Why AI Should Solve Real Problems, Not Just Add Features

I have a simple view on AI. If it does not make something meaningfully easier, clearer, or more useful, it should not be there. Right now, we are seeing AI added to almost everything. Apps are racing to introduce it, products are being redesigned around it, and entire businesses are being positioned through it. Too often, it feels like AI is being used to decorate a product rather than improve it. Features are being added because they can, not because they need to be, and users can feel the difference.

I did not set out to build an AI product. I set out to solve a very specific problem. When I started playing golf, I wanted to understand whether I was actually improving. I was taking lessons, practising regularly, and investing time into the game, but I had no clear way to track progress in a way that made sense. There was no shortage of information. Tips were everywhere, and data existed, but none of it answered the question I cared about most: Am I getting better? That gap is where I think AI becomes valuable. Not as a headline, but as a tool to remove friction.

The role of AI should not be to give you more, but should be to help you focus. It should take something that is currently unclear or time-consuming and make it obvious. If it cannot do that, it is just adding noise. We often assume that more data leads to better decisions. In reality, the opposite is often true. When people are overwhelmed with information, they either ignore it or default to what they already know. Neither leads to improvement. What actually changes behaviour is clarity. This is where I think many products get it wrong. They build for capability instead of usefulness. They ask what AI can do, rather than what the user actually needs. There is a big difference between those two things.

For example, showing someone ten different performance metrics might feel valuable, but if none of them clearly point to what they should do next, they are not helping. On the other hand, one simple insight that highlights where they are losing time, money, or effort can change behaviour immediately. That is the difference between information and guidance. AI should sit on the side of guidance. It should reduce the mental load, not increase it. It should help people make decisions faster, with more confidence. Most importantly, it should fit into how people already behave, rather than forcing them to learn something new. If a feature requires too much explanation, it is already too complicated.

The best products I have used remove friction in a way that makes you wonder why things were ever done differently. That is what good use of AI looks like. There is also a tendency right now to build for perception and to have AI in the product because it signals innovation. However, users do not stay because something is labelled as “AI”. They stay because it works. And they leave just as quickly when it does not.

As founders, that is where the real discipline comes in. It is easy to add features, but it is much harder to decide what not to build. The products that will last are not the ones with the most advanced technology but the ones that solve a clear problem in a way that feels simple and intuitive. AI should support that, not distract from it. It comes down to one question: “Does this help someone move forward?” If the answer is yes, it is worth building. If the answer is no, it is just another feature, so hold off and leave it out.

​​About the author

Keila Doyle is the founder of Golffily, a UAE-founded golf technology platform that helps players track progress, improve performance, and connect with other golfers. The platform transforms traditional paper scorecards into clear performance insights through simple scanning and data analysis. Golffily aims to make golf more accessible, social, and easier to understand for players at every level.

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