What makes an AI start-up fail?

Who doesn’t want to work for an AI startup? Everyone, right? And there are so many of them at the moment. But what you may not realize is that AI is a hard business and most start-ups fail. But no one talks about that. No one wants to talk about failures.

But I do. So, let’s talk about those failures and focus on one particular problem. Why? Because we can learn a lot from these mistakes. Here is an example. We know a number of AI startups with really brilliant people. These people develop incredibly sophisticated algorithms and have found a way to commercialize them, which is no small achievement.

But the story doesn’t end there. When you are working with an AI modeler or an AI algorithm developer, data scientist, or whatever you want to brand him, it is absolutely crucial to understand that these roles, whatever you call them, require a forward-thinking mindset. That R program or Python program that they are developing must be scalable, sustainable, and secure. That requires people, multiple people, with the right mindset.

This is where a lot of startups go wrong: the same person who creates those clever algorithms is often the person who needs to make sure that the program is scalable, secure and safe.

But we have seen too many companies getting caught out because once the algorithm is done, they believe the hard work is done. But you need something else. You need different types of engineers with a forward-thinking mindset. Be aware of this because many companies fall into this trap and have a hard time resolving it because they become dependent on one or two people.

Be watchful and remember to grow the right team. Then, you can enter into a successful AI endeavor.

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