This is the first edition of the State of EHS+ Technology Maturity Matrix. A sample of 2,000 (18+) professionals at B2B organizations was surveyed for this study. Respondents were scored across four dimensions — Leadership & Culture, Processes & Data, Technology, and AI — and grouped into top, middle, and bottom thirds based on their combined score. The analysis in this report focuses on how the top third is operating differently from the rest of the sample.
The report was developed in partnership with Censuswide, a member of ESOMAR, a global association and voice of the data, research, and insights industry. They comply with the Market Research Society code of conduct.
ABOUT
Amanda Smith, EVP, Strategy
Carrie Young, VP, Strategic Solutions
Dan Timpson, CTO
Sean Baldry, Sr Director, Product Marketing
Srikanth Venkataseshu, Sr Director, AI Product
Ryan Magee, CEO
CONTRIBUTORS
“The window to build the right foundation is right now.
You probably noticed that the Outperformers aren’t those organizations with the most AI tools deployed. They are the organizations that are building a converged foundation fastest — supportive leadership, engaged workforces, centralized data, modern platforms, and governance that keeps pace with how their teams work.
Wherever you’re starting from today, you’re not behind. The window to build the right foundation is right now.
RYAN MAGEE | CEO, CORITY
The race to apply AI in EHS+ is on, but it will not be won by the organizations that move the fastest. It will be won by the ones who move the smartest, and those who put people at the center of the work.
The outperformers in this report are not the teams with the most pilots, the biggest models, or the flashiest demos. They are the teams that focused on the fundamentals —engaged leadership, connected platforms and data, and responsible controls to earn trust.
They understand that intelligence, artificial or otherwise, is only as good as the foundation beneath it and the judgment on top of it.
The next 12 to 18 months will separate organizations that treat AI as a sprinkle-on capability from those that use it to fundamentally rethink how they work for the good of their people and their performance.
5. CONCLUSION
Centralizing AI strategy and governance
Outperformers prioritize trust and impact over fast innovation. One of the starkest contrasts in the report was the approach top performers took in implementing AI.
While many organizations are experimenting and piloting, outperformers are taking a disciplined, top-down approach. First, they make sure the right leaders are at the table when the AI strategy is set. The report showed 45% have made AI a strategic priority at the executive level, compared to just 14% of their peers. They’re also 2.5X more likely to have embedded AI across workflows instead of focusing on point solutions.
They’re not just planning centrally, though. They’re also prioritizing strong governance. Their first priority is data and privacy security, prioritizing nearly 10 points higher than their peers. The payoff for the centralized approach is trust. The outperformers are consistently willing to allow AI to execute actions with human review, while laggards overwhelmingly believe AI should only provide insights.
With a top-down strategy, focus on embedding AI across workflows, and a heavier investment in governance, it appears outperformers are more willing to trust their AI agents and more likely to see meaningful impact on their operations.
Nearly 60% “always” use EHS+ data for corporate reporting vs 18% of lower-performing peers
45% have made AI a strategic priority at the executive level, compared to just 14% of lower-performing peers
Their top AI priority:
Data Privacy & Security
BRYN JENSSON
CORPORATE DIRECTOR OF EHS OPERATIONS
“Having a centralized system gives leadership clear visibility into what's happening across the organization, while still enabling action at the local level. That balance is important for maintaining accountability, consistency, and trust as new capabilities are introduced.”
Engaging leadership and the frontlines
Outperformers benefit from strong partnerships at the executive and frontline levels. In EHS+, the focus for AI is not replacing people, like it is in some other industries. Rather, the focus appears to be on turning people across the business into change agents who can see and prevent risks wherever they are.
Outperformers benefit from operating in engaged environments where they’ve built strong partnerships with executive leaders and frontline teams.
This is one of the big gaps in the report dataset. Outperformers are 3X more likely to have consistently engaged leadership. This translates into better buy-in on their change initiatives and a far more strategic approach to AI implementation.
The rest of the workforce showed similar trends. More than 50% were regularly engaged vs 22% for their peers. Preparedness to act on risk indicators mirrored the same pattern (34% vs 13%). As every organization seeks to apply AI, the ones in the lead have focused on ensuring their workforce is primed to see the benefits.
CHRIS LAYTON
MANAGER, EMPLOYEE HEALTH
3x more likely to have consistently engaged leadership
More than 50% were regularly engaged vs 22% of lower-performing peers
34% were prepared to act on risk indicators vs 13% of lower-performing peers
“We want the team to think about what’s going to make their day-to-day easier. If we can do just steps one and two to make a couple of things easier, it gives them time to reassess.”
Building strong data practices in day-to-day operations
Applying AI presents an opportunity to unlock unrealized value of data, both because of its ability to surface insights and make recommendations and because it can help clean up messy and unstructured data that was previously hard to use.
The outperformers are readying themselves for that potential by building good practices for managing and applying EHS+ data right now. In the survey, 28% said they have centralized, trusted data vs less than 7% of their lower performing peers. They’re also putting that data to work. Nearly 60% of the outperformers “always” use EHS+ data for corporate reporting (vs 18%) and 46% always use leading indicators proactively (vs 26%).
They’re not just prioritizing the hard work of centralizing data. They’re doing it with purpose, focusing on using that data to guide faster, earlier, and better decisions in their operations right now.
46% always use leading indicators proactively vs 26% of lower-performing peers
Nearly 60% “always” use EHS+ data for corporate reporting vs 18% of lower-performing peers
28% have centralized, trusted data vs less than 7% of lower-performing peers
NICHOLAS MOSSOP
DIGITAL ECOSYSTEM PLATFORM OWNER
“[Before] we couldn’t distinguish failures in behavior from failures in the system, from failures in process. The data and the richness of the quality data set enable us to get to the heart of what the issues are."
Embracing modern platforms
Outperformers understand that intelligence is only as strong as the platform underneath it.
AI is unlikely to make a significant impact if it’s bolted on top of disconnected and outdated solutions. The outperformers appear to have learned that early. While the report shows a general trend toward consolidation, the top companies are further along on that journey.
In fact, they’re 3X more likely to be running on a modern platform. They’ve cut usage of disconnected tools in half vs their peers and are 17X more likely to run on an integrated platform with advanced analytics.
3x more likely to be running on a modern platform
17X more likely to run on an integrated platform with advanced analytics
ANA FERREIRA
SYSTEMS AND GOVERNANCE GLOBAL DIRECTOR FOR ENVIRONMENT, HEALTH, SAFETY, AND SUSTAINABILITY
"Before we had the transformation, it was very challenging to maintain multiple systems that were supporting us in the different regions. Those were outdated, not effective, and far away from having a centralized and standard system that will allow us to have an aggregation of that, understanding of common needs.”
Research from EY shows that companies with the most mature EHS+ programs perform better. They’re 10-15% more efficient, generate 12% more revenue, and have a 3% edge in employee retention.
The Outperformer's Playbook
4. OUTPERFORMERS
Outperformers are applying a four-point playbook →
Our research with Censuswide provides a blueprint for reaching those results, highlighting what the most mature organizations across all four dimensions are doing differently in this moment.
The data clearly shows they aren’t sprinkling AI on top of the old way of doing things. Instead, they’re taking this moment to rethink how they manage risk in their operations.
They’re engaging their workforce from leaders to the frontlines, building strong platform and data foundations, and taking a strategic, executive-led approach to ensure AI is governed, trusted, and impactful.
The EHS+ Technology Maturity Matrix is a benchmarking framework for how organizations are using technology to manage environmental, occupational health, safety, and sustainability performance.
It measures 2,000 EHS+ leaders against four dimensions that build on one another: Leadership & Culture, Processes & Data, Technology, and AI. Here's where the industry sits on each today.
From passive sponsorship → to strategic collaboration.
The industry is early in the shift from leadership-driven passive sponsorship to co-authoring strategy with leaders at the top. Only 23% of EHS+ leaders say their executives are consistently engaged and support timely resolution, yet the top performers are consistently aligning on AI and data strategy with senior leaders now.
LEADERSHIP & CULTURE
From fragmented data → to consolidated views.
The industry is moving from siloed, compliance-driven data to consolidated, decision-ready data — but it has a long way to go. Just 5% of organizations have EHS+ data centralized and integrated with enterprise data.
PROCESSES & DATA
From point solutions → to modern platforms.
The industry is shifting from point solutions to broad adoption of integrated platforms, and the transition is where most of the friction sits today. 27% cite integration complexity as their biggest technology issue — more than any other challenge reported.
TECHNOLOGY
From AI experimentation → to AI embedded in processes
The industry is moving from experimentation to embedded use, and most organizations are still in the earliest stages. Only 15% of organizations are using AI across multiple EHS+ workflows.
AI
THE 4 MATURITY DIMENSIONS
The EHS+ Technology Maturity Matrix
3. MATURITY MATRIX
Using overall maturity scores, we grouped organizations into three equal bands. The top third — the highest-performing 33% — stand apart from the rest on every dimension we looked at, and in a way that may be surprising. They aren't the ones using the most AI tools. They're the ones that fixed the fundamentals first: executive alignment, clean and connected data, and integrated platforms.
That foundation is what positions them to scale AI as the technology matures. Across every dimension of our maturity model, they're further along than the rest of the industry — and the gap is widest on the foundations that determine whether AI can work at all.
The rest of this report unpacks what that playbook looks like in practice.
The outperformers are the ones who fixed the fundamentals first: executive alignment, clean and connected data, and integrated platforms.
While fully autonomous, agentic AI shows great promise in many industries, EHS+ professionals view it as too risky for their work. Only 15% of respondents said they trust agentic AI to make and act on decisions, even in well-defined scenarios.
And 85% prefer to keep humans in command when AI is involved in decision-making, whether that means reviewing AI actions, approving AI recommendations, or treating AI as an advisor while humans retain the final call.
That choice shows up clearly in how investment priorities are being set.
When asked what matters most in AI investment decisions, leaders named explainability, vendor domain expertise, and human control alongside security and scalability.
It’s a signal that impactful AI in EHS+ has to be built around the expert, not around replacing them.
“
AI should make experts faster and sharper, not replace their judgment.
Only 15% of EHS+ leaders trust fully autonomous AI decision-making, even in defined scenarios. The other 85% are telling us something the rest of the enterprise is still learning: in EHS+, "mostly right" isn't acceptable.
Making sure an expert understands and agrees with an AI-generated recommendation isn't optional in EHS+. An AI prompt can return an answer that's right on point, but when you're dealing with complex regulatory scenarios, cross-departmental workflows, and unique operating environments, contextualization is everything. And contextualization requires deep subject matter expertise, either applied to the output or used to sharpen the next prompt.
The consequences of the wrong answer aren't theoretical. They're non-compliance, reputational damage, or a worker getting hurt. That's why the tools that matter most in an EHS+ AI strategy are the ones that keep humans in control: deciding when to use AI in the first place, shaping the prompts, and choosing whether to act on the results.
Fully autonomous AI agents aren't where smart EHS+ teams are focused right now, and honestly, they shouldn't be. The real value is in AI that makes experts faster and sharper, not AI that tries to replace their judgment."
CARRIE YOUNG | VP STRATEGIC SOLUTIONS, CORITY
AI Agents Can't Replace EHS+ Experts. But They Can Make Them Superhuman.
The consensus view in EHS+ is that AI agents shouldn’t – and in fact can’t – replace the experts. In fact, the perspective is that human talent and deep domain expertise are more valuable now than ever, and the real opportunity is in amplifying that expertise to make a greater impact.
05
04
While AI solutions can and will work standalone, the data suggests companies are quickly realizing broad adoption and governance require a more centralized approach.
Consolidation is no longer optional.
As teams race to apply AI, the trend of consolidating tech and data on modern platforms is not only accelerating — it's shifting from a best practice to a necessity. Most teams still work in fragmented systems, and frustration with that reality has been growing. Today, 85% report operating on manual, disconnected, or partially integrated tools.
Those problems are no longer just operational. Most see applying AI on top of a fragmented foundation as untenable for a range of reasons, including the need to manage agents centrally and govern who can do what, demands for a consistent user experience to ensure adoption, and requirements for quality and consistent data across agents.
The trend toward consolidation has been happening for some time, driven by the recognition that risks live between workflows and the need to reduce cost and complexity. AI has shifted it from a good strategy to an urgent imperative.
Together, the collection of use cases tells a story of a movement from responding to risks to preventing them.
Root cause analysis and hazard identification directly target prevention, while others, like data analysis and audit prep, free EHS+ professionals from low-value admin work and help them find insights earlier.
91%
of organizations are deploying AI for both 'Predictive or preventative risk monitoring' and 'Hazard identification or risk analysis' in some form over the next 12–18 months.
Where AI is being deployed in EHS+ today
The industry is putting AI to work in the same eight areas.
Looking ahead, leaders across the industry are converging on a shared set of AI use cases they are hoping to scale. More than half of respondents say they are already piloting or expanding eight key use cases — signaling that the industry has moved past thinking about AI and is now in the middle of putting it to work.
03
Real-time decision-making could be AI's biggest payoff in operations.
The biggest unsolved problem in operations isn’t in reports, it’s in the next 10 minutes – the real-time decision-making that happens everywhere, all the time, by people outside the EHS department.
02
82%
of organizations always or often rely on lagging indicators (incidents, violations, claims) for after-the-fact reporting and corrective actions for EHS+.
When asked what they saw as the biggest opportunity for AI-driven data and insights, leaders pointed to a range of real-time decisions:
Production pressure or schedule changes:
When deadlines shift or new tasks are added after a shift has begun, the work no longer matches the plan the crew prepared for that morning.
Availability or suitability of tools and PPE:
When the right equipment isn't at the point of work, crews generally proceed with what they have, and those substitutions are rarely captured in formal reporting.
Communication quality between teams or shifts:
Context gathered by one crew, including informal adjustments and emerging equipment issues, doesn't always make it to the next.
Focus, alertness, and fatigue over the course of a shift:
Long hours, overnight work, and consecutive days on appear to have a cumulative effect on attention and judgment that supervisors often cannot assess in real time.
Moments that matter, when the day stops matching the plan.
The biggest unsolved problem in operations isn’t in reports, it’s in the next 10 minutes – the real-time decision-making that happens everywhere, all the time, by people outside the EHS department. Here's a sample of how those risks might arise over a single shift.
The biggest barrier to widespread adoption appears to be earning the trust of the people who stand to benefit from it.
Asked what's limiting their ability to use AI effectively, the top concerns all center on the foundation underneath: inconsistent or non-standardized processes, limited workforce trust in AI-generated insights, and a lack of leadership alignment or support for AI initiatives.
To overcome that, the focus shifts from the AI solutions themselves to the foundation underneath. Asked what would make AI more effective, the top priorities point to the underpinning explainability, security, and human governance.
While it appears most view experimentation as important at this stage, they also recognize scale requires more discipline: a clear strategy, AI embedded directly into workflows, and strong security and governance underneath.
01
95% report that teams or frontline workers are using AI outside of approved systems, and only 5% of organizations have restricted or prohibited that use. This is shadow AI — tools adopted without IT or security approval. Beyond the usual risks around governance, monitoring, and compliance, it introduces new ones: hallucinations, errors, and AI bias.
Use of AI tools is nearly universal: 97% of professionals report applying AI in some form, and nearly half (49%) use it widely. But almost all of that activity is happening off-book. Only 5% of organizations have AI embedded in their workflows; the rest is shadow AI — tools picked up without formal approval, governance, or oversight.
Only 5% of organizations have AI embedded in their workflows; the rest is shadow AI.
2. KEY INSIGHTS
Everyone's using AI, but almost no one trusts it enough to scale.
While nearly everyone admits to experimenting with AI in EHS+, those same people recognize that making a real impact from it will require a more disciplined approach.
We surveyed 2,000 senior leaders of environmental, occupational health, safety, and sustainability practices across industries and geographies to understand where EHS+ technology, data, and AI stand today. The results show an industry at an inflection point: AI is rapidly reshaping how organizations manage risk, how they buy technology, and what they expect from the people and systems underneath it.
The data shows how uneven that shift already is. 95% of leaders say their teams are already using AI tools outside approved systems, and 94% say AI has already influenced how they approach their EHS+ technology purchasing. But only 5% have AI embedded across their workflows, and 85% are still running on manual, disconnected, or partially integrated tools.
The race to apply AI is moving fast, but for most organizations, its use is outpacing the foundation required to make it effective in the moments that matter.
To ground the comparison, we built the first EHS+ Technology Maturity Model, a benchmarking framework that measures organizations across four dimensions: leadership and culture, processes and data, technology, and AI. The dimensions build on one another, and together they show where the industry stands today.
Alongside the broader sample, we identified a group of outperformers: the top third of respondents across every dimension of our maturity model. We studied what they are doing differently to offer a view from the front and a playbook the rest of the industry can follow.
The research surfaced a consistent pattern. Appetite for AI is universal across the industry, but the foundations required to scale it — security & governance, trusted data, integrated platforms, and domain expertise — remain inconsistent. The organizations pulling ahead are not the ones running the most AI experiments. They are the ones focused on strong fundamentals across all four dimensions of maturity.
We hope this report can be an annual snapshot of where we stand as an industry, and a helpful guide for the talented people who keep operations moving by protecting employees, customers, and communities from risk.
1. INTRODUCTION
AMANDA SMITH
EVP STRATEGY,
CORITY
SURVEY DETAILS
2,000
Global EHS+ Leaders
10
Industries
Done in partnership with leading research firm, Censuswide
The State of
EHS+ Technology
A global benchmark of technology, AI, and data maturity, and what separates outperformers from the pack
The top insights from a global survey of 2,000+ EHS+ leaders.
AI Agents can't replace EHS+ experts. But they can make them superhuman.
INSIGHT 05
The industry is putting AI to work in the same eight areas.
INSIGHT 03
Real-time decision-making could be AI's biggest payoff in operations.
INSIGHT 02
Everyone's using AI, but almost no one trusts it enough to scale.
INSIGHT 01
Consolidation is no longer optional.
INSIGHT 04
“
AI gives you a rare opportunity to manage risk closer to the reality of work.
Not the work as imagined in a procedure, but the work as it is actually being done by a real person, in a real environment, as conditions change. Some of the biggest risks emerge in the transitions, when teams hand work off, when operating conditions shift, or when the day no longer looks like the plan.
The value of AI is not just in improving visibility or speed. It is in helping capture the real context of work and translating it into insights that better reflect how people, processes, and assets interact in the moment. It extends the reach of EHS+ professionals and helps organizations take smarter, more targeted action to better protect people, operations, and the communities connected to them."
AMANDA SMITH | EVP STRATEGY, CORITY
“
An AI output that sounds confident but is not grounded in your reality is a liability.
The most innovative EHS+ organizations are the most eager to experiment with AI, and that appetite is healthy. But most pilots and proofs of concept are not designed with production in mind. Security, governance, data access controls, reliability, and scale rarely come up during experimentation.
Running AI in a regulated operating environment demands all of them, and more. Responses need to be grounded in your own operational data, not general knowledge. An AI output that sounds confident but is not grounded in your reality is a liability. Organizations that skip this step move fast and pay for it later."
SRIK VENKATASESHU | SR. DIRECTOR OF AI, CORITY
“
If AI is going to make an impact, you’ll need to design it to earn trust.
If AI is going to support real-time, in-the-moment decisions, teams need to know they can trust their systems. There's less time to review, and the stakes are immediate. When I evaluate whether AI is ready for that kind of work, I look at three things:
First, data security. EHS+ data is personal, regulated, and persistent. Any AI operating in this space should follow the principle of least privilege—every operation sees the minimum data required for the specific task.
Second, flexibility. The right model for analyzing a chemical hazard isn't the right model for completing an inspection. Organizations should be able to use the best tool for the job and adopt new ones as they emerge, without being locked into a single vendor's AI or rebuilding every time something better comes along.
Third, transparency. This is the one that matters most to the people I talk to. If AI is involved in how a finding, a corrective action, or a compliance decision was reached, there needs to be a paper trail of what was used, what was produced, and what context informed it.
These are the minimum standards for AI in work where people are accountable for outcomes."