Practical AI in P&C Insurance: Where Should Insurers Begin?
- By: Amy Gillespie
- November 3, 2026
- 5 min read
Insurance professionals spend considerable time collecting, reviewing, and entering information before they can make the decisions their jobs require. From processing commercial insurance applications to organizing claims documentation, these necessary but repetitive activities can slow operations and consume valuable resources.
Artificial intelligence (AI) offers opportunities to reduce that administrative burden. But for small and midsize property and casualty (P&C) insurers and managing general agents (MGAs), determining where to begin can be as challenging as evaluating the technology itself.
The question is not necessarily how to implement AI throughout the organization, but where to begin. A practical approach starts with identifying specific processes where AI can reduce repetitive work, improve information handling, and allow employees to concentrate on decisions requiring their expertise. From there, insurers can evaluate the improvements and build on measurable results.
Key Takeaways
- AI can streamline application intake, organize submission information, identify missing data, and support automated quoting through established underwriting and rating processes.
- In claims, AI reduces repetitive documentation and information-handling tasks, allowing adjusters to focus more attention on evaluating and resolving claims.
- AI can also improve policy servicing, billing operations, and access to institutional knowledge.
- Adopting AI does not always require replacing existing insurance systems, but successful integration depends on reliable data, system capabilities, and appropriate controls.
- The strongest starting point is a clearly defined operational problem with measurable results and appropriate human oversight.
Start With the Business Problem, Not the Technology
AI discussions often begin with the technology itself. For insurers evaluating where to invest, however, the better approach is to examine existing workflows.
Where do employees repeatedly enter information from documents? Which activities require reviewing large volumes of material? Where do incomplete submissions, missing documentation, or manual handoffs create delays?
These questions can help identify opportunities with the most measurable operational value.
According to McKinsey & Company, generative AI is reshaping document-intensive insurance activities, including submissions, policy issuance, and claims handling. AI-enabled solutions can also connect multiple steps within an established workflow, helping insurers move information from one activity to the next with less manual intervention.
For insurers with limited technology resources, starting with a clearly defined activity offers an opportunity to evaluate AI without taking on the complexity of a companywide initiative.
Application Intake and Underwriting: From Documents to Usable Data
One of the most promising applications of AI in P&C insurance is the processing of new business submissions.
Commercial insurance applications often arrive in different formats and may include supplemental questionnaires, statements of values, loss runs, and other supporting materials. Before underwriting can proceed, the information must be organized, reviewed, and entered into the insurer’s systems.
AI-enabled application intake streamlines this process.
AI reads submitted applications and supporting documents, extracts relevant information, and populates the appropriate fields in an insurance system. It identifies missing or inconsistent information, flags exceptions requiring attention, and routes submissions through established underwriting workflows. Instead of manually reviewing and entering information from multiple documents, underwriters begin their evaluation with organized data and a clearer picture of the proposed risk.
For insurers, this means less time spent on administrative tasks, fewer opportunities for manual data-entry errors, and faster movement from submission to underwriting review. It also allows experienced underwriters to spend more time evaluating risk, making decisions, and responding to agents and policyholders.
Extending Automation from Submission to Quote
AI-assisted submission processing extends beyond data collection and underwriting preparation to support automated quote generation.
When an application contains the required information and meets established underwriting and eligibility criteria, an integrated workflow moves that information directly into the insurer’s rating process for automated quote generation. Submissions that require additional information or fall outside established guidelines can be flagged and routed for underwriting review.
By connecting application intake, data validation, and quoting, insurers reduce processing time, respond to agents more quickly, and allow underwriters to focus on submissions requiring their expertise.
McKinsey & Company’s June 2026 analysis of commercial and specialty underwriting describes a similar progression, from extracting submission information and checking completeness to supporting risk selection, pricing, and quote issuance.
AI in Claims: Reducing Administrative Work, Supporting Better Decisions
Claims handling is another area where AI is improving insurance operations, particularly in the collection, organization, and review of information.
A claim file may contain first notice of loss (FNOL) details, adjuster notes, photographs, estimates, correspondence, and supporting documentation. Reviewing and organizing this information takes time, especially when adjusters are managing multiple claims.
AI-enabled claims workflows streamline these activities by extracting information from incoming loss reports, populating claim fields, organizing supporting documents, and preparing summaries of claim activity.
For claims teams, these capabilities reduce repetitive administrative work, improve access to information, and help adjusters move claims through the process more efficiently. Adjusters can devote more time to evaluating losses, communicating with policyholders, and resolving claims.
However, organizing claim information is different from determining the outcome of a claim. Coverage interpretation, liability assessment, claim valuation, and settlement decisions require appropriate controls and human oversight.
For a broader discussion of improving claims operations, read our article, How to Improve Claims Handling.
Additional Opportunities in Policy Servicing and Insurance Operations
eyond underwriting and claims, AI is helping insurers streamline everyday operations, improve access to information, and reduce administrative work.
In policy servicing, AI reviews endorsement requests, identifies relevant policy information, and prepares draft communications for employee review. This reduces time spent gathering information and helps service representatives respond more efficiently to agents and policyholders.
AI also makes institutional knowledge more accessible. Instead of searching through multiple documents for procedures, underwriting guidelines, or operational instructions, employees can retrieve relevant information and receive summaries with references to the original sources. This is particularly valuable as experienced employees retire or responsibilities change, helping insurers preserve knowledge and maintain consistency across their operations.
For more on preserving institutional knowledge and building repeatable processes, read The Future of Insurance Talent — Part Two.
In billing and other administrative functions, AI classifies inquiries, summarizes account information, and identifies items requiring attention. These capabilities help employees manage routine requests more efficiently and concentrate on issues that require additional review.
The greatest value comes from applying AI where it addresses a genuine operational need—not introducing it into every process simply because the technology is available.
The opportunities for AI extend across insurance operations. The following table highlights several practical applications and the operational benefits they provide.
Insurance Activity | How AI Supports the Process | Operational Benefit |
Application intake and underwriting | Extracts application data, populates system fields, identifies missing information, and prepares validated information for automated quoting through established rating processes | Faster submission processing, reduced manual entry, and more time for underwriting decisions |
Claims Handling | Organizes claim documentation, extracts FNOL information, prepares summaries, and flags missing information | Less administrative work, faster access to information, and more efficient claims handling |
Policy Servicing | Reviews endorsement requests, retrieves policy information, and prepares draft communications | Faster responses and reduced administrative effort |
Internal Knowledge Management | Retrieves procedures, guidelines, and operational information from approved sources | Improved access to institutional knowledge and greater process consistency |
Billing and Administrative Operations | Classifies inquiries, summarizes account information, and identifies exceptions requiring attention | More efficient handling of routine requests and improved workflow visibility |
Does AI Require Replacing Existing Insurance Systems?
Adopting AI does not necessarily require insurers to replace their existing technology. AI solutions can work alongside established policy administration, claims, billing, and other business systems, extending their capabilities without requiring a complete technology overhaul.
Through appropriate integrations, AI can access information, process documents, exchange data, and support workflows across multiple systems. This allows insurers to introduce new capabilities while continuing to use the technology and business processes they already rely on.
Successful implementation, however, requires more than connecting an AI tool to an existing system. Data must be accurate, accessible, and secure, with AI-generated information properly validated before it becomes part of official system records.
An insurer’s ability to introduce AI depends in part on the flexibility of its core systems, the availability of appropriate integrations, and access to reliable data. Technology that accommodates changing business needs gives insurers greater freedom to adopt new capabilities without unnecessary disruption.
Choosing the Right First AI Project
For insurers considering an initial AI initiative, a narrowly defined pilot is often more manageable than attempting to automate an entire business function.
A practical evaluation should begin with four questions:
- Is the process repetitive and clearly defined? Activities involving recurring document review, data extraction, or information retrieval may be easier to evaluate than highly subjective decisions.
- Is the necessary information available and reliable? AI cannot consistently produce dependable results when source information is incomplete, inaccessible, or poorly organized.
- Can the results be measured? Establish a baseline for processing time, error rates, rework, or employee effort before beginning.
- Can employees review and correct the output? A pilot should include clear procedures for exceptions, validation, and accountability.
An insurer might begin by using AI to extract selected fields from incoming applications, with employees validating the results before information is entered into production systems. By measuring accuracy, processing time, and the frequency of exceptions, the insurer can determine whether the solution delivers meaningful improvements.
Once the process demonstrates reliable results, the insurer can expand its use to additional activities. This approach allows organizations to evaluate both the technology and the operational changes needed to use it effectively.
Human Oversight Remains Essential
AI introduces new opportunities, but it also creates responsibilities involving accuracy, data security, transparency, and regulatory compliance.
AI-generated information can be incomplete or incorrect, even when presented confidently. An extracted property value, summarized policy provision, or suggested claim classification must therefore be subject to controls appropriate to its potential impact.
The National Association of Insurance Commissioners emphasizes that insurers remain responsible for complying with applicable insurance laws when using AI, including requirements involving fairness and unfair discrimination. Its Model Bulletin on the Use of Artificial Intelligence Systems by Insurers also addresses governance, risk management, and regulatory oversight.
Insurers should define where automated assistance is appropriate, when human review is required, and how decisions and supporting information will be documented.
These safeguards are especially important when AI does more than organize or summarize information and begins initiating actions within an insurer’s business systems.
For a closer look at the governance, risk management, and operational considerations involved in expanding AI initiatives, read our article, From AI Pilot to Production: What P&C Insurers Need Before They Scale.
Summary
AI offers practical opportunities to improve P&C insurance operations, from application intake and automated quoting to claims handling, policy servicing, and administrative workflows.
For small and midsize insurers and MGAs, the most effective approach is to begin with a clearly defined business need, evaluate the results, and expand where the benefits are demonstrated.
The goal is not to introduce AI into every process. It is to reduce unnecessary administrative work, improve access to information, and give insurance professionals more time to focus on decisions and relationships that matter.
Frequently Asked Questions
What is the best place for a P&C insurer to start using AI?
A good starting point is a repetitive, information-intensive process with measurable outcomes. Submission intake, document review, and claims information handling are potential candidates, depending on the insurer’s workflows and available data.
Can AI help generate insurance quotes?
Yes. AI can extract and validate application information, which can then be used by established underwriting and rating systems. Automated quote generation depends on the insurer’s eligibility rules, data completeness, system capabilities, and required approvals.
Can AI be used with an existing policy administration system?
Yes. AI applications can work alongside existing policy administration systems through supported integrations and workflows. Implementation depends on the system’s capabilities, data accessibility, security requirements, and the intended use.
Will AI replace insurance underwriters and claims adjusters?
AI reduces repetitive work and helps professionals access and evaluate information. Underwriting and claims decisions still require appropriate human judgment, accountability, and regulatory controls.
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