AI can understand a customer who says, “Someone backed into me in the grocery store parking lot.” That’s impressive. But here's a harder problem: Can it tell whether HO-1234567, 1234567-HO and 001234567 are valid policy numbers?
Insurance is full of small variations like these. Policy numbers follow different structures. Claim numbers change by line of business. Dates arrive in different formats. One system calls someone an “insured,” another calls them a “policyholder” and a third stores them under an internal code nobody outside the company would recognize.
A human insurance professional barely notices most of this. After enough time on the job, people learn the patterns. AI doesn't get that luxury. When AI moves from answering questions to taking action inside an insurance operation, those seemingly insignificant details become a very big deal.
The hard part isn't always the AI
There's a tendency to think about insurance AI implementation as a model problem.
How accurately does it understand the caller? Can it interpret an email? Can it extract information from a document? Can it carry on a natural conversation?
Those capabilities matter. But they are only part of what has to happen when AI goes into production. Consider something as mundane as a policy number. One insurer might use: ABC-1234567. Another might use: 1234567-ABC. Another might use a 10-digit numeric identifier with leading zeros: 0012345678.
Even within the same insurer, structures can vary because of acquisitions, legacy systems, product lines or policy administration platforms.
To an experienced employee, those differences may be second nature. To an AI system attempting to retrieve the right policy, they're rules that have to be understood, tested and handled correctly, because being almost right isn't particularly useful when you're looking for someone's insurance policy.
Insurance is full of tiny exceptions
Take a moment to think about what happens during a first notice of loss:
- A policyholder might give an address as: 1825 SW Market Street
- The policy administration system might store: 1825 S.W. Market St.
- A third-party data source could return: 1825 Southwest Market Street
A human recognizes the same address instantly. A production AI system needs to know how to normalize, match and validate the information without accidentally associating the claim with the wrong property.
Now multiply that problem across names, dates, VINs, phone numbers, vehicle descriptions, coverage codes, cause-of-loss categories and every other piece of information involved in a claim.
Then add the insurer's own operating rules and terminology. “Effective date” sounds straightforward until one system calls it an inception date. Another database uses EFF_DT. An acquired book of business may carry information in a legacy field that doesn't match either convention.
None of these are spectacular AI problems. They're insurance problems, and there are thousands of them.
A demo doesn't have to know any of this
This is one reason insurance AI can look much easier in a demonstration than it does in production: A demo gets a clean scenario.
The policyholder provides the expected information > The policy is easy to find > The claim follows the anticipated path > The systems cooperate.
Real insurance operations rarely work that way. There are countless details that can go wrong:
- Someone gives you the name they go by instead of the legal name on the policy.
- A caller says “twenty-four dash oh-six-seven-eight” when the system expects 2400678.
- The policy was migrated from a legacy system.
- The address has changed.
- Two customers have nearly identical names.
- The vehicle on the call isn't the vehicle the system expected.
- A required field is blank.
- The claims platform times out halfway through the transaction.
Individually, these aren't extraordinary situations. Collectively, they are the environment in which insurance AI has to operate. That's why AI engineers have to anticipate what might go wrong, and what will happen when service scenarios don’t go as expected.
Your data doesn't have to be perfect to get started
Insurers know their data is complicated, and many see that as a reason to hesitate on AI.
In a 2026 AM Best survey of more than 150 rated insurers and MGAs, 45% identified data readiness as a top challenge to AI deployment, ahead of security and privacy at 43% and legacy system integration at 41%.
Those concerns are understandable. Decades of systems, acquisitions, migrations and changing data standards have left many insurers with information living in different places and different formats.
But data readiness doesn't have to mean data perfection. For many operational AI use cases, an insurer doesn't need to clean every database, consolidate every source or complete a multiyear core modernization project before getting started.
When an AI agent integrates with the systems already running the business, the immediate job is to understand the data environment it needs to operate within.
The goal isn't to make decades of insurance data pristine. It's to understand the structures, rules and exceptions the AI will encounter so it can work reliably with the environment that exists today.
That distinction matters. If “data readiness” means fixing everything first, AI can remain perpetually one modernization project away. This is where your AI partner’s insurance depth starts to matter. They can help provide the operational context required to use data correctly.
This is why implementation starts before the kickoff
The fastest insurance AI implementations are often the ones where these details have been surfaced early.
Before implementation begins, teams can start answering questions like:
- Which systems will the AI need to access?
- What identifiers does each system use?
- Where does the same information appear in different formats?
- Which workflows vary by state, product, policy type or claim type?
- What happens when information doesn't match?
- Which exceptions can the AI resolve?
- Which ones require escalation?
- Who owns the decision when a rule isn't clear?
That work isn't glamorous, but it is where a surprising amount of implementation risk can be removed. Every undocumented variation discovered before build is one less surprise waiting in production.
The last mile is where insurance AI gets real
Large language models have made the front end of AI astonishingly good. They can understand messy sentences, interpret intent and conduct conversations that would have seemed impossible a few years ago.
But insurance doesn't end when the conversation ends. The information has to go somewhere, which means the correct policy has to be identified and the right fields have to be populated.
Business rules have to be followed. Actions have to occur in downstream systems. Exceptions have to be recognized. Decisions need to be reconstructable.
That last mile is where a general-purpose AI capability becomes an insurance operation.
Sometimes the thing standing between a great demo and a successful launch is knowing that one book of business puts two zeros in front of the policy number and another doesn't. Those details look small right up until the AI encounters one it wasn't prepared for.
Looking for more insights about your company’s AI readiness?
Download this white paper: “From Demo to Production in Weeks.”
Source
- AM Best, Artificial Intelligence Appears to Be Ready, But Most Insurers Are Not, April 27, 2026. Survey of more than 150 rated insurers and MGAs.
