In Occupational Health and Safety, AI is beginning to have a significant impact by unifying criteria, reducing biases and strengthening the quality of analysis. In the day-to-day life of any Safety and Health team, Preventive Safety Observations (PSOs) are an essential tool for understanding what is really happening on the ground. However, even with solid processes, there may be small variations in how to observe, interpret or prioritize. They do not generate large noises, but they do affect the consistency of the analysis. In this context, artificial intelligence is beginning to provide valuable support: unifying criteria, reducing biases and strengthening the quality of prevention without adding more burden to the team.
The root of the problem: bias in PSOs analysis
In many Health and Safety teams the same pattern is repeated, although it is rarely verbalized: two people may observe the same situation and come to different conclusions.
It is not a lack of rigor.
It is natural when the analysis depends on the experience, the context or even the moment in which it is reviewed.
What causes this variability?
- Differences between observers.
- Disparate interpretations of similar situations.
- Different prioritization according to plant, shift or reviewer.
- Lack of time due to accumulation of PSO.
The result is well known: inconsistent consistency, decisions that do not always reflect reality and delays that impact daily prevention.
This is one of the big blind spots that digitalization is beginning to solve.
Why AI is making a strong entry into OHS management
Artificial intelligence does not replace the technical judgment of the teams.
What it does do is reinforce it, eliminating variations that distort the analysis.
Cuando se integra en procesos como las OPS, ocurren tres cambios clave:
1. Human bias is reduced (even if unintentional).
The IA analyzes each observation under the same criteria, regardless of who made the review or where the observation occurs.
2. Consistency between plants, shifts and equipment is gained.
What used to depend on “how each person interprets it” now depends on a model trained to apply the same rules in all cases.
Analysis is accelerated without loss of quality.
Automation allows processing large volumes of PSO in minutes, maintaining consistency and avoiding delays.
The result is a clearer, more transparent process with more solid decisions.
The Tubacex case: less bias, more transparency, more prevention
Before introducing AI into its OPS analysis, Tubacex faced challenges common to many industrial organizations:
- Accumulation of observations that delayed the analysis.
- Differences in criteria between teams or plants.
- Manual processes were time consuming and took focus away from the field.
- Lack of standardization in the review of information.
By incorporating AI to automate analysis:
- They unified criteria throughout the organization.
- They reduce both interpretation and language biases.
- They increase transparency in preventive decisions.
- They free up time to focus on accompanying, observing and acting.
A change that, without modifying the underlying process, transformed the way of leading prevention.
