Apr 23, 2025
The 3 Knowledge Gaps That Can Break Your AI Product Experience
Flavia Marsano
Product & CX
If you’re working with AI in any capacity building with it, using it daily, or testing it with real users you’ve probably heard or said this phrase: “The AI is not working.”
And while sometimes it's a tech issue, most of the time it's not. It's a knowledge issue. A mismatch between what the AI knows and what the user expects it to know. We call that a knowledge gap.
Once you understand this, it completely reframes how you build and maintain AI products. It stops being a tech problem and starts being a content and experience challenge.
There are three common types of knowledge gaps. Here’s how to spot them and what to do about them.
1. Missing content
This gap shows up when users ask questions your knowledge base just can’t answer because the info simply isn’t there.
Example:
You run a hotel listing platform. A user asks: “Does this hotel have parking and allow pets?” The AI responds with: “Sorry, I don’t have that information.”
That’s a dead end. The user is left uncertain, and instead of digging further, they switch platforms booking through a competitor who does provide that info. You didn’t lose the sale because of the AI... you lost it because you don't have that information in your knowledge base.
What helps:
Map common unanswered questions. Track what your users are asking that gets no response or vague answers. You’ll start to see patterns and spot the real gaps.
Let the AI enrich your knowledge base. Once you know the patterns, AI can help fill in missing pieces by crawling trusted sources or rephrasing existing content to fit more queries.
Set up a feedback-to-content pipeline. Create a simple system where “unanswered” = “content request,” so your team (or your AI) can start closing the loop.
2. Outdated content
This happens when the data the AI uses is no longer accurate or aligned with reality.
Example:
A user asks your bank’s AI assistant: “Where’s the nearest branch?” The system replies with: “You can go to the branch on Street A.” But that branch actually moved to Street B three months ago. No one updated your CMS, so the AI confidently gives the wrong location. The user goes to Street A, finds nothing, gets annoyed, and decides the AI assistant is useless so never uses it again.
What helps:
Treat user feedback like gold. If a user flags something as incorrect, that’s a live insight that something in your knowledge base is off. Build simple ways to collect and review feedback regularly.
Detect errors with AI and set up alerts
Continuously update your data
3. Lack of business rules
Even with good data, the AI can struggle if it doesn’t know what to prioritize or how to respond based on your goals.
Example:
A user visits your insurance website and types: “How can I file a car accident claim quickly?” The assistant answers with a wall of text listing every possible option: app, hotline, email, printed form. While the answer is technically correct, it’s overwhelming. The user is already stressed and instead of getting clarity, they get more confusion.
What helps:
Give the AI clear instructions on what to prioritize (for your users and your business)
Show direct actions. If someone wants to withdraw cash or file a claim, they don’t want a paragraph—they want an action: a button, a link, a shortcut. Less talk, more action.
So how do you close these gaps?
It’s all about having better, smarter content that's curated with intention and kept fresh with the help of the very AI you’re using. 😉
But here’s the real key: get into production fast. Don’t wait for perfection. Launch, learn, and keep iterating. That’s how you stay close to what users actually need and make sure your content stays useful for them.
You don’t need a massive content team writing 24/7. With the right tools, AI can help you:
Detect knowledge gaps as they happen
Enrich your knowledge base with validated, relevant data.
Apply business rules that reflect your users and your goals
Adjust your content constantly based on real-world usage and feedback
Because in the end, building great AI products isn’t just about good tech. It’s about the experience people have when they use it.