One million two hundred fifty thousand autonomous hospital deliveries have already taken place and construction robots are in production. Yet a significant number of American factories operate in 2026 without autonomous robots. Law is an obstacle among several: coverage and liability attribution for damages caused by a robot raise difficulties linked to fragmented value chains, system opacity, and insurers’ risk assessment. These uncertainties slow large-scale deployment.
The essentials
- Autonomous physical robotics is operational, but large-scale deployment runs into legal uncertainties: liability applicable to robots falls under existing regimes and, in the EU, updated rules covering notably software and AI systems.
- In 2026, a majority of American factories operate without autonomous robotics. According to PYMNTS, a significant share of certain AI-related damage exclusion requests have been approved by regulators, a signal of an insurance market confronted with uncertainty (PYMNTS, 2026).
- Insurers are rebuilding their models from scratch: autonomous physical systems do not correspond to any risk category that actuaries have learned to assess.
- Establishing a clear liability regime could facilitate technology diffusion; in its absence, some players negotiate contractual exclusions while others stay away.
- Key indicators to monitor are the share of deployments covered by standardized insurance and the number of jurisdictions that have adopted a legal framework specific to autonomous systems.
Robots work, lawyers are still searching
Technical maturity is established for certain autonomous robotics applications, but remains uneven depending on uses. In American hospitals, robot fleets have been delivering medications and equipment between floors for several years, without major documented incidents at scale. On construction sites, autonomous machines lay bricks and weld structures. In logistics warehouses, navigation without human operators has become standard among several major e-commerce players.
What blocks the passage to industrial scale is of another nature. When an autonomous robot damages a machine tool, injures an employee, or causes costly delivery delays, the chain of responsibility remains complex. The robot manufacturer, the company that programmed it, the operator who deployed it, the client who defined the task: each can invoke various contractual clauses. Regulators and legislators have adopted rules applicable to certain robotic and AI systems, notably in Europe.
Insurers, for their part, have found a temporary solution: say no.
According to PYMNTS, a significant share of certain AI-related damage exclusion requests have been approved by regulators, a signal of an insurance market confronted with uncertainty. Some companies that deploy autonomous robots operate with limited coverage or subject to contractual exclusions.
Actuaries start from scratch
Traditional insurance models rest on historical claims data, established risk classes, and predictable behaviors. A car has known accident statistics. An escalator, a press machine, a forklift operated by a human: all risks that actuaries have learned to quantify over decades. An autonomous robot that interacts with humans in a changing industrial space, makes real-time decisions, and can be remotely updated by its manufacturer—nothing in actuarial tables resembles it.
Governance work published on arXiv points to a structural problem: the evolving nature of physical AI systems complicates the assessment of fixed risk. A robot delivered in January can behave differently in December, after several software updates. The question of whether the January robot and the December robot constitute the same product and the same risk exposure raises substantial legal difficulties.
Shift Technology, a specialist in AI applied to fraud detection in the insurance sector, documents the same difficulty: insurance decision-support systems produce decisions whose traceability remains opaque, even for their designers. Attributing a cause to an incident—design defect, programming defect, operational error, unforeseen environment—requires lengthy and costly investigations that neither insurers nor courts are equipped to conduct quickly.
The practical result is visible in adoption figures. While the technology is available and its performance is documented, a majority of American factories operate without autonomous robotics in 2026. Some large companies that deploy nonetheless negotiate specific contractual conditions and assume residual legal risk. SMEs encounter greater obstacles to adoption.
A legal vacuum that chooses its winners
Uncertainty over the civil liability framework favors players with substantial legal and financial resources. Major logistics platforms, multinational industrial groups, and integrated hospital systems can negotiate contracts or absorb residual risks. Subcontractors, manufacturing SMEs, and personal service providers encounter increased obstacles to adoption.
This selection mechanism reproduces a logic documented elsewhere in AI diffusion: productivity gains concentrate first where the means to capture them already exist. Legal uncertainty structurally advantages large-scale players.
Governance work published by Springer on regulatory frameworks for autonomous systems identifies three recurring bottlenecks. First point: the autonomous robotics value chain is long and multinational—an American robot can be designed in Germany, manufactured in Asia, programmed by an Indian provider, and deployed by a French SME. Multinational chains raise questions of jurisdiction and applicable law, but liability attribution does not necessarily require a global inter-jurisdictional agreement. Second point: commonly documented contracts shift responsibility downward to the end operator, who often has limited means to assume it. Third point: potential victims may encounter delays or obstacles to compensation depending on applicable contractual clauses.
According to analyses of physical robotics financing, civil liability uncertainties influence valuations and financing cycles, particularly for applications in direct contact with the public.
Two trajectories for the next ten years
The 2028-2032 horizon opens two distinct scenarios, and signals that will allow distinguishing them are beginning to appear.
In the first, a liability framework clarifies for economic operators in cases of defective products, consistent with EU directive 2024/2853. Insurers then have a more stable contractual basis for offering standardized products. Economic and legal conditions become more readable for deployment and technology access extends to players of diverse sizes.
In the second scenario, legal uncertainty persists. Some players continue to deploy despite residual risks. Autonomous robotics remains principally accessible to players with substantial legal and financial resources. Deployment encounters brakes in SMEs and sectors with lower capital intensity. Productivity inequalities between large and small businesses widen, with effects on employment and competitiveness of local industrial fabrics—a risk that emerging economies are all the less able to ignore.
The two deployment trajectories are distinguished by the clarification of the civil liability framework and conditions for insurers’ access to a stable contractual basis.
Conditions for a functioning market
The collective needs that emerge from this debate are concrete. The first is an indemnification mechanism accessible to persons injured or harmed by an autonomous system. The no-fault liability model, which exists in several European countries for workplace accidents or nuclear accidents, offers a lead: the manufacturer contributes to a fund, the victim is quickly compensated, the debate over fault is then settled among industrial players. Transposing this model to autonomous robotics would suppose an obligatory manufacturer contribution proportional to the volume of robots deployed, a form of collective premium on deployment.
The second need is that of transparency on exploitation conditions. An autonomous robot deployed in a space shared with humans should be subject to independent certification, verifiable and regularly updated. Several governance works referenced at the SAE World Congress 2026 propose the creation of a certification authority whose approval would condition access to public markets and shared spaces. This type of mechanism exists for medicines, for medical devices, for vehicles: extending it to autonomous physical systems is a logical extension, not a rupture.
Two signals will allow measuring progress toward a functioning regime. First, the share of autonomous robot deployments covered by a standardized insurance contract rather than by case-by-case negotiated coverage exclusions. Its increase would signal that the insurance market has found a contractual basis sufficiently solid to propose replicable products. Next, the number of jurisdictions that have adopted a legal framework specific to autonomous systems.
Deployment awaits a political decision, not a technical breakthrough
Autonomous robotics is in an unusual position for an industrial technology. It is operational. It has proven itself in demanding environments—hospitals, construction sites, warehouses. Its deployment cost is falling. And yet, its deployment encounters obstacles in several industrial sectors, confronted with a question that is not technical.
The decision to be made is political and legal in nature: establish the allocation of civil liability in a fragmented, multinational, and evolving value chain. This allocation will influence the economic and legal conditions of deployment as well as occupational risks for workers in direct contact with autonomous robots.
The first decisive institutional move will be that of the legislator, jurisdiction, or regulatory authority that proposes a sufficiently complete framework so that insurers see it as a working basis. The European Union has a framework in the process of being deployed: the AI Act applies in phases, while directive 2024/2853 on product liability, applicable to products placed on the market or in service after December 8, 2026, covers software and AI systems. A European framework without American or Asian equivalent will nonetheless only resolve part of the problem, in an industry whose value chains exceed the borders of a single market.
Sources
- PYMNTS, Physical AI Is Forcing Insurers to Start From Scratch, 2026, https://www.pymnts.com/news/artificial-intelligence/2026/physical-ai-is-forcing-insurers-to-start-from-scratch
- SAE World Congress 2026, work on governance of autonomous physical systems (no verifiable URL)
- Springer, governance frameworks for autonomous systems (no verifiable URL)
- ArXiv, studies on civil liability allocation in autonomous systems (no verifiable URL)
- Shift Technology, AI applied to fraud detection and traceability of autonomous decisions (no verifiable URL)
- Venture Forward Capital, documentation on physical robotics financing cycles (no verifiable URL)
