{"id":10444,"date":"2026-05-05T09:00:52","date_gmt":"2026-05-05T08:00:52","guid":{"rendered":"https:\/\/holisticds.com\/?post_type=articulo&p=10444"},"modified":"2026-05-05T09:00:52","modified_gmt":"2026-05-05T08:00:52","slug":"predictive-ai-business-applications","status":"publish","type":"articulo","link":"https:\/\/holisticds.com\/en\/articulo\/predictive-ai-business-applications\/","title":{"rendered":"Predictive AI in companies: real-world use cases and applications by Holistic Data Solutions"},"content":{"rendered":"

Predictive artificial intelligence <\/b>is one of the most useful tools for improving decision-making in organizations. Gone are the days when it was an experimental technology: today, it is already generating concrete, real-world results in companies across different sectors, helping them anticipate situations, optimize resources, and reduce uncertainty.<\/span><\/p>\n

However, although many organizations understand the theory, it is not always clear how to apply it in practice. Where should you start? What kinds of problems can it really solve? What impact can it have on day-to-day operations?<\/span><\/p>\n

In this article, we answer these questions through <\/span>real-world use cases developed by Holistic Data Solutions<\/b>, showing how we have applied predictive AI in very different contexts, but with a common logic: starting from a specific problem, using available data, and generating tangible value from the outset.<\/span><\/p>\n

What is predictive AI and how is it applied in business.<\/span><\/h2>\n

Before discussing specific cases, let\u2019s remember that predictive AI is a branch of artificial intelligence that uses historical data and machine learning models to <\/span>anticipate future behaviors<\/b>, with the aim of improving decision quality through data-based estimates.<\/span><\/p>\n

In other words, it enables organizations to anticipate scenarios such as product demand, the emergence of incidents, customer behavior, or even internal situations such as absenteeism, instead of reacting once events have already occurred.<\/span><\/p>\n

We have already discussed predictive AI on our blog, so if you would like to explore how it works and how it differs from other types of AI, you can read this article where we discuss <\/span>Artificial Intelligence in companies.<\/span><\/a><\/p>\n

The key point here is to understand that predictive AI does not replace human judgment, but rather complements it with a quantitative foundation that would be difficult for our \u201corganic\u201d brains to handle, helping us make more informed and consistent decisions.<\/span><\/p>\n

Let\u2019s now look at some experiences and real-world cases we have developed at Holistic Data Solutions.<\/span><\/p>\n

Absenteeism prediction with AI at Can Cet.<\/span><\/h2>\n

Context and challenge.<\/span><\/h3>\n

Can Cet is an organization committed to the labor market integration of people with functional diversity, with operations distributed across different work centers. In this environment, the organization faced a recurring problem: unplanned absences.<\/span><\/p>\n

When someone failed to show up for work without prior notice, the ability to react was limited. This created imbalances across teams, increased pressure on other workers, and generated additional costs from urgent solutions.<\/span><\/p>\n

Solution implemented.<\/span><\/h3>\n

To address this situation, a <\/span>predictive model based on historical data <\/b>was developed, capable of estimating the expected volume of absences in each center and period.<\/span><\/p>\n

This model enabled the organization to move from reactive management to anticipatory planning, incorporating behavioral patterns that had previously been invisible to the organization.<\/span><\/p>\n

Results and impact.<\/span><\/h3>\n

The implementation of the solution delivered the following results:<\/span><\/p>\n