The role of high-quality data in AI for EHS

At Enhesa, we’ve been incorporating AI into our services and solutions since 2020, making great strides in optimizing technology to further enhance our offerings. Today, our team of 20 dedicated engineers, developers, and legal experts develop the AI algorithms that help our 160 in-house regulatory analysts to monitor, flag, and interpret new EHS requirements across more than 400 jurisdictions worldwide.

For a look behind the scenes at how it’s done, Enhesa’s Ana Sofia Rolim, AI Innovation Lead, and Andrea Pennisi, Senior AI Engineering Manager, explore:

• The importance of high-quality EHS data to support the use of AI in EHS compliance solutions
• How better EHS data and Enhesa’s use of AI helps our clients improve risk management
• Enhesa’s approach to AI with a technical deep dive into our solutions and AI tools

It all starts with high-quality data

High-quality data is crucial because it directly impacts the accuracy, reliability, and effectiveness of AI models. Here’s why good data is a necessary foundation for using AI to support EHS compliance:

1. Accuracy in hazard identification and risk assessment

AI systems designed for EHS applications process vast amounts of data to identify potential workplace hazards, assess risk hazards, and recommend appropriate and necessary control measures. At the center of this process lies the assumption that the input data (for example, incident logs, environmental monitoring, and employee feedback) is accurate, consistent, and relevant. If this fails, the system’s ability to make proper decisions will deteriorate rapidly.

The quality of data has a crucial role in hazard identification and risk assessment. Low-quality data can alter the entire risk profile of a workplace or site. AI systems rely on historical reports, sensor data, inspection records, and regulatory information. If there are underreported incidents or inconsistently labeled hazards, for example, the AI model can underweight the frequency or severity of certain risks. Additionally, if the system is fed outdated safety procedures or legacy data, it may make recommendations that aren’t in line with current operations or regulatory expectations.

The consequences of having poor data manifest in two ways ­— false positives and false negatives:

• False positives can be described as flagging non-existent risks, and can result in wasted resources or unnecessary work stoppages
• False negatives happen when the system overlooks a genuine and true risk. This can lead to real-world harm, such as injuries, environmental damage, or regulatory violations.

High-quality data not only enhances model performance, but it also ensures it has contextual awareness, meaning the ability to differentiate between routine anomalies and critical red flags.

In short, good data allows EHS AI systems to go beyond surface-level pattern recognition: they can deliver timely, targeted, and relevant insights that help safety managers and compliance teams to act. This results in a more intelligent and more resilient approach to risk management.

With accurate data, companies ensure EHS AI systems can correctly identify patterns, understand context, and make meaningful and actionable predictions. This level of precision will then help managers prioritize interventions, allocate resources more effectively, and have a proactive safety culture based on concrete evidence.

2. Regulatory compliance and legal liability

Beyond risk assessment, regulatory compliance is another important area where data quality has a direct impact on the performance and trustworthiness of EHS AI systems. Across the globe, companies must navigate a complex and ever-evolving web of legal requirements. For example, in the USA, regulations from OSHA and the EPA set strict expectations. Internationally, ISO 45001 provides a framework for occupational health and safety management. At the same time, the European Union (with the Seveso III Directive and REACH), Asia, and Latin America each have their own strict requirements.

AI models are often used to automate compliance workflows — for example, generating reports, tracking corrective actions, and identifying gaps in safety programs. However, if these models are trained or operate on incomplete, outdated, or inaccurate data, they may generate reports that don’t meet regulatory standards. An incident misclassified because of poor labelling or a missing timestamp in emissions monitoring data can render a whole compliance report invalid. While these kinds of errors may seem small, they can have enormous consequences, including:

• Regulatory penalties
• Increased scrutiny from auditors
• Legal liability

Compliance isn’t just about “ticking boxes” — it includes demonstrating due diligence and traceability. Regulators and stakeholders expect organizations to be able to explain what actions were taken and — most importantly — why they were taken. If an AI system flags a hazard, suppresses an alert, or recommends a control, for example, its logic must be transparent and defensible — requiring a foundation of trustworthy data. Without it, organizations risk relying on AI-generated outputs that cannot be verified, raising concerns about auditability and accountability.

As AI becomes more embedded in EHS management, the quality of the data feeding these models becomes a compliance concern in itself. Regulatory trends are moving to a bigger oversight of AI usage in high-stakes environments. One good example of this is the EU AI Act, which forbids high-risk AI systems from being used, for example, for hiring.

3. AI-driven safety: Predicting incidents, enhancing training, and boosting efficiency

AI systems in EHS rely on historical data to foresee potential accidents, near misses, and environmental risks. However, these predictions depend on the quality of the historical data they analyze. If the data is biased, inconsistent, or lacks context, the predictions become unreliable and therefore affect the effectiveness of the system in preventing incidents. For instance, biased data may overlook certain risk factors, leading to inaccurate predictions and potentially hazardous situations. Inconsistent data can limit the understanding of the AI model, thus generating flawed insights and recommendations.

AI-driven safety recommendations must be based on accurate and high-quality data to be effective. Poor data can lead to several issues, including:

• Misidentifying workplace hazards, which can result in failing to correctly identify risks
• Recommending ineffective corrective actions, which may not mitigate the risks
• Slowing response times in emergencies, which can lead to severe consequences

By addressing data quality issues, organizations can leverage AI to make more accurate predictions, provide effective training, and ultimately create a safer and more efficient work environment. This involves implementing robust data collection and validation processes, continuously monitoring and updating the data, and ensuring that the AI models are trained on diverse and representative datasets.

By doing this, organizations can enhance worker safety and operational efficiency, thereby maximizing the benefits of AI in EHS.

4. Data-driven decision-making and continuous improvement

EHS AI systems are increasingly embedded in the strategic processes that organizations use to improve safety policies, training programs, and overall workplace conditions. By analyzing large volumes of incident data, audit findings, behavioral observations, and environmental monitoring inputs, these systems can surface patterns and trends that may otherwise go unnoticed.

These data-driven insights are essential for continuous improvement, helping organization leaders to target interventions more precisely, refine procedures, and proactively adjust resources before issues escalate. In this way, EHS AI doesn’t just support compliance, but it becomes a tool for smarter, evidence-based decision-making that enhances organizational resilience and safety culture.

However, when the underlying data is flawed, it can lead to poor decision-making, reducing the strategic value of AI. If the data is inaccurate, incomplete, or biased, the resulting insights can be misleading. This can cause leaders to implement ineffective policies, invest in inadequate training, or overlook critical areas that need attention. As a result, the AI’s recommendations become unreliable, and its potential to drive meaningful change is compromised. Therefore, maintaining high-quality data is essential for making informed decisions that truly benefit the organization and its employees.

5. How does Enhesa train its models?

At Enhesa, we place strong emphasis on ensuring the accuracy and reliability of our models by meticulously cleaning and double-checking the data before training. This involves removing inconsistencies, errors, or irrelevant information from the datasets — thereby enhancing the quality of the input data. By doing so, we ensure our models are built on a solid foundation of high-quality, error-free data, which ultimately leads to more precise and dependable outcomes.

We’re also deeply committed to maintaining privacy and safeguarding sensitive information throughout the training process. We take proactive measures to hide any personal or confidential data, ensuring all sensitive information is anonymized or removed before it’s used in model training. This approach not only complies with privacy standards but also protects the integrity of the information we handle.

By focusing on data cleanliness and privacy, we’re able to develop robust and reliable models that provide valuable insights while maintaining the confidentiality of the data sources.

Conclusion: Ensuring high-quality data for EHS AI

Ensuring high-quality data for EHS AI is crucial for achieving accurate and reliable outcomes. To this end, we perform several steps:

1. Data validation and cleaning

This removes duplicates, corrects inconsistencies, and validates sources. This process helps maintain the integrity of the data and ensures that it is free from errors.

2. Standardization

This is another critical aspect, where data formats, terminology, and units are made consistent across datasets to facilitate the seamless integration and comparison of data from different sources.

3. Cross-verification

This combines human expertise with AI-driven audits to enhance accuracy. This approach ensures that the data is thoroughly checked and validated.

4. Bias detection and correction

Bias detection and correction play an important role in identifying and mitigating biases in data collection and analysis, ensuring that the AI models are fair and unbiased, leading to more trustworthy results.

All these steps are fundamental to maintaining high-quality data in EHS AI and achieving more effective and reliable AI-driven solutions.

By prioritizing high-quality data, Enhesa ensures its models are accurate, trustworthy, and effective in protecting workers and the environment.