Artificial Intelligence (AI) is no longer a futuristic concept; it’s here, and it’s transforming every corner of the workplace—including occupational health and safety (OHS). From predictive analytics to real-time monitoring, AI is increasingly used to prevent workplace injuries, illnesses, and fatalities. But as AI reshapes the safety landscape, it also raises important questions: What does this mean for OHS professionals? How can they stay relevant and effective in an AI-driven environment? And what are the risks associated with relying on AI in safety-critical domains?
The Rise of AI in Workplace Safety
AI in occupational health and safety typically involves systems that learn from data to identify patterns, detect anomalies, and predict potential hazards. These tools may include:
- Wearable technology that tracks worker biometrics and environmental conditions.
- Computer vision systems that monitor high-risk areas via video and CCTV and detect unsafe behaviours in real-time.
- Predictive analytics that analyse historical incident data to forecast future risks.
- Natural language processing (NLP) tools that scan safety reports, inspection records, or maintenance logs to flag concerns.
Such innovations have already started to improve workplace health and safety by automating hazard identification, minimizing human error, and allowing for earlier interventions. However, this shift also means that OHS professionals need to evolve alongside the technology.
What This Means for the OHS Professional of the Future
As AI tools become more integral to health and safety management systems, the role of OHS professionals is poised to transform in several ways:
- From Enforcers to Strategists
Rather than just enforcing compliance and conducting inspections, OHS professionals will take on a more strategic role—interpreting AI outputs, integrating data insights into risk management plans, and collaborating with IT and data teams.
- Multidisciplinary Skillsets
The health and safety expert of tomorrow must be fluent not only in health and safety legislation and human behaviour, but also in data science fundamentals. Understanding how AI models work, what they measure, and how they can fail will be crucial.
- Data Stewardship
With AI relying heavily on data, OHS professionals will need to ensure the quality, relevance, and ethical use of health and safety-related data. They’ll play a pivotal role in managing data governance and ensuring AI systems are used responsibly.
Future-Proofing Your Safety Career and Skills
To stay competitive and effective in the AI era, safety professionals should consider the following strategies:
- Develop Data Literacy
You don’t need to become a data scientist, but understanding key concepts—such as machine learning, probability, data bias, and algorithms—is essential. Online courses, certifications, and workshops can bridge this knowledge gap.
- Embrace Continuous Learning
The pace of technological change is fast. Safety professionals should commit to lifelong learning through professional development, industry conferences, and cross-disciplinary collaboration.
- Build Interdisciplinary Relationships
Safety now intersects with IT, HR, operations, and data science. Building partnerships across these domains will be essential for implementing AI tools successfully and interpreting results in context.
- Advocate for Ethical AI Use
OHS professionals must ensure that AI systems uphold health and safety standards without compromising privacy, fairness, or worker autonomy. Being a voice for ethical and responsible AI deployment is a growing part of the health and safety function.
- Refocus on Human Factors
AI may help spot patterns, but it’s still humans who make critical decisions. Understanding human behaviour, motivation, and error remains core to the profession. AI should augment—not replace—human-centred health and safety practices.
- Strengthen Critical Thinking Skills
As AI delivers data-driven insights, professionals must be equipped to question, interpret, and contextualize those outputs. Critical thinking ensures that health and safety decisions are not made blindly based on algorithms but are evaluated with logic, evidence, and ethical consideration. Being able to spot inconsistencies, challenge assumptions, and weigh alternative explanations will remain a vital skill in the age of automation.
The Risks of Using AI as a Predictive Risk Tool
While AI holds great promise, it also introduces new risks—especially when used as a predictive risk tool. These risks can affect not only the accuracy of AI models but also the health and safety outcomes they aim to improve.
- Data Quality and Bias
AI is only as good as the data it learns from. If historical safety data is incomplete, outdated, or biased, AI models can perpetuate these issues—leading to inaccurate predictions or skewed risk assessments. For instance, underreporting of near misses or incidents in certain departments may lead an AI system to incorrectly classify them as low-risk areas.
Mitigation: OHS professionals should be deeply involved in data collection processes, auditing data sources regularly for completeness and bias.
- Hidden Latency Errors
One of the most dangerous aspects of AI is the possibility of long-latency errors—systemic flaws in the AI model or data pipeline that remain hidden until a serious incident occurs. These can be caused by unnoticed shifts in workplace processes, sensor failures, or changes in human behaviour that the model isn’t trained to recognize.
Mitigation: Regular validation and updating of AI models are critical. This includes real-world testing and scenario planning to identify blind spots.
- AI Over-Reliance
There is a risk that organizations may come to over-rely on AI, assuming it is infallible. This can lead to complacency or diminished situational awareness among workers and supervisors.
Mitigation: AI tools should be viewed as decision-support systems—not decision-makers. Training programs must emphasize the importance of human judgment and critical thinking.
- Ethical and Privacy Concerns
AI systems that monitor workers’ behaviour or health data can raise ethical and legal questions around surveillance, autonomy, data privacy and consent. Poorly designed systems can erode trust and potentially violate legal or cultural norms.
Mitigation: Transparent communication with workers, anonymization of data where possible, understanding data and privacy laws and clear consent protocols are essential.
- Lack of Explainability
Some AI systems—especially deep learning models—operate as “black boxes,” providing outputs without clear reasoning. In a safety-critical domain, this lack of explainability can undermine trust and hinder incident investigations.
Mitigation: Prioritize the use of explainable AI models where possible and always document how safety-related AI systems function and make predictions.
- Inadequate AI Root Cause Analysis in Incidents
When an incident occurs in a workplace where AI plays any role—whether through monitoring, prediction, or automation—it is critical that the investigation includes a review of the AI systems involved. Overlooking AI contributions can lead to incomplete root cause analyses, missed lessons, and repeat incidents. Faulty data inputs, misclassifications, or overlooked alerts from AI tools can all play a part in failure.
Mitigation: Ensure that health and safety incident investigations are updated to include a structured evaluation of any AI system involved. This includes checking data integrity, algorithm behaviour, decision pathways, and human interaction with the system to ensure a robust and accountable root cause analysis.
A Balanced Path Forward
AI is set to become an indispensable tool in the OHS professional’s toolkit, offering unprecedented capabilities to anticipate, detect, and mitigate risk. However, its power must be balanced with vigilance, critical thinking, and a commitment to human well-being.
Occupational health and safety professionals who embrace AI, understand its limitations, and continue to advocate for ethical and human-centred practices will not only future-proof their careers—they will also lead the next generation of safer, healthier and smarter workplaces.
Conclusion
The integration of AI into occupational health and safety is both an opportunity and a challenge. While the tools themselves are powerful, their effectiveness depends on the professionals who wield them. For the OHS professional of the future, success will hinge on blending technological fluency with traditional health and safety expertise, ethical judgment, and a deep understanding of the human factors at play in every workplace. As AI evolves, so too must the role of health and safety professionals—ensuring not just that the workplace is healthy and safe, but that health and safety itself remains rooted in both innovation and humanity.
About the Author
Kate Field: Global Head Health, Safety and Wellbeing at BRITISH STANDARDS INSTITUTION
Kate Field is the Global Head of Health, Safety, and Wellbeing at BSI Group. Kate leads the business development for BSI’s health safety and wellbeing portfolio and is an industry expert on psychological health and safety and ISO 45003.
Kate champions the business benefits of developing trust in an organisation and we are pleased to have her as a contributor for HSE Network.
