AI's Role in Evolving Workplace Safety: Building Trust in AI for Chemical Compliance

In today's data-driven world, AI isn't just on the map - it's the fast lane. From mining critical insights and tracking regulatory shifts to forecasting risk, monitoring employee health and safety metrics, and streamlining reporting, AI is steering the future of EHS, sustainability, and compliance. But as professionals take the wheel, what roadblocks and green lights lie ahead on the journey? This series of articles will help you navigate the future of AI in EHS.

Artificial intelligence (AI) is rapidly reshaping industries. From customer service to financial analysis, AI has proven its ability to process vast amounts of data and deliver insights at unprecedented speed. Yet, in highly regulated and safety-critical domains such as chemical compliance, one truth stands above all others: trust is paramount.

In conversations about AI in chemical compliance, trust is non-negotiable. But trust must be built deliberately. For AI to become a dependable partner in regulatory decision-making, three pillars must be in place: content, data, and expertise. Those three pillars are the foundation of trust.

More Than a Regulatory Requirement

Chemical compliance is not just a regulatory requirement - it is a matter of safety, sustainability, and corporate responsibility. Errors or misinformation in this domain can lead to:

• Fines and legal penalties for noncompliance.
• Reputational damage that erodes customer trust.
• Safety incidents that put workers, communities, and the environment at risk.

In such a high-stakes environment, there is no room for “black box” answers from AI that you cannot verify, explain, or trust, with the key word being “trust.”

Generic AI models are powerful, but when applied to chemical regulations, they offer a number of risks. One risk is known as “hallucinations,” which means the AI model produces answers that sound authoritative but are factually wrong. Another risk is incomplete coverage that misses region-specific or industry-specific requirements.

A lack of traceability is a significant issue with generic AI models. The information provided by an AI tool might offer no auditable path back to the source of regulatory truth. This is why it is important to use a proprietary tool, rather than one that is publicly available and that is pulling information from its datasets that other users have fed into it. Potentially, that information exists behind a corporate or organizational firewall and cannot be confirmed or verified.

For chemical compliance, this is unacceptable. A misinterpreted regulation can trigger cascading risks across global supply chains. You need to trust your AI system and the employees entering information into the system and helping manage EHS based on the AI system's output. The only way to do that is to incorporate a system that is built on trusted regulatory data, maintained by domain experts, and validated through rigorous governance.

This means authoritative sources, which ensure that the AI draws only from verified regulatory datasets. Trustworthy AI doesn't just provide an answer; it provides the “why” behind the answer. AI in compliance is only as strong as the content it delivers. To earn trust, the system must be anchored in verified regulatory sources that ensure responses are drawn from authoritative sources and current regulations.

Domain expertise is vital in order to embed chemical safety and compliance knowledge directly into the solution. Domain experts understand and have knowledge of the history, context, resources, and subject matter expertise that generic AI cannot provide. (More about “human intelligence” a little later in this article.)

Transparency is important so users have the ability to trace every answer back to its origin. Additionally, the system should offer contextual clarity, offering not just directives but also the reasoning behind them. Any AI model you choose should provide transparency that offers clear citations, context, and audit trails for every AI-driven response.

When compliance professionals can audit and understand AI-driven data and content, confidence in the AI model grows.

Trust Through Data: Quality, Integrity, Coverage

Data is the foundation on which trust in your EHS and risk management systems is built. Without rigorous data management, even advanced AI fails. The essentials include:

• High-quality data that provides accuracy and harmonization across all jurisdictions.
• Integrity, which requires safeguards against corruption, bias, and gaps in information.
• Comprehensive coverage, which means a global scope that accounts for regional regulations, sector-specific standards, and evolving requirements.

In chemical compliance, incomplete or flawed data introduces unacceptable risk. Flawed or incomplete data will contribute to decisions that could impact your workers, your facilities, the surrounding environment, and your company's reputation based on bad data.

AI alone cannot interpret the nuance of complex regulations. Human expertise must remain central to governance. Whether internal or external, regulatory specialists can curate, validate, and update frameworks. This expert oversight ensures exceptions and ambiguities are resolved responsibly. An added benefit is that continuous learning loops between experts and your AI solution strengthen reliability over time. When AI is paired with the knowledge of domain professionals, it becomes a trusted extension of human judgment rather than a replacement.

The future of AI in chemical compliance is not about replacing human expertise; it's about amplifying it. AI must become a trusted partner, ensuring that organizations can navigate complex regulatory landscapes with confidence.

Trust as Competitive Advantage

Companies that have adopted AI solutions for chemical compliance they can trust will find themselves ahead of the curve. They can:

• Reduce compliance risks with confidence in every response.
• Accelerate decision-making without sacrificing accuracy.
• Strengthen stakeholder trust - from regulators to customers - by demonstrating that compliance is proactive, transparent, and resilient.

Trust is not just a requirement; it's a strategic advantage. In a business climate where sustainability, ESG reporting, and regulatory scrutiny are only intensifying, the ability to rely on trusted AI solutions can differentiate leaders from laggards.

It’s important to remember that trustworthy AI is not the product of clever algorithms alone. It is the result of verified content, strong data governance, and expert validation working together. Organizations that demand these standards will not only meet compliance requirements but also gain resilience, efficiency, and credibility with regulators, customers, and stakeholders.

In the end, in chemical compliance in particular - and in safety-critical domains in general - trust is the only answer.

About the author: Alan L. Johnson is managing director, Chemical Management & Workplace Safety, 3E. At 3E, he champions intelligent compliance strategies that foster sustainable progress, ensuring safety across products, workplaces, and communities. With a focus on actionable intelligence, his work directly contributes to enhancing product sustainability and ESG performance.