Stop Fixing Your Broken Chatbot — Start Learning From It!

Author(s): Louis Dupont

Originally published on Towards AI.

Why building a broken chatbot might be the best thing you’ve done.

You built a chatbot. You launched it. And now? It’s failing.

Users are frustrated. The bot feels incomplete. Your team is asking, “Was this even a good idea?”

Let me reframe this: your chatbot isn’t failing — it’s teaching you. Every frustration, every gap, every friction point is revealing what your users actually need and how to deliver real value.

In my last post, The Chatbots Trap, I broke down why so many AI projects fail — teams treat chatbots as the default AI solution, even when they don’t align with the problem they’re trying to solve.

But here’s the twist: even if your chatbot isn’t perfect, it’s a goldmine of insights. Why? Because it’s a discovery engine.

By the end of this post, you’ll know how to turn frustration into insights and use your chatbot as a tool to uncover user needs — and maybe even pivot to the perfect solution.

Your Chatbot’s Failure is Your Biggest Opportunity

Distilling the key insights from my previous post, I listed three main reasons why chatbots often fail to deliver value:

• Assumptions, Not Evidence. Teams design features they think users want — like automated replies or summaries — without seeing how users actually work. The result? Features that miss the mark.
• Overpromising, Underdelivering. “Ask me anything!” sounds great — until your bot struggles to deliver. An unlimited scope makes it impossible to improve or set clear expectations.
• Mismatch of Expectations. Users expect perfection; developers expect flexibility. When these mental models clash, trust breaks down.

But here’s the reality — even a failing chatbot is a diagnostic tool. It shows:

• What’s blocking users — Gaps in your bot often reveal gaps in their workflows.
• Where your assumptions broke down — Misaligned responses highlight misaligned priorities.
• What users care about most — Patterns in queries expose their most pressing needs.

Instead of seeing failure as the end, treat it as the start of discovery.

Failure isn’t the end — it’s your entry point to uncovering the real opportunities. Let’s break down how to transform these insights into actionable strategies that drive real results.

1. Start Where It Matters Most

Before even fixing the bot, step back. The only way to understand the problem your bot is solving is to watch users handle it without the bot.

Here’s how:

1. Shadow your users. Watch how they navigate the task you’re trying to automate.
2. Identify friction points. Where do they hesitate, struggle, or repeat manual steps?
3. Clarify their end goal. What are they trying to achieve, and how far are they from success?

Users don’t care about chatbots. They care about outcomes. If you don’t understand their workflow, your bot will always feel disconnected.

2. Test the Chatbot Like a Social Experiment

Once you understand your users’ workflows, the next step is observing how they interact with the chatbot in practice.

Treat this as a chance to observe human behavior in real time: deploy the chatbot, watch how users interact naturally, and focus on moments of hesitation or frustration. These subtle behaviors often uncover insights that raw data alone can’t reveal

Afterward, ask probing questions: What worked? What didn’t? What were they thinking? These reflections will uncover deeper insights to guide improvements.

Note: If you can’t physically observe users, consider setting up a screen-sharing call to watch them interact with the chatbot. Alternatively, use session recordings or shadow workflows indirectly.

3. Think in Scenarios, Not Features

Here’s the trap most teams fall into: they design features. “The chatbot can summarize documents!” “It can answer follow-up questions!” But features are tools, not solutions.

Instead, focus on scenarios — real-world situations where the bot helps users achieve a goal.

Here’s the distinction:

• Feature: “The bot summarizes a legal document.”
• Scenario: “The user uploads two contracts and receives a clear comparison of key differences within 1s.”

Scenarios align your chatbot with user workflows, ensuring it delivers outcomes — not just capabilities.

4. Let Friction Guide Your Next Move

Your chatbot is failing. Good. That failure is full of clues.

Start by categorizing the points of friction:

• Ambiguous queries. Are users struggling to phrase their questions?
• Misaligned intents. Is the bot misunderstanding what they’re asking?
• Unsupported scenarios. Are there workflows the bot doesn’t address?

Here’s the thing: not all friction is equal. Some failures are minor annoyances; others are critical blockers. Your job is to identify the high-impact failures — the ones that affect the most users or block key workflows — and prioritize those.

5. Focus on Less to Achieve More

If your chatbot tries to do everything, it will fail at everything. That’s not an exaggeration — it’s the reality of AI.

Here’s how to avoid that trap:

1. Find dominant use cases. Look for patterns in your logs. What are the top 2–3 scenarios users care about?
2. Constrain scope. Focus on making the bot exceptional at those scenarios first.
3. Measure success by scenario. Forget “overall accuracy.” Ask, “How well does the bot handle our most critical workflows?”

By narrowing focus, you simplify development and deliver tangible wins. Users notice when one thing works perfectly — and that builds trust.

6. Treat Your Chatbot as a Discovery Tool

Your chatbot isn’t just a product. It’s a lens into user behavior.

Every interaction generates data. That data isn’t just about chatbot performance — it reveals:

• What users need most. Recurring queries highlight their priorities.
• Where workflows break down. Patterns in friction point to inefficiencies.
• When the chatbot isn’t the answer. Some problems are better solved with dashboards, reports, or other tools.

Sometimes, the biggest insight your chatbot provides is that it’s not the right solution. That’s not failure — it’s clarity.

7. Know When to Pivot

Sometimes, pivoting away from the chatbot is the smartest move.

Here are 2 simple heuristics that should alert you to maybe switch gears:

• Users expects specific outputs. If users repeatedly ask for specific format, like summaries or comparative reports, a dashboard or report generator may serve them better than a chatbot.
• Queries require precision. If users consistently struggle to phrase questions correctly, a chatbot may introduce friction rather than reduce it. A more structured interface could eliminate ambiguity.

The goal isn’t to force the chatbot to succeed — it’s to deliver the solution that fits the problem best.

Your Broken Chatbot Is Just the Beginning

Here’s the mindset shift: your chatbot isn’t a failure — it’s a flashlight. It illuminates gaps, pain points, and opportunities for building something better. This is how to leverage it:

1. Observe users without the bot to uncover their real workflows.
2. Watch users interact with the chatbot to see identify why they struggle.
3. Design for scenarios, not features, to align with outcomes.
4. Use friction to guide your priorities — focus on what matters most.
5. Narrow scope to deliver tangible success.
6. Treat your chatbot as a discovery tool for broader user needs.

Chatbots aren’t the endpoint — they’re a stepping stone. Whether yours evolves into the perfect tool or leads you to something entirely different, the value lies in what it teaches you.

If this resonates, let’s talk. I’ve helped teams turn chatbot failures into strategic breakthroughs — and I’d love to help you do the same.

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Published via Towards AI