Why many AI features fail to meet expectations (and how to make sure yours don’t)

Many applications today are adding AI features in an attempt to stay competitive. Yet, users are often left underwhelmed, since most AI implementations are short-sighted. Instead of truly improving the user experience, they offer only superficial benefits, like auto-filling a field or generating a simple suggestion.

Explore why many AI features fall short and how you can make sure the AI in your application delivers real, tangible value.

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Why AI features often disappoint

Many AI initiatives start from the wrong question: "How can we add AI?" rather than "How can we make the user experience better?"

Management teams often feel the pressure to "do something with AI" without taking the time to truly understand how it can serve their users. As a result, features get added that technically involve AI, but don't actually help users complete tasks faster, easier, or more efficiently.

Simply adding a prompt or auto-complete function doesn’t improve the overall workflow. Real value comes from understanding the user's process and identifying where AI can streamline, automate, or simplify meaningful parts of their journey.

If an AI feature doesn’t make the user's life easier, it becomes just another button they ignore.

This thinking aligns with a broader product management principle discussed in our article on when to retire features in an application. Features, AI-driven or not, need to serve a purpose or risk becoming dead weight.

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How to ensure AI features add real value

If you want your AI features to truly improve your product, start by looking at where users deal with unstructured data. Unstructured information — like free-text motivation letters, resumes, or support requests — can be time-consuming to process manually.

AI can add significant value by transforming that unstructured data into structured, actionable insights.

For example, in a recruitment application, instead of just helping users store candidate profiles, AI could automatically assess competencies. By analyzing a LinkedIn profile, a motivation letter, and previous job descriptions, AI could generate a 0-5 score for competencies like "team leadership" or "technical proficiency."

This way, recruiters don't have to sift through every piece of information themselves. They get a clear, structured overview that helps them make decisions faster and more confidently.

Or, for instance, emails can be ranked by urgency in a customer support application with the help of AI analysis, like in the example below:  

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Structuring unstructured data not only saves time but also creates a seamless process, where users experience the benefit without feeling the burden of the technology behind it.

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In conclusion

Adding AI to your application isn’t about ticking a box. It’s about improving the user's workflow in meaningful ways.

By focusing on streamlining processes, automating repetitive tasks, and structuring unstructured data, you can create AI features that actually make a difference. When done right, AI doesn't just enhance your product — it transforms how users interact with it.