Artificial intelligence does not need to be hacked, poisoned, kidnapped, or persuaded by a villain wearing a hoodie to create a serious commercial loss. Sometimes the system runs exactly as built, processes the available data, produces an answer on scheduleand delivers a result that is inaccurate, biased, unreliable, or spectacularly unhelpful. In insurance terms, that is where AI performance failure becomes a separate problem from the cyber threats that usually grab the headlines.

IA Magazine highlighted this distinction in 2024, noting that an AI system can operate correctly from a technical standpoint while still failing to meet expected performance standards. Since then, commercial insurance has continued moving from broad assumptions toward sharper questions: Which policy responds? What event triggers coverage? Is the loss caused by a security failure, a professional error, a defective product, misleading content, or a broken performance promise?

The answers matter because businesses are placing AI inside customer service, hiring, underwriting, medical support, logistics, pricing, manufacturing, marketing, and financial decision-making. When the output is wrong, the financial damage may travel through several departmentsand several insurance policiesbefore anyone finds the off switch.

What Is an AI Performance Failure?

An AI performance failure occurs when an AI-enabled system does not achieve its intended or promised result, even though there may be no malicious attack and no conventional software crash. The model may be available, the servers may be humming, and the dashboard may be glowing a reassuring shade of green. The problem is that the output is not good enough for the job.

Common examples include:

• A customer-service chatbot invents a refund policy or gives incorrect account instructions.
• A hiring model produces discriminatory recommendations because of biased data or poorly selected variables.
• A medical-support tool misses a condition or generates an unreliable treatment suggestion.
• A fraud-detection model drifts over time and begins approving transactions it should block.
• An autonomous purchasing agent orders the wrong quantity, accepts unfavorable terms, or acts outside its delegated authority.
• A generative AI tool publishes defamatory, confidential, or infringing content.

These events differ from ransomware, unauthorized access, and data theft. A cyber policy is usually designed around defined security or privacy events. Performance failure may involve none of those. The model simply makes a bad decision at machine speed, which is efficient in the same way a conveyor belt is efficient when it is carrying every package toward the wrong truck.

Why Traditional Commercial Policies Struggle With AI

Insurance policies are organized around causes of loss, legal duties, covered damages, and carefully defined triggers. AI, meanwhile, is usually embedded across business functions rather than sitting inside one tidy risk category. A single AI incident can create pure financial loss, privacy liability, bodily injury, intellectual property allegations, regulatory scrutiny, reputational harm, and business interruption.

That creates what the market often calls silent AI risk: an existing policy neither clearly includes nor clearly excludes a particular AI exposure. Silence can look comforting at renewal, but it becomes much less charming when several insurers point toward one another after a claim.

The July 2024 CrowdStrike outage, although not an AI event, illustrated the scale of nonmalicious technology failure. One faulty update affected millions of Windows devices and disrupted airlines, banks, hospitals, and other organizations. AI dependency adds a similar accumulation concern. If thousands of companies rely on the same foundation model, cloud platform, data provider, or embedded agent, one upstream failure can produce many claims at once.

How Major Commercial Coverage Lines May Respond

Cyber Insurance

Cyber insurance remains important when AI is involved in a privacy breach, network security failure, unauthorized access event, cyber extortion incident, or covered system interruption. It may also respond when attackers use AI to improve phishing, impersonation, or malware.

The gap appears when the AI model underperforms without a covered cyber trigger. Standard wording may not pay for retraining a flawed model, validating corrupted outputs, replacing unsuitable training data, refunding customers, or repairing a decision process that was inaccurate but never breached. Contingent business interruption can also be narrow if the failed AI vendor does not fit the policy’s definition of a dependent provider.

Technology Errors and Omissions

Technology errors and omissions insurance is often the most natural home for third-party financial loss caused by a defective technology product or failed technology service. An AI vendor facing a client claim over inaccurate output, missed performance standards, or operational failure may look first to tech E&O.

However, coverage still depends on the definition of technology services, the wrongful-act trigger, contractual liability exclusions, performance guarantees, damages language, and any AI-specific endorsement. A promise that a model will deliver 99% accuracy is not automatically insured merely because it appears in a sales deck with an impressive gradient background.

Professional Liability and Traditional E&O

Professional firms may face claims when employees rely on AI while delivering advice or services. Accountants, consultants, lawyers, insurance agencies, architects, health care organizations, and financial professionals can all create exposure if an AI-assisted recommendation harms a client.

Traditional professional liability may respond when the allegation is a negligent act, error, or omission in professional services. Yet autonomous operation complicates the analysis. Was the AI output part of the insured service? Was a human required to review it? Did the firm disclose its use? Did the professional make an independent judgment, or merely forward the robot’s homework?

Commercial General Liability and Product Liability

Commercial general liability coverage is generally designed for bodily injury, property damage, and certain personal or advertising injury claims. It may become relevant when an AI-enabled product injures someone, damages property, or generates allegedly defamatory advertising content.

But CGL is not designed to cover every form of economic underperformance. Pure financial loss from bad advice or a failed model may fall elsewhere. The market is also moving toward clearer AI language. Verisk has filed optional generative AI exclusion endorsements for its ISO general liability program, giving insurers standardized tools to restrict specified exposures. Optional does not mean universal, but it does mean buyers should stop assuming that renewal wording will quietly remain unchanged.

Directors and Officers Liability

D&O insurance may be implicated when shareholders, regulators, or other claimants allege that executives misrepresented AI capabilities, failed to oversee a high-risk deployment, ignored known control weaknesses, or made misleading public statements about an AI strategy.

Regulators have already demonstrated interest in exaggerated AI claims. That means “AI washing” can become more than a marketing embarrassment. It can create investigations, defense costs, and management liability questions. Boards should know where AI is used, who owns the risk, what controls exist, and which statements about the technology can be supported by evidence.

Employment Practices, Media, Crime, and Other Policies

AI-related losses may also touch employment practices liability when automated hiring or workplace decisions allegedly discriminate; media liability when generated content creates defamation, privacy, or intellectual property claims; and crime insurance when deepfake impersonation leads to fraudulent transfers.

These policies contain very different triggers. A crime policy may require a qualifying fraudulent instruction. An EPLI policy may address a discrimination claim but not the cost of rebuilding the model. A media policy may cover defense of a defamation allegation while excluding intentional conduct or certain intellectual property rights. The technology is shared; the insurance logic is not.

The Market Shift: From Silent AI to Explicit Terms

Commercial coverage is developing along three tracks.

1. Clarifying Existing Policies

Carriers are asking more questions about AI use and refining definitions, exclusions, sublimits, and endorsements. Some may affirm limited coverage for specified events. Others may restrict autonomous systems, generative output, high-risk applications, regulatory violations, or performance guarantees. The result is not one universal “AI exclusion,” but a growing collection of policy-specific approaches.

2. Building Affirmative AI Coverage

Specialist products are emerging to address model error, hallucinations, drift, discrimination, privacy leakage, and related liability. Munich Re’s aiSure offerings demonstrate a performance-focused approach, while Armilla and insurance partners have developed AI performance warranties and affirmative liability products. Other markets are experimenting with AI-focused cyber, tech E&O, difference-in-conditions, and first-party response solutions.

A performance warranty and liability insurance are not identical. A warranty may promise a remedy if defined metrics are missed, such as reimbursement of a license fee. Liability insurance may address defense costs and damages arising from third-party claims. Buyers should understand which instrument they are purchasing instead of treating every shiny document as a magical umbrella.

3. Making Governance Part of Insurability

Underwriting is becoming more technical. Insurers increasingly want to know the model’s purpose, decision authority, data sources, testing history, monitoring process, vendor dependencies, fallback procedures, and level of human oversight.

Frameworks such as the NIST AI Risk Management Framework help organizations document reliability, security, transparency, accountability, privacy, and fairness. Strong governance does not guarantee a claim will be covered, but it can make the risk easier to evaluate and may support better terms. Insurance applications are slowly becoming less like “Do you use AI? Yes or no?” and more like a technical interview with follow-up questions.

A Practical AI Failure Scenario

Consider a regional financial-services company that deploys an AI agent to answer customer questions and recommend account actions. The agent works without interruption but gives hundreds of customers incorrect information about fees and withdrawal restrictions. It also reveals limited confidential information in several responses. Customers demand reimbursement, a regulator opens an inquiry, and a class-action complaint alleges misleading practices.

Several policies may be involved:

• Cyber: potentially relevant to the confidential information disclosure and response costs.
• Professional liability or E&O: potentially relevant to customer financial loss caused by incorrect service or advice.
• Media liability: potentially relevant if generated statements create covered publication claims.
• D&O: potentially relevant if management is accused of inadequate oversight or misleading disclosures.
• Affirmative AI coverage: potentially relevant if the policy expressly covers model underperformance or output liability.

The final outcome would depend on actual wording, facts, retentions, exclusions, notice requirements, and allocation rules. That is precisely the point: “We have cyber insurance” is not a complete AI risk-transfer strategy.

What Businesses and Brokers Should Do Before Renewal

Build an AI Inventory

List every AI system the organization develops, buys, embeds, or permits employees to use. Include shadow AI, vendor features added through routine software updates, and autonomous agents connected to email, payment, customer, or production systems.

Map Failure Scenarios to Policies

For each important use case, test at least five outcomes: incorrect advice, data disclosure, discriminatory decision, business interruption, and physical injury or property damage. Identify the policy expected to respond and the exact trigger that must occur.

Review Contracts Alongside Insurance

Vendor agreements may contain broad indemnities, warranty disclaimers, liability caps, data-use rights, and responsibility splits that materially affect recovery. Insurance cannot repair a contract that assigns nearly every risk to the buyer and limits the vendor’s liability to one month of subscription fees.

Examine Definitions, Exclusions, and Sublimits

Search policies and endorsements for terms such as artificial intelligence, algorithm, automated decision system, generative AI, model, software, technology service, professional service, dependent business, electronic data, and contractual liability. Small wording changes can move a loss from “probably covered” to “please meet our coverage counsel.”

Document Human Oversight and Incident Response

Define when humans must review output, when the system must escalate, who can disable it, how logs are preserved, and how customers are notified. Underwriters favor controls that reduce both frequency and severity, especially for high-impact decisions.

Where Commercial AI Coverage Is Heading

The market is unlikely to settle on one policy that covers every AI risk. AI is too widely distributed across operations, and its losses resemble too many established insurance categories. The more likely future is a coordinated insurance stack: cyber for security and privacy events, E&O for service failures, casualty for physical harm, D&O and EPLI for governance and employment claims, plus affirmative AI endorsements or specialty products for identified gaps.

At the same time, insurers must manage accumulation risk. A failure in one widely used foundation model could affect thousands of insureds simultaneously. That concern will encourage sublimits, dependency analysis, event definitions, dedicated aggregates, and technical monitoring. In other words, coverage may become more available for well-controlled, measurable AI use cases while becoming more restrictive for opaque, highly autonomous, or systemically concentrated exposures.

Experience-Based Lessons From AI Coverage Reviews

The following composite lessons reflect common issues that appear when businesses, brokers, technology teams, and legal advisers review AI risk together. They are not a description of one confidential client or claim.

The First Surprise Is Usually the AI Inventory

A company may initially report that it uses one approved chatbot. After a more detailed review, the list often expands to include AI features in customer relationship software, fraud tools, recruiting platforms, call-center transcription, marketing systems, document review, coding assistants, and vendor analytics. The risk manager thought the organization had one AI tool; the organization had an AI petting zoo.

This matters because each tool has a different function and consequence. A writing assistant that drafts internal summaries presents a different exposure from an agent authorized to issue refunds or change customer records. Coverage analysis improves immediately when the organization classifies systems by authority, data sensitivity, user population, and worst credible outcome.

The Second Surprise Is That “Failure” Means Different Things

Technology teams often define failure as downtime, corrupted code, or an unavailable service. Business teams may define failure as a missed revenue target. Legal teams focus on breached duties, misleading statements, discrimination, or regulatory violations. Insurance policies may require a wrongful act, security failure, occurrence, claim, or direct physical loss.

A productive review translates one scenario into every language. For example, “the recommendation model became less accurate” may also mean “customers received unsuitable advice,” “the company breached a service-level commitment,” “a regulator alleges unfair treatment,” and “the vendor must retrain the model.” Those are different losses, and they may not share a policy.

The Third Surprise Is Hidden in the Contract

Businesses frequently spend more time negotiating subscription price than liability allocation. Yet the vendor agreement may disclaim accuracy, reject warranties, exclude consequential damages, cap recovery, and require the customer to defend claims arising from its use of outputs. When a performance failure occurs, the buyer may discover that its legal remedy is tiny while its operational loss is enormous.

The strongest insurance reviews therefore include procurement contracts. A clear performance warranty, meaningful indemnity, audit right, incident-notification duty, and access to logs can be as important as the policy itself. Risk transfer works best when contracts and insurance point in the same direction rather than staging a three-way argument.

The Fourth Surprise Is How Much Underwriters Value Operational Detail

Generic statements such as “we follow responsible AI principles” are less useful than evidence. Underwriters respond better to documented testing thresholds, validation reports, red-team results, model-change controls, bias assessments, fallback procedures, and named decision owners. The question is not whether the company has a beautiful AI policy PDF. The question is whether anyone follows it on a Tuesday afternoon when the model starts behaving strangely.

Organizations that can show monitoring, logging, escalation, human review, and controlled deployment give insurers more ways to define and price the exposure. They may also detect problems earlier, reducing the size of a loss before insurance becomes necessary.

The Final Lesson: Run the Claim Before the Claim

A tabletop exercise can expose coverage gaps without the inconvenience of an actual lawsuit. Give the team a realistic scenario: an AI agent sends incorrect instructions to 10,000 customers, leaks confidential information, and causes a two-day shutdown. Ask who declares the incident, which insurer receives notice, which vendor preserves evidence, which policy pays first-party costs, and which policy defends customer claims.

If the answer is a long silence followed by “probably cyber,” the program needs work. The goal is not to predict every AI failure. It is to know who acts, what evidence is preserved, and where the financial response is expected to come from.

Conclusion

Commercial coverage for AI performance failure is shifting from assumption to specification. Traditional policies remain essential, but businesses can no longer rely on the idea that an AI loss will automatically fit inside cyber insurance. The same event may involve E&O, professional liability, general liability, media, employment, crime, D&O, and specialty AI coverage.

The winning strategy is practical: inventory AI use, model realistic failures, align contracts with insurance, review every endorsement, and document governance. AI may be new, but the insurance lesson is wonderfully old-fashionedread the policy before the robot makes a mistake.

Note: This article provides general educational information and is not legal, insurance, or risk-management advice. Actual coverage depends on policy language, endorsements, facts, applicable law, and insurer interpretation.