Insurance premiums in trucking are on the rise, driven by nuclear verdicts, regulatory scrutiny, and rising claims costs. But amid this volatility, a new opportunity is emerging: the ability to use AI and telematics data to demonstrate proactive risk management. This article explores how the insurance landscape is changing, how technologies like Readi provide valuable operational insights, and what forward-thinking fleets are doing to prepare for the future.
Fleet insurance in 2025 is facing unprecedented pressure from multiple angles:
Nuclear verdicts — defined as settlements exceeding $10 million — are more frequent and impacting underwriting assumptions. A notable example was the $100 million verdict against Werner Enterprises, which was only recently reversed after years of appeals.
Premium increases are a structural trend. According to Hub International, 2025 marks the fifth consecutive year of rising fleet insurance costs, with many policies seeing double-digit increases.
Coverage contraction is accelerating. Excess liability coverage that once offered $10M is now often capped at $1M, with many carriers pulling out of transportation altogether (source).
Small and midsize fleets are disproportionately affected, as they lack the resources to form captives or self-insure.
"Even if you’re operating perfectly, you’re going to have increasing insurance rates. It’s punishing everyone." — Matthew Payne, Lockton
In this context, insurers now seek more than traditional safety protocols. To even be considered insurable at a competitive rate, fleets are expected to show:
Documented driver qualification and hiring processes
Use of Advanced Driver Assistance Systems (ADAS) and camera systems
Proactive safety program engagement with traceable data and audits
Artificial intelligence (AI) is emerging as a transformative force in fleet insurance. Telematics and ELDs already generate vast quantities of operational data, but AI enables fleets and insurers to make sense of it in real time.
AI applications in this space include:
Real-time behavioral analytics: Detects trends in hard braking, speeding, lane drift, and near misses before they result in incidents
Predictive fatigue modeling: Uses historical and rolling data (e.g. 10-day sleep + work patterns) to forecast risk windows
Claims triage and fraud detection: Reduces costs by quickly identifying high-probability claim risks or exonerating video data
Driver coaching: Enables proactive intervention when fatigue or distraction indicators are high
"The industry is struggling to absorb AI, but those who do are gaining sharper actuarial insights and risk models." — Steve Miller, Hub International
Some insurers have adopted an AI-first underwriting approach, ingesting real-time data from ELDs, dashcams, and ADAS to dynamically price policies.
Readi from Fatigue Science is built to address one of the most persistent, least visible risks in transportation: fatigue. Unlike traditional tools that react to drowsiness in real time, Readi predicts fatigue before it becomes a risk.
Key features include:
18-hour advance fatigue forecasts, based on a validated SAFTE model and real-world data
Software-only deployment using ELD and schedule data (ReadiML), or optional wearables (ReadiWatch)
Shift-level supervisor dashboards (ReadiSupervise) that show which drivers are at risk before dispatch
Union-approved privacy model with no biometric tracking or personal health data collection
Seamless integration with fleet tools, e.g., dispatch software, safety dashboards, even existing camera systems
"At Fatigue Science, we believe predictive fatigue insights should be as operationally fundamental as telematics. Readi equips safety leaders with decision-ready data to intervene before fatigue becomes a liability." — Andrew Morden, CEO, Fatigue Science
Comparison: Reactive vs. Predictive Systems
| Feature | Traditional Cameras | Readi (Predictive AI) |
|---|---|---|
| Detects fatigue | Only after it occurs | Before the shift begins |
| Requires driver input | Often intrusive | Passive (via ML + schedule) |
| Supports shift planning | No | Yes |
| Union-friendly | Mixed | Yes |
| Offline compatible | No | Yes |
| Supervisor action | Post-incident review | Pre-shift risk mitigation |
Readi complements existing camera systems by providing the missing predictive context that cameras can’t capture.
One example is Day & Ross, a major Canadian transportation provider. After deploying Readi, Day & Ross reported greater visibility into driver fatigue and added ReadiScores into their daily safety meetings and pre-trip planning. This proactive approach reflects their commitment to safety innovation.
Amid tightening margins and rising exposure, proactive fleets aren’t waiting for mandates. They’re taking steps to manage risk more intelligently:
Blending reactive and predictive tools: Readi is often deployed alongside fatigue-detection cameras, offering a more complete picture of risk.
Adding fatigue insights to pre-start checklists: Supervisors use ReadiScores during toolbox talks or daily safety meetings to identify drivers who may need reassignment or additional breaks.
Using fatigue data to support scheduling decisions: Dispatchers reference fatigue forecasts to avoid stacking high-risk shifts or scheduling back-to-back fatigue-heavy runs.
Documenting fatigue countermeasures/counteractions: From task rotation to stand-downs, fleets use Readi's shift logs to show insurers and auditors a record of intervention.
These steps don’t just align with operational best practices, they support the story insurers want to hear: "We don’t just react to risk, we anticipate it."
Can Readi lower our insurance premiums?
Readi is not an insurance product and does not make claims about lowering premiums. However, some fleets report that it strengthens their safety posture and supports data-driven conversations with underwriters.
What data does Readi use?
Readi can operate using just schedule and ELD data (via ReadiML). All data is processed through a union-compliant, privacy-safe model.
Do insurers accept Readi data?
While Readi is not a certified insurance tool, its data has been used in internal investigations, incident reviews, and audits. Its real-time logs may support documentation of fatigue mitigation.
Is Readi only for large fleets?
No. Readi has been deployed in both small regional carriers and global mining and logistics operators. It is scalable and adaptable to different fleet sizes and risk profiles.
Explore how Readi works:
ELD Whitepaper