J.S. Held acquiert Element Forensic Engineering, renforçant ainsi ses capacités dans le domaine de l'assurance pour les pertes matérielles importantes et les sinistres touchant les entreprises de taille moyenne partout au Canada
En savoir plusAI may operate behind the scenes, but its failures can cause real-world injury and damage. Physical AI losses raise engineering, coverage, human factors, and damages questions - and J.S. Held answers all four.
AI risk is often considered a software problem: an issue of copyright, training data, trade secrets, algorithmic bias, and hallucination. The losses are perceived as purely economic and the disputes contractual.
However, the risk profile has evolved beyond software AI disputes to include physical losses where AI is embedded in machines that move, lift, drive, cut, inspect, and operate alongside people. Machines such as industrial robots, warehouse automation, autonomous vehicles, commercial drones, autonomous heavy and agricultural equipment, surgical and caregiving robotics, and AI-controlled building and process infrastructure.
When physical AI systems fail, they can produce an injury, fire, property loss, product recall, a regulatory investigation, or an insurance claim.
Addressing these losses requires an integrated view of the physical event, the machine and model evidence, the human interaction, and the resulting financial impact.
J.S. Held combines these disciplines to help insurance claims professionals and legal counsel determine:
We bring our deep, courtroom-tested technical and financial expertise – built over decades – to a new class of claims and disputes.
Four Quadrants. One Firm.
J.S. Held is a global consulting firm organized across two axes: financial and technical expertise, applied to insurance claims and complex disputes.
These four quadrants share a single underlying competency: determining the value and consequence of an event under a governing document. In litigation that document is a contract, a patent, or a statute. In insurance it is a policy. The analytical discipline is the same. Only the instrument changes.
A physical AI loss lands in all four quadrants at once.
The Next Generation of a Market J.S. Held Already Leads
J.S. Held identified the first generation of AI disputes early, published a framework, built an index, and now serves as damages expert in matters that are helping shape how AI losses are valued.
Physical AI is the second generation of that same market — more deeply connected to the core capabilities J.S. Held delivers. Every significant autonomous-system failure generates a chain of technical, operational, insurance, human-factors, economic, regulatory, and litigation issues that align with the multidisciplinary model our firm has spent decades building.
Cause and origin; materials and component analysis; laboratory examination; fire, thermal-runaway, and energy-storage events; structural, electrical, and mechanical failure in automated environments.
Human-robot interaction and reasonable reliance; automation bias and over-reliance on automated systems; warning and interface adequacy for machines with model-driven behavior; safety-control bypass and foreseeability; shared-workspace design.
Autonomous and driver-assisted vehicle collision; delivery robot, drone, and autonomous forklift incidents; mining and agricultural machinery; premises liability; integration of physical evidence with perception and control data into a demonstrable sequence.
Robot and automation failure analysis; sensor, actuator, drive, and battery failure modes; industrial, warehouse, port, mining, and agricultural systems; valuation and condition assessment; repair-versus-replace determination for autonomous systems, including retraining, recalibration, and revalidation cost.
Preservation, extraction, and authentication of sensor logs, video, perception outputs, decision records, model prompts and outputs, and telemetry; operational-technology and industrial-control-system forensics; autonomous-agent incident investigation; chain of custody for machine and model evidence. Determining what the machine knew, when it knew it, and why it acted.
Bodily injury and wrongful-death economics; life-care planning; business interruption and contingent business interruption; lost profits; recall and withdrawal cost; subrogation support.
Patent and trade-secret damages across robotics, autonomous navigation, sensor fusion, perception, edge-AI silicon, battery technology, and human-machine interfaces; licensing and FRAND; ITC matters; valuation of AI-enabled machinery, training data, and model assets; model provenance and derivation analysis.
Autonomous survey drones, AI equipment monitoring, autonomous machinery, robotic fabrication, and AI scheduling; delay and productivity claims; defective execution; safety incidents; allocation where a schedule or sequence was generated by a model.
Workplace safety; autonomous hazardous-material handling; chemical and process-plant automation; mining automation; OSHA investigation and response; root-cause analysis; safety program design; regulatory compliance.
Autonomous Systems Risk Review — board-level assessment of deployment safety, liability allocation, control environment, and incident-response readiness; AI-governance frameworks for physical systems; disclosure and AI-claim substantiation review.
Physical AI systems record what they do, producing an evidentiary record before anyone knows there will be a dispute. Every autonomous machine continuously logs sensor inputs, perception outputs, planning decisions, actuator commands, software and model versions, and operator interactions.
Physical AI may be the first category in which the machine itself holds the answer — if it is preserved, extracted, and authenticated before it is overwritten.
The Loss
The Evidence
Who Pays
Who Investigates
Physical AI losses do not fit within a single discipline. They can involve engineering failure, human interaction, digital evidence, insurance claims, economic damages, and litigation. J.S. Held brings technical and financial expertise across insurance claims and complex disputes, enabling our experts to examine the event and its consequences through a coordinated, multidisciplinary approach.
Our intake protocol specifies scene and machine preservation, the digital evidence to be captured before it is overwritten, chain of custody for model artifacts and telemetry, the sequence in which disciplines engage, and a single point of contact for the client. One call, one team, one accountable party.
Investigators can reconstruct an autonomous system's actions by connecting physical evidence with its perception, planning, control, and operator-interaction data. This may involve examining what the system sensed, how it interpreted its environment, what decision it made, which actuator commands followed, and whether an operator intervened. The objective is to establish what the machine knew, when it knew it, and why it acted.
Commercial general liability
Robot strike and crush injury; public-interaction failure; autonomous delivery incident; premises exposure in automated facilities
Propriété
Robot-caused fire; battery and energy-storage thermal events; AI-controlled building and process-system failure
Product liability
Design, manufacturing, and software defect; sensor and perception failure; inadequate warning and interface design
Professional and E&O
Erroneous automated recommendation; medical and surgical robotics; AI-assisted engineering and design
Auto and fleet
Autonomous and driver-assisted vehicle collision; platooning and yard-automation incidents
Internet
Compromise of operational technology and control systems producing physical consequence
Workers’ compensation and EHS
Injury in shared human-robot workspaces; OSHA action and root cause
Recall and product withdrawal
Fleet grounding; software recall; regulatory withdrawal
D'autres questions ?
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