AI: why less is more

Every startup is instructed to reach for more. The more you do, the more you offer, the bigger you get, and the more success you achieve.  That’s why so many AI startups seek extensive functionality. A platform with a narrow focus doesn’t seem to have the potential to raise much revenue. But the reality is usually the opposite. When you reach too far in tech, you spread yourself too thin, compromising quality in favour of quantity. But when you stay small, focusing on a single use case and getting the details right, you begin to solve real problems for a real audience. And that’s where sustainability comes from.

 

 

How doing less can lead to success

Small doesn’t mean limited in AI. It means detailed. And that’s what customers want. When you’re bootstrapped, you can’t afford to create a highly proficient generic platform that answers the explicit needs of every customer group. That leads to nothing more than mediocrity at best. But when you invest all of your resources into developing a product that works seamlessly for a specific customer group, building improved outcomes and functionality, you create a product with a waiting audience that maximises the potential of your investment.

 

That smaller target group is easier to please because you are better able to identify needs, workflows, and annoyances. You can build your AI product around their unique data, and train your models to deliver more accurate, reliable outputs because of that. And it’s that high degree of accuracy that provides real value to your customers, and keeps them coming back, making your service an integral part of their operational setup.

 

But more than that, when you work with a narrower focus, you gain the opportunity to make your product easier to use. When you design experiences tailored to a specific task or workflow, users are presented with an understandable interface that takes them exactly where they need to go, rather than a blank prompt. This makes it simpler and faster for users to gain value for your platform, which leads to more engagement and faster adoption.

 

Then there’s the matter of targeting. If you want to onboard interested customers, you have to let them know that you’re there. And a smaller target is easier to reach with tailored marketing. You can easily research a specific group, finding out what they do and what they see, so you can get your product in front of them. This makes marketing cheaper, faster. And more efficient, generating better returns.

 

 

Answer your niche’s needs

The first thing you have to do when targeting any market is find out how to give them value; how to create a platform that will solve their problems, or make life easier - not just now, but continuously, as their needs evolve. That’s the only way to become indispensable.

 

In AI, the best way to do that is to collaborate closely with a clearly defined group of users. With their input, you can continually refine the product to tackle more nuanced challenges in that space, through steady iteration and tight feedback loops. Having a focused use case makes every update count, improving both the system’s performance and the overall user experience. And often, when you solve one problem really well, you start to spot related needs your AI can handle too, creating organic growth within that niche.

 

If you’re building an AI platform for independent fitness studios to manage class scheduling and attendance, for example, you might initially focus on optimising timetables and reducing no-shows. Over time, as you analyse usage data and gather feedback, you may discover that many studio owners also struggle with member retention. By expanding the AI to flag customer loss, using signals like attendance frequency, booking patterns, and engagement trends, you can help owners proactively reach out with tailored offers or class recommendations. Instead of branching into a completely new market, you deepen your value within the same customer base, solving a closely related problem they already care about.

 

That said, value doesn’t have to be features-led. By focusing on the improvement and iteration of the things that matter most to your customers, helping them to achieve better results more easily, you can increase the perceived value of your product, building loyalty, authority, confidence, and value. And that’s how you achieve worthwhile returns as a bootstrapped startup.

 

For enterprise-level businesses, a wide reach is always a sign of success. But you don’t have to be Google or Meta to gain a sense of accomplishment. If you’re bootstrapped, that level of [often criticised] notoriety is beyond you… Until you put in the groundwork. And to do that, you need to maximise the potential of your limited resources by serving your smaller market exceptionally. 

 

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Kacper Staniul is Co-Founder and CEO of MyArchitectAI, an AI rendering software that enables any architect or interior designer to create stunning renderings fast,  without specialised technical skills or expensive hardware

 

Main image courtesy of iStockPhoto.com and narvo vexar