What predictive lone worker safety lookslike in practice: hotspots, context-aware alerts, and human-in-the-loop AIinside Field Safe.
Lone worker safety hastraditionally been built around response: missed check-ins, alerts, andescalation workflows designed to get help moving fast when something goeswrong. That lifeline matters, and it always will. But with today’s volume ofdigital field signals, safety leaders can do more than respond. They cananticipate.
Predictive safety is thepractical next step. It doesn’t claim to predict the exact moment an incidentwill happen. Instead, it helps teams see where risk is elevated today, based onleading indicators such as check-in patterns, journey conditions, recurringhazards, control effectiveness, near misses, and environmental context. Whenthose signals cluster, AI can flag emerging hotspots and give supervisors aprioritized view of what needs attention first.
In this second part ofour series, we’ll break down what that looks like in real operations: how AImodels identify risk hotspots across routes, sites, and job types; howgeofencing can deliver location-specific safety intelligence in the moment; andhow Field Safe is building AI into a connected worker platform to help teamsact sooner—without adding admin or sacrificing trust and privacy.
AI models can scan years ofcheck-ins, journey logs, incidents, and corrective actions to spot patternsfast:
According to Safety Inc.,in its article “AI-Powered Workplace Safety: How PredictiveAnalytics and Wearables Can Prevent Accidents," by combining environmental data (like weather andremoteness), task type, worker history, and equipment information, predictiveanalytics can estimate where risk is elevated on any given day.
That doesn’t meanpredicting the exact moment of an incident. It means giving HSE leaders aprioritized view of risks that require closer attention.
Now that you haveidentified areas of elevated risk, you can use AI to create more intelligentworkflows.
As field workers travelbetween job sites, geofencing and AI combine to proactively protect them byoffering real-time, location-specific safety intelligence. Using predefinedgeofenced zones around assets, facilities, easements, or high-risk areas, the systemautomatically detects when a worker enters a zone and evaluates the conditionsat that location.
For example, when a workerapproaches a regulator station, pipeline ROW, or active construction area, theAI engine pulls from historical hazard submissions, recent high-energy events,weather feeds, and equipment status reports. If the area has a history ofpressure-related incidents, elevated line strike risk, or recent wildlifeactivity, the system instantly pushes a mobile alert such as:
“Caution: You areentering a high-risk zone. Previous incidents in this area include pressurereleases and excavation hazards. High winds were reported within the last 60minutes. Additional controls are recommended.”
This approach convertssafety from passive to predictive. Instead of relying on workers to rememberpast site concerns or wait for updated safety briefings, the system deliverscontext-aware alerts at the exact moment they are needed, helping ensure at-riskworkers are better prepared as they arrive on site.
Field Safe Solutions isalready a connected worker platform: we digitize lone worker monitoring, journeymanagement, safety forms, and corrective actions into a single system. Here arefour ways we are integrating AI to improve the safety of lone and at-riskworkers.
Related Video: Operational Efficiency
1. Building on a StrongData Foundation
Our mobile app integrateswith the wearables workers use in the field, such as the Apple Watch, todeliver health and safety notifications. This allows us to capture rich,time-stamped data about:
That data gives AI areliable source of learning, and it also makes Field Safe more valuable day today. The more complete and consistent the data, the easier it is for oursolution to surface leading risk signals, spot repeat hazards or weak controls,and help supervisors prioritize prevention actions early, without adding excesspaperwork for workers
2. Moving from Laggingto Leading Indicators to Measure Risk
Field Safe is also focusedon helping safety teams move beyond TRIR and other lagging indicators. Throughconversations with our clients, we heard a consistent challenge: traditionaldashboards often explain what happened last month, but they do not reliablyshow where risk is building today. That insight, combined with learnings fromsector specialists and current research, is forming our approach to AI as apractical tool to strengthen leading indicators, identify rising exposure, andimprove prevention.
One of the clearestexamples of this shift is the CSRA work mentioned earlier, led by ExecutiveDirector Dr. Matthew Hallowell. Their research has appealed to many safety leadersbecause it questions a long-standing assumption that many organizations stilltreat as the scoreboard for safety performance: TRIR (Total Recordable IncidentRate).
CSRA’s “Tyranny of TRIR” work explains how TRIR has dominated safety performance conversationsfor nearly 50 years, yet can be misleading when used to compare teams,projects, or companies. Their research argues TRIR is often statisticallyinvalid as a comparative metric and shows no discernible association betweenTRIR and fatalities, meaning you can “improve TRIR” without necessarilyreducing exposure to the events that matter most.
From there, CSRA points theindustry toward alternatives: metrics and methods that better reflect prevention, controleffectiveness, and serious-injury-and-fatality exposure.
This ties directly back tothe core promise of AI in safety: it’s not just analyzing what happened, it'shelping leaders see risk building earlier by learning from leading-indicatordata (hazards, controls, near misses, and work conditions) before harm occurs.
Hazard + ControlsIntelligence
Once you digitizefield-level hazard assessments and controls, AI can help safety teams move from“forms filed” to “signals learned.” The goal is to surface trends likerecurring hazard types, weak or missing controls, or sites where riskconditions are changing faster than normal. CSRA’s work on Quality of Safety Leading Indicators reinforces this idea: measuring the quality ofsafety activities (not just whether they happened) is a stronger path tomeaningful prevention.
Over time, this becomes apractical leading-indicator engine that helps teams improve hazard recognitionand strengthen controls before an incident.
Field Safe is bringing thisapproach into our platform by using AI to turn day-to-day field data, such ashazard reports, FLHAs, near misses, check-ins, and corrective actions, intoleading risk signals, so HSE teams can spot rising exposure earlier and takeaction before an incident happens.
Related Content: FLHAs:Where Safety and Technology Converge
3. SmarterPrioritization for Lone Worker Prevention
AI can combine journeyplans, check-in patterns, location context, and operating conditions (likeweather/road risk) to give supervisors a simple daily view of where risk iselevated and what needs attention first: extra check-ins, adjusted timing,added controls, or a different route. That’s how AI supports prevention withoutflooding teams with more admin.
Field Safe makes thispractical by bringing together lone worker monitoring, journey management, anddigital safety workflows into a single, easy-to-use mobile app, so workers havefewer tools to juggle, and supervisors can see the full picture in one place.With everything captured in real time and tied to the same worker, task, andlocation, it becomes much easier to act early and apply the right controls.
4. Human in the Loop,Always
AI will never replacecompetent safety professionals. Instead, Field Safe strives to leverage it to:
We also recognize thatpersistent monitoring can raise valid privacy and trust questions. In itsarticle “The Role of AI in Predicting Workplace Hazards andPreventing Accidents,” the HSENetwork advises that responsible AI in safety means being transparent aboutwhat’s being tracked, why it’s being tracked, and how it benefits workers,rather than using it as a disciplinary tool.
Field Safe safeguardsprivacy through tight data governance (SOC-2 Compliance), encryption of alldata in transit and at rest, role-specific access controls, and secure datahosting on AWS, making certain that only authorized users can access theminimum information required for their work while fully complying with industryand statutory standards.
What AI-Powered SafetyMeans for HSE Leaders
For leaders responsible forlone and remote workers, AI-powered safety isn’t about chasing a buzzword. It’sabout strengthening the fundamentals you already care about:
Field Safe’s vision issimple: combine a strong safety culture with modern, AI-powered tools so thatevery lone worker is connected and has a digital safety net around them, onethat can see risk coming and help the team act sooner.
If you’d like to explorewhat AI could do with your existing safety data, we’re ready to have thatconversation.
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About the Author:
Doug Junor has been drivingbusiness transformation across North America for more than 35 years, helpingorganizations spot and act on emerging innovations that challenge the statusquo. Clients value Doug’s ability to translate new technology into practicalstrategies that create operational and competitive impact.
Previously, Doug served asChief Business Officer at Robots & Pencils, an award-winning mobile development firm, where heoversaw digital initiatives across multiple industries and led digitaltransformation work long before it had a name. Doug also brings his experienceinto the classroom, having developed and delivered programs through SAIT’s School of Advanced Digital Technology, including Transformational Leadership forExecutives and Digital Strategy and Leadership.
Follow Doug on LinkedIn: https://www.linkedin.com/in/dougjunor/