Predictive or generative: how to choose the right artificial intelligence for your business?

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Artificial intelligence (AI) is a powerful tool that has the ability to transform business operations, optimizing decision-making, streamlining processes and creating new opportunities and competitive advantages.

Although companies are aware of its benefits, they are often unclear about whether they should adopt predictive AI-based tools, designed to predict outcomes based on historical data, or implement generative AI solutions, focused on creating new content.

While both types of AI use models that are trained from historical data, seeking to learn from it, understanding their particularities and specific use cases allows us to extract their full potential and make a more appropriate selection of the tool.

What is predictive artificial intelligence?
It is a data analytics method that combines statistical analysis with machine learning algorithms with the aim of finding data patterns that allow predicting future results.

Predictive AI extracts information from historical namibia phone number lead data and uses it to make accurate predictions about the most likely events, outcomes, or trends.

With this approach, organizations improve the speed and accuracy of predictive analytics and are therefore able to make informed business decisions .

Supervised learning techniques are used to train predictive AI models, which allow the models to learn from labeled training data. The most commonly used algorithms in this method are logistic regression, decision trees, and neural networks, among others.

What is predictive artificial intelligence used for? Broadly speaking, it is applied in retail, e-commerce and the manufacturing industry for, for example:

Make financial predictions about market trends , stock prices, and other economic factors.
Detect fraud and suspicious transactions in real time.
Manage inventories , helping to plan and control stock levels.
Get personalized recommendations based on customer behavioral data, with the aim of enhancing Customer Experience (CX).
Manage the supply chain , optimizing logistics, operations, production plans, resource allocation and workload scheduling.
Generative artificial intelligence: definition and use cases
Unlike predictive AI, generative AI is used to create new content in different formats (images, audio, text, video, software code, and much more).

It is a powerful tool for content creation and natural language processing.

Models of this type of artificial intelligence are trained with massive volumes of raw data and learn the underlying patterns and structures of these records, using this knowledge to produce original results.

These models are often built using different techniques, such as generative adversarial networks (GANs), variational autoencoders (VAEs), and transformer-based models such as the Generative Pretrained Transformer (GPT). These models are trained on large datasets with the goal of learning the details needed to generate new, consistent, and context-appropriate content.

Due to its characteristics, generative AI contemplates multiple and diverse use cases:

Creating question and answer systems , such as chatbots and virtual agents. These solutions offer real-time assistance and provide personalized answers.
Automatic synthesis of documents such as reports, articles or extensive research papers.
Creation of virtual environments , with realistic landscapes and characters, dynamic animations and visual effects for video games and simulators.
Development of personalized messages , based on specific data and expected customer behaviors, in order to engage with them more effectively.
Creating marketing and advertising pieces , designing images and writing attractive and personalized ads and sales texts for each buyer persona.
Creating context-specific knowledge bases , adapting to new information and offering relevant perspectives.
Software development , providing the ability to write new code and automate the debugging and testing phases.
Bridging the gap: the benefits of combining predictive AI with generative AI
As with other technologies, the choice between predictive AI and generative AI depends on the specific goals and needs of each organization.

Although some companies opt for one or the other, the truth is that these are not mutually exclusive models. In fact, their combination can lead to more robust and complete solutions. That is why companies that use them in tandem obtain extensive benefits for their businesses.

For example, a predictive AI framework helps detect potential customer loss, while a generative model helps develop personalized communication pieces that prevent customer churn and mitigate the negative impact of this scenario.