The Best Way For Independent Insurance Surveyors To Use AI In 2025 Without Leaking Client Data?

Insurance lives on documents. Policies, claims, forms, receipts, reports, and emails all flow through thousands of human decisions every single day.

When those decisions depend on manual review, copy‑pasting, or rekeying data, things slow down fast. Backlogs grow, errors creep in, and compliance risks rise. That is exactly where AI agents are starting to change how insurers work.

In this guide, we will walk through how StackAI’s no-code AI agent platform helps insurers automate underwriting, policy Q&A, and handwritten claim forms, while saving tens of thousands of staff hours every week. You will also see how the same ideas point to the best way for independent insurance surveyors to use AI in their own work.

If you work in underwriting, claims, or policy service, or you run an independent practice, this will show you what is possible right now, not in some distant future.

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The challenge with manual insurance workflows

Most insurance teams still run on paper-era processes, even when the documents are digital.

A single claim or policy change might pass through email, shared drives, desktop folders, and multiple systems before anything is approved. Along the way, people read, retype, compare, and check the same information again and again.

In practice, that often looks like:

• Manual, document-heavy, hard-to-scale workflows
• Slower cycle times for underwriting, claims, and endorsements
• Increased risk of errors, misinterpretation, and compliance gaps

For large insurers this can mean thousands of work hours each week just to keep up. For independent surveyors and small teams, it means late nights, missed opportunities, and less time in the field.

This is where AI agents make the biggest difference. Instead of asking people to chase information across documents, AI agents can read, extract, validate, and act on that information in a consistent way.

If you want a high-level view of where this is heading, McKinsey has a helpful overview in its piece on the future of AI for the insurance industry.

How AI agents turn insurance workflows into fast, scalable systems

StackAI’s no-code AI agent platform is designed around a simple idea: let people describe how work should flow, then let AI handle the heavy lifting.

Insurers can build agents that understand documents, apply rules, and complete full workflows without writing a single line of code. These agents sit inside existing processes and systems, not outside them.

Across global insurance clients, this approach has delivered results such as:

• 25,000 to 30,000 staff hours saved every week
• Faster cycle times for underwriting and claims decisions
• Higher accuracy, consistency, and compliance
• Better customer response times and fewer back-and-forth emails

The outcome is a set of fast, auditable, fully scalable systems where each AI agent handles a specific workflow. For example, one agent may manage underwriting submissions, another may answer coverage questions, and another may process handwritten claim forms.

This idea aligns with what many industry experts see. For instance, Sikich outlines how agentic AI can support underwriting, claims, and operations in its article on agentic AI in insurance underwriting and claims.

Real-world impact at a glance

Here are a few headline numbers from real insurance operations using StackAI:

• 10,000+ hours saved every week from a single underwriting workflow
• 25,000 hours saved every week from handwritten claims processing
• More than 90% improvement in transcription accuracy on scanned forms

These are not small optimizations. They let teams rethink how work is organized and where humans add the most value.

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Automating underwriting: from days to minutes

Underwriting is one of the most document-heavy parts of insurance. Every submission pulls in customer information, receipts, eligibility criteria, and risk indicators.

One of the world’s largest device insurers used StackAI to automate its entire underwriting submission process from end to end.

Before AI agents

Previously, underwriters had to:

• Manually review customer information and receipts
• Check eligibility rules and risk tiers
• Calculate premiums based on internal guidelines
• Draft policy summaries and approval emails

For complex cases, this could take days. Even when volume was high, the work still depended on people reading and re-checking the same documents.

How the underwriting submission AI agent works

The insurer built a no-code AI agent that handles the full flow:

1. Extracts data from receipts using OCR
   The agent reads receipt images and PDFs, pulls out fields like item type, purchase date, and price.
2. Validates that data with a large language model (LLM)
   The LLM checks if the extracted values make sense in context, such as matching currency symbols or item descriptions.
3. Checks eligibility automatically
   The agent applies the insurer’s rules to see if the device is eligible for coverage based on age, value, and other conditions.
4. Assigns risk tiers
   Based on predefined criteria, the agent classifies the submission into standard risk levels.
5. Calculates premiums
   Using those risk tiers and business logic, the agent calculates the correct premium amounts.
6. Generates a policy summary and approval email
   The agent drafts the policy summary and a customer-ready approval email, all without any human intervention for standard cases.

Underwriters still handle edge cases and complex decisions, but the routine work no longer piles up.

Results for the insurer

Once this underwriting agent went live:

• Average processing time dropped from days to minutes
• The workflow now saves more than 10,000 staff hours every week
• Consistency improved across decisions, which reduced regulatory risk

If you want to explore how a similar setup might look in your environment, you can use StackAI’s site to book a no-code AI agent demo for insurance workflows.

For a broader industry view on underwriting automation, Salesforce shares practical guidance in its AI in insurance underwriting guide.

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Solving policy interpretation with instant Q&A

Policy wording is dense. Staff and customers often ask the same coverage questions over and over. When different people give slightly different answers, confusion and compliance risks grow.

A major insurer used StackAI to build a policy Q&A and coverage validation agent to handle these questions.

The problem with manual answers

Before the agent:

• Staff spent hours every week answering repetitive coverage questions
• Responses differed based on who picked up the phone or replied to the email
• Compliance teams worried about misstatements or missed updates

This also limited how quickly front-line staff could resolve customer queries.

How the policy Q&A agent works

The policy agent acts like a focused, policy-aware assistant that:

• Searches the insurer’s full policy library
• Retrieves the most relevant sections of wording
• Generates a clear, closed-ended answer that points back to exact policy text
• If the knowledge base does not contain a clear answer, the agent says so, instead of guessing

This gives staff and customers a consistent way to get answers that are both fast and grounded in the actual policy language.

The key outcome is simple: coverage questions are answered instantly, every time, or clearly flagged as unclear when the documents do not support a definite answer.

If you want a wider view of how agents like this fit into insurance operations, Binariks has a useful article on the role of AI agents in transforming insurance operations.

Streamlining claims intake: handwritten forms no more

Handwritten claim forms are one of the biggest bottlenecks in claims intake. Many insurers still receive scanned or faxed reports that someone must retype into a system.

A national insurance client used StackAI to build a handwritten form processing agent, turning this into a fully automated flow.

How the handwritten form processing agent works

The agent follows a simple but powerful pattern:

1. Reads scanned forms
   The agent takes in images or PDFs of handwritten claim reports and incident forms.
2. Extracts key fields
   It pulls out important data such as:
   • Claimant name
   • Policy number
   • Contact information
   • Incident date and description
3. Structures the data into a clear, searchable report
   The output is a clean, consistent record that can go straight into claims systems or downstream workflows.

Claims staff can then focus on investigation and judgment, instead of typing and formatting.

The impact on claims teams

For this national insurer, the results were dramatic:

• The workflow now saves 25,000 staff hours every week
• Transcription accuracy improved by more than 90%
• Claim intake keeps pace with volume without constant overtime

This reflects a wider trend where AI agents sit at the front of the claim pipeline, reading and structuring data, while humans focus on empathy, judgment, and complex disputes. Deloitte touches on this broader shift in its review of AI-driven transformation in commercial insurance.

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The best way for independent insurance surveyors to use AI

Independent surveyors often feel the same pressure as large insurers, just without the extra staff. Field visits, site photos, reports, and policy checks all compete for attention.

The best way for independent insurance surveyors to use AI is to borrow the same patterns that large carriers use, but apply them on a smaller, focused scale.

Here are three practical starting points that mirror the agents above:

1. Turn field notes and forms into structured reports
   Use OCR and AI models to read your site notes, checklists, and simple handwritten forms. The AI can turn them into a clean, searchable report that you can paste into carrier templates or your own system.
2. Use a policy-aware Q&A assistant during assessments
   Load relevant policy documents into a private AI assistant. Then, when you are writing a report, you can ask focused questions like, “What are the conditions for water damage coverage in this section?” and get the exact clauses back, instead of scanning pages.
3. Standardize your own risk tiers and recommendations
   Define your usual risk categories and recommendation templates. An AI agent can then draft consistent summaries based on your field notes and photos. You stay in control and edit the final wording, but you start from a strong first draft every time.

These steps echo how underwriters, claims teams, and policy staff already use AI agents at scale. The difference is that independent surveyors can start small, focus on their own bottlenecks, and grow from there.

If you want to understand industry-wide use cases, StackAI’s whitepaper on real-world AI agent use cases in insurance is a helpful resource.

Also Read: The AI Of 2026 Will Be Different (And Far More Independent Than You Think)

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The future of AI-powered insurance operations

Across underwriting, claims, policy service, and field surveys, a clear pattern is emerging. AI agents take on repetitive, document-heavy work so people can focus on decisions, judgment, and customer care.

For insurers, this means:

• Faster and more fair decisions
• Higher-quality data, which supports better pricing and reserving
• Better customer experiences at every touchpoint

For independent surveyors and smaller teams, it means more time in the field, less time glued to spreadsheets, and calmer workdays.

StackAI is helping insurers make this shift one intelligent agent, one workflow at a time. If you want to explore what that might look like in your own work, you can start with an overview at StackAI’s AI agent platform for insurance automation.

AI is not here to replace the human side of insurance. It is here to clear away the manual noise so you can focus on better decisions and better service.