Today’s insurance markets face mounting pressures ranging from increased loss severity and CAT volatility to shifting risk profiles and rising operational costs. In this environment, underwriting accuracy and speed have become critical levers for maintaining profitability and competitive positioning.
To stay ahead of the pack, carriers must be more precise, agile, and customer-focused while also being able to write larger, more complex risks with confidence.
As competition intensifies, many insurers are looking to artificial intelligence (AI) as a strategic advantage to modernize decision-making and strengthen underwriting performance.
Managing Incoming Submissions with AI
The role of an underwriter has become increasingly complex. Today’s underwriters are expected to handle growing volumes of submissions faster, more accurately, and to accomplish all this with limited resources.
A growing number of brokers and agents now use agency management systems to distribute submissions to multiple carriers at once. As a result, underwriters are inundated with opportunities, many of which fall outside their appetite or guidelines. In this new landscape, the first to hit the client’s inbox often wins the business, making manual review of each submission infeasible.
AI can increase underwriting velocity by eliminating this “noise.” An AI agent trained to understand your underwriting guidelines can automatically filter out submissions that don’t align with your appetite. Beyond filtering, AI can also prioritize submissions based on the criteria that matter most to your business – e.g., risk alignment, premium potential, agent relationships, etc. – depending on how the system is configured.
The flexibility created by AI opens immediate capacity, allowing underwriters to focus on higher-quality risks with greater potential to convert. As a result, they can dedicate more time to analyzing complex accounts and ensuring quotes are both competitive and appropriately priced.
How AI Tackles Incomplete Submissions
Another common underwriting challenge is managing incomplete submissions. A significant portion of an underwriter’s time is spent tracking down missing information, often waiting on responses from busy brokers and agents who may not reply promptly. This slows down the underwriting process and drains valuable capacity. AI reduces this inefficiency by automatically identifying incomplete submissions, initiating requests for missing data, and following up as needed. These submissions can also be deprioritized in the queue until the required information is received, allowing underwriting teams to focus on submissions that are ready for evaluation.
Often, submissions come packaged with large volumes of data, necessitating underwriters to weed through complex documents for details essential to evaluating risks and writing quality policies. An integrated AI solution can surface key risk indicators, compare them against historical benchmarks, and highlight outliers to support faster and more informed decision-making.
Using AI to Minimize Inconsistent Risk Evaluation
Underwriters often rely on their individual experience, judgment, and personal underwriting style when evaluating risks. While this expertise is valuable, it can also lead to inconsistencies in how similar submissions are assessed, particularly when underwriting teams are distributed across regions or levels of experience.
For example, one underwriter may decline a risk that another would quote or apply different pricing assumptions for a comparable exposure. These inconsistencies can reduce profitability, create confusion with distribution partners, and complicate the uniform application of portfolio-level strategies. Compounding this issue, underwriting guidelines are frequently updated, but those changes may not always be communicated clearly or consistently across all teams, causing further variation in decision-making and risk selection.
AI-powered underwriting introduces standardized, model-supported risk scoring that works alongside human judgment. This helps reduce inconsistencies and brings greater accuracy, clarity, and transparency to decision-making.
Overcoming Email-Driven Underwriting Challenges with AI
Much of underwriting communication remains trapped in email inboxes, causing inefficiency throughout the submission process. Critical information from agents, brokers, and internal teams is often scattered across lengthy email threads, making it difficult to track updates, retrieve key documents, or ensure timely follow-up.
Fragmented communication also slows collaboration, delays decision-making, and increases the risk of overlooked details and missed opportunities. Without centralized, automated processes for managing these interactions, underwriters waste valuable time digging through emails instead of focusing on evaluating and quoting quality risks.
AI can also streamline underwriting communication by extracting key information from emails, automatically organizing it into structured formats, and flagging action items. AI identifies attachments, pulls relevant data points, and associates messages with the appropriate submission files at up to 90% accuracy, reducing the need for manual tracking and coordination. It can also create reminders for outstanding tasks, suggest follow-up actions, and even draft responses based on context.
AI frees underwriters from inbox overload, bringing visibility and automation to email-driven workflows to support faster, more informed collaboration across teams.
AI Can Deliver Smarter Pricing Decisions
Underwriters often lack access to information they need to price large or nuanced risks competitively. In many cases, this data exists but is scattered across multiple systems, buried in documents, or delayed by manual workflows. Without easy access to this information, underwriters sometimes are forced to rely on incomplete data or outdated assumptions, which can result in mispriced risks, slower quote turnaround times, and missed opportunities – especially for complex accounts where deeper insight is critical to making confident, competitive decisions.
The absence of streamlined tools limits underwriters’ ability to act efficiently and effectively in today’s fast-paced market. AI can bridge these gaps by accelerating data aggregating from multiple sources, extracting key insights from documents, and presenting underwriters with a clear, organized view of the risk.
AI tools can instantly analyze historical claims, third-party data, and contextual risk factors, empowering underwriters to make faster and more accurate pricing decisions. With AI, underwriters can reduce the time spent searching for information to enhance visibility into complex risks, enabling experts to respond more competitively and confidently in a market increasingly differentiated by speed and precision.
AI Turns Data Overload into Actionable Underwriting Insights
Underwriters often lack the tools to turn large volumes of data into actionable insights. Submission files, loss runs, supplemental applications, and third-party data sources all contain valuable details, but without intelligent systems in place, much of that information goes underutilized. Manually sorting through lengthy documents to identify key risk factors slows the underwriting process and increases the likelihood of overlooking important variables.
This lack of visibility can lead to inconsistent decision-making, delayed quotes, and missed opportunities to differentiate on pricing or coverage strategy. The challenge is not the absence of data, but the inability to efficiently extract and apply it in a meaningful way.
AI addresses this challenge by quickly finding, organizing, and analyzing data from a wide range of sources. It can also integrate third-party data to enrich the risk profile, helping underwriters make more informed decisions with greater speed and accuracy.
By transforming raw data into actionable intelligence, AI empowers underwriters to respond faster, price more effectively, and ensure consistency across accounts, all while reducing the time spent on routine data processing.
AI Streamlines Underwriting Support
Another common cause of lost underwriting capacity is the significant time spent on administrative tasks. From logging case details and organizing documentation to ensuring files meet internal and regulatory standards, these responsibilities are time-consuming and repetitive. Adding to the challenge, underwriters often manually rekey data from ACORD forms, loss runs, emails, and supporting documents into internal systems.
This not only slows decision-making but also introduces potential for errors and inconsistency. Time is spent on administrative work instead of evaluating risks or collaborating with brokers creates significant drag that creates a backlog, delays outcomes, and reduces overall efficiency.
This is where AI can deliver meaningful impact by automating data extraction, streamlining documentation, flagging missing information, and ensuring records are complete and compliant. By substantially cutting the administrative burden, AI allows underwriters to focus on strategic analysis, pricing decisions, and stronger broker engagement, ultimately improving both productivity and underwriting outcomes.
AI is no longer a future concept; it is the technology of today. Across industries, businesses are adopting AI to gain a competitive edge, and insurance is no exception. There are several practical ways to integrate AI into your underwriting workflows to enhance both performance and efficiency. By leveraging AI, underwriting teams can manage business more effectively, respond to customers with greater speed and accuracy, and ensure pricing aligns with risk. Just as importantly, AI enables underwriters to shift their focus to higher-value tasks while supporting a healthier work-life balance through reduced administrative burden and smarter task allocation.
If you need even further reason to transform your business, know this: 75% of your competitors are making AI-powered underwriting a top strategic priority in 2025. However, it’s not too late for you to gain the same advantage. Start the conversation with an AI partner like Roots and give your team the edge to work smarter, not harder.
Curious how insurers are putting Roots to work? Check out our case studies to see insurance-specific AI in action.