According to various studies, it is possible that in the coming years around 50% of the analysis predictions carried out by companies will be based on methods that use Artificial Intelligence. Therefore, we can assume that expenses in this area will also increase and that investment in this phenomenon will be a high priority for different companies.
We are at a time when we need to pay special attention to predictive artificial intelligence , although it is possible that this definition, as happens with everything that evolves at a dizzying pace, is already out of date.
When we talk about predictive analytics, advanced artificial intelligence systems inevitably come to mind, and we think of technological business solutions . We can therefore speak of a close link between this area and Machine Learning . The main difference between the two is that we use Machine Learning to identify the fact that machines can recognise patterns among complex data systems, draw conclusions and thus predict future behaviour in an automated way. We use the term Predictive Artificial Intelligence to define how this technology is applied to the business world.
If one thing is clear, it is that Big Data is booming, and with it comes a world of belize phone number possibilities with regard to behavioural predictions and obtaining business data. In other words, companies are now able to carry out much more complete analyses of their strategies and make future decisions based on predictions that turn out to be much more reliable than those they have obtained up to now.
But we must not be carried away by excessive enthusiasm, since there is what some have already called the “data bubble” . It is true that these tools can help us in decision-making, but it is still early days; we must wait to see what limitations affect this technology. In addition, we cannot forget the human factor and its capacity for analysis. There are billions of data, but a human brain is needed to decide what needs a company has and how to position itself with respect to the competition.
We must not forget another key factor: how we apply this data. We can foresee the emergence of ethical and moral questions, especially if we consider such sensitive issues as our privacy. There will surely be certain limits and barriers that will define what the algorithms and machines we use can (or cannot) do. The question that remains, for the moment, in the air is: what will these barriers be like?
Regardless of the responses that may arise and the progress that is made, the truth is that companies in this not-too-distant future will need to have technical experience in these processes in order to adapt to the new social, economic and moral needs that may arise.