Every week, yet another AI startup emerges with cool demos and grandiose promises, but most of these companies vanish in a few months’ time not because of technological failures, but because they built something to solve problems nobody needed to be solved.
Today, developing your own AI application is easier than ever. Large language models, open-source libraries, and cloud computing infrastructure make the development process much faster. However, the issue is not in creating another AI-based product; the issue is in creating something people need.
The Worst Thing to Do When Building a Startup
Too often, entrepreneurs think in terms of solutions instead of problems.
It sounds like this:
"AI can do it… Let’s create it"
The right way would be:
"Which problem should we solve with the help of AI?"
Speak With Potential Users Before Coding
It is very tempting to start building right away; especially now when the availability of technology allows creating prototypes in several days.
However, before that, make sure to spend some time communicating with future users.
Try to ask such questions:
- What is the most time-consuming element of your workflow?
- What are your tedious tasks?
- What software applications are you using?
- Which task would you want to automate?
Such communications will show many aspects which you wouldn't even think about during brainstorming sessions.
Create the Smallest MVP Possible
Your initial product does not have to cover all the possible problems.
Instead, try to develop an MVP, which will answer just one question:
Will people use it?
Such MVP will help you:
- Get demand validation quickly
- Save on engineering work
- Collect useful feedback
- Find the feature priorities
- Improve product market fit
Measure the Right Signals
Founders love metrics which do not really prove success.
Instead of being focused on:
- Website traffic
- Social media engagement
- Upvotes for the product
Try paying more attention to:
- Active users per day
- Customer retention
- Repetition
- Customers for piloting
- Willingness to pay
These metrics will be much better proof that you're delivering value.
AI Needs to Generate Measurable Results
AI is not the product itself but the technology powering the product.
People won't use AI just because it's cool.
They will prefer to use a solution which helps them:
- Save time
- Save money
- Boost precision
- Become faster
- Increase productivity
And when AI is generating measurable results, people will definitely stick to it.
Always Be Prepared to Pivot
Few startups are successful right from the start.
Customers can show you that:
- The audience is not the right one
- Another feature may be even more valuable
- There is a completely different use-case
This is not failure.
It is progress.
The founders who learn quickly will perform better than those who keep building without validation.
Strategy beats Speed
Fast shipping is great.
Fast learning is even greater.
Speed of shipping doesn't mean speed of learning, and you don't want to waste time developing something nobody really wants.
Tech is progressing at a blistering pace, but the needs of your customers remain the basis for any startup's success.
For entrepreneurs who are building ventures with AI technology and looking for startup strategies, Aperture Venture Studio offers great tips for building tech companies that scale.
Final Thought
Before coding anything else, make sure you ask yourself one question:
Am I creating this AI product because the technology itself is amazing, or because it helps solve a problem?
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