When conducting a risk assessment, weighing how a business uses AI is increasingly paramount. Directors may face claims related to failures in AI governance or "AI-washing" (i.e., overstating the use of AI in products or services), while manufacturers may face product liability claims for AI-enhanced products.

Research indicates that the global AI insurance market size is expected to surpass around 141 billion dollars by 2034. Broadly, there are two ways AI risks may be covered by insurance:

• Silent AI cover is where coverage of AI risks is already provided via existing standard policies such as professional indemnity, business interruption, D&O, product liability and employers liability insurances. However, as the AI landscape develops and insurers' risk appetites adapt, it is possible that AI-specific exclusions may be implemented, reducing the cover available.
• Affirmative AI cover provides standalone or bespoke insurance protection for AI risks. The insurance market is showing signs of evolution in this space. Recently, Armilla Insurance Services launched an AI liability insurance product underwritten by Lloyd's underwriters: one of the first to offer affirmative coverage for unique AI-related losses. Those businesses adopting AI may need to seek affirmative cover, including where exclusions might be introduced to "traditional" insurance policies, limiting the protection they provide.

Life sciences companies face particularly acute AI insurance considerations. Companies developing AI-based medical devices, for example, could face product liability exposure if the AI fails to detect a condition or produces a false positive. Traditional product liability insurance policies may not adequately address AI-based software as a medical device, particularly where systems continuously learn post market.

Digital health companies using AI for patient monitoring or predictive analytics may face claims arising from algorithmic errors, data breaches, or regulatory non-compliance and will need to evaluate whether traditional insurance coverage for claims of this sort cover AI-related risks. Pharmaceutical companies deploying AI in clinical trials or drug discovery should scrutinize whether existing insurance cover extends to AI-related failures or regulatory enforcement.

The intersection of AI risk with data privacy regulations and sector-specific requirements creates additional complexity. Here are some practical considerations to ensure a proactive approach:

• Identify AI risks: Assess current and future threats; distinguish where AI use creates a new risk, or amplifies an existing risk.
• Policy and training: Implement robust AI governance processes and internal training.
• Insurance review: Evaluate insurance coverage when purchasing or renewing policies to address identified AI risks; focusing especially on any new AI-related exclusions proposed by insurers and consider whether affirmative cover is required.