Artificial intelligence (AI) is rapidly becoming embedded in every phase of the construction life cycle—from design and scheduling to quality assurance, claims investigation, and litigation. Insurers, contractors, sureties, and forensic consultants are turning to AI-enabled platforms to analyze photographs, contracts, schedules, building information modeling (BIM) files, weather data, and inspection reports faster than ever before. These capabilities promise faster investigations, lower loss-adjustment expenses, and better-informed decisions.
Yet despite significant investment in AI-enabled technologies, many organizations remain focused on what AI can do rather than what it should do. Insurance risk managers’ and claims professionals’ responsibility extends beyond adopting innovative technology. They are entrusted with protecting enterprise value, preserving stakeholder confidence, and ensuring innovation does not outpace governance. Construction claims provide one of the clearest examples of this challenge, and they offer a useful lens through which to examine AI-assisted investigations, AI-generated expert reports, drone inspections, AI hallucinations, chain of custody, and enterprise risk governance.
The stakes are not theoretical: A single large construction claim can involve millions of dollars in exposure, years’ worth of scheduling and change-order documentation, dozens of subcontractors, and competing expert opinions on causation. Introducing AI into that environment without a governance structure does not simply create operational risk—it creates legal, regulatory, and reputational risk that can outlast the claim itself. The question is no longer whether AI belongs in construction claims; it is how organizations ensure that AI is deployed in a way that withstands scrutiny from regulators, courts, and the public.
AI-Assisted Claims Investigations
Computer vision, natural language processing, and predictive analytics now allow investigators to review thousands of photographs, reconstruct accident scenes using drone imagery, detect subtle defects through automated pattern recognition, and draft technical summaries in a fraction of the time once required. Predictive models can flag anomalies in a construction schedule, cross-reference weather data against a delay claim, or surface inconsistencies buried across thousands of pages of contract documents and correspondence.
These efficiencies are real and meaningful. A claims team that once needed weeks to review a project's full documentary record can now generate a first-pass summary in hours. But speed is not the same as reliability, and volume is not the same as insight. AI improves efficiency but should augment—not replace—the professional judgment of engineers, adjusters, and claims professionals who understand the full factual and contractual context of a dispute. Construction claims have always demanded the integration of engineering, insurance, contract interpretation, forensic investigation, and legal analysis. AI accelerates these processes but does not change the underlying professional standard of care that governs them.
Consider a delay claim involving a multi-phase commercial project. An AI tool can ingest the entire critical-path schedule, every schedule update, and every weather report for the site, then flag the days on which weather deviated meaningfully from historical norms. That is a genuine time-saving contribution. But determining if that weather caused critical-path delay—as opposed to a concurrent delay caused by a subcontractor's own inefficiency, a late owner-furnished design decision, or a labor shortage—requires a scheduling expert’s judgment, informed by site conditions, contract language, and industry practice that models may not currently capture on their own. The same caution applies to defect detection: computer vision can flag a crack pattern or a moisture stain, but distinguishing a construction defect from ordinary wear, deferred maintenance, or a preexisting condition still requires an engineer who understands the building's history and construction sequence.
AI-Generated Expert Reports and Evidence Admissibility
Modern platforms can evaluate drone imagery, review BIM files, analyze schedules, summarize contracts, and identify anomalies across vast data sets. AI can organize technical evidence and draft analytical summaries that would once have taken a forensic engineer days to compile manually. This is a genuine advance in efficiency.
However, expert opinions remain the responsibility of licensed, credentialed professionals. AI cannot replace professional judgment shaped by years of field experience. Under Federal Rule of Evidence 702 and the Daubert reliability standard, experts must independently validate any AI-assisted findings and be prepared to explain, under cross-examination, the methodology behind their conclusions. An expert who cannot articulate how an AI tool reached a given result—or who cannot distinguish the tool's output from their own independent analysis—risks having that opinion excluded or given little weight. Technology should augment expertise, not substitute it.
As AI-generated analyses become more common in construction disputes, courts will increasingly be asked to evaluate their admissibility. Federal Rule of Evidence 702 requires expert opinions to rest on reliable methodology, while Rule 901 requires authentication of digital evidence before it can be considered by a factfinder. The Daubert standard, in turn, emphasizes testing, peer review, known error rates, and general acceptance of the methods underlying expert testimony.
Courts will scrutinize transparency, validation, reproducibility, and reliability, and black-box outputs offered without meaningful human verification are likely to face significant challenges. Organizations relying on AI must therefore document how their models were trained, validate outputs through qualified professionals, and preserve transparent audit trails showing exactly how a given conclusion was reached. Governance—not automation alone—is what ultimately makes AI-assisted evidence defensible in a courtroom.
Drones, Digital Twins, and AI Damage Analysis
The convergence of drones, digital twins, and AI-powered analytics is fundamentally changing how construction investigations are conducted. Drone imagery combined with AI and photogrammetry enables rapid, high-resolution documentation of roofs, facades, bridges, and large or hard-to-access construction sites. High-resolution imagery, LiDAR scanning, and photogrammetric modeling allow investigators to document entire projects with a level of precision that was difficult to achieve through manual inspection alone.
When combined with AI, these technologies create digital records—often integrated with BIM models to form true digital twins—that improve causation analysis and support more efficient dispute resolution. A digital twin created shortly after a loss event can preserve conditions that would otherwise change or be remediated before litigation begins, giving experts, adjusters, and courts a durable, three-dimensional record to work from months or years later. However, metadata preservation, cybersecurity, and chain-of-custody documentation are now essential components of enterprise risk management, not merely IT functions relegated to a technology department.
AI Hallucinations: A Governance Risk
One of AI's greatest risks is hallucination: the confident generation of inaccurate facts, citations, or technical conclusions. Generative AI systems are designed to produce plausible-sounding output, and a fabricated code citation, a misstated measurement, or an invented precedent can appear just as authoritative as an accurate one. Within construction claims, these errors could influence coverage decisions, settlement strategy, or expert testimony in ways that are difficult to detect after the fact.
Left unchecked, hallucinated content may expose organizations to professional liability, regulatory scrutiny, reputational damage, and allegations of bad-faith claims handling. A denial letter built in part on a fabricated policy provision, or an expert report citing a standard that does not actually exist, is not a hypothetical risk—it is a foreseeable consequence of deploying generative AI without adequate review. Every AI-generated conclusion should therefore be independently reviewed by experienced engineers, adjusters, attorneys, or claims professionals.
Chain of Custody and Evidentiary Integrity
Preserving the evidentiary value of AI-assisted work product requires the same discipline construction claims professionals already apply to physical evidence. Organizations should document the source data used, the specific software and model version employed, the prompts or queries submitted, the human review process applied to the output, and the metadata and audit logs generated along the way.
This documentation matters for two distinct reasons. First, it allows an expert or claims professional to reconstruct exactly how a conclusion was reached if it is later challenged. Second, it demonstrates to a court, a regulator, or an auditor that the organization applied a consistent, defensible process rather than relying on an unverified machine output. In construction claims, where causation disputes often turn on subtle timing and sequencing questions, a well-documented chain of custody for AI-assisted evidence can be the difference between a defensible position and an indefensible one.
Enterprise Governance Frameworks
Responsible AI requires a governance framework, not merely a procurement decision. Before deploying AI within construction claims, executives should ask five foundational questions: Who validates AI outputs? Can the methodologies be explained? Is evidentiary integrity protected throughout the process? Are sensitive data secure? And are models continuously monitored for accuracy and bias over time?
These principles align with established, internationally recognized standards, including the National Institute of Standards and Technology (NIST) AI Risk Management Framework (AI RMF 1.0); the International Organization for Standardization/International Electrotechnical Commission (ISO/IEC) 42001 for AI management systems; and ISO 31000 for enterprise risk management (ERM). Boards and executive leadership should align AI initiatives with these frameworks to ensure accountability, transparency, cybersecurity, and ethical oversight are built into AI adoption from the outset, rather than layered on afterward in response to a claim, an audit, or a courtroom challenge.
Five Principles of Responsible AI
Distilled into practice, responsible AI adoption in construction claims rests on five core principles:
- Human judgment first. AI supports the analysis; licensed professionals remain accountable for the conclusions and the decisions built upon them.
- Transparency. Methodologies, training data, and model limitations must be explainable in plain language, including under cross-examination.
- Evidence integrity. Metadata, source data, and audit trails must be preserved from the moment AI is used through final resolution of the claim.
- Governance. AI deployment should follow a documented framework tied to recognized standards, not ad hoc adoption by individual teams.
- Continuous validation. Models must be monitored on an ongoing basis for accuracy, drift, and bias, with periodic independent review.
A Practical Roadmap for Chief Risk Officers
Translating these principles into practice does not require a wholesale technology overhaul. It requires a deliberate, staged approach. Organizations should begin by inventorying every point in the claims lifecycle where AI is already in use, formally approved or not. Claims teams and outside experts often adopt AI tools independently, well before enterprise risk or legal departments are aware of them. From there, a tiered review process can be established, distinguishing low-risk uses, such as summarizing internal correspondence, from high-risk uses, such as generating conclusions that may inform coverage decisions or expert testimony.
Training is equally important. Claims professionals, adjusters, and panel counsel need practical guidance on what AI tools can and cannot reliably do, how to recognize signs of hallucination, and when independent verification is mandatory rather than optional. Vendor contracts should be reviewed to confirm that AI providers can explain their model's methodology, disclose training data limitations, and support the audit trail requirements described above. Finally, governance should not be a one-time exercise. As models are updated, retrained, or replaced, the organization's risk assessment must be revisited, and periodic independent audits should confirm that AI outputs remain accurate, unbiased, and properly documented over time.
AI will reshape construction claims, but its greatest value lies in enhancing—not replacing—human expertise. AI cannot testify under oath, evaluate witness credibility, or exercise the professional judgment that comes from decades of field experience. What it can do is compress weeks of document review into hours, surface patterns a human reviewer might miss, and give experts a stronger evidentiary foundation to build upon.
Organizations that succeed will combine technological innovation with disciplined governance, legal integrity, and professional accountability. An insurance executive’s role is not merely to adopt AI; it is to establish the ethical and operational framework that ensures AI strengthens decision-making, protects stakeholders, and advances resilient enterprise risk management. The organizations that integrate AI responsibly—with rigorous professional oversight at every step—will lead the next generation of construction risk management.
About the Author:
Nikki-Ann Thomson, J.M., MBA, is a chief risk officer at Priderock Capital Partners, LLC. thomsonna@prssllc.com