Takeaways

• Most EHS and ESG teams are in the early stages of adopting artificial intelligence but are actively exploring its potential to improve safety, sustainability, and efficiency.
• Data quality and integration remain the biggest barriers to scaling AI, outweighing external regulatory concerns.
• Responsible AI deployment is top of mind, with professionals emphasizing human oversight, accountability, and transparency.

Environment, health, and safety (EHS) and environmental, social, and governance (ESG) professionals are taking deliberate steps toward responsible AI adoption, according to a new poll by Wolters Kluwer. Conducted during the Enablon Engage EMEA conference in Paris, the survey polled around 60 EHS, operational risk management (ORM), and ESG experts, revealing that while most organizations are in the early stages of their AI journeys, they are laying the groundwork for scalable and ethical AI transformation.

Read More: AI Meets ESG: Compliance & Risks Launches Next-Gen Sustainability Solution

Early but Intentional Adoption

The poll found that 41% of respondents said their organizations are just beginning to explore AI use cases. Only 3% are scaling AI across multiple processes, and a mere 2% report that AI is already integrated and delivering measurable value. This shows that while AI adoption is still in its infancy, awareness and readiness are growing.

As EHS, ORM, and ESG teams explore AI’s potential, success appears closely tied to data quality and integration. “The Enablon platform is uniquely positioned to address this challenge because it delivers clean, reliable, asset-level data across EHS, ESG, and operational domains,” said Richard Pulliam, General Manager of Wolters Kluwer EHS & ESG.

Challenges and Opportunities

Data quality emerged as the leading obstacle to AI deployment, cited by 23% of respondents. Other key challenges include change management (18%) and resource constraints (12%). Interestingly, only 1% identified regulatory complexity as a major concern, suggesting that internal operational barriers outweigh external compliance issues.

When asked about top AI use cases, respondents prioritized automating routine processes (19%), improving data accessibility (17%), and predictive forecasting (15%), indicating a growing focus on efficiency and data-driven decision-making.

The survey also found that 43% view data quality and integration as the main barrier to scaling AI, while 19% pointed to a lack of internal skills and literacy. Other challenges included change management, unclear ROI, and ethical or security concerns.

Building Responsible AI Frameworks

EHS and ESG leaders are also establishing safeguards for responsible AI deployment. Nearly half (48%) emphasized the importance of human review and sign-offs at defined checkpoints, while 41% called for clear accountability for AI-assisted decisions. Transparency with workers and stakeholders was also a recurring theme, cited by 36% of respondents.

Also Read: From Data to Impact: How AI Adoption Fuels Sustainable Business Growth

These findings suggest that while AI’s full potential in EHS and ESG remains untapped, organizations are approaching its integration thoughtfully, balancing innovation with ethical oversight. With platforms like Enablon providing reliable data and integrated risk management, many teams are well-positioned to move from experimentation to execution in their AI-driven transformation journeys.

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Source: MarketScreener