What Can Insurers Actually Automate With AI Agents Today: Claims, Underwriting, or Both?
The useful question is not whether AI agents can help insurance. It is which specific tasks they can handle today, in claims, in underwriting, or both. Claims and underwriting are not equally far along, and an honest answer names the tasks rather than making a blanket claim. This piece breaks down what AI agents can do in each function, where the two compare, and where a human still has to be in the loop.
What can AI agents actually do in insurance today?
| AI agents in insurance are software that can read unstructured information, a claim document, an underwriting submission, decide what it means, and take the next action, requesting a missing document, flagging a risk, routing a case, without a human scripting a rule for every scenario. That differs from traditional automation, which runs a fixed sequence and cannot handle a case it was not explicitly built for. |
The core distinction is that agents reason and decide; they are not a faster version of rules-based automation. An emerging pattern is multi-agent collaboration: specialized agents handling intake, fraud analysis, and communication, coordinating with each other rather than one agent doing everything. And the human-in-the-loop principle belongs up front, not as a late caveat: agents own the routine and the well-defined, while people stay involved for the ambiguous and the high-risk.
Claims: What AI agents can automate right now
| AI agents can handle claims triage, document and image intake, coverage validation, severity estimation, and routing to the right adjuster today, and increasingly full settlement for simple, low-value, clearly-covered claims. |
Item 6 needs honesty. Full settlement automation today is realistic for a narrow band of claims, small, unambiguous, no coverage dispute, not the general case. Overstating it is the most common credibility problem in this kind of content. Fraud detection sits in a similar place: agents support it by flagging anomalies, but a human still makes the final call on a flagged case. Even where an AI claims adjuster assists throughout, the judgment on disputed files stays human.
| Claims Task | How Automatable Today | |
| 1 | Triage and prioritization by urgency and complexity | Mostly |
| 2 | Document and image intake with extraction | Mostly |
| 3 | Coverage validation against the policy | Mostly |
| 4 | Severity or damage estimation | Partially |
| 5 | Routing to the right adjuster | Fully |
| 6 | Full settlement for simple, clearly-covered claims | Partially |
Underwriting: What AI agents can automate right now
| AI agents can extract and organize submission data, validate completeness, pull third-party data, summarize risk, and recommend a decision for straightforward risks today, with automated risk assessment handling the narrowest, lowest-complexity risk bands end to end. |
What still needs a human underwriter is worth stating plainly: complex, high-value, or ambiguous risks, anything requiring judgment the agent was not trained on, and any genuinely novel risk profile. Can AI agents fully automate underwriting? Not fully, and not for complex risks, though they can fully decision well-understood bands.
| Underwriting Task | How Automatable Today | |
| 1 | Submission data extraction and organization | Mostly |
| 2 | Completeness validation | Fully |
| 3 | Risk summarization for the underwriter | Mostly |
| 4 | Decision recommendation for straightforward risks | Partially |
| 5 | Full straight-through decisioning, lowest-complexity band | Partially |
Claims vs. Underwriting: Where AI Agents are further along
| Claims automation is further along overall, because most claims data is more structured, policy details, standard forms, defined coverage rules, and the decision space is narrower for the majority of cases. Underwriting is catching up quickly on straightforward risk bands, but complex or novel risks still lean on human judgment more than most claims scenarios do. |
The “or both” part of the question answers itself: most insurers run AI agents in both functions at once. This is not a sequential either/or, and the two tracks typically move in parallel at different speeds. One caution: further along does not mean more valuable. The relative gain in underwriting on eligible risks can be larger even if the overall function is less automated than claims.
| Dimension | Claims | Underwriting |
| Data structure | More structured | More variable |
| Decision complexity | Narrower for most cases | Wider, more judgment |
| Current automation ceiling | Higher overall | Rising fast on simple risks |
| Where humans stay involved | Disputed, high-value claims | Complex, novel risks |
What still needs a human
| A human stays in the loop for high-value or high-complexity cases, anything with a coverage dispute or ambiguous language, novel risk profiles the agent has no pattern for, and any decision with material customer or regulatory impact. AI agents hand off; they do not replace the final call on judgment-heavy work. |
This matters for buyers because the honest scope of human-in-the-loop determines real staffing impact, not the marketing framing of full automation. The comparison is sharpest in claims, where fully automated applies to the simple, clearly-covered band and everything else routes to an adjuster with the agent handling the surrounding work.
| Agent handles | Human handles |
| Routine, well-defined cases | Disputed or ambiguous cases |
| Low-to-moderate value | High-value exposure |
| Known patterns | Novel risk profiles |
| Prep, intake, routing | Regulatory-impact decisions |
How to measure whether it’s working
| Track four things: Straight-through processing rate on the specific task the agent owns, cycle time from intake to resolution, the rate at which cases get correctly escalated to a human rather than mishandled, and cost per case. Avoid a single blended “percent automated” number. |
That blended number hides which tasks are actually automated and gets these initiatives judged unfairly. Measure at the task level, not the function level, because that is the only way to know which specific claims or underwriting task is delivering.
The takeaway
AI agents can automate specific, nameable tasks in both claims and underwriting today, claims further along overall, underwriting closing the gap fast on straightforward risks, with humans still owning the disputed and the novel. Map your own task list against the two checklists above to find your realistic near-term candidates.
Frequently asked questions
They can handle claims triage, document intake and extraction, coverage validation, severity estimation, and routing, plus underwriting submission extraction, completeness checks, and risk summarization. Full end-to-end automation is realistic today only for simple, low-complexity cases in both functions.
Not fully, and not yet for complex risks. Agents can fully decision straightforward, well-understood risk bands, but complex or novel risks still need a human underwriter to make the final call.
Agents can automate triage, intake, coverage validation, severity estimation, and routing across most claims, plus full settlement for simple, low-value, clearly-covered claims specifically, a meaningful but still narrow band of total claim volume.
Mostly human-in-the-loop today. Agents fully automate simple, clearly-covered claims, but disputed, high-value, or ambiguous claims route to a human adjuster, with the agent handling intake and prep either way
Claims, overall, because the data is more structured and the decision space is narrower for most cases. Underwriting is catching up fast on straightforward risks, and most insurers run both in parallel.
