Top 6 reasons for failures in Artificial Intelligence Projects
  • byAjay Ray

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Deployment of an artificial intelligence system means the digital transformation of your company. You are hoping to enter a new era, where machine learning will boost all of your business’s operations. However, you have noticed that the system you have developed isn’t bringing the results you were expecting. This might happen due to various reasons. The most common reasons why an artificial intelligence system is failing are the following:

Lack of a Firm Data Strategy 

Data are extremely important for the development and training of a machine learning algorithm. For this reason, it is important that, before you start developing it, you have a firm data strategy. This includes the type of data you need, their location, and their review. If you don’t have a pre-agreed data strategy, you might end with erroneous or incomplete data.

Development of the Wrong Algorithm

There are many things that can go wrong with the development of the artificial intelligence algorithm. This type of system is influenced a lot by its creator, as by its nature needs to work in a similar way to humans. That’s why it is a common problem that the biases of the developer are noticed on the AI as well. Another thing that could go wrong is that the developer might want to simplify the program by removing some data extraction processes and having humans manually add them. This will mess up the data and bring wrong results. On the other end of the spectrum, the algorithm might be too complicated for the application it is needed.

Use of Incomplete Datasets

This is often due to the lack of a data strategy, even though its cause is not limited to that. Incomplete datasets will bring the wrong results, as the machine learning system will be unable to make an informed prediction. When compiling your databases be extra careful that you don’t rush the process so that you have everything you need.

Absence of an AI Expert

It goes without saying that there must be an AI expert as long as the artificial intelligence system is in use. The AI professional isn’t essential just for consulting while developing the system. He will be able to analyze the data that come from the algorithm and notice potential problems with it.

Unrealistic Expectations

This point usually refers to the managers of the organization. If they are not familiar with the way that a machine learning system works, they might have the wrong impression that it will immediately show results. Instead, they should be prepared for a long trial and error process, from which every successful AI system emerges.

Lack of Complete Understanding of the Business Problem

Even if you have done all of the technical things correctly, there is still one more thing that can make your artificial intelligence system fail. This is the lack of understanding of the business problem you are trying to solve with it. In fact, the business problem should become your first point of research and then move on to the data and algorithm.

Pay attention to above points, and you will end up with a successful artificial intelligence system. Be prepared to enter into a digital transformation of your company.

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