3 Key Considerations for EHS Leaders When Adopting AI Tools

Are you ready to to take the next step with AI in EHS?

Now that we've covered the different types of AI and how it can transform key areas of Environment, Health and Safety (EHS)—from compliance and risk assessment to training and reporting—it’s time to shift gears. Implementing AI successfully isn’t just about choosing the right tools. It requires thoughtful planning, clean data, and clear human oversight.

In Part 2 of our series, we’ll explore three foundational considerations every EHS leader must keep in mind when adopting AI tools. These principles can help you avoid common pitfalls, boost AI effectiveness, and ensure your efforts drive real, measurable impact—without compromising on safety or ethics.

AI is most effective when applied to specific, well-defined goals. Instead of starting with a broad question, like "How can I use AI?", focus on identifying the problems you want to solve. Ask questions like:

• What tasks delay safety compliance the most?
• What repetitive tasks consume the most time?
• Where do we struggle with incident management, risk assessment, or emergency response?  

Example: An EHS compliance officer at a hydrogen facility might struggle to keep SOPs audit-ready amid fast-changing regulations. Failure to comply could result in fines, legal consequences, and operational delays. By understanding the specific challenges before turning to AI—in this case, ensuring safety documentation is current—EHS leaders can more effectively determine how AI can drive improvements and ensure compliance.

AI is only as good as the quality of data it processes. If the input is poor, then the output will be poor. Before integrating AI, ask questions like: 

• What safety data is available (e.g., incident reports, audits, compliance logs, IoT data, etc.)?
• Where is it stored (e.g., spreadsheets, cloud platforms, IoT devices, regulatory databases)?
• Is it structured and accessible for automation (e.g., is data in a structured database that can be easily analyzed by AI algorithms)?

Example: If a company uses AI tools like H2O.ai to predict safety incidents but its data is scattered across paper records, emails, and disorganized spreadsheets, generating meaningful insights becomes difficult. Centralizing data in a structured format—like a cloud platform or database—makes analysis, automation, and actionable insights far more achievable.

Centralized, structured data not only improves AI performance but also supports responsible AI use. As you integrate AI tools, be mindful of corporate data governance, especially around ethics and security. Avoid sharing sensitive information with public GenAI platforms like ChatGPT. Emerging regulations—such as the EU Artificial Intelligence Act—are setting standards for privacy and data protection and can serve as valuable references for compliant AI practices.

» Blackline Safety’s cloud-connected safety platform streamlines data collection and ensures the data is securely stored, managed and easy to access and interpret «

AI is a valuable decision-support tool, not a replacement for human judgment. In safety, particularly, it’s crucial to recognize both the strengths and limitations of AI, as these decisions directly affect human lives and infrastructure 

The safest approach is to use AI to assist decision-making, not make decisions on its own. Always be mindful of its limitations, especially issues like ‘hallucinations’ (instances where Gen AI produces inaccurate or fabricated information that seems plausible but is not based on real data or facts). 

Example: Web scraping AI can be set up to monitor changes to regulatory compliance from a source like government websites or OSHA and update policies automatically to keep up. Use AI to notify you of a change, and even suggest policy updates, but you do not use it to directly publish updates to the policy

The National Institute of Standards and Technology, a US government body released its Artificial Intelligence Risk Management Framework (AI RMF 1.0) in 2023 helping organizations understand AI risks and mitigate them before implementation.

Partner With an Expert to Make AI in EHS Work for You

AI has enormous potential in EHS with the ability to help leaders better manage compliance, assess risk and predict incidents, strengthen training, develop policies and create in-depth reports. It works best when applied to well-defined challenges, backed by quality data, and used as a support tool.

While safety professionals don’t need to be AI experts—they do need to understand their own challenges, data, AI’s capabilities and their own responsibility in driving AI transformation. Partnering with a safety data expert, like Blackline Safety, can help too.

IF YOU MISSED IT, READ PART 1: Unlocking AI for EHS - Choose the Right Tool

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