AI underwriting has moved from buzzword to boardroom priority. A recent study of 430 senior insurance underwriting leaders indicates a significant shift: AI is set to transform the field, with insurers projecting AI adoption to grow from 14% to 70% within the next three years.
When we talk with P&C insurance leaders, they consistently agree on why they need to modernize with AI underwriting workflows: rising submission volumes, broker expectations for faster turnaround, growing data complexity, and workforce constraints all demand a more modern underwriting approach.
Yet knowing how AI will help underwriters quote faster and more accurately is very different than knowing how to actually start moving forward with AI for data ingestion, submission prep and underwriting workflows.
Across the industry, many AI underwriting initiatives stall before they deliver real impact. Not because leaders doubt the potential, but because execution feels risky, disruptive, or uncertain.
It’s in that gap between intent and action that underwriting modernization efforts most often lose momentum.
This article offers a practical leadership perspective on how insurance organizations move AI underwriting from idea to execution—without triggering large-scale disruption, accuracy concerns, or another stalled pilot.
Why AI underwriting projects often struggle to move forward
Insurance leaders tell us AI underwriting rarely stalls because leaders lack conviction. More often, it slows when execution feels uncertain, especially in an industry where accuracy, trust, and consistency are non-negotiable.
That hesitation isn’t unique to insurance. MIT’s State of AI in Business 2025 report found that 95% of GenAI pilots never make it to production—not because AI doesn’t work, but because most tools aren’t built for real operational friction. MIT refers to this gap as the “GenAI Divide.”
Submission prep and underwriting workflows are squarely in that friction. Submissions are messy, accuracy is a must, and workflows carry real risk. When AI isn’t consistently trained by millions of complex insurance documents annually, projects stall or fail in insurance.
In practice, that stall can usually be traced back to a few common causes.
Competing priorities across the business
Growth, profitability, regulatory pressure, broker expectations, and core system initiatives all pull attention in different directions.
Technology fatigue and AI skepticism
Years of long, costly implementations have made teams cautious about anything that resembles another drawn-out transformation.
Fear of disrupting underwriting accuracy or broker trust
Even small errors can have outsized consequences, making leaders hesitant to introduce automation without proof.
Misalignment across underwriting, operations, IT, and leadership
Each group has different goals, timelines, and risk tolerances—stalling momentum before execution begins.
None of this signals resistance to innovation. It explains why progress depends less on ambition—and more on how execution risk is addressed.
What leaders need to see before committing to execution
What we’ve found for AI underwriting to move forward is that leaders need more than technical capability. They need to see results early, have confidence in the data accuracy, and trust the process for scaling enterprise wide.
Clear, contained scope
Projects that try to modernize everything at once often feel unmanageable. Leaders want a defined starting point that addresses a real bottleneck without overextending teams.
Measurable outcomes
Execution requires visibility into success. Leaders need concrete metrics—faster quote cycles, reduced manual effort, improved data quality—not abstract promises of efficiency.
Low disruption to existing workflows
Underwriting teams don’t have the capacity for large-scale workflow changes or disruption to daily operations. The safest AI initiatives integrate into existing workflows and improve them incrementally.
Confidence in data accuracy and transparency
Experience working with seven of the top 15 commercial P&C carriers tells us that AI underwriting only works when underwriting teams trust the data. Transparency, auditability, and explainable AI are essential for adoption, especially when decisions affect pricing, risk selection, and broker relationships.
Early proof of value
Leaders continually tell us they are far more likely to scale AI initiatives once they see tangible results in production, not just in a pilot environment. They want to know how soon they can expect to start quoting faster and pricing more accurately, without adding headcount
This is why leaders start small: one high-impact use case, minimal IT lift, then scale—rather than launching a risky, IT-heavy transformation.
Why data ingestion is the turning point for AI underwriting execution
Many AI underwriting conversations focus on advanced analytics, predictive models, or automation downstream in the workflow. In reality, execution usually succeeds—or fails—much earlier.
Submission data is where underwriting bottlenecks and data inaccuracy accumulate, making it the best place to start with modernization.
Broker submissions arrive in endless formats. Loss runs, exposure schedules, and supplemental documents are often incomplete, inconsistent, or difficult to interpret. Before underwriting decisions can improve, this data must be transformed into something usable.
This is why data ingestion becomes the turning point:
- It’s the first true operational bottleneck in underwriting.
- Manual preparation slows quote cycles and increases rework.
- Poor data quality undermines trust in every downstream AI output.
- Clean, structured data enables everything that follows.
For many carriers, execution doesn’t start with advanced AI models. It starts by fixing how submission data enters the underwriting process.
How successful teams move from pilot to real-world impact
Organizations that move AI underwriting initiatives into production tend to follow a disciplined, incremental approach. Rather than pursuing broad transformation upfront, they focus on reducing execution risk, proving value in targeted areas, and building confidence through measurable operational gains.
Over time, this creates the momentum and alignment needed to extend AI from pilot projects into durable, real-world impact.
Start now and grow confidently
Rather than replacing entire underwriting systems, successful teams focus on one high-friction use case—often loss run ingestion or submission intake—and expand only after proving value.
Use modular, focused implementations
Modular approaches allow carriers to modernize at their own pace, layering improvements without disrupting core platforms or workflows.
Build internal champions through operational relief
When underwriters experience immediate relief—less manual data entry, faster access to usable information—they become advocates rather than skeptics of AI initiatives.
Let results drive alignment
Early, measurable wins create momentum. Within months, teams often see dramatic reductions in manual prep time and faster turnaround without sacrificing accuracy.
Start now and grow confidently. In real-world deployments, this approach has delivered outcomes such as:
- Loss run summaries completed 24x faster within three months
- 75% reductions in rating preparation time within six months
- Over 50% time savings across submission intake within a year
By deliberately sequencing change and proving value early, teams lay the foundation for lasting operational impact.
What “execution” actually looks like in practice
When AI underwriting is implemented successfully, its impact shows up quickly in everyday operations. Execution is less about introducing new capabilities and more about removing friction from existing workflows: shortening turnaround times, reducing manual effort, and improving the quality and consistency of underwriting decisions.
These outcomes provide a clear signal that AI has moved beyond experimentation and is delivering practical, production-level value.
Faster quote cycles
When AI underwriting moves into production, one of the earliest and most visible impacts is speed. By removing manual data preparation from the front of the workflow, carriers can move from prolonged review cycles to materially faster quoting without sacrificing underwriting rigor.
Proven impact
Quoting accelerated 5x (20 days → 4 days) in highly manual, and triple-keying environments, and 2.5x faster (5 days → 2 days) in already-optimized workflows.
Reduced manual preparation
Execution also shows up in how underwriting time is reclaimed. Automating data ingestion and preparation eliminates hours—or days—of rekeying, document review, and reconciliation. As a result, underwriters spend less time preparing submissions and more time evaluating risk, improving both throughput and consistency across the book.
Proven impact
One mid-sized P&C carrier saw these results with a start now and grow confidently approach:
- 24x faster loss run summaries within 3 months
- 75% reduction in rating preparation time within 6 months
- 54% time savings across overall submission intake within 12 months.
Better broker responsiveness
As manual bottlenecks are removed, carriers can respond to brokers in near real time. Instead of waiting hours or days to identify missing or inconsistent information, teams can surface issues and provide feedback within minutes. This level of responsiveness materially improves broker experience—even on accounts that don’t ultimately bind—and plays a critical role in earning placement on preferred carrier lists.
Proven impact
Broker responses delivered within minutes of submission when documentation or key fields are incomplete, rather than hours or days later.
More confident underwriting decisions
Perhaps the most important aspect we’ve learned from over 50 AI underwriting implementations is that execution strengthens confidence. With structured, verified data delivered consistently at the point of decision, underwriters can trust what they’re reviewing and focus on judgment rather than validation. This clarity supports more consistent pricing, better risk selection, and greater confidence across the underwriting organization.
Proven impact
97%+ accuracy on automated extractions of insurance-specific documents, giving underwriters trusted insights to price policies and assess risk with confidence.
Execution doesn’t require a full transformation. It requires sustained momentum built on trusted results.
Moving AI underwriting from idea to execution
AI underwriting doesn’t stall because leaders lack ambition. It stalls when the path forward feels uncertain or risky.
The most successful carriers don’t wait for perfect conditions or comprehensive platform replacements. They start with a focused, low-disruption step that delivers clarity, confidence, and measurable results, and build from there.
We find the most effective path to leverage AI for faster quoting, increased risk assessment, and triaging growing submission volumes starts by solving the most immediate bottleneck, proving value quickly, and creating the trust needed to scale.
Want a deeper, step-by-step roadmap?
Download Underwriting Modernization with Confidence: A Roadmap to Leading Transformation to explore how insurance leaders are sequencing change, reducing risk, and building alignment across their organizations.