Over the past few years, artificial intelligence (AI) has evolved from a futuristic concept into a core engine of modern enterprise strategy. Organizations across every major industry are now using AI to automate complex workflows, augment customer service operations, drive predictive decision-making, and unlock greater operational productivity.
Understanding AI Risk
AI is not an easily defined category, as it spans several dimensions that traditional risk frames are not built to accommodate. The Gallagher report, Smart Systems, Blind Spots: Rethinking Insurance for the AI Era, found that the pace of AI adoption surpassed the insurance industry’s capacity to develop responsive products.
What makes AI unique is that risks associated with it emerge from the way systems learn, generate outputs, and make decisions to influence customers, employees, and business outcomes.
Modern businesses face several distinct risk vectors:
- Biased or discriminatory decisions
Automated recruitment, lending, or credit-scoring models trained on flawed data can produce systematically unfair outcomes. This can result in regulatory penalties, civil rights litigation, and damaged brand reputation. - Hallucinations and inaccurate outputs
AI models can confidently generate inaccurate or misleading information. A customer-facing AI assistant that provides incorrect financial, legal or medical guidance could create significant liability exposure. - Intellectual property and copyright disputes
Models trained on vast, unvetted datasets reproduce copyrighted material, exposing organizations to costly intellectual property infringement claims. - Data privacy violations
Unintentional exposure of proprietary trade secrets or personally identifiable information (PII) during model training can trigger regulatory investigations under frameworks such as the EU AI Act, the General Data Protection Regulation (GDPR), or state-level privacy laws. - Cybersecurity vulnerabilities
AI introduces new attack vectors, including prompt injection, data poisoning, and model extraction. Malicious actors can exploit these to compromise business integrity. - Financial losses
Autonomous trading agents or algorithmic pricing models operating at high speeds can execute erroneous transactions, leading to immediate financial losses.
Why Traditional Insurance May Not Be Enough
Existing coverage was not designed for current AI issues. Cyber policies were designed around data breaches and network intrusion. This does not cover an AI model making a biased hiring decision or fabricating a financial projection.
Professional indemnity and E&O policies assume a human professional exercised judgment. So, when an algorithm makes a mistake, an insurer may dispute whether the policy was intended to respond. For general liability policies, the focus is on bodily injury and property damage. If an AI program causes bodily injury, insurers can debate whether the policy applies.
Several incidents have caused some insurance companies to exclude AI from their corporate policies. For instance, Google was sued by a Minnesota-based company after its AI Overviews feature named it as a defendant in a lawsuit. This is just one case that highlights the growing concern around “silent insurance” when policies do not explicitly address AI-related risks. However, businesses may assume they are covered when they are not.
The challenge is compounded by the rapidly evolving legal landscape, with governments worldwide introducing new regulations.
The Rise of AI Liability Coverage
In response, a new category is beginning to take shape. This is AI liability insurance. These policies are designed to explicitly address the development, deployment, and use of AI systems. While offerings may vary across providers, AI liability covers incidents such as AI-driven discrimination claims, IP infringement from generative outputs, financial losses from automated decision-making, and regulatory penalties tied to AI non-compliance.
Insurers are approaching underwriting as they did with early cyber policies. They are starting cautiously, requiring detailed disclosure of how AI is used, existing governance controls, and how models are tested and monitored.
Beyond Insurance: Building Comprehensive AI Resilience
Insurance alone cannot eliminate AI risk and should not be a substitute for operational resilience. Organizations building genuine AI resilience are investing in:
- Formal AI governance frameworks
- Meaningful oversight of consequential decisions
- Ongoing model monitoring and auditing
- Employee training on responsible AI use
- Clearly articulated responsible AI principles
- Tested incident response plans specifically for AI-related failures.
A well-governed AI program will also make a business significantly more insurable, as underwriters increasingly price risk based on demonstrated controls.
Conclusion
AI has become one of the greatest sources of competitive advantage as well as a new source of liability. As regulatory scrutiny increases and AI-driven decisions become more consequential, executives must broaden their understanding of enterprise risk. Insurance should not be viewed as a substitute for governance, oversight or responsible AI practices.
For businesses increasingly relying on AI, the question is no longer whether AI creates liability risk, but whether existing insurance is equipped to respond to it.

21st Century ROAD to Housing Act (HR 6644) – This bipartisan, White House-endorsed bill addresses housing affordability by placing ownership restrictions on large institutional investors and expanding financing for homebuyers. Introduced by Rep. French Hill (R-AR) on Dec. 11, 2025, it passed in the House on Feb. 9 and in the Senate with changes on March 12. The bill went back and forth between the two chambers until both agreed to the final form on June 23. However, during that time frame, the President withheld his support, and the bill was enacted on July 11 by the 10-day rule (meaning it was neither signed nor vetoed by the President within 10 days of receiving the bill from Congress).
For the first time since 2022, the IRS is changing standard mileage rates in the middle of the tax year. If you track business, medical or moving miles, this matters. Starting July 1, 2026, the numbers go up, and your recordkeeping needs to get more precise.
With more than $57 trillion in mergers and acquisitions, according to the
No matter how well you know someone, you usually learn a lot more once you’ve traveled with them. We are all different in this activity, from people who prefer aisle seats over window seats, to Airbnb renters or hotel enthusiasts, to the outdoorsy versus museum aficionados.
Surprising as it may seem, Q4 is at your doorstep, knocking and asking for attention. What’s more, it’s that time of year when everything starts getting busy: kids go back to school, football starts, and then the holidays are just up ahead. During this time, you might also be hearing “cha-ching, cha-ching” as what lies ahead can be financially challenging. Consider a few ways to frame this and strategies to set up goals as you bring the year to a close.
For two decades, enterprise software has been built around a simple assumption: people log into multiple applications to retrieve information, make decisions, and complete work. A CRM, a project tracker, a business intelligence dashboard, a support ticketing system, and more. All this is because these applications operate in isolation.
Sunshine Protection Act of 2025 (HR 139) – The purpose of this legislation is to make daylight savings time (DST) permanent for most of the country. States and territories presently exempt from DST may choose the standard time for those areas. This latest version of the bill was introduced by Rep. Vern Buchanan (R-FL) on Jan. 3, 2025. The Act passed in the House on July 14 and faces a mix of cross-aisle opposition and support in the Senate.
This accounting and tax method refers to a treatment used by the Internal Revenue Service (IRS) to obtain tax remittances on sales of depreciated property. Understanding how it works is essential for filers to make the most of it.
A county in Michigan was owed about $2,200 in back taxes. To collect it, the government took a home worth close to $200,000, auctioned it for a fraction of that, and called the matter settled. The family is now putting a simple question to the Supreme Court: when the state sells your house over a small debt, does it owe you the real worth of what it took or only whatever the auction happened to fetch?