AI may increase the scope of work tasks where a worker can be removed from a situation that carries risk. In a sense, while traditional automation can replace the functions of a worker's body with a robot, AI effectively replaces the functions of their brain with a computer. Hazards that can be avoided include stress, overwork, musculoskeletal injuries, and boredom. This can expand the range of affected job sectors into white-collar and service sector jobs such as in medicine, finance, and information technology. As an example, call center workers face extensive health and safety risks due to its repetitive and demanding nature and its high rates of micro-surveillance. AI-enabled chatbots lower the need for humans to perform the most basic call center tasks. Machine learning is used for people analytics to make predictions about worker behavior to assist management decision-making, such as hiring and performance assessment.

 These could also be used to improve worker health. The analytics may be based on inputs such as online activities, monitoring of communications, location tracking, and voice analysis and body language analysis of filmed interviews. For example, sentiment analysis may be used to spot fatigue to prevent overwork.   Decision support systems have a similar ability to be used to, for example, prevent industrial disasters or make disaster response more efficient.  For manual material handling workers, predictive analytics and artificial intelligence may be used to reduce musculoskeletal injury. Traditional guidelines are based on statistical averages and are geared towards anthropometrically typical humans. The analysis of large amounts of data from wearable sensors may allow real-time, personalized calculation of ergonomic risk and fatigue management, as well as better analysis of the risk associated with specific job roles. Wearable sensors may also enable earlier intervention against exposure to toxic substances than is possible with area or breathing zone testing on a periodic basis. Furthermore, the large data sets generated could improve workplace health surveillance, risk assessment, and research. AI can also be used to make the workplace safety and health workflow more efficient. One example is coding of workers' compensation claims, which are submitted in a prose narrative form and must manually be assigned standardized codes. AI is being investigated to perform this task faster, more cheaply, and with fewer errors.AI‐enabled virtual reality systems may be useful for safety training for hazard recognition. Artificial intelligence may be used to more efficiently detect near misses. Reporting and analysis of near misses are important in reducing accident rates, but they are often underreported because they are not noticed by humans, or are not reported by workers due to social factors.