t magic. It’s not about mimicking humans. It’s a tool to deliver outcomes.

If you’re starting with “we need a chatbot,” you’re starting in the wrong place. Instead:

Start with the problem.

• What pain point are you solving?
• What outcome does the user really care about?

Constrain your scope.

• The smaller and clearer the scope, the easier it will be to measure, improve, and deliver.
• Ask: What will the AI do, and what won’t it do?

Build measurable, closed systems first.

• Focus on structured outputs, like generating a report, extracting key insights, or delivering a targeted answer.
• Closed systems are measurable. Measurable systems are improvable. And improvable systems are what succeed.

When Is a Chatbot the Right Answer?

Let me be clear: Chatbots aren’t useless. For narrow, well-defined use cases, they can work brilliantly.

But here’s the thing: a chatbot should never be your default solution.

If you’re building a chatbot, ask yourself:

• What’s the scope? Can we define clear boundaries for what it will and won’t do?
• What’s the expectation? Will users understand what they can / cannot do?
• What’s the outcome? Are we solving a real, measurable problem, or are we just adding AI because it looks good?

In most cases, a simpler, clearer solution — like a report or a structured output — will deliver more value, faster.

The Simplest Solution Is Usually the Best

If your user needs insights, don’t force them to “chat” for it. Stop overcomplicating things.

Here’s the truth: people don’t want tools — they want outcomes. They don’t care about asking questions or how “smart” the AI looks. They care about getting the information they need to do their job better, faster, and with less friction. And AI can help with that.

So instead of building something flashy, focus on what delivers:

• A report with exactly what they’re looking for.
• A dashboard that gives them the right data in seconds.
• Clear outputs they don’t need to “phrase correctly” to understand.

That’s the magic of simplicity.

When you keep your solution simple and focused, a few things happen:

• You set clear expectations. Users know what they’ll get, so they’re not frustrated.
• You can measure success. And if you can measure it, you can improve it.

AI doesn’t need to feel like magic to be valuable. In fact, the most valuable AI often feels simple — like it “just works.”

Final Thought

So before you default to building a chatbot, ask yourself:

• What does the user actually need?
• What’s the simplest, most direct way to give it to them?

Because when you strip away the noise and deliver outcomes that matter, you’re not just building AI.

You’re solving real problems. And that’s where the value is.

Are you stuck in the chatbot trap? I’ve helped teams rethink their AI strategy and deliver real results. If this resonates, let’s talk.

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Published via Towards AI