AI-powered workflows: Solving insurance operational backlogs

Here is each of the top backlog areas identified in that polling, with specific, practical applications of current AI technology.

A. Policy checking: From 90 minutes to 10 minutes

The traditional policy checking workflow requires an insurance professional to:

• Download and open the policy document
• Open the application or submission information in the AMS
• Manually compare dozens or hundreds of data points
• Create a spreadsheet or document noting discrepancies
• Follow up on issues and document corrections

The AI-centric approach turns this into a shorter workflow:

• Upload the policy document to an AI platform (via portal, API, email, or AMS integration)
• AI extracts and structures the relevant policy data
• AI compares policy data against submission information
• AI generates a check report highlighting discrepancies
• Insurance professional reviews and validates the AI's findings (5 to 10 minutes)
• System delivers results via preferred method (document download, API, AMS integration)

The time savings matter, but so does the consistency. Manual checking varies with who is doing the work and how much time they have. AI-powered checking applies the same standards to every policy, whether it is a $500 premium or a $50,000 premium account.

B. Quote comparisons: Replacing the spreadsheet grind

Anyone who has built quote comparison spreadsheets knows the work: manually extracting data from multiple carrier quotes, keeping formatting consistent, double-checking figures, and creating a professional comparison that clients can use.

AI-powered quote comparison replaces that manual work by:

• Automatically extracting coverage details, premiums, and terms from multiple carrier quotes
• Standardizing data for apples-to-apples comparison
• Highlighting key differences and coverage gaps
• Generating professional comparison documents in minutes instead of hours

For agencies managing dozens of quotes daily, that turns a bottleneck into a competitive advantage: faster turnaround that impresses clients and wins business.

C. Coverage gap analysis: From reactive to proactive

Coverage gap analysis has traditionally been labor-intensive enough that many agencies only perform it at renewal or when a client asks. With AI-powered structured data extraction, agencies can:

• Automatically identify potential coverage gaps across client portfolios
• Compare existing coverage against industry benchmarks or client risk profiles
• Prioritize outreach based on exposure levels
• Move from reactive service to proactive risk consulting

That shifts the agency's role from policy administrator to trusted risk advisor, a real differentiation in a competitive market.

D. Contract review and compliance: Consistency at scale

Reviewing contracts, policy forms, and compliance documents requires deep insurance knowledge and careful attention to detail. AI does not replace that expertise. It supports it by:

• Identifying key terms, conditions, and compliance requirements consistently
• Flagging unusual provisions or potential issues for human review
• Maintaining audit trails and documentation automatically
• Supporting faster turnaround on contract reviews without sacrificing thoroughness

This is where AI implementation gets strategic. Not every policy check needs the same level of service. Not every quote comparison requires human oversight. The agencies furthest along are matching the service model to the work, adjusting how AI-powered processing and human-in-the-loop review combine based on what each situation requires.

Think of it as having multiple gears instead of one fixed speed. You can shift between approaches based on:

Risk profile and complexity

• Lower premium policies with straightforward coverage might run through AI-only processing
• High-premium, complex accounts get full human review with AI assistance
• Everything else falls somewhere in between based on your risk tolerance

Speed requirements

• Urgent turnaround needs? AI-only or self-service might be appropriate
• Standard processing timeframes? Full-service with human verification, for the most thorough check
• Complex custom work? White-glove service with tailored workflows and comprehensive oversight

Cost considerations

• High-volume, lower-value work suits AI-only processing
• Strategic accounts justify the investment in full-service with E&O coverage
• Most work sits in the middle with self-service or standard full-service options

Lines of business

• Standard commercial lines such as BOP run well through automated workflows
• Specialty lines or non-standard coverages may require custom approaches
• Personal lines suit high-volume, lower-touch processing

1. AI-only processing: Straight-through processing with no human review. AI extracts, analyzes, and delivers results in minutes. This works for agencies with internal expertise who want to keep oversight of the review process and are comfortable with moderate risk assumption. It is the fastest and most cost-effective option for high-volume, routine work.

2. Self-service: In-house teams access an AI-powered platform directly through an interface they control. Insurance professionals keep control of the process while using the AI automation behind it. Results are immediate, risk assumption is low because your team validates the output, and turnaround is typically around 10 minutes. This suits agencies that want AI with internal oversight.

3. Full-service: AI technology combined with dedicated insurance professionals who manage the process and provide final verification. The service provider assumes E&O risk, delivers results in under one business day, and carries the lowest risk assumption for your agency. This suits agencies that want to free their teams from document-heavy work while holding quality standards.

4. Custom/white-glove service: Comprehensive processing with customized quality control measures, reporting, and service level agreements aligned to your operational needs. As with full-service, the provider assumes E&O risk, but workflows are designed specifically for your requirements. Turnaround times are longer, typically under 15 business days, but support all commercial and personal lines, including specialty coverage.

Agencies that mix these options are not locked into a single approach. They can use AI-only for routine personal lines policies, self-service for standard commercial accounts where their team wants oversight, and full-service for complex or high-premium business. That balances cost against risk management.

Understanding how other agencies approach AI implementation helps demystify the process. Here is how one large agency applied AI-powered policy checking to expand capacity without additional headcount.

The starting point in April 2025

• Large multi-location agency managing complex commercial accounts
• Initial implementation with 50,000 policies
• Evaluated the service options and selected a mix of self-service and full-service based on account complexity and premium levels

Year one results

• Achieved 75% time reduction on policy checking workflows
• Reduced policy checking expense by $300,000 annually
• Gained 30% handling time savings
• Redirected staff time to client-facing activities and new business development
• Implemented policy checking across their entire book, including lower-premium accounts that were previously skipped

Year two projections

• Expanding to 90,000 policies as confidence grows
• Projected annual policy checking cost reduction of $500,000
• No additional headcount required despite significant book growth
• Enhanced service quality driving client retention and referrals

The key to success: the agency did not try to implement everything at once. They started with a substantial but manageable volume, validated the results, refined their approach, and then scaled. They also used different service levels for distinct types of policies based on complexity, premium size, and risk tolerance.

This phased approach reduced implementation risk while building organizational confidence in AI-powered workflows. Staff who were initially skeptical became advocates once they experienced the time savings and improved work quality.

The commercial insurance lines of business included in their project were BOP/CPP, Commercial Automobile, Commercial General Liability, Commercial Property, Commercial Umbrella, Cyber Liability, Directors and Officers Liability, Employment Practices Liability Insurance, Excess Liability, Professional Liability, and Workers Compensation.

Based on the webinar polling data, here is guidance for each segment of the AI adoption curve.

1. For the 50% already using AI

Ask yourself these questions:

• Are you seeing measurable ROI, or just marginal efficiency gains?
• Does your current technology manage document variation and non-standard forms effectively?
• Are you still doing extensive manual cleanup or verification?
• Can it scale to address your biggest operational backlogs?

If you are answering no, you may be working with foundation-level tools where AI-centric workflows would deliver more. Consider evaluating current-generation technology built on LLMs and advanced AI models trained specifically on insurance documents.

Key evaluation criteria:

• Accuracy rates on your specific lines of business
• Ability to manage complex forms and endorsements
• Integration capabilities with your existing systems
• Service options to match different use cases
• Provider track record and insurance industry expertise

2. For the 41% actively researching in 2025

You are in a strong position to learn from early adopters while implementing proven technology. Focus your evaluation on:

Technical capabilities:

• What percentage of fields can it accurately extract from your policy documents?
• How does it manage non-standard forms, endorsements, and manuscript coverage?
• What validation mechanisms support accuracy?
• How does the technology adapt and improve over time?

Implementation approach:

• What is the typical implementation timeline?
• What integration options are available (API, AMS connectors, portal upload, email)?
• How much internal IT resource will be required?
• What training and change management support are provided?

Business model and risk:

• What pricing models are available (per-transaction, subscription, volume-based)?
• What service options exist to match your risk tolerance?
• Does the provider offer E&O coverage on their work?
• What service level agreements are standard?

Vendor considerations:

• How long has the provider been focused on insurance?
• What is their track record with agencies your size?
• Are they a technology vendor or an insurance services company with AI capabilities?
• What is their product roadmap and continued investment?

Start strategically: like the case study agency, consider beginning with a specific use case, such as policy checking for your top commercial lines, where the pain point is clear and the ROI is measurable. Validate results, build confidence, and then expand.

3. For the 9% planning for 2026

You have the advantage of learning from early adopters, but do not wait too long. The competitive landscape is shifting as agencies with AI-powered operations deliver faster service, more consistent quality, and greater capacity than those still operating manually.

Consider these realities:

• Your competitors in the 50% cohort are already capturing efficiency gains
• The technology is proven and mature enough for production use
• Labor market constraints make hiring additional staff increasingly difficult
• Client expectations for speed and service quality continue to rise

Your planning should focus on:

• Identifying your highest-impact use case (policy checking, based on industry data)
• Budgeting for both technology costs and implementation resources
• Beginning vendor evaluation now so you can move quickly when ready
• Assessing internal change management needs and preparing your team

The polling data from NetVU's insurance community tells a clear story: AI has moved from experimental technology to operational necessity. Half the industry is already implementing, and another 41% are actively researching, not because of technology hype but because operational realities demand new approaches.

Policy checking, quote comparison, coverage gap analysis, and contract review create backlogs that manual processes cannot manage at scale. The agencies succeeding with AI are not looking for futuristic autonomous agents; they are implementing proven, AI-centric workflows that deliver measurable results today.

The key is understanding where you are on the adoption curve, what level of AI you need, and how to match service models to your operational requirements. Whether you are at the foundation level looking to upgrade, actively researching, or planning your 2026 strategy, the path forward is clear: start with your biggest backlog, choose proven technology that offers a range of service models, implement strategically, and scale based on results.

The agencies that master this approach will not just solve operational bottlenecks. They will turn document-heavy work into a competitive advantage, freeing their teams to focus on advising clients, building relationships, and growing their business.

Artificial Intelligence (AI)

A machine's ability to perform cognitive functions usually associated with human minds — like understanding documents, making comparisons, and identifying patterns.

Machine Learning (ML)

A subset of AI that analyzes data to make predictions and improve performance over time without being explicitly programmed for every scenario.

Natural Language Processing (NLP)

Technology that enables computers to understand human language, including the complex terminology and structure of insurance documents.

Large Language Model (LLM)

Advanced AI systems that understand and generate human language to perform tasks like answering questions, summarizing text, and extracting structured data from unstructured documents.

Agentic AI

AI systems designed to act autonomously within defined parameters, making tactical decisions without constant human intervention — like routing different document types to appropriate processing workflows.

AI-Centric Workflows

Complete operational processes that combine advanced AI models with structured human oversight to manage document-heavy work from start to finish.