Why AI-powered compliance needs an expert at the wheel

AI’s role in EHS

To be clear, AI itself is not the problem. It can in practice transform EHS compliance tracking. The scale of the challenge of keeping abreast of regulatory details that impact an enterprise-level company is staggering and that’s compounded by the fact that regulations, standards, and guidelines spanning hundreds of jurisdictions worldwide, are in constant flux. In just a single year, EHS professionals will need to navigate delayed EU sustainability directives, Carbon Border Adjustment Mechanism updates, emerging AI governance frameworks, and Asia-Pacific circularity mandates, all while contending with rules that frequently conflict, overlap, or change with little warning. It is one of the most operationally demanding functions in any large organization. 

Used well, AI can meet this challenge head-on. It continuously monitors regulatory changes, flags relevant updates, reduces manual effort, and cuts non-compliance risk. Tasks that machine learning performs with aplomb include pattern recognition, alert triage, change detection, preliminary summarization and bounded action, to name just a few. It’s not an exaggeration to say that the workflow improvements brought by AI for EHS programs have redefined expectations. It buys compliance teams time and cognitive bandwidth, drawing out those precise details that are specifically relevant from ‘mountains’ of complex documentation. AI becomes a superpower when assessing the applicability of laws to facilities, matching the unique fingerprint of an office, factory, or datacenter to a set of legal requirements. 

However, for all its technological expediency, it remains the case that whoever is behind the wheel of that AI process, is the one who will drive the results towards success, or failure. 

Keeping AI in its lane

For AI to work well it needs to perform a synchronized manoeuvre with its professional human counterparts. Validation of relevance, quality, and regulatory implications requires qualified human analysts to lead, critique, and filter outputs. This is the approach Enhesa takes, keeping AI strictly in its lane as a tool, not elevating it to an untouchable, unquestionable leadership role. That distinction matters enormously when you are interpreting law and determining its applicability to your business, not least because AI cannot be held professionally accountable for an interpretation.  

Your AI platform cannot appear before a regulator, explain its reasoning, be named on a filing, or hold a qualification. Ask yourself: would you hand your verbatim AI outputs to an auditor or regulator with full confidence in how they were validated? Defensibility requires a human expert with deep, jurisdiction-specific knowledge and a proven track record. It’s better to think of the AI in this context and scenario as the accelerator, albeit with a human manager. Remove the human interpreter and you have not increased your capability; you have increased your exposure while creating a false sense of security. Courts and regulators will want to know who owns the system, how is risk managed and what happens when it fails? “The AI told us”, is not a defensible answer. 

Govern your AI for defensibility

It’s obvious that however sophisticated your AI model is, if it is fed poor regulatory data, it produces confident sounding but poor outputs, and this happens out there in the real world all too easily. In contrast, a well-governed AI system for regulatory intelligence has underlying data curated, continuously updated, and validated by human experts across specific jurisdictions. The AI models must be grounded in that curated data, not a general corpus, and be reviewed by qualified regulatory specialists before any consequential action is taken. There should be a clear audit trail of who validated which interpretation, so when an auditor asks how a regulatory requirement was interpreted, the answer can be tracked back to a named expert who stands behind it. This is the recommended steer for AI and experts, and it’s important for accuracy, and as guard-rails for the direction and success of your EHS program. 

Alex Sadovsky is Chief AI Officer at Enhesa, where he oversees the architecture, governance, and deployment of AI systems across the company’s global regulatory intelligence platform.