While AI is moving quickly in insurance, trust is struggling to keep pace. Recent research found that 90% of senior insurance professionals in the UK and Europe expect end-to-end claims administration to be managed by AI within the next 24 months. Yet, 87% are concerned about bias or unfair outcomes, and 99% believe there should still be some level of human oversight.
This contradiction is at the center of conversations around deploying AI in insurance. Firms aren't reluctant to introduce the technology. That is happening. What they are less certain on is how to govern it.
Why insurance faces a unique AI challenge
Insurance may be similar to other industries in that it is exploring and actively deploying AI, but where it differs is regulation. By its very nature, insurance is a highly regulated industry, and for good reason. Each decision affects customer outcomes directly and must operate within strict expectations around fairness and transparency.
The key challenge for insurers deploying AI lies in its probabilistic nature. AI identifies patterns, generates outputs and makes predictions based on statistical inference. That is great in areas such as fraud detection and data extraction, but regulated claims decisions require something more rigid. Firms must be able to show and explain exactly how and why a decision was reached. Regulators will not accept "our AI decided" as a sufficient explanation, nor should customers. This is why 39% of the industry say that transparent algorithms and decision logs would help reassure them about the use of AI in insurance.
The issue facing insurance firms is whether they can deploy AI within these regulated processes without creating unacceptable operational, reputational or compliance risks.
The governance conundrum
Nowhere is this tension more obvious than in claims. The research found that the industry feels least comfortable automating claims submissions, with 40% identifying it as an area they would not feel comfortable handing over to AI, ahead of underwriting recommendations and customer interactions.
Claims decisions are among the most sensitive moments in the insurance workflow. They need to be consistent, transparent and capable of being mapped back to explicit rules and policy terms.
This doesn't mean that AI has no place in claims. Used properly, AI can extract structured data from unstructured sources, detect anomalies and flag potentially fraudulent claims, enrich claims data with external sources and prioritize cases for human or automated rules-based assessment.
But when it comes to claims decisions, the only compliant way to leverage AI is to use a rules engine. Because these are fully configured and controlled by the insurer, rules engines remove the unpredictability of machine learning models and instead apply deterministic, auditable business logic to every claim.
As a result, each decision is documented against explicit rules. This ensures transparency, compliance and reinforces customer trust in the fairness of the insurer and the industry.
The moment AI moves from assistant to judge, firms risk crossing a line that governance frameworks are not yet ready to support.
AI deployment with accountability
While the industry is keen to advance the use of AI, it's clear that compliance teams do have genuine concerns. This is driving a focus on how to make AI adoption viable in practice, which is showing up in procurement decisions.
Insurers are willing to compromise on cost to find the right AI solutions, prioritizing ease of integration and strong vendor support. In fact just 10% of senior professionals said cost would strongly influence their decision.
This is the sign of a necessarily cautious market. For all the noise around AI, insurers are becoming more discerning. They are not just asking only what a system can automate but whether it can be trusted in a regulated setting, whether it can integrate with existing workflows and whether it gives them enough visibility and control to stand behind the outcomes it produces.
Those firms driving genuine innovation in insurance won't be the firms making the boldest claims about total automation but those building systems that are fit for purpose within this highly regulated industry. In practice, that means combining AI with deterministic rules, strong oversight, clear escalation paths and audit-ready decision making. It means using AI to improve speed and efficiency without surrendering control over outcomes that need to remain consistent and accountable.
Insurance should absolutely embrace AI – the gains are too significant to ignore, and the appetite across the market is undeniable. The real contest is not whether the insurance industry can deploy AI quickly, it is whether governance can keep pace as it does.
