Why your Artificial intelligence Project may fail? Top 5 reasons for failures in AI!

Covid-10 Pandemic has pushed all companies to digital transformation and work towards automation. Ai and Machine Learning helps companies to manage their operation more efficiently. The company are hoping to enter a new era with Ai and Machine learning, that may 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:

01. 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.

02. 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.

03. 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.

04. 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.

05. 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.

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