The six most common mistakes when implementing AI projects

As we have already mentioned in other writings on this blog, artificial intelligence (AI) is an innovative proposal that, in recent years, has gained strength in different industries. However, this is not necessarily quick and easy to adopt. A series of steps must be followed for it to work correctly, starting with collecting the most appropriate data for the work to be done. Likewise, it is necessary to find the tools that best adapt to the industry model where it will be implemented. Training on adequately handling the devices must be carried out for all personnel. It is essential to highlight that business productivity increases when we correctly use AI. Still, it also has certain risks with different levels of severity that can lead to not getting what is required. Next, we will explain the six most common mistakes when implementing AI projects:

1. One of the most prominent mistakes when adopting AI technology for companies is trying to adapt different tools at the same time. Some entrepreneurs believe that using several devices without improvements is the best way to start incorporating AI in their fields. In the end, they do not obtain positive results.
2. Defining a clear goal is always the best way to start. When you don't have identified problems you want to solve and ways to measure the impact that AI solutions will bring to the company, you lose track of work, obtaining negative results.
3. One of the feeding principles of AI is data; having a reliable, structured, and sufficient amount of information is always paramount. The AI algorithms will not give the best results if you do not have a reliable source of data and an adequate number of data. It is important to remember that new data must be continually fed into the algorithm to produce more efficient renewal and results.
4. Another common mistake is limiting the number of team members who can use or access the implemented AI. When incorporating new AI-based technologies into a company, it is essential to segment the internal parties by defining who the stakeholders and beneficiaries are. This segmentation is necessary to establish each area's knowledge, ensuring that data and technology are used appropriately.
5. Although some companies may have many structural and operational problems, focusing on the most significant problem(s) is essential. Some believe that AI is a God that solves all problems, so they do not focus on what is most important and focus the technology on widespread use. This can lead to bugs by not fully addressing a specific problem.
6. Some companies do not have the right technology infrastructure to implement AI. Therefore, before solving other problems, the first thing is to update the basic digital technological infrastructure. Well, if you don't have the structure, you can't take full advantage of AI's benefits.

References:

1. Panel®, E. (2021, February 24). 13 Common Mistakes Can Derail Your AI Initiatives. Forbes. https://www.forbes.com/sites/forbestechcouncil/2021/02/24/13-common-mistakes-that-can-derail-your-ai-initiatives/?sh=7a1c845f68b0

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