AI is transforming fleet safety, but not always for the better. While tools like dashcams and driver-monitoring systems promise greater protection, they also introduce a new layer of risk: false positives, automation overreach, and decision-making without human context.
A recent industry warning highlights three major concerns:
Risk Totality: AI that sees everything but understands nothing.
False Positives: Constant alerts that dull response and reduce trust.
AI Coaching: Systems that take the wheel on driver behavior, with no supervisor insight or context.
These are not theoretical concerns. Fleets are already experiencing the side effects of reactive AI. Fortunately, there’s a better way forward—one that puts humans back in control and ensures AI enhances safety without undermining it.
That solution is Readi, a predictive fatigue management system that brings AI into the safety workflow without taking humans out of it.
AI-enabled video and monitoring systems collect vast amounts of data. But more data doesn’t mean better decisions. “Risk totality” is the fallacy of believing that seeing everything equates to understanding what matters.
For example, an AI system might flag eye closures or head tilts as fatigue, but what it can’t tell is whether a driver was pre-fatigued before the shift even started.
Readi solves this by predicting risk before a shift begins. Using validated SAFTE biomathematical modeling and ML-derived sleep profiles, Readi predicts hour-by-hour fatigue risk for every driver, up to 18 hours ahead. This gives supervisors and dispatchers true situational context before the first key is turned, avoiding guesswork entirely.
Fleet managers consistently report issues with in-cab cameras that trigger too frequently, flagging fatigue when drivers are alert, and missing it when they’re not. This constant stream of false positives erodes driver trust, increases alarm fatigue, and disrupts operations.
Readi reduces false alarms by acting as a pre-filter, identifying high-risk drivers before they hit the road. In fact, one mining contractor using Readi alongside in-cab cameras saw 58% fewer camera fatigue alarms among Readi-enabled operators. Fewer false positives means more confidence, better compliance, and less operational disruption.
Some AI systems now offer “real-time coaching,” issuing behavior corrections directly to drivers without intervention from a dispatcher or supervisor. While well-intentioned, these systems often lack the context to deliver fair, safe, and accurate guidance. Worse, they exclude supervisors from the loop, creating liability risks and morale issues.
Readi keeps humans in control. Supervisors receive fatigue predictions via ReadiDispatch, allowing them to:
Review fatigue scores per driver before the shift
Adjust task assignments or shift timing proactively
Log countermeasures like rest breaks, task swaps, or stand-downs
Rather than removing supervisors from the equation, Readi empowers them with predictive insights and gives them tools to make and document better decisions.
Readi is not just another AI safety tool. It’s a full-cycle fatigue management platform designed to prevent risk, not just monitor it. Key features include:
Predictive ReadiScores: Hour-by-hour cognitive fatigue forecasts
No hardware required: Deploys via ELD and schedule data, not cameras or wearables
Integrated workflows: Fatigue scores shown in dispatch tools or supervisor dashboards
Privacy-first model: No biometric tracking or sleep surveillance; union-approved
It’s the opposite of “black box AI.” Readi gives transparency, control, and measurable ROI to every stakeholder.
What’s the biggest AI risk in fleet safety?
The biggest AI risks in fleet safety are systems that take action without human oversight, leading to false positives, coaching without context, and operational backlash.
How does Readi reduce false alarms?
Readi reduces camera false alarms by predicting fatigue risk before a shift, Readi helps supervisors filter out which operators are truly at risk, preventing unnecessary camera alerts and disruptions.
Does Readi use AI coaching or intervene with drivers directly?
No. Readi empowers supervisors and dispatchers to make proactive, documented decisions based on predictive fatigue data.
Is Readi compatible with ELD systems?
Yes. Readi integrates with all major ELD providers and uses schedule + HOS data to model fatigue risk (no hardware needed).
Can Readi replace our camera system?
Readi is not a replacement, it’s a complement. It adds predictive context to reactive systems, improving their accuracy and reducing their alert burden.
AI in fleet safety should serve operators rather than override them. The real risk isn’t that AI is watching but that it’s acting without context.
With Readi, fleets get the power of predictive fatigue risk combined with human-in-the-loop decision-making. No false positives. No AI overreach. Just smarter, safer operations, delivered by people, powered by science.