Generative AI vs Predictive AI: Features & Applications

Generative AI is a type of AI that is capable of creating new and original content based on existing data with which it has been trained. In this sense, generative AI can generate and process information in text, image, video, and/or audio format.

For its part, predictive AI is another type of AI that is designed to analyze a history of data and detect patterns with which to make future predictions.

Beyond these basic definitions, generative AI and predictive AI have many other differences that make them distinct, both in their purposes and their usefulness. In this article, we want to delve deeper into these two types of AI so that you understand when to use one and when to use the other. 

Key differences between generative AI and predictive AI

Aim

As we mentioned in the definition, generative AI aims to create new and original content in different formats or in just one (depending on the model with which it has been developed). Thanks to having been trained with a large amount of data, it learns patterns that it then applies to create other non-existent combinations, thus promoting creativity and innovation.

Predictive AI, on the other hand, does not have that purpose of originality. It is an Artificial Intelligence that has the sole purpose of analyzing large amounts of data and identifying patterns and trends in them. However, it does not use them to generate new content but rather to anticipate or predict future behaviors.

Types of Data Used

Generative AI is trained with data that must be diverse and of high quality to learn the greatest variety of possible combinations within the same topic. This allows it to generate more realistic and original content.

The data used to train predictive AI must have other characteristics. In this case, it must be precise and relevant, since this is the only way it can make the most accurate predictions possible.

Limitations and Challenges

One of the main challenges facing generative AI is the generation of biased or untruthful content. If the content it has been trained on contains some kind of personal opinion, this technology may end up generating content that is not very objective.

Predictive AI, on the other hand, has the limitation that it may not be able to predict unexpected trends due to very abrupt and sudden changes that have not been reflected in the data analyzed by it.

Applications of Generative AI and Predictive AI

While generative AI is used in more creative fields, such as writing, art, or design, predictive AI is intended to be used in more analytical sectors. In that sense, both have multiple applications.

Applications of Generative AI

All creative jobs (writers, marketers, artists, designers, musicians…) can find in generative AI a great tool for inspiration. Generative AI can provide new ideas and even improve existing content. Therefore, professionals who are dedicated to creative jobs can greatly improve their work using this type of technology. Not only that, they can also speed up the entire creative process.

On the other hand, in the teaching and student world, this technology can also be tremendously useful. For its part, it can help teachers to better focus their classes and organize their time better. And, on the other hand, it can help students to better understand certain concepts not understood in class, to better organize their study time, and, even, to study better since there is generative AI designed especially to become an expert in a specific subject and be able to explain it only.

Finally, generative AI has also proven to be tremendously useful in the medical field. This technology can help professionals improve medical images, generate detailed reports, make more accurate diagnoses, and plan treatments.

Applications of Predictive AI

Predictive AI can also be used in a wide variety of industries, helping all of them to anticipate events, optimize processes, and improve decision-making. For example, in the field of retail and logistics, predictive AI can anticipate the approximate demand that there will be for certain products, which helps companies adjust their stock levels and optimize their distribution routes.

Again, in the healthcare and medical sector we also find that predictive AI has a lot to say. By analyzing patient data, it can identify patterns and anticipate possible future illnesses or complications. This facilitates early care and enhances preventive medicine, personalizing healthcare much more.

And of course, in the business and marketing world, predictive AI is used to analyze customer behavior and predict their future needs, allowing for even more personalized user experiences.