AI is adding tremendous benefit to the insurance industry, but claims adjusters dislike it more than workers in any other occupation. Between June 2025 and May 2026, 98% of adjuster comments about AI in Glassdoor reviews were negative. For comparison, the figure for all occupations was 53%. Insurance overall came in at 81%, the third-most AI-critical industry Glassdoor tracked.
But AI technology isn't the problem. Routine servicing, such as billing questions, ID cards, policy changes and status checks, already completes on its own more than 85% of the time. Also, if you read those comments on Glassdoor closely, you'll see that the complaints read mostly like product reviews. One reviewer said they spent 15 to 20 minutes correcting AI-generated mistakes "because supervisors don't verify anything." Another wrote that the new tools left them "dubious about the accuracy of some claim activities."
Unrelated factors add to the on-the-job frustration: Experienced adjusters are leaving their positions. They are quitting at about 20% per year, with each departure taking roughly six years of experience with them, according to a September 2025 Deloitte study. The Bureau of Labor Statistics projects 21,600 openings a year in the same occupation through 2035, and by its own accounting, every opening exists to replace someone who has transferred out or left the workforce. The desks stay full while the people at them keep starting over, learning how to use AI software products from scratch. Meanwhile, 58% of adjusters who remain in place spend more than a fifth of their day on manual data entry and compliance, per Verisk.
That's the day-to-day work experience for insurance adjusters, while special cases such as wildfires and storms add still more stress. One of our clients absorbed 14 times its usual claim volume in the two weeks between hurricanes Helene and Milton. No company can staff for that.
Compounding matters, carriers are hiring experience rather than developing it. Job posting data show junior adjuster listings are down by half since early 2024, while listings for senior adjusters run roughly 80% above their 2017 level. That means everyone is recruiting from the same thinning pool of veteran adjusters, but no one is replenishing it from below.
While AI is the logical response to that shortage, most carriers are implementing it on an ad hoc basis. A poorly thought-through implementation can cost more in claims than it would in most other industries, too, because that policyholder on the other end of the call is likely experiencing the worst thing that's happened to them all year.
Mistake 1: Bolting AI Onto the Call Center Instead of the Core
Five percent of the value of an AI-managed phone call comes from conversations with policyholders. The other 95% is the orchestration happening underneath: pulling the policy, checking coverage, opening the claim, dispatching the vendor and writing all of it into the system of record. Carriers that buy voice AI as a contact-center feature are buying the 5%.
If that stripped-down AI agent can't reach Guidewire, Duck Creek or Snapsheet directly, it doesn't relieve the adjuster caseload because, while it can hold a conversation, it doesn't resolve anything. In the deployments we've measured, routine status calls consume more than 40% of an adjuster's time. An AI agent that answers calls but just hands the work to a (likely overworked) adjuster isn't adding much value. That adjuster must still take the message, rekey it and then work the open loop.
These are the adjusters writing the anti-AI Glassdoor reviews and searching LinkedIn for new opportunities.
Mistake 2: Skipping Governance to Move Faster
Governance allows pilots to become deployments. An agent with no way to flag when it's unsure, no rule for when it hands off to a person and no record of what it said hides risk. Nobody sees the mistakes until a market conduct examiner requests the call records.
Claims handling accounted for 65% of complaints filed by consumers with state regulators in 2024, according to ValuePenguin's analysis of NAIC closed-complaint data. Delays alone were 22%. While hallucination rates below 1% are achievable in production, that's still not zero.
The question a chief claims officer should ask a vendor is: What happens the moment the model gets something wrong? That officer should also find out whether confidence is scored in real time, and whether a low score triggers a warm transfer with the full context attached. Or does it just dump the policyholder back into the queue? Also: is every action logged against authority limits, and is there a kill switch?
Mistake 3: Starting With the Hardest Use Case
Carriers go to claims first because that's where the pain is. It's also the hardest problem to automate. FNOL, or first notice of loss, requires an AI agent to collect regulated information from someone (who is often upset), while working across several systems to open the claim. In our deployments, our agent completes the FNOL 45% to 55% of the time. Routine servicing, such as billing questions, ID cards and policy changes, completes more than 85% of the time.
We initiated an FNOL activation once at a carrier's request. The results came in under half, and it took nearly a year to rebuild the case for automating anything else with that company. That experience points to a broader risk because claims draw more internal scrutiny than any other function at a carrier.
A project's first result carries unusual weight, and if that number looks like a failure, leadership grows reluctant to keep funding the work, so the project ends before the team has a chance to show what the AI tool can do.
Start with servicing. The first result looks like a win, which gives a carrier something concrete to point to before asking adjusters to trust anything that's harder.
The Common Thread: Treating AI as a Bolt-On Feature
All three of these mistakes share a root cause: they don't treat AI as infrastructure.
Voice AI purchased as a contact-center bolt-on feature never reaches the systems of record. Pilots that skip governance don't ever earn the right to scale, and projects that start with the hardest use case never survive long enough to reach the easier ones.
These are sequencing failures, not model failures. An AI agent can handle routine calls at a volume no hiring plan can match while the adjuster speaks with policyholders who genuinely require a person. Those conversations, the ones where someone is having the worst week of their year, begin with the claim already open and the context already assembled.
AI can lighten the adjuster's workload if the work is sequenced properly. It will give back the 40% or more of the day now eaten up by routine status calls. Negative sentiment toward AI will then turn for the better once adjusters see that their Monday morning queues are shorter.
