Predictive AI in companies: real-world use cases and applications by Holistic Data Solutions

Predictive artificial intelligence 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.

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?

In this article, we answer these questions through real-world use cases developed by Holistic Data Solutions, 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.

What is predictive AI and how is it applied in business.

Before discussing specific cases, let’s remember that predictive AI is a branch of artificial intelligence that uses historical data and machine learning models to anticipate future behaviors, with the aim of improving decision quality through data-based estimates.

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.

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 Artificial Intelligence in companies.

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 “organic” brains to handle, helping us make more informed and consistent decisions.

Let’s now look at some experiences and real-world cases we have developed at Holistic Data Solutions.

Absenteeism prediction with AI at Can Cet.

Context and challenge.

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.

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.

Solution implemented.

To address this situation, a predictive model based on historical data was developed, capable of estimating the expected volume of absences in each center and period.

This model enabled the organization to move from reactive management to anticipatory planning, incorporating behavioral patterns that had previously been invisible to the organization.

Results and impact.

The implementation of the solution delivered the following results:

• Improved workforce planning.
• Reduced costs associated with urgent replacements.
• Greater operational stability across centers.

In addition, the project generated relevant added value, strengthening the sustainability of the organizational model and the organization’s social mission.

Demand forecasting in retail with AI at Brownie.

Context and challenge.

Brownie, a fashion brand with an extensive network of physical stores, needed to improve accuracy in commercial planning. In a retail environment, small deviations can create issues such as overstock, stockouts, or failure to meet targets.

In addition, factors such as campaigns, seasonality, or store changes made forecasting with traditional methods more difficult.

Solution implemented.

Holistic developed a demand forecasting proof of concept based on Machine Learning models trained with historical data from 2019 onward.

The project included:

• Data extraction and cleansing processes.
• Exploratory analysis and feature engineering.
• Evaluation of different algorithms.
• Model automation with daily updates.
• Visualization through Power BI dashboards.

Results and impact.

The solution provided a solid quantitative foundation for commercial decision-making, enabling predictions to be analyzed by store, week, and month, while distinguishing between new and established stores. This translated into:

• Better sales forecasts and reduced uncertainty.
• Inventory optimization.
• Greater ability to react to changes.

This project consolidated the use of AI as a strategic tool within the company, demonstrating its direct impact on business results.

Logistics optimization with predictive AI at Grupo Akoma.

Context and challenge.

Grupo Akoma operates in a complex logistics environment involving the preparation and dispatch of multi-reference orders, combining manual order picking with automated systems such as AutoStore. Although efficient, this structure created difficulties when deciding how to distribute stock between both systems in order to maintain optimal operational capacity.

Demand variability and the coexistence of different capacities caused imbalances that affected overall performance. For this reason, Akoma was looking for a solution to optimize stock and stabilize daily production.

Solution implemented.

A predictive AI-based solution was developed to more accurately estimate future outbound flows and the optimal distribution for each product and moment across both warehouses, conventional and AutoStore.

The solution also makes it possible to dynamically adjust the demand curve based on actual sales, providing a daily calculation of the recommended stock for each product.

The model was integrated into the company’s systems through an API, enabling real-time queries on the best distribution for each product.

Results and impact.

The implementation made it possible to achieve:

• Greater stability in daily production.
• Optimized stock management across the company’s different warehouses.
• Improved coordination between manual and automated systems, as well as greater accuracy in estimates.

The company can now make logistics decisions based on dynamic quantitative criteria that are applicable to day-to-day operations, instead of relying on static rules.

Applications of predictive AI in companies: what these cases have in common.

Despite belonging to very different sectors, these projects share the same implementation logic:

• They start from a clear business problem, not from the technology itself as a trend or buzzword.
• They leverage existing data, without requiring major initial infrastructure.
• They are developed progressively, starting with pilots or proofs of concept.
• They generate direct impact, whether in costs, efficiency, or planning capacity.

How to get started with predictive AI in your organization.

As you have seen, predictive AI is already proving its value in both public and private organizations. Starting from a specific need, working with available data, and applying predictive AI through business knowledge and technological expertise makes it possible to find solutions without relying on major investments or advanced technological maturity.

At Holistic Data Solutions, we help organizations navigate this journey: from identifying opportunities to developing predictive models and integrating them into business processes.

If you would like to learn more or explore how to apply predictive AI in your company, let’s talk.