Where agencies stand on AI adoption in insurance

The polling results were striking: 50% of agencies are already using some form of AI, and another 41% are actively researching for 2025. That is 91% of the insurance community either implementing or seriously evaluating AI technology right now.

The second poll question, about operational backlogs, was more telling. Asked which processes create the biggest bottlenecks, 52% pointed to policy checking — the time-intensive, document-heavy work that can consume 60 to 90 minutes per policy and often gets skipped entirely on lower-premium accounts.

The connection between these two data points tells the real story of AI adoption in insurance: agencies are not chasing technology for its own sake. They are looking for answers to specific, costly operational problems that manual processes can no longer manage at scale.

When half of insurance agencies say they are "already using AI," what does that mean? The reality is nuanced. Many agencies are using basic tools such as OCR document scanning, simple data extraction, and rule-based automation, and calling it AI. Those tools provide value, but they sit at the foundation level of what AI can accomplish for insurance operations.

The 41% who are "researching in 2025" are a more advanced cohort. These agencies have seen the limitations of basic automation and are looking for technology that can manage the complexity and variability of insurance documents. They are asking harder questions: How accurate is it? What kind of oversight is needed? Can it manage non-standard forms and endorsements? What is the actual ROI?

The remaining 9% planning for 2026 are not necessarily behind; they may be taking a strategic approach, watching early adopters, and learning from their experiences before committing resources.

That 52% is no surprise to anyone working in agency operations. Policy checking combines four operational problems at once:

• Time-intensive: A thorough policy check requires 60 to 90 minutes of focused work
• Detail-oriented: Small errors can lead to significant E&O exposure
• High-volume: Agencies managing thousands of policies face a workload that outstrips capacity
• Low-premium problem: Many agencies skip checking lower-premium policies entirely because the cost does not justify the time

The result? Agencies either invest in additional headcount, increasingly difficult in today's tight labor market, accept E&O risk by skipping checks, or watch service quality decline as staff rush through reviews.

Quote comparisons (19%), coverage gap analysis (16%), and contract review and compliance (13%) round out the backlog challenges, each a document-heavy process consuming time that could go to client-facing work.

Not all AI is created equal, and understanding the evolution of AI adoption in insurance helps agencies separate what they need from what vendors are selling.

A. Foundation: Intelligent data extraction

This is where most agencies currently operate. These tools use OCR, rule-based logic, and early-stage machine learning to extract data from insurance documents. They help with basic data entry tasks but have significant limitations:

• Heavily reliant on traditional programming techniques
• Not reliable without extensive human review
• Struggle with document variation and non-standard forms
• Cannot manage complex, document-centric processes from start to finish

If you are using a tool that requires significant manual cleanup or frequent error correction, you are likely at this foundation stage.

B. Present: AI-centric workflows (the current sweet spot)

This is where real operational change happens. AI-centric workflows combine advanced AI models, including large language models (LLMs), with structured human oversight to process and analyze unstructured insurance documents.

Key characteristics:

• Adapts to different document types and formats
• Can manage complex policy documents with high accuracy
• Still requires human oversight for validation and quality control
• Suitable for complete, document-centric processes with defined workflows

This level delivers measurable ROI because it addresses the full workflow, not just isolated data extraction tasks. For policy checking, that means reducing a 60- to 90-minute manual process to 5 to 10 minutes of AI-assisted work with human-in-the-loop verification, maintaining 100% accuracy while cutting time investment.

C. Research: Guided agents (pre-production stage)

The next evolution involves agent frameworks with defined guardrails that allow AI to make tactical decisions within constrained problem spaces. These systems can:

• Detect document types and adjust processing paths accordingly
• Make quality assessments and route work appropriately
• Handle workflows that shift with document characteristics

This technology exists in research environments and early production pilots, but it is not yet widely available or proven at scale. Agencies evaluating technology in this category should ask tough questions about production readiness and real-world validation.

D. Future: Autonomous agents will drive AI adoption in insurance

Fully autonomous systems that can manage complex, undefined tasks on the fly, including transacting with legacy systems and adapting workflows based on context, remain in the research phase. These systems are not currently feasible without further advancements in AI reliability and trust mechanisms.

The bottom line for agencies: the gap between foundation and present is the difference between marginal efficiency gains and real operational change. If you are in the 50% already using AI but not seeing results, you may be working with foundation-level tools where AI-centric workflows would deliver more.