The biggest challenges for applying AI in business

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As has become clear throughout the text, there are several challenges to the application of Artificial Intelligence in companies, and most of them are structural.

At the very least, it’s worth remembering that AI is machine learning and not an off-the-shelf solution. Adopting it in your organization requires specific sets of resources and skills, as we’ll see below:

The AI ​​talent gap
One of the first challenges for organizations is finding talent. You will need to have a team with the technical skills needed to train AI systems: how to use marketing data to optimize campaigns or leverage customer support data to automate feedback.

This type of training requires very specific skills and, unfortunately, talents on the market are still scarce.

Creating AI culture in companies
While recruiting talent is a huge challenge, incorporating Artificial Intelligence into the company can be easier.

However, as noted, the organization may encounter structural issues, such as research and development in favor of adopting AI in a real corporate environment.

Most IT companies' options are the software or hardware you want to use to get the job done.

The problem with Artificial Intelligence is that it requires a lot of training, at least at the beginning, and working with data, to deliver the expected results.

In this sense, it will be necessary to invest switzerland phone number list heavily in R&D (Research and Development) and this is not something that is within the reach of most companies.

After all, they never had to do anything like that to make the technology work. In other words, there are costs, and most organizations aren't prepared to bear them.

Adopt or wait?
Faced with this crossroads, the question remains: should we adopt Artificial Intelligence in the company or not? In general, there are five types of actors:

innovators;
early adopters;
early majority;
late majority;
newcomers.
As a general rule, most managers will shy away from being innovators (given uncertainties, talent, and costs) or laggards (in which case, the rest of the industry will have already taken advantage of the ROI of AI).