J.S. Held adquiere Element Forensic Engineering, ampliando así sus capacidades especializadas en seguros para reclamos de daños a la propiedad en el segmento de empresas medianas y de grandes en todo Canadá
LEER MÁSOur multidisciplinary team connects technical and financial evidence to help clients evaluate complex AI-related claims across insurance lines. The result is an independent, evidence-based analysis that supports coverage evaluation, claims resolution, subrogation and recovery, litigation, arbitration, and expert testimony.
When AI contributes to a loss, it may not be identified in the initial claim notice. J.S. Held helps insurance professionals reconstruct how AI, connected systems, organizational controls, third-party technologies, and human decisions interacted; determine whether AI materially affected causation, severity, duration, or liability; and quantify the resulting financial exposure.
AI is increasingly embedded in corporate and product workflows, from assistive tools and decision-support systems to decision-making and autonomous applications. As authority shifts from people to AI-enabled systems, the need for controls, accountability, testing, logs, override mechanisms, and preserved evidence increases.
AI-related losses may involve more than a model output alone. They can reflect the interaction of model behavior, system integrations, data flows, vendor dependencies, governance controls, and human reliance. Claims may therefore involve blended questions across property and business interruption, casualty and product liability, professional liability, financial lines, cyber and privacy, and other coverage pathways.
J.S. Held examines the relevant AI outputs, system and action logs, approvals, overrides, data handoffs, operational records, digital evidence, physical evidence, and financial information. We help clients establish what happened, identify the triggering event and contributing failures, assess responsibility, and determine how AI affected the resulting loss.
AI exposure is not always visible at first notice; it may be embedded in the systems, workflows, decisions, or third-party technologies that contributed to the loss.
The central claims question is not simply whether AI was used. It is:
Four Levels of AI Authority
Three Interacting Failure Layers
Reconstructing the AI-Human-System Pathway
Tracing the loss from the triggering event through AI outputs or actions, human reliance, system handoffs, downstream propagation, and resulting damage. This analysis helps reveal whether AI contributed materially to the loss, even when its causal role was not apparent at first notice.
Determining Causation and Contributing Failures
Evaluating whether the event involved model behavior, a system integration failure, weak data or vendor handoffs, failed controls, human over-reliance, deficient oversight, or a combination of factors. Where physical damage is involved, our technical specialists can also examine the affected equipment, products, structures, or operating environment.
Preserving and Analyzing Digital Evidence
Identifying and analyzing available logs, model outputs, prompts, action records, approvals, overrides, access records, system telemetry, data flows, configurations, and related digital evidence needed to establish what occurred and support a defensible reconstruction.
Quantifying Financial Exposure
Measuring business interruption, extra expense, remediation costs, lost profits, economic damages, operational disruption, customer harm, and other financial consequences, while evaluating whether AI affected the severity, duration, or scope of the loss.
Supporting Coverage, Apportionment, and Recovery Decisions
Providing independent analysis that helps clients evaluate the relevant coverage pathway, causation, quantum, responsibility, subrogation, recovery, and reserving uncertainty. Policy interpretation and coverage determinations remain the responsibility of insurers and counsel.
Delivering Defensible Findings
Integrating technical, digital, investigative, and financial analysis into a coherent evidence-based narrative for claims resolution, litigation, arbitration, mediation, and expert testimony.
Integrated Technical, Digital, Investigative, and Financial Expertise
AI-related claims can cross traditional expert disciplines and insurance lines. J.S. Held brings the relevant specialists together to connect the technical cause, digital pathway, operational impact, and financial outcome rather than treating each issue in isolation.
Depth in Complex Insurance Claims
Our insurance professionals understand the evidence, documentation, and independent analysis required in complex claims. Five decades of claims consulting experience helps the team focus the AI-related investigation on questions that matter to causation, damages, liability, recovery, and defensibility.
Analysis Beyond the AI Application
We evaluate AI in the context of the broader system in which it operates, including data inputs, integrations, workflows, third-party platforms, organizational controls, decision owners, physical processes, and human actions.
Objective, Evidence-Based Outcomes
Our work is grounded in available records and tested against the factual and financial evidence. Where evidence is incomplete, the analysis identifies the limitation rather than assuming how an AI system performed.
Property and Business Interruption
AI may be embedded in maintenance, control, optimization, monitoring, or building and equipment systems. A claim may involve missed warnings, unsafe automated actions, equipment damage, downtime, or an increase in the severity or duration of interruption. J.S. Held investigates the physical and digital evidence, assess the interaction among technology, operations, and human decisions, and quantify property and time-element impacts.
Casualty and Product Liability
AI can influence product design, safety workflows, routing, access control, autonomous functions, or intelligent devices. Our engineers, accident reconstructionists, human factors specialists, digital investigators, and financial experts evaluate whether AI-assisted design, automated actions, system failures, or human reliance contributed to a product defect, unsafe condition, third-party harm, or related damages.
Responsabilidad profesional
Claims may arise from alleged errors, omissions, inappropriate recommendations, faulty analysis, classification, drafting, or review involving AI-generated or AI-assisted outputs. J.S. Held reconstructs the workflow, evaluates how the output was produced and used, examines review and escalation controls, and analyzes resulting damages and responsibility.
Financial Lines and Directors and Officers (D&O)
AI may affect approvals, payment controls, fraud detection, disclosures, management reporting, underwriting, trading, or capital-allocation decisions. Our team investigates the relevant systems, governance and oversight, data and approval pathways, alleged misstatements or omissions, fraudulent instructions, and associated financial impacts.
Cyber and Privacy
AI-related cyber events can involve agents, prompts, APIs, data flows, third-party models, unauthorized actions, model manipulation, prompt injection, or data exposure. J.S. Held preserves and analyzes digital evidence, reconstructs system activity, assesses access and control failures, and supports the evaluation of technology, privacy, operational, and financial impacts.
Blended and Multi-Policy Claims
A single event may implicate more than one coverage area. Our integrated approach helps clients understand which parts of the causal chain relate to physical damage, digital activity, professional services, third-party harm, operational interruption, or financial loss, while keeping the underlying facts and quantum aligned across workstreams.
Additional specialists are engaged based on the facts of the claim, including professionals in equipment consulting; forensic engineering; accident reconstruction; human factors; cyber and digital forensics; forensic accounting; economic damages; intellectual property; environmental, health, and safety; surety; building consulting; and builder’s risk.
An AI-related insurance claim is a loss in which an AI-enabled tool, automated decision, model output, intelligent product, agent, or AI-supported process may have contributed to causation, severity, duration, liability, or damages. The claim may arise under property, business interruption, casualty, product liability, professional liability, financial lines, cyber, privacy, or multiple coverage pathways. It may not be identified as AI-related until the underlying workflow is reconstructed.
AI may be embedded within a broader workflow and may not appear in the claim notice or policy file. The relevant event may involve an interaction among a model output, system integration, third-party platform, human decision, operational process, and organizational control. Identifying AI involvement requires looking beyond the final failure to the outputs, actions, handoffs, approvals, overrides, and decisions that preceded it.
AI exposure is not limited to a digital incident. AI can influence physical equipment, products, safety processes, professional services, financial decisions, operations, and customer outcomes. A single event can therefore combine physical, digital, human, governance, and financial issues. The analysis must determine where AI affected the causal chain rather than assuming that every loss involving AI is a cyber claim.
Relevant evidence depends on the event and the system involved. It may include model outputs, prompts, action and audit logs, access records, data inputs, system telemetry, approval and override records, testing and monitoring records, configurations, vendor documentation, APIs and data handoffs, policies and procedures, operational records, physical evidence, financial records, and witness information. The availability and reliability of this evidence can materially affect whether the event can be reconstructed.
An assistive tool typically leaves a person responsible for the final decision, while decision-making or autonomous systems may act with limited human intervention. As AI authority increases, the investigation may place greater emphasis on action logs, authority boundaries, override controls, monitoring, fallback procedures, vendor dependencies, and incident-response evidence. The level of authority also helps frame questions about responsibility and control.
AI involvement does not automatically change claim value or coverage. It can, however, change the analysis by introducing additional questions about causation, exclusions or conditions, severity, duration, responsibility, and available evidence. J.S. Held provides factual, technical, digital, and financial analysis; insurers and counsel make policy interpretation and coverage determinations.
J.S. Held can help reconstruct the AI-human-system pathway, investigate the triggering event and contributing failures, preserve and analyze digital and physical evidence, assess causation and responsibility, quantify business interruption and other damages, and present independent findings for claims resolution, subrogation and recovery, litigation, arbitration, mediation, and expert testimony.
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