Many times executives and data scientists can get caught up in all the new technology fads, the fanciest algorithms, and focus on those artificial intelligence (AI) buzzwords. It is great to innovate and expand on a regular basis, but this becomes an issue when this fancy technology is applied to every project that arises within the company. It is an even bigger issue when this technology is used to create solutions with problems to find instead of solutions for well thought out projects. This can cause resources, especially labor, to be greatly misused.
Artificial Intelligence is the area of technology where computer or computer-controlled robots perform tasks commonly associated with intelligent beings by utilizing data. Generally data scientists code machine learning algorithms to achieve artificial intelligence by providing the computer data and having it use a certain method and parameters to determine what decisions to make from it. This can be very helpful when it comes to solving problems using the power of technology instead of having a person review all the data on their own or using a more basic tool like a spreadsheet to perform their analysis. Depending on the size of the problem though, these methods can become very expensive to use and implement solutions with due to the labor and technology costs.
The best way to determine whether you should use artificial intelligence or not with a problem is to make it a clearly defined project. Take some time to meet with the key stakeholders to determine what the problem is, what the desired outcome is, and what constraints there are for working on this problem such as budget. Then discuss with the data scientist(s) to determine what methods and tools make sense to use with this problem to make sure the technology used is advanced enough, but not overkill for the desired outcome. Sometimes projects just need a quick spreadsheet analysis or an interactive dashboard made for monitoring. The initial time taken to do this due diligence will save a lot of headaches in the long-term versus trying to have a one size fits all solution with fancy technology for every project that arises in the company.
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