How generative AI is impacting lone-worker safety

Generative AI can bring several benefits to lone worker safety apps, enhancing their functionality and effectiveness in safeguarding individuals who work alone in potentially hazardous environments. Here are some of the key advantages:

1. Real-time Risk Assessment: Generative AI can analyze real-time data from various sources, such as sensors, GPS, and weather forecasts, to assess the current risk level for a lone worker. This enables the app to provide timely warnings and recommendations for safer practices.
2. Predictive Analytics: By analyzing historical data and patterns, generative AI can predict potential safety risks and incidents. This allows the app to proactively suggest preventive measures or recommend alternative routes or work schedules to reduce risks.
3. Natural Language Processing (NLP): Generative AI can incorporate NLP capabilities to facilitate communication between lone workers and the app. This enables workers to report incidents or request assistance using voice or text commands, making it easier to call for help when needed.
4. Personalized Safety Plans: Generative AI can create personalized safety plans for each lone worker based on their specific tasks, location, and individual characteristics. These plans can include step-by-step safety procedures and emergency response protocols.
5. Incident Reporting and Documentation: The AI can assist lone workers in documenting incidents by generating incident reports in real-time. This documentation is valuable for compliance purposes and can also serve as a record of what occurred in case of investigations or legal issues.
6. Contextual Alerts: Generative AI can send contextual alerts and reminders to lone workers based on their current situation. For example, if a worker enters a high-risk area, the app can automatically remind them to wear appropriate safety gear or inform them about nearby hazards.
7. Enhanced Emergency Response: In the event of an emergency, generative AI can assist emergency services by providing precise location data, worker profiles, and real-time information about the situation. This aids in faster and more efficient responses.
8. Continuous Improvement: Over time, generative AI can learn from data and user feedback to improve safety recommendations and incident response. It can adapt to changing conditions and become more effective in protecting lone workers.
9. Remote Monitoring: Lone worker safety apps can use generative AI to enable remote monitoring by supervisors or safety teams. This allows for a higher level of oversight and immediate intervention if a worker encounters a critical situation.
10. Scalability and Cost-Efficiency: Generative AI can help automate many aspects of lone worker safety apps, making them more scalable and cost-effective. It can handle a large volume of data and tasks, reducing the need for extensive manual monitoring and intervention.

Overall, generative AI can significantly enhance the capabilities of lone worker safety apps, making them more intelligent, responsive, and adaptable to the unique challenges faced by individuals working alone in various environments. This technology has the potential to save lives and improve the overall safety and well-being of lone workers – however – like any significant technological advancement, generative AI brings with it new risks, and it can also exacerbate existing risks. According to McKinsey research, The state of AI in 2023: Generative AI’s breakout year, only 32 percent of respondents say they’re taking measures to prevent inaccuracy, while 38 percent, down from 51 percent last year, are managing cybersecurity risks.

Read the full article at https://www.mckinsey.com/about-us/new-at-mckinsey-blog/an-inside-look-at-how-businesses-are-or-not-managing-ai-risk.