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EHS professionals share how they are using AI to make workplaces safer

Early adopters across the safety profession are using AI to save time and surface patterns in incident data, while most practitioners are still learning. This page sets out Benchmark Gensuite’s view on turning that interest into a first pilot that earns funding and keeps professional judgment in charge.

published

February 23, 2026

Originally covered by

TOPIC

AI in EHS

· Adoption Strategy

Natasha Porter
Chief Customer Officer

As covered in

Risk & Insurance

━ Overview

Risk & Insurance reports on a new white paper from the ASSP AI Task Force. It shows that members who have started using AI are seeing practical gains, even though most safety professionals are still learning about the technology rather than deploying it. Natasha Porter, Benchmark Gensuite’s Chief Customer Officer, is among the practitioners and vendors quoted.

A news story has limited room for the question the white paper leaves open: how does a cautious EHS team get from interest to a first pilot that survives budget review? This page adds a practical view on where to start, how to keep experienced judgment in the loop, and what to measure.

━ Benchmark Gensuite Perspective

AI adoption stalls when it begins as a technology project instead of a fix for a named problem

The pattern across the profession is broad awareness and narrow deployment. Individual practitioners experiment on their own time, but few organizations can say what those experiments were meant to fix, so the work rarely earns a budget line.

Our view is that the first pilot should target the two or three EHS problems that already consume the most hours or carry the most exposure, such as permit tracking, incident report quality, or audit follow-up. A pilot aimed at a known pain point produces a before-and-after comparison that sponsors can read without a technical briefing, and that comparison is what secures the next round of support.

Caution is an asset here. EHS professionals are trained to question inputs, verify sources, and keep accountability with a named person, which is exactly the discipline AI output needs. For a stretched team, the goal is simple: remove hours from work already on the calendar, so the saving shows up in next quarter’s schedule rather than in a slide deck.

━ Key themes

Three things to understand about starting with AI in EHS

1

The best first use cases are tasks your team already times

Permit reviews, incident write-ups, and training preparation usually have a known duration. That baseline turns any improvement into evidence instead of opinion.
2

Risk aversion is a design input, not an obstacle

Build review steps, source links, and sign-off into the pilot from day one. Cautious decision-makers approve what they can audit.
3

Hands-on time changes minds faster than strategy documents

Teams that work with real files form realistic expectations of both the strengths and the errors. A short, structured trial on live work builds more confidence than a presentation about potential.

━ Featured executive

Natasha Porter

Chief Customer Officer - Benchmark Gensuite

Natasha Porter is Chief Customer Officer at Benchmark Gensuite, where she has been a driving force since the company's inception — more than 25 years ago. Beginning her career as an EHS leader, she has played a pivotal role in shaping Benchmark Gensuite into the global SaaS leader it is today, supporting 480+ companies worldwide.

A recognized innovator in applying AI and machine learning to workplace safety, Natasha has led initiatives that transform how organizations manage risk, compliance, and operational excellence. She is passionate about bringing practical, people-centered AI solutions into the real world to make workplaces safer, smarter, and more efficient.

Natasha is the lead inventor of the PSI AI Advisor — a patented AI technology developed by Benchmark Gensuite that leverages machine learning to identify and prioritize incidents with potential for serious injuries and fatalities. She was also recognized with the ASSP President's Award for her leadership on the ASSP AI Task Force and her contributions to advancing workplace safety through the application of AI technologies.

Originally covered in

Risk & Insurance

February 23, 2026

What EHS leaders are asking

How should an EHS team start using AI?

Start with one or two problems that already cost your team the most time or create the most exposure, such as permit tracking backlogs, incident report quality, or audit follow-up. Run a small pilot with a named owner, record how long the task takes today, and compare after a few weeks. A narrow, measured pilot is easier to review, easier to fund, and easier to stop if it does not deliver.

How do you get leadership buy-in and funding for AI in EHS?

Tie the pilot to a business problem leadership already cares about, such as incident rates, audit findings, or overtime, rather than presenting AI as a standalone initiative. Show a baseline, a defined review process, and a result that can be expressed in hours saved or risk reduced. Decision-makers in safety tend to be cautious, so evidence they can audit carries more weight than a technology demo.

How do you check AI output for errors in safety work?

Treat every AI output as a draft that a qualified person reviews against the source document, standard, or regulation before it is used. Keep a named reviewer accountable, and require the tool to link back to its sources where possible. Experienced professionals catch plausible but wrong answers fastest, so pair newer team members with them during the pilot.

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EHS professionals share how they are using AI to make workplaces safer

AI adoption stalls when it begins as a technology project instead of a fix for a named problem

Three things to understand about starting with AI in EHS

What EHS leaders are asking

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