Editorial
As artificial intelligence sweeps across every industry, insurance is no different. There are places AI is already being integrated, places where it holds huge potential and also areas where experts urge caution.
AI tools — such as ChatGPT-style large language models (LLMs), generative AI, natural language understanding, as well as audio and image recognition — are all thought to hold keys to the future of the insurance industry.
The best analysis presumes that as these tools present opportunities, they will be rolled out in phases, rather than as a tidal wave of change.
How Insurers Are Using AI Today
In the current phase, AI tools are being used in two major ways.
First is to aid human decision-makers, picking up much of the mundane and repetitive tasks that are prone to errors and omissions. AI tools can research policy language and summarize huge documents and data sets, enabling the human decision-makers to work more efficiently. The rule of thumb is that the first jobs to be most impacted will be the most tedious and repetitive ones.
The other area AI is currently being rolled out is with the tasks that have most recently been outsourced to call centers, such as front-line customer service. AI voice assistants are able to do many routine tasks without being transferred to a human, and web bots handle increasingly more complex chats to engage and gain customers.
Sales reps and agents can also now leverage AI tools that are integrated into their customer relationship management software to identify warm sales leads by analyzing demographics or buying patterns that might suggest future sales opportunities.
Related Article: AI and Human Teams: Smarter Contact Centers and Better Customer Service
What’s Coming Next for AI in Insurance
What comes next are more advanced and increasingly autonomous use cases, several of which are already starting to appear across the industry.
AI for Quality Assurance
The next phase of the AI rollout will likely be where humans are feeding their work back into AI as a quality check tool. It could, for example, read coverage decisions and policy briefs to ensure they are compliant and adhere to best practices.
AI-Assisted Decision-Making
After that is when AI might be ready to make suggestions for final decisions, such as what should be covered or excluded and what rates should apply. At this point the AI will essentially be moving toward making the decisions with human oversight. AI could conceivably be writing briefs and coverage opinions, with humans giving the final sign-off.
While AI is still susceptible to hallucinations, humans will likely have to stay in the loop for the foreseeable future. Guardrails are important right now because courts have been holding things those AI tools say as legally binding to the company.
AI Claims Processing
From a customer-facing perspective, one of the use cases with the most potential is in claims processing. Already, claims are often started in a company’s mobile app. But further AI integration could take that even further. The customer could take a picture of a damaged vehicle, or a property after a disaster, and AI image recognition could get to work.
The automated tool could analyze what was damaged and to what extent, and then cross-reference real-time parts and repair pricing and could conceivably return a near-real-time settlement offer. Along with the uploaded picture, the AI agent could also tap into any onboard sensors or IoT devices to paint a nuanced picture of what was damaged and what is required to repair it.
Then, with the rich database of damage data, AI tools could then mine that historical data and come up with suggestions to pass along to manufacturers, presenting design changes or damage-prone components that might benefit from re-engineering.
AI-Backed Accident Avoidance
AI could also be integral to avoiding crashes to begin with, along with rewarding good driving in real time. An AI agent could be integrated into a vehicle’s navigation system and tied back to their insurance policy. Then, each morning before the commute, the driver would punch in the destination and the AI agent could return not only the best route, but also apply different premium amounts depending on the route chosen. Routes with less traffic, or those that are less prone to accidents, could be rewarded with discounts.
Pattern and Fraud Detection
The other place AI could shine is in evaluating the company’s claims patterns, identifying and flagging potential fraudulent claims that might have slipped past a human auditor.
This is not all to say that AI tools are ready to be deployed widely throughout the insurance industry. Because insurance is so heavily regulated, and the rules sometimes differ widely between states, rules regarding when and how AI can be integrated need to be standardized.
Related Article: Reimagining Traditional Workflows With AI Agents
Balancing AI Risk and Opportunities
One of the most perilous areas of AI integration is its potential for bias. Because much of what goes into an AI decision is done inside a black box, it wouldn’t necessarily be clear if the premiums or coverage decisions were based on assumptions that discriminate against a protected class, such as race or gender.
The insurance industry’s future with AI will almost certainly be transformative, but it won’t be instantaneous. The technology is already helping professionals work faster and smarter, and its next phases promise even more sophisticated support, from real-time claims analysis to proactive risk reduction.
But the industry’s uniquely high stakes demand a careful, deliberate rollout. Regulators, carriers and technologists will need to ensure these tools operate transparently and without bias, and that human judgment remains in place where it matters most.
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If insurers can strike that balance, AI won’t replace the expertise at the heart of the industry, it will enhance it, opening the door to more efficient, fair and responsive coverage for everyone.
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