Why do insurance AI projects fail?

Most stall on process and people, not technology. AI repeats whatever process it's given, so undocumented steps and inconsistent handoffs become errors at scale. BCG finds two-thirds of the AI transformation challenge hinges on people.

Which insurance processes should be automated with AI first?

Start with high-volume work that brokers and policyholders feel directly, such as new business submissions or claims intake. These have clear inputs, measurable turnaround, and fast payback once the process is consistent.

How do you measure whether AI improved an insurance process?

Set a baseline before you automate. Track turnaround time, error rates, and missed deadlines, then compare the same metrics after AI is running. Broker complaints and rework volume show whether the change holds up in practice.

Does AI replace insurance staff?

No. AI agents take on repetitive tasks like data entry and document review, so people can focus on judgment, relationships, and service. Human reviewers still handle exceptions and own final decisions.

What is an insurance-trained AI model?

An insurance-trained AI model learns from real insurance documents such as loss runs, claim forms, and policies, rather than general web data. That training teaches it the field names, layouts, and conventions carriers actually use. Bevaya's InsurGPT is trained on more than 300 million insurance documents.

Can ChatGPT or other general AI read claims documents?

It can read the text, but it often misses the fields that matter most. In Bevaya's benchmark, the strongest general-purpose model read claim-tying fields correctly 72.8% of the time, against 86.6% for an insurance-trained model. General models also return answers without confidence checks or an audit trail.

Does claims AI make coverage or reserve decisions?

No. Claims AI reads documents, sets up files, and recommends next steps, but adjusters keep every claims decision. Values the AI isn't confident about go to a person before they reach the claim system.

What should claims leaders ask AI vendors about accuracy?

Ask how well the model reads the fields that tie a document to a claim, not just its overall accuracy. One wrong claim number barely moves a headline percentage but misfiles the whole record. Ask to see results field by field.

Can AI combine loss runs from several carriers into one loss history?

Yes. AI pulls every claim from each carrier's loss run into the same fields, whatever the format. The underwriter sees one consolidated loss history across carriers and policy years instead of reconciling them by hand.

Why can't general-purpose AI read loss runs accurately?

Loss run formats and conventions live in private carrier documents that general models never trained on. Values like total incurred often have to be calculated rather than read, so general models guess or leave the field blank. Newer general model releases improved by only a point or two.

How accurate is AI on loss runs in production?

With confidence scoring, a second Verifier model, and human review of flagged values, Bevaya's loss run AI reaches 98%+ accuracy in production. More than 90% of loss runs complete without anyone touching them.

Does AI replace underwriters in loss run review?

No. AI reads and reconciles the loss runs and sends uncertain values to a reviewer. Underwriters still judge the risk, set the price, and decide whether to quote.

Which general-purpose AI models did Bevaya test?

Twelve leading general-purpose models from four model families and three generations, tested in 18 configurations, some with and without reasoning. Each was accessed through its API with an optimized prompt and no insurance-specific training. The full list and every result are on Bevaya's benchmarks page.

How was field accuracy scored in the benchmark?

Field accuracy is the share of fields read correctly on each loss run, averaged across all 346 documents. Every model read the same documents and was scored the same way. Bevaya also measured the share of loss runs needing no correction, where the gap was wider.

Can verification and human review make up for a general-purpose model?

Only partly. Verification and review catch errors, but they start from the model's accuracy, and a model reading at 85% leaves roughly twice as many wrong fields as one at 93%. That means more files opened by people and less work that goes straight through.

Will AI replace jobs at insurance companies?

AI agents take over repetitive tasks like data extraction and document intake, while judgment calls stay with people. Staff typically shift to higher-value work like complex claims and risk assessment.

How should insurance staff be trained on AI tools?

Tie training to how each role changes, not a generic tool walkthrough. Give people permission to ask basic questions well past week one.

Who should lead change management for an AI rollout?

Leadership should own it visibly, since leadership support strongly predicts whether staff trust a new tool. Day-to-day managers should handle questions and fix early friction quickly.

How can you tell if staff are accepting a new AI tool?

Watch whether people keep using it and reporting issues, or quietly go back to the old process. Silence is often a warning sign, not a good one.

How long does it take to get an insurance AI agent into production?

Most Bevaya deployments take 8 to 12 weeks. Organizations that start with a single, well-scoped workflow consistently reach production on a defined timeline.

What KPIs should define success for an insurance AI pilot?

Tie success criteria to KPIs already in the operating review, such as cycle time, STP rate, handling time, and FTE hours recovered. Define them, along with a baseline and a go threshold, before the pilot starts so the production decision is already made when it ends.

Do you need perfect data before starting an insurance AI pilot?

No. No data environment is perfect, and most workflows have enough clean, accessible data to support a reliable AI deployment. Assess whether the data for one specific workflow is sufficient, accessible, and reliable before the pilot starts, then move it to production and build data discipline from there.

Where should insurers start when redesigning a workflow for AI?

Start by asking which steps exist only because of old system limitations, such as duplicate data entry, manual handoffs, and rework, and fix those first. Then simplify inputs, classifications, and decision paths before you scale.

Should teams be able to choose whether to use AI?

No. When AI is optional, it gets skipped. Building it into the workflow so it is part of how work gets done is what makes it the default.

Does an AI-designed workflow stay the same after launch?

No. What looks right in design will change once your team is using it. As AI processes real submissions and claims, you will find steps to eliminate, decisions to refine, and patterns in where exceptions occur, so treat workflows as living processes.

What are the top barriers to AI adoption in insurance?

In Bevaya’s 2026 report, data quality and availability is the top barrier at 51%, followed by security and privacy concerns at 48% and unclear ROI at 40%. Only 2% of respondents say they have no confidence in AI at all.

Which insurance AI use cases are most common?

Workflow and process automation leads at 53%, followed by unstructured document processing (46%) and AI copilots for employees (45%). Adoption concentrates in high-volume, document-heavy workflows where the value is clear and implementation risk is relatively low.

How many insurers have an AI governance committee?

In Bevaya’s 2026 report, 73% have a committee established or in progress, and 30% have a fully operational committee with a clear charter and defined responsibilities. Another 25% of respondents have no governance plans.

Where in the quote-to-bind process do most delays happen?

The earliest stages, including submission intake, clearance and appetite alignment, and submission preparation. These steps are usually handled by hand, and the time spent organizing documents and confirming completeness happens before a single underwriter looks at the file.

How does AI help brokers get faster quotes?

AI flags missing or incomplete information before submissions go to carriers, extracts exposure data from applications, loss runs, and schedules of values, organizes documents into a consistent structure, and compares risk characteristics with historical placement outcomes so brokers can focus on carriers more likely to quote the risk.

How does AI change the underwriting intake workflow for carriers and MGAs?

AI identifies submission emails, separates attachments, classifies document types, and extracts key exposure information. Applications, financial statements, and loss histories are converted into structured data that feeds underwriting systems, and submissions aligned with appetite are prioritized so underwriters begin evaluation sooner.

Why does document preparation take so much time in insurance operations?

Submissions, claims files, and servicing requests arrive with multiple attachments in varied formats. Employees must sort them, identify document types, extract data, and organize files before they can begin the work that requires their expertise, and these tasks are rarely the focus of operational improvement initiatives.

Which core systems can AI-supported extraction populate?

AI-supported extraction and integration can populate a wide range of core systems, including Guidewire InsuranceSuite, Insurity SureSuite, and Duck Creek Suite, automatically, reducing repetitive data entry and minimizing the potential for error.

How does AI handle incomplete submissions or claims files?

AI can identify incomplete materials and automatically request missing documents or information from the sender before the file reaches an underwriter or claims professional, helping prevent delays later in the workflow.

Which underwriting tasks are insurers using AI agents for?

Insurers use AI agents for submission intake, loss run summarization, supplemental applications, schedules and statements of values, policy renewals, and ACORD forms processing. Agents classify, extract, and validate data, and route low-confidence outputs or unclear information to underwriters for judgment.

Where do humans stay in the loop when insurers use AI?

Human-in-the-loop review is a core design principle: low-confidence outputs and exceptions are routed to experienced underwriters, adjusters, and other employees, and decision authority remains with them. AI handles repetitive, document-heavy work so experts have more bandwidth for exceptions, decisioning, and accountability.

What separates successful AI programs from stalled ones in insurance?

Successful insurers align AI use cases to real operational work, embed AI in standard workflows rather than running it in parallel, route low-confidence outputs to human experts, measure outcomes tied to business goals, and maintain effective, responsive governance.

Do insurers only hire data scientists for AI roles?

No. Employers are not only hiring data scientists and machine learning engineers; they are looking for professionals who can use AI tools effectively, interpret AI-generated outputs, and apply judgment in AI-supported workflows. In many cases, insurers focus more on practical fluency with AI in real insurance workflows than on deep technical specialization.

Which US states lead in AI job demand?

A 2025 analysis pointed to California, Texas, and New York as continuing leaders in overall AI job volume. States such as Washington and Virginia benefit from close ties between government and private industry, defense and research funding, and a focus on analytics and digital modernization, and the District of Columbia area is also seeing elevated AI demand.

How should employers respond to rising demand for AI skills?

By upskilling current employees. Many of the most effective AI roles should be filled by people who already understand the organization and can adapt to new tools.

Which insurance roles are most affected by AI?

Roles built on repetitive, structured, data-heavy tasks are changing most, including underwriting assistants, policy services representatives, claims intake specialists, data entry and loss-run processors, and operations managers. As AI takes over extraction, triage, and reporting, these roles shift toward judgment, oversight, relationships, and quality control.

What new roles are emerging in AI-enabled insurance operations?

Insurers can create hybrid roles such as AI Workflow Owner/Digital Workforce Manager, AI-Supported Customer Service Representative, Insurance Data Translator/Risk Insights Partner, Change and Workforce Transformation Lead, and Responsible AI Officer. The right roles depend on each business’s needs, structure, and AI maturity.

Which skills should insurers prioritize when upskilling teams for AI?

Core areas include AI literacy, analytical thinking, emotional intelligence, consultative communication, change leadership, and cross-functional awareness. Upskilling turns anxiety about automation into confidence and curiosity, and reinforces a culture of collaboration between people and AI.

Why do insurers still rely on BPO arrangements for manual processing?

Many carriers and brokers have historically relied on BPO arrangements to manage volume when automation is limited. These models can provide short-term relief, but they often invite complexity and other challenges demanding attention from internal teams.

What gains are insurers reporting from automating manual processes?

Insurers across underwriting, policy servicing, and claims report a 25% workload reduction, 10% reduction in underwriting costs, 200% boost to submission processing volume, 90% faster loss run processing, a 60% increase in team capacity, and 98% data extraction accuracy.

Why does AI still need human oversight in insurance workflows?

AI excels at handling volume, enforcing consistency, and applying rules at scale, but it is not designed to independently resolve ambiguity, regulatory nuance, internal compliance standards, or ethical judgment. Human-in-the-loop systems surface exceptions, flag uncertainty, and escalate edge cases to human experts.

Why should insurers optimize workflows before automating them?

Automation amplifies whatever process it touches. Clarifying business rules, eliminating manual detours, and reducing unnecessary handoffs first ensures AI enhances efficiency instead of just hiding dysfunction.

Which metrics should insurers use to measure AI performance?

Insurers should adopt modern measurements such as accuracy, cycle-time reduction, compliance confidence, human-in-the-loop review percentages, customer sentiment/NPS, error reduction, and straight-through processing rates to accurately measure AI ROI and total cost of ownership.

Which operational AI use cases should insurers scale in 2026?

Insurers should prioritize scaling successful use cases such as FNOL automation, loss runs, endorsements, COIs, claims triage, and billing inquiries into governed, measurable production workflows, including total cost of ownership planning so programs stay sustainable and cost-effective.

How does AI help independent agents with quoting?

AI-powered tools eliminate manual data entry by extracting key information from ACORD forms, email attachments, and application PDFs, providing more consistent and accurate submissions and reducing turnaround times. For commercial lines customers, where small data gaps or mismatched details can stall quoting, this is especially valuable.

Can AI tools integrate with an agency management system?

In many cases, yes. These tools integrate directly with an AMS, enabling faster responses, improved quote-to-bind ratios, and expanded focus on delivering customer value. Recent advances also embed AI tools in many CRMs, agency management systems, and small commercial quoting platforms.

Insurance AI Blog | Bevaya | AI Program Best Practices

Bevaya ExpertsSeptember 29, 20265 min read

Insurance AI Results Depend on Process Quality

Insurance organizations deploy AI to move submissions, claims, and service requests ...

Start Reading

Bevaya ExpertsSeptember 23, 20267 min read

Why Claims AI Needs Insurance-Trained Models, Not General AI

Claims teams are absorbing more complexity with fewer senior people. High deductibles ...

Start Reading

Bevaya ExpertsSeptember 23, 20266 min read

AI for Loss Runs: What Bevaya's Benchmark Means for Underwriting Teams

A commercial submission rarely arrives with one loss run. It arrives with three or four, ...

Start Reading

Bevaya ExpertsSeptember 23, 20266 min read

Insurance-Trained vs. General AI on Loss Runs: Bevaya's Benchmark

With the attention general-purpose AI has drawn, a fair question follows: is it good ...

Start Reading

Bevaya ExpertsSeptember 22, 20267 min read

5 Reasons Insurance Staff Resist AI and How to Plan for Each One

Before you roll out an AI agent, plan for this. A veteran adjuster will double-check ...

Start Reading

Bevaya ExpertsJuly 7, 20265 min read

3 Reasons Insurance AI Pilots Stall and How to Reach Production

Eighty-six percent of insurers are confident AI can deliver on their business goals, but ...

Start Reading

Bevaya ExpertsJune 10, 20267 min read

How to Design Insurance Workflows Around AI

Adding AI to existing insurance workflows and expecting different results is a common ...

Start Reading

Bevaya ExpertsApril 21, 20267 min read

93% of Insurers Are Now Actively Using or Exploring AI

It's Monday morning. Your underwriting team is staring down 200 submissions that came in ...

Start Reading

Bevaya ExpertsMarch 31, 20267 min read

How to Accelerate Quote-to-Bind Cycles Using AI

Speed has long been the gold standard for insurance placement decisions. Clients need ...

Start Reading

Bevaya ExpertsMarch 11, 20264 min read

Spring Reset: 8 Routine Tasks AI Removes from Insurance Operations

Spring often encourages organizations to clear out accumulated clutter and bring greater ...

Start Reading

Bevaya ExpertsMarch 3, 20266 min read

How Are Insurers Using AI?

Across insurance operations, insurers are exploring and evaluating how AI can be applied ...

Start Reading

Bevaya ExpertsFebruary 10, 20266 min read

AI Skills Are Now a Baseline: How AI Is Reshaping Insurance Jobs

AI exerted a tremendous impact in 2025 – and the technology’s influence on US labor ...

Start Reading

Bevaya ExpertsJanuary 20, 202610 min read

How to Restructure the Insurance Workforce for an AI-Enabled Future

AI in insurance is reshaping how white-collar work gets done – and it’s happening faster ...

Start Reading

Bevaya ExpertsJanuary 13, 20267 min read

8 Manual Processes You Should Not Still Be Doing

For decades, insurance underwriters, claims adjusters, and other skilled experts have ...

Start Reading

Bevaya ExpertsJanuary 6, 20265 min read

8 AI-Driven Resolutions for Insurance Leaders in 2026

2026 will be a defining year for insurance tech transformation – you can see our ...

Start Reading

Bevaya ExpertsDecember 31, 202512 min read

10 Insurance AI Predictions for 2026: From Promise to Performance

If 2025 was a year of learning about artificial intelligence and notching early wins then ...

Start Reading

Bevaya ExpertsDecember 23, 20255 min read

Survive the Holiday Rush: AI Gives Insurance Teams the Gift of Relief

The end of each year brings the same challenges: heavy renewal cycles, weather-driven ...

Start Reading

Bevaya ExpertsDecember 9, 20256 min read

7 Ways AI Supports Independent Insurance Agents

Independent agents are a vital link in the insurance value chain, facilitating, ...

Start Reading

Bevaya ExpertsNovember 25, 20256 min read

AI Ends the Holiday Backlog Tradition

The holidays are a time for family, rest, and reflection. For us insurance folks, ...

Start Reading

Bevaya ExpertsNovember 12, 20257 min read

How to Avoid 6 Common Insurance AI Implementation Challenges

When a broker submits an account or a policyholder files a claim, they expect a fast, ...

Start Reading

Bevaya ExpertsNovember 4, 20256 min read

Using AI to Simplify and Speed Up Insurance Book of Business Transfers

When it comes to acquiring business, insurance operates like many other businesses. ...

Start Reading

Bevaya ExpertsOctober 14, 20257 min read

Redefining Small Commercial Insurance: Closing Gaps and Driving Growth

Small businesses are both the backbone of the U.S. economy and one of its greatest ...

Start Reading

Bevaya ExpertsOctober 8, 20258 min read

The Hidden Security Risks of Deploying AI in Insurance

Artificial intelligence (AI) has quickly become one of the most talked-about technologies ...

Start Reading

Bevaya ExpertsOctober 1, 20256 min read

How AI Helps Teams Stay on the Offense During Peak Demand Times

Now that football season is in full swing, all eyes are turned to the gridiron. When you ...

Start Reading

Bevaya ExpertsAugust 19, 20254 min read

Sharpen Your Insurance Workflow with AI

Get those pencils sharpened, tablets charged, and backpacks packed, it’s that time of ...

Start Reading

The Platform →

Build

Run

Review

Govern

Underwriting Automation →

Claims Automation →

Policy Servicing →

AI Agent Library →

Technology

Accuracy

Trust

Stories

Who It's For

Why Bevaya

Featured Case Study →

Learn

Research

Updates

Featured Report →

About

News & Trust

Connect

Blog

Insurance AI Results Depend on Process Quality

Why Claims AI Needs Insurance-Trained Models, Not General AI

AI for Loss Runs: What Bevaya's Benchmark Means for Underwriting Teams

Insurance-Trained vs. General AI on Loss Runs: Bevaya's Benchmark

5 Reasons Insurance Staff Resist AI and How to Plan for Each One

3 Reasons Insurance AI Pilots Stall and How to Reach Production

How to Design Insurance Workflows Around AI

93% of Insurers Are Now Actively Using or Exploring AI

How to Accelerate Quote-to-Bind Cycles Using AI

Spring Reset: 8 Routine Tasks AI Removes from Insurance Operations

How Are Insurers Using AI?

AI Skills Are Now a Baseline: How AI Is Reshaping Insurance Jobs

How to Restructure the Insurance Workforce for an AI-Enabled Future

8 Manual Processes You Should Not Still Be Doing

8 AI-Driven Resolutions for Insurance Leaders in 2026

10 Insurance AI Predictions for 2026: From Promise to Performance

Survive the Holiday Rush: AI Gives Insurance Teams the Gift of Relief

7 Ways AI Supports Independent Insurance Agents

AI Ends the Holiday Backlog Tradition

How to Avoid 6 Common Insurance AI Implementation Challenges

Using AI to Simplify and Speed Up Insurance Book of Business Transfers

Redefining Small Commercial Insurance: Closing Gaps and Driving Growth

The Hidden Security Risks of Deploying AI in Insurance

How AI Helps Teams Stay on the Offense During Peak Demand Times

Sharpen Your Insurance Workflow with AI