Compliance in the chemical process industry (CPI) has become an increasingly complex endeavor, but artificial intelligence (AI) promises to help effectively navigate modern regulatory landscapes
When it comes to compliance and safety in the chemical process industries (CPI), product stewardship, compliance and safety teams are surfing rough waters. Companies face mounting challenges meeting global regulations, tracking substances of concern and adapting to constantly evolving reporting requirements and deadlines related to environmental, health and safety (EHS) issues. The number of global product regulations is surging. Government agencies and non-governmental organizations (NGOs) around the world publish scores of regulations, appoint new substances of concern and issue updated reporting standards on a weekly basis. New reporting frameworks require specific formats, detailed information and documentation in local languages. Compliance teams need tools to help them stay afloat in the surging tidal wave of data, requirements and deadlines (Figure 1).
FIGURE 1. Investments in compliance, workplace safety and increased transparency ultimately streamline the R&D process, position companies for successful product launches, enable regional expansion and support advanced sustainability performance that builds brand trust
According to a survey conducted in 2024 [1], 68% of respondents said they are currently using internal teams to ensure compliance with regulations. Only 30% relied on third-party services or automated software to manage compliance. Yet 92% of all survey participants reported the need for regulatory monitoring (Figure 2). Growing demand for sustainability reporting in the chemical industry also amplifies the need for real-time data about substance and material use across the supply chain. Both the E.U. Corporate Sustainability Reporting Directive (CSRD) and the Globally Harmonized Standard (GHS) for Reporting version 7 [2, 3] require more granular reporting on which chemicals are used, in what amounts, and how they are handled throughout the product lifecycle, from sourcing to end of life.
FIGURE 2. Survey results illuminate compliance management strategies, highlighting the universal need for regulatory monitoring across the industry (Source: 3E)
Given the volume of products, regulations, substances of concern and supply-chain stakeholders, consolidating and processing all the required data in a meaningful way is virtually impossible with humans alone. Even technology systems that were once sufficient for ensuring single-product compliance with a limited number of local regulations have become inadequate for handling the elevated scope and volume of information required.
Fortunately, AI for regulatory monitoring is rising to meet the demands of this complex, fast-moving chemical-compliance landscape, offering scalability that is not possible with traditional resources. Balanced with expert data curation by product stewards and regulatory experts, AI empowers the chemical industry to navigate the regulatory landscape with competence and confidence.
Staying on top of regulatory data
AI is often touted as a revolutionary do-it-all tool. But, like all technologies before and since, AI is finding its place in practical applications. In the context of advancing chemical compliance and greater sustainability, AI has several capabilities that empower teams to achieve more with fewer resources.
Scalability. AI can process data from more sources more quickly than is possible with humans or traditional automated systems. With the right data sources fueling AI, it is possible to produce meaningful summaries of global chemical regulations, information about substances of concern and reporting requirements from multiple regulatory frameworks. AI is essentially unlimited in its ability to handle growing volumes of data to meet the dynamic demands of regulatory compliance.
Data cleansing. AI can effectively validate and cleanse nonstandard data inputs to enhance the quality of outputs. It can eliminate duplicates, standardize input formats and remove non-uniform data with speed and consistency. Thanks to machine learning, AI can also learn associations between different phrases that refer to the same concept, allowing it to correctly classify disparate references. The time investment required for this type of detailed data-cleansing exercise would be prohibitive for a team of individuals.
Intuitive use of a complex tool. AI enables natural language queries that make it easy to retrieve needed information in meaningful ways. When information is maintained in systems or spreadsheets, extracting the data often requires complex formulas, in-depth system training or technical expertise. AI simplifies user interaction with intuitive query capabilities.
Multilingual configurations. Translation of technical documentation into multiple languages is expensive and time consuming. AI reaps tremendous efficiencies with the innate accommodation of multiple languages for both inputs and outputs.
AI’s promise: Key applications
AI’s scalability and speed are a boon to product stewards, compliance managers and EHS professionals, as detailed in the following section.
Generate complex reporting. Gaining momentum across the E.U., CSRD is just one example of a sustainability reporting framework in the CPI that demands greater transparency across the entire lifecycle of chemicals, from initial development through end-of-life disposal. Reporting frameworks like CSRD, GHS, the Toxic Substances Control Act (TSCA) in the U.S. and others require that companies provide increasingly granular information about sustainability performance and material use across the supply chain. Collecting, synthesizing and reporting on information ranging from the use of substances of concern to Scope 3 emissions, is a complicated endeavor. The dynamic nature of supply chains also means that information may change on a weekly basis. AI is positioned to empower companies to warehouse, synthesize and interpret data in ways that allow for greater transparency and flexibility to meet frequently updated reporting standards without duplicating product stewardship and compliance teams.
Enhance workplace safety. Supplied with data ranging from historic events to employee safety history, monitoring from IoT sensors, and real-time video, AI can synthesize and analyze data from disparate sources to generate meaningful predictions that detect safety hazards before they cause accidents. AI can predict equipment failures. It can leverage video to reveal a potential chemical leak early before employees are exposed. If a problem is detected, AI empowers employees to quickly and easily access required safety datasheets (SDS) and relevant labels and safety instructions or contacts in an instant. AI can easily harmonize information about safety requirements for an unlimited number of chemicals, making it easier to maintain workplace safety.
Engineer safer chemicals. As new chemicals are engineered to meet standards for safety and performance, there may be a risk that new substances trigger negative health and ecological impacts. Fortunately, AI has the capability to help predict material suitability based on the synthesis of tens of millions of SDS and dozens of substances-of-concern lists. This provides product managers with decision-making information that expedites product development and avoids last-minute discovery of product unsuitability. This saves time, money and rework in the process.
Expedite ad hoc queries. Product stewards and regulatory managers receive regular requests from marketing teams to validate the acceptability of products or chemicals for sales in specific regions. Without the help of AI, this can set off a wild goose chase, searching for documentation, reaching out to suppliers, and studying recent regulatory standards. With AI, managers can simply enter a natural language query to produce the needed information and then summarize that data to deliver back to product and marketing teams. What would have taken days, now takes minutes, thanks to the power of AI.
Manage SDS. Chemical SDS are the foundation of successful chemical compliance and workplace safety initiatives. Yet authoring and updating SDS is an increasingly complicated endeavor, with the list of substances of concern in flux, frequent supplier changes, and regular regulatory updates. AI-powered SDS authoring tools can now author and update SDS systematically with embedded intelligence that automatically incorporates new regulatory requirements, cross-references information with millions of other SDS and generates SDS in multiple languages. Thanks to AI, SDS can become the dependable foundation for product content inquiries, workplace safety actions, and sustainability reporting.
Predict what’s next. The dynamic and ever-changing nature of the regulatory landscape can leave chemical companies feeling as if they’re always behind. Leveraging information from NGO and agency meetings, news stories, reports, trends, historical data and global regulations, AI can effectively offer predictions on regulatory changes to come. This positions companies to better prepare for potential changes.
Limitations of AI: Junk in, junk out
While AI holds much promise, it is not a silver-bullet solution that generates compliance effortlessly. Rather, it is a powerful tool in the hands of an educated user, and its insights are still only as good as the data fueling it.
Validate data. The regulatory world for chemicals is messy and ever-changing. Changes to lists of substances of concern, threshold calculations, and reporting deadlines mean that things evolve. Document decisions about how calculations were made and how reports were generated. Validate sources of studies that are used to power AI, as some are unreliable or incomplete. AI systems require well-defined inputs to generate accurate outputs.
Use the best sources. Feed your system with data sources that are trustworthy and extensive. Verify that data sources include the latest global regulatory updates from reliable authorities, deadlines and information on emerging regulations. Outdated sources can result in inaccurate information in system output.
Keep experts on hand for interpretation. Integrate AI into your workflows but don’t over-depend on it. Experts are needed to validate outputs. It’s important to evaluate AI-generated feedback and identify errors and inconsistencies prior to making business decisions. For example, generic AI tools are trained only on widely available information which can’t guarantee knowledge of complete, accurate, nor up-to-date datasets. Generic AI tools might misclassify chemicals, resulting in accurate SDS or incorrect hazard communications.
Enhance existing processes. AI is used most effectively in the context of existing processes and systems. AI enhances business logic, expedites processes, and is easily integrated into existing applications. It is not a stand-alone solution to chemical compliance outside of the context of existing best practices within your organization.
Navigating waves of change
Compliance is not simply a gate that requires unlocking to achieve the right to sell products. Savvy firms understand that compliance is a strategic endeavor that delivers competitive advantage, and AI use has the power to improve efficiency, speed and accuracy. ■
References
1. 3E, Q2 2025 Market Perspectives: Chemical compliance trends and SDS management insights for faster market access, June 2025.
2. PWC, What U.S. companies need to know about the EU’s CSRD/
3. United Nations Economic Commission for Europe (UNECE), Globally Harmonized System of Classification and Labelling of Chemicals (GHS), 7th Ed., 2017.
Author
Alan L. Johnson is the managing director of chemical & workplace safety at 3E (Email: alan.johnson@3eco.com). Johnson has more than 15 years of leadership in business development, strategic alliances and product management. Prior to joining 3E, he held senior business development and product positions with market-leading software companies, including Turning Technologies, BlueTie Inc. and NASVF.