Today, as inflation strains both household and commercial budgets, insurance companies are beginning to explore ways for artificial intelligence (AI) to address rising operational and financial challenges. The perfect storm of premium increases, claims severity, and rising customer expectations around digital experiences call for agile solutions. Despite this, many insurers are still in the early stages of their digital transformation – the “finding out” phase about AI’s capabilities and applications.
AI-driven automation presents a promising opportunity to streamline workflows, reduce manual effort, and enhance service delivery – and knowing where to start is as important as selecting a solution. As insurers explore ways to remain competitive and responsive amid economic uncertainty and widening affordability gaps, now is the time to build foundational knowledge about AI’s potential.
Learning how AI can support cost control, effective decision-making, and long-term adaptability is the first step toward preparing for a more resilient and efficient future.
What AI Is Used for In Insurance
The examples below illustrate areas where AI is having the greatest impact on insurance operations.
1. Processing and analyzing large amounts of data at high speed
AI systems excel at ingesting and interpreting massive datasets in a fraction of the time it would take human experts. In insurance, this means faster processing of policyholder information, claims data, sensor inputs (e.g., telematics), and unstructured content like emails or scanned documents.
Example: An AI engine can analyze thousands of incoming claims after a natural disaster, prioritize them based on severity, identify duplicate entries, and flag inconsistencies – all within minutes – allowing adjusters to focus on complex or high-risk cases first and accelerating claims settlement to help policyholders rebound from a loss sooner.
2. Making predictions based on historical information
Machine learning models can identify patterns from historical data and use those patterns to forecast future outcomes. This is particularly useful for underwriting, risk assessment, and fraud detection.
Example: By analyzing past claims data, an AI model can predict the likelihood of a new auto policyholder filing a claim within the first year, helping underwriters price policies more accurately or trigger additional risk review steps.
3. Greater objectivity and consistency
AI systems apply predefined logic uniformly across all cases, eliminating inconsistencies that can arise from human interpretation.
Example: In underwriting, an AI model can assess applications using the same risk factors and scoring criteria every time – such as credit scores, loss history, and property characteristics – ensuring that all applicants with similar profiles receive the same evaluation and rate offer.
4. 24x7 automation
AI-powered agents can operate around the clock, completing repetitive or rules-based tasks without interruption.
Example: An AI agent can monitor an inbox for FNOL (First Notice of Loss) submissions, extract relevant details, prefill the claim in the system, and assign it to the appropriate team – all in real time, even outside regular business hours.
5. Process optimization
AI can identify workflow inefficiencies by analyzing process data and recommending improvements or reconfiguring steps dynamically.
Example: A worker's comp carrier experienced a 30% reduction to claim cycle times by using AI to eliminate manual processing of data for complex or high-value claims.
6. Improving user experiences
AI enables P&C insurers to use customer’s data to deliver personalized experiences for greater satisfaction and loyalty.
Example: A homeowners insurance carrier uses AI to personalize policyholder communications based on their location, third-party CAT risk exposure data, and other information (e.g., claims history, property features, flood zone, etc.). Using this data, carriers can automatically send tailored preparedness tips, coverage reviews, and recommendations to update limits or add endorsements. This personalized outreach not only boosts engagement but also helps reduce claim severity through early risk mitigation.
Potential Drawbacks of AI for Insurance
While AI presents significant benefits, it also comes with important limitations and trade-offs that insurers must understand before making it a cornerstone of their operations. The points below highlight critical considerations and potential pitfalls of overreliance on insurance AI.
1. Lack of critical thinking skills
AI excels at pattern recognition, automation, and data-driven decision-making – but it lacks human intuition, judgment, and creativity.
Example: In a complex liability claim involving multiple parties and ambiguous policy language, an AI model might struggle to interpret legal nuances or foresee long-term reputational consequences. A seasoned adjuster, on the other hand, can weigh unique circumstances, ask clarifying questions, and craft a resolution strategy with empathy and strategic foresight.
2. Significant cost outlays
Developing, training, and maintaining AI systems – especially advanced ones – requires significant investment in technology, talent, and ongoing oversight.
Example: A mid-size regional carrier looking to automate its underwriting process may face high upfront costs for licensing AI platforms, integrating them with legacy systems, and hiring specialized staff (e.g., data scientists or AI governance leads). These costs can be a barrier to entry, especially for firms with tight IT budgets.
3. Building Large Language Models (LLMs) is resource intensive
Building and training LLMs from scratch requires massive volumes of data, computing power, and financial resources that go well beyond the capacity of most individual insurers.
Example: A carrier interested in creating a custom LLM for their specific use cases (e.g., policy summarization, coverage recommendations) can expect to face steep financial and engineering demands. Instead, most opt to fine-tune existing models (e.g., ChatGPT and other public, non-insurance-specific models) rather than build from the ground up.
4. Building Infrastructure
AI systems demand robust digital infrastructure, including cloud computing, scalable data storage, real-time APIs, and cybersecurity protections.
Example: An insurer attempting to deploy real-time fraud detection during claims intake must ensure that its core systems can handle high data throughput, connect with third-party data sources, and maintain uptime – something many legacy platforms were never designed to support.
5. Lack of transparency
Many AI systems, especially deep learning models, can operate as "black boxes," making it difficult to understand how decisions are made.
Example: If a customer is denied coverage or a claim is flagged as suspicious by an AI system, the insurer may struggle to explain the rationale behind the decision – undermining customer trust and increasing regulatory scrutiny.
6. Quality of training data and potential for bias
AI is only as good as the data it learns from. Poor, incomplete, or biased data can lead to inaccurate or skewed outcomes. If the underlying data reflects historical inequities, the AI may unintentionally perpetuate or amplify those biases in pricing, underwriting, or claims decisions.
Example: If an auto insurance AI model is trained primarily on urban driver data, it may perform poorly when evaluating risks for rural drivers, leading to unfair pricing or increased underwriting errors.
A way to avoid the stress of these drawbacks is to partner with an insurance-specific AI provider like Roots.
AI presents powerful opportunities for the insurance industry by delivering rapid data analysis, predictive insights, 24/7 task automation, process optimization, personalized customer experiences, and other capabilities. These advancements can drive efficiency, reduce operational costs, and improve service delivery in a challenging economic climate.
However, realizing these benefits requires careful navigation of key challenges.
High implementation costs, infrastructure demands, limited transparency, and potential for biased or ethically questionable outcomes must all be addressed. Additionally, regulatory scrutiny and the need for quality data and explainable models add layers of complexity.
Importantly, AI is not a set-it-and-forget-it solution – ongoing human oversight is essential. Internal teams must regularly monitor outputs, retrain models, and ensure decisions align with business goals, compliance standards, and ethical expectations. As insurers begin exploring AI adoption, balancing innovation with accountability, fairness, and continual human input will be critical to building sustainable and trustworthy AI strategies.
Curious how insurers are putting Roots to work? Check out our case studies to see insurance-specific AI in action.