To talk about Machine Learning is to talk about Artificial Intelligence and its correlation in the world of technology. Something to highlight is that there is the very common mistake of not knowing the difference between Artificial Intelligence and Machine Learning. Therefore, these terms are usually used without distinction of organizations, which prevents having a clear perspective of what these disciplines really mean.
In simple words, we will define Machine Learning as that learning process based on exposing the computer to a lot of data for processing, analysis and learning from them.
Now, to the business world, the Machine Learning has surpassed all expectations and has become a true generator of value where more than half of the companies that have had a contact and they have implemented projects based on artificial intelligence (AI) and Machine Learning claim that this technology has increased their productivity.
With this it is clear that ML plays an important role in any business transformation that any organization wishes to implement. But, when choosing the right use case it is necessary to consider some factors.
First of all, you need to look for a balance between speed and the optimal value. It is possible that if a proof of concept is carried out by a data scientist independently, there is little chance of getting Machine Learning to arouse much enthusiasm and that necessary attention. But, if you show how Machine Learning can solve the everyday problems that any organization faces, it will be easier to get the necessary commitment.
The second step is to make a correct choice of a use case to solve in which you have access to a lot of data, so that this is verifiable and not a frustration by not having the necessary data.
Finally, it is necessary to validate if the problem to be solved is really solved by using Machine Learning, as well as really obtaining the expected results that can translate into cost reduction, productivity or a better experience for our customers.
Main use cases
Automation of data extraction and analysis from documents.
Intelligence applied to the contact center.
Personalization of recommendations for customers.
Increased value for multimedia assets.
Prediction of demand metrics.
Identification of fraudulent online activities.
If you want to know more about these use cases, we share the following link.