Chaz Perera is the Co-Founder and CEO of Bevaya, an AI agent platform built for insurance.
Insurance companies have been used to buying software for so long that when it comes time to transition over to insurance AI tools, they continue their same approach. You evaluate options, select a vendor, implement, go live and consider yourself done and move on to the next initiative.
That works fine for software. Software either does what it’s supposed to do, or it doesn’t. You know when it breaks, and you know quickly.
But AI doesn’t work like that. When it fails, it often does so unnoticed. And it gets better or worse depending on what you do after you deploy it. It’s not a product you install, but rather an operational capability you manage over time. And organizations that haven’t figured out the difference are going to discover it the hard way.
In insurance, the stakes make this more urgent. A silent error in underwriting is years of exposure shaped by bad data. An unnoticed failure in claims processing isn’t just a bad user experience. It’s a policyholder affected at a moment of real need. The industry can’t afford the software mindset.
Why Insurance AI Implementation Failures Go Unnoticed
With software, failure is visible. The system crashes, or the report doesn’t generate. You know something is wrong because things stop working.
With AI, failure is quiet, meaning the system keeps running. Output keeps flowing so everything looks normal. The error is in the content of what the system produced, not in whether it produced something. And because AI output looks confident and well-formatted whether it’s right or wrong, there’s no natural signal telling anyone to look twice.
When a claims adjuster is unsure about something, they say so. They add a note, flag the file or ask for a second opinion. That signal travels with the work and tells the next person where to focus. AI doesn’t work that way. It produces the same clean, confident-looking output whether it understood the document perfectly or made a string of uncertain inferences on something it had never seen before. There’s no flag or note. The reviewer sees a finished result and has no way of knowing which situation they’re in.
Then on top of that, there’s drift. I’ve seen deployments that looked great at launch performing very differently six months later. AI keeps evolving post-deployment, and insurance is not a static industry. The documents an insurance organization encounters in production aren’t always the ones the model trained on. Policy language and document formats change. The data an AI encounters months later into production often looks different from what it trained on. When that happens, accuracy drops. And without a monitoring layer in place, no one knows until an error surfaces that’s already worked its way downstream.
What An Operational Mindset Actually Looks Like
The software playbook doesn’t work for insurance AI implementation because AI is not static. Software does what it was programmed to do, indefinitely. AI performs based on the data it sees, the workflows it’s embedded in and the feedback it receives after deployment. If you treat it like a one-time install, you get one-time results, followed by slow and invisible degradation. But if you treat it as an ongoing operational capability, it will continue to improve.
Insurers have to make a specific shift: They need to stop treating deployment as the finish line and start treating it as the starting point.
That means you need to define accuracy requirements before go-live, per workflow, based on what happens when the system is wrong in each one. For example, initial submission triage can tolerate more variance than final coverage determination. Getting that calibration right up front determines whether the verification layer actually catches the right things.
It means you need to build confidence scoring into the output at the field level. A system that tells you it’s confident about the claim number and policy dates but uncertain about the coverage interpretation on page four is giving you something actionable. An aggregate accuracy score isn’t.
You need to incorporate human-in-the-loop within workflows, meaning route work to humans as a checkpoint. Design that in from day one, rather than bolting it on later. When the AI is confident in its output, let it move through the workflow automatically without requiring any human review. Anything below the threshold goes to a human reviewer before it touches a downstream system. Structuring your system in this way will help you catch the right things at the right points.
And an extra important piece is to understand that every correction is a training event. When a reviewer adjusts an AI output, that feedback improves the model on the specific document types and edge cases that appear in your operations. The system gets more accurate over time, on your data, for your book of business. Organizations that build this in from the start avoid the problems that show up in month six and have a model that compounds. The gap between their performance and an organization that plugged in a general model and walked away widens every month.
The Insurance AI Implementation Question Worth Asking
If your organization is running AI in any part of insurance operations, one question is worth sitting with: Are you managing it like software or like an operational capability?
In my experience, a lot of the time the answer is software. You implemented it, and the main interaction with it is checking whether it’s still on. The accuracy gap is probably already there. You just don’t have visibility into it yet.
The fix is adding the operational layer that should have been there from the start. Add in confidence scoring, traceability, human-in-the-loop routing and a feedback loop that makes the system better over time. None of it is complicated. It just requires treating AI as something you run, not something you bought.
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