Artificial intelligence has moved past the “someday” conversation in insurance. It’s already helping carriers and insurance organizations analyze information, automate repetitive work, improve customer interactions, detect patterns, and speed up routine processes.
Recent reports indicate that AI is widely used across insurance markets. For example, 88% of auto insurers and 92% of health insurers surveyed say they use, plan to use, or are considering AI or machine learning. Still, using AI is only part of the story.
The more important question for insurance leaders is where AI should actually make work easier and where people should remain firmly in the driver’s seat.
AI Fits Best Where the Work Is Repetitive
Think about the amount of time insurance teams spend finding information, organizing documents, summarizing records, routing requests, reviewing large datasets, or preparing routine communications. These are exactly the kinds of areas where AI creates meaningful operational leverage.
AI is already being used throughout the insurance lifecycle, including marketing, sales and distribution, underwriting, pricing, policy servicing, claims management, and fraud detection. That doesn’t necessarily mean handing an entire process over to an algorithm.
For instance, a claims team could use AI to sort through documents and highlight key information before a person reviews the case. In contrast, a service team might use AI to produce a summary of a lengthy customer interaction before the next representative takes over. The goal is to remove issues from the workflow.
AI Is Also Good at Finding the Signal in the Noise
Insurance organizations generate enormous amounts of information. The challenge is figuring out what deserves attention.
AI identifies patterns, exceptions, inconsistencies, and unusual activity across datasets far faster than a person can manually review thousands of records. That makes AI particularly useful as an early-warning system.
Instead of asking AI to make the final call, organizations use it to tell employees, “You should probably look at this.”
Where AI Doesn’t Fit is Unsupervised High-Stakes Decisions
Speed becomes much less valuable when nobody explains why the organization makes a decision. AI-supported decisions must still comply with applicable insurance laws and regulations, including requirements regarding unfair trade practices and unfair discrimination.
Risks include inaccurate results, discrimination, data vulnerability, and limited transparency or explainability. That should influence where companies draw their AI boundaries.
If an AI-assisted process could significantly affect a customer’s coverage, price, claims, access, or treatment, human oversight and clearly defined controls become much more important. AI informs the decision without automatically owning the decision.
Generative AI Needs Its Own Guardrails
Tools that generate text, summaries, recommendations, code, images, or other content introduce another layer of risk that needs additional attention to human review, tracking, documentation, and management oversight when organizations use generative AI. That matters for something as simple as an AI-generated customer email.
An AI-generated draft might save an employee five minutes, which is helpful. But if it provides incorrect details about coverage, benefits, eligibility, a rule, or the claims process, those five minutes could end up costing a lot.
Use AI to create the first draft, but keep knowledgeable people responsible for what ultimately reaches the customer.
Don’t Automate Your Way Out of the Human Experience
The other boundary that doesn’t show up neatly on an efficiency spreadsheet is trust. Insurance customers often contact their companies when something important is happening in their lives, with a health concern, a damaged home, an accident, a financial decision, or an unexpected bill.
Sometimes the most efficient interaction isn’t the best interaction. AI gathers information before the conversation.
It summarizes what happened previously and gives an employee faster access to relevant information. But empathy, judgment, reassurance, and context remain deeply human strengths.
Technology should help employees spend more time doing those things, not less.
The Better AI Question
The strongest AI strategies probably won’t begin with, “What can we automate?” A better starting point is, “Where are our people spending time on work that doesn’t require their judgment?”
That shift changes the entire conversation. Responsible AI management revolves around four ongoing functions: Govern, Map, Measure, and Manage.
This framework stresses that AI risk management should happen throughout the system’s life, not just as a one-time review. Modern insurance operations don’t need AI in every area.
It should be used in the areas where it has the greatest impact. Then have the AI carry out sorting, summarizing, searching, detecting, organizing, and speeding up tasks.
Let individuals handle judgment, accountability, exceptions, relationships, and decisions when context is important because it is in those areas that AI becomes more than just another piece of technology. It becomes a way to give your team time to focus on the work that actually needs them, something much more valuable.
The insurance industry needs to adapt quickly and remain open to new ideas. Agility Holdings Group makes investments in InsurTech, HealthTech, and other companies that are aimed at improving care and outcomes.
If you’d like to learn how we help your organization innovate, achieve your objectives, and stay ahead in the ever-changing insurance industry, feel free to connect with us on LinkedIn. Contact us today to start.