Before Scaling AI in EHS, Fix the Contractor Data Behind It
Most EHS departments already use AI, but few have scaled it. Keeping contractor data current, worker-level and standardized gives AI a reliable base for spotting serious risks earlier.
- By Joe Schloesser
- Oct 21, 2026
AI is already widespread in environmental health and safety (EHS): Ninety-four percent of EHS and safety departments across manufacturers, retailers, and pharma organizations now use AI to some extent. However, only 9% have scaled and governed its use, according to EHS data and software provider 3E.
As many organizations are discovering, scaling AI across the enterprise is not as simple as applying it to more use cases. AI's output is only as useful as the information it has to work with, so applying it broadly without addressing underlying data gaps can undermine the very insights organizations expect it to deliver.
By relying on outdated, incomplete, or inconsistent data, scaling AI use too soon risks producing unreliable safety insights at greater scale.
The most pressing challenge many organizations face is making sure underlying data can support meaningful analysis as the volume and range of information AI processes grows. Only with a solid data foundation in place can AI help EHS teams analyze safety trends across larger datasets, identify leading indicators, and proactively surface risks.
For many organizations, the foundation is incomplete without contractors. Employee data is further along, but contractors often perform some of the highest-risk work, and their information is the hardest to keep current and consistent. For EHS leaders working to reduce serious injuries and fatalities (SIFs), contractor data is where AI has the most to offer and the most to lose. Before asking AI to do more, make sure it has something reliable to work with.
Give AI current data to uncover emerging safety risks
Current data is hardest to come by with contractors. Many organizations are making progress with internal employees, where job planning is under direct control and field data is easier to capture. Contractor work is often a different story, with limited visibility into field activity, and data is rarely captured consistently or at the scale needed for accurate insights.
When contractor data is detailed and consistent, AI can surface outliers, recurring trends, and relationships across large datasets much faster than a person could manually, allowing EHS professionals to spend less time searching for patterns and more time evaluating what they mean and where action may be needed.
This is particularly valuable for identifying leading indicators, such as signals pointing to growing risk before an incident occurs.
Suppose several projects report workers improperly securing loads or positioning equipment, or not following material-handling procedures. With worker-level data, AI could connect those observations to workers who recently changed roles or whose training is nearing expiration. EHS teams could then close the training gap before those behaviors contribute to an injury. Many organizations do not yet have the data foundation to reliably perform this kind of analysis. Contractor programs may capture data on safety performance, worker qualifications, training, incidents, and projects, but that information often lives across disparate systems, follows inconsistent standards, or quickly becomes outdated.
For example, an annual qualification may not account for changes in a contractor's work or workforce since the last review. Company-level records may indicate a contractor meets qualification requirements without showing whether individual workers have the necessary training or credentials, and if similar safety observations are categorized differently across projects, AI may treat them as separate issues rather than recognize a broader pattern.
Those gaps limit what AI can see, regardless of how much information it processes. A model cannot account for a qualification change that was never recorded or accurately connect information that lacks a consistent structure.
Documentation also rarely matches how work is performed. Job plans, procedures, and qualification records describe work as intended, but field conditions shift, crews change, and controls are applied unevenly. Understanding real risk requires more data and signals from the jobsite itself, including the conditions workers face, the controls implemented, and how effective those controls are.
For AI to deliver value at scale, it doesn't have to, nor should it, make every safety decision. It needs accurate, up-to-date data to give EHS professionals earlier visibility into potential risks so they can determine where attention or action is needed.
3 ways to build a stronger data foundation for AI
Capturing reliable safety insights from AI at scale starts with improving the contractor data available for analysis. EHS leaders should focus on three principles:
1. Keep contractor data current
Contractor data shouldn't be treated as a static record. Safety performance, training status, personnel, jobsite conditions, and the work a contractor performs can change between formal reviews, making an annual snapshot increasingly disconnected from current conditions.
Rather than waiting for the next review cycle, update contractor records as changes occur and connect central records with other safety systems through APIs, avoiding periodic manual data transfers. Records should also capture contractor performance metrics tied to safer outcomes, including job planning quality, control implementation, and focus on the high-risk activities known to cause the most harm to workers and operations. Where possible, add signals from the jobsite itself, such as field conditions and how well controls are working, since documentation describes work as intended, not as performed.
The result is a more current picture of the contractor environment. AI can analyze changes and emerging patterns as they develop, giving EHS teams an earlier opportunity to investigate potential risks.
2. Capture data at the individual worker level
Even current data has limitations when it reflects the contractor as a whole rather than individual workers. A contractor may meet an organization's requirements while individual workers have different training, qualifications, and experience relevant to their assignments.
Worker-level data adds the missing layer. Connecting worker qualifications and training to specific roles or scopes of work helps EHS teams determine whether the people performing a job are prepared for its requirements. This includes training on topics that help workers better identify hazards and implement controls on the highest risk tasks or situations.
This specificity also gives AI more meaningful relationships to analyze. Patterns can be examined by worker qualification, assignment, and type and risk level of work, so EHS teams can identify concentrations of risk that may not surface when data is viewed only at the contractor level.
3. Standardize information to be comparable
AI cannot reliably compare contractor and safety data if the same information is recorded differently across sites or projects. Without common definitions and categories, it becomes harder to recognize when separate records point to the same issue.
Organizations should standardize how qualifications, safety observations, incidents, and performance information are captured, ideally within a centralized contractor record. This gives equivalent information the same meaning regardless of which contractor, site, or project it comes from.
AI then has a stronger basis for identifying trends across the broader contractor population, helping EHS teams recognize patterns early that may not be visible within a single project.
Prepare contractor data for AI at scale
Many organizations already have vast amounts of data to work with, but processing more of it does not automatically lead to a better understanding of risk. Getting value from AI at scale requires data to be current, consistent, and complete, with contractors in the picture. AI helps give EHS teams earlier visibility and can impact decisions on where to act.