04.22.26 By Bridgenext Think Tank
Underwriting has always been a judgment-driven discipline. That hasn’t changed. What has changed is the sheer volume of thinking underwriters must do before they can even apply that judgment. They’re navigating fragmented submissions, shifting risk signals, tighter regulations, and escalating loss volatility. The pressure is real: global insured catastrophe losses reached $80 billion in the first half of 2025, nearly double the 10-year average, continuing into 2026 with annual losses consistently exceeding $100 billion.1 In this environment, speed, clarity, and confidence aren’t just nice-to-haves, they’re essential.
Underwriting remains one of the most critical drivers of profitability, with performance improving in 72% of global markets in 2024, even as margin pressures intensify.2 That makes the real challenge clear: it isn’t access to information; it’s the ability to interpret it fast enough to make sound decisions. This is where AI copilots make their mark. They’re not here to replace underwriting expertise. They’re here to lighten the cognitive load that’s slowing it down.
This blog explores the growing strain on underwriting, how it shows up in everyday work, and how AI copilots can help teams move faster without compromising judgment.
The underwriting environment has become significantly more demanding, and the numbers make that clear. Data is pouring in from an ever-expanding array of sources, while 76% of US insurers have already implemented generative AI in at least one business function, adding even more inputs, signals, and expectations into underwriting workflows.3 At the same time, risks are evolving faster. Climate-related losses have consistently crossed $100 billion annually in recent years, making risk assessment more volatile and harder to predict.4
Emerging exposures like climate and cyber are not just adding complexity, they are reshaping underwriting itself. At the same time, regulators are increasing scrutiny around governance, transparency, and model explainability, especially as AI adoption accelerates across the industry. Clients, meanwhile, expect faster responses and a seamless experience.
All of this creates real pressure. Underwriters are now evaluating more variables, in less time, with greater accountability. The rhythm of their work has changed and the result is a cognitive bottleneck, one that directly impacts decision quality, profitability, and competitiveness.
The strain is most visible in the day-to-day realities of underwriting. Instead of focusing on risk judgment, underwriters are often consumed by:
While individually these tasks are necessary, collectively they can be exhausting and the impact is real. In many cases, underwriting still takes 3–5 days for a decision, largely due to manual effort and fragmented workflows. With AI, that can drop to minutes with over 99% accuracy, showing just how much time is lost in the current process.5 This friction shows up as:
For leaders, the trade-off is clear- more time spent gathering information, less time spent making decisions. When that balance shifts, outcomes improve. One global insurer’s AI assistant handled over 13,000 queries post-rollout, saving 65,000 minutes (135 working days) in information gathering. And at scale, the upside is even bigger: AI can reduce manual underwriting tasks by up to 67% and cut cycle times by 30%+. That’s not just efficiency. That’s capacity coming back to where it belongs: decision-making.
AI copilots are gaining traction because insurers need a smarter way to navigate complexity. The value is clear: copilots help underwriters think with less friction. They summarize submissions, retrieve relevant guidance, surface anomalies, and recommend next steps, removing the clutter around decisions so underwriters can focus on making them with clarity and confidence.
This is critical in a market where speed, accuracy, and consistency directly impact performance. The industry has already moved beyond experimentation. While many insurers are adopting generative AI in business functions, only a fraction of initiatives progress beyond proof of concept. This highlights a key insight: the tool itself isn’t enough; workflow integration is equally vital.
A copilot that operates outside the process adds another system to manage. A copilot embedded within the process becomes an integral part of how work gets done.
This isn’t about removing people from underwriting. It’s about helping them do their best work. AI is good at speed. It can process large volumes of information, spot patterns across thousands of data points, and flag potential risks in seconds. But underwriting decisions don’t live in clean datasets. They live in context and that’s where human judgment comes in. Across the industry, even as AI adoption grows, insurers are not taking humans out of the loop. In fact, recent studies show that most AI systems in insurance are still designed with human oversight built in, especially in underwriting and pricing decisions.6
There’s a reason for that. Underwriters don’t just assess risk. They interpret incomplete information, balance trade-offs, negotiate with brokers, and make calls where there is no obvious “right” answer. This is especially true in commercial and specialty lines, where every risk has its own story. Regulation reinforces this reality. AI systems used in underwriting are increasingly classified as “high-risk,” requiring explainability, strong governance, and clear human oversight.7 Trust plays a role too. Nearly 1 in 4 consumers (24%) say they don’t fully trust how insurers use their data, which makes human accountability even more important in decision-making.8
So while AI can accelerate analysis, it cannot own the outcome. Underwriting still depends on knowing when to say yes, when to pause, and when to walk away. It’s about judgment, not just calculation. AI copilots support that process. They take away the repetitive load, surface what matters, and give underwriters the space to think more clearly. The decision, however, still belongs to the human.
The future of underwriting is AI-augmented. In this future, underwriters spend less time on repetitive manual tasks and more on strategy, portfolio management, and broker advisory work. AI takes on the heavy lifting of data synthesis and retrieval, while human expertise remains focused on decisions that demand context, judgment, and accountability. This shift delivers tangible business value. It enables insurers to move faster, select risks with greater precision, price more competitively, and respond more agilely to market changes. It also elevates the insurer’s external presence. Brokers, clients, and investors see a company that is more responsive, capable, and future-ready. The opportunity is significant. Industry estimates show that AI-enabled underwriting can improve loss ratios, reduce expense ratios, boost productivity, and accelerate quote turnaround times. These aren’t incremental gains, they redefine how insurers compete.
AI copilots will only deliver value when they are seamlessly integrated into the operating model, not simply layered on top of it. This requires three key elements:
For CIOs and CTOs, legacy systems and siloed data won’t provide the real-time context copilots need to succeed. For heads of Data and AI, the focus must shift from merely storing data securely to making it actionable in decision-making. For operations leaders, the opportunity lies in removing friction and reducing the cost per decision. For business leaders, the priority is to track measurable improvements in speed, quality, and consistency. The insurers that will lead the market are those who align people, platforms, data, and governance into one cohesive, high-performing system.
Underwriting is facing a cognitive overload. That’s the real challenge. AI copilots matter because they alleviate this pressure. They cut through the noise, enhance decision-making, and give underwriters the freedom to focus on judgment, relationships, and risk quality. But technology alone isn’t the solution. The true advantage lies in creating a connected underwriting ecosystem where people, data, platforms, and governance seamlessly align.
This is where Bridgenext makes the difference. By removing system friction and embedding intelligence into underwriting workflows, insurers can transform cognitive overload into faster, higher-quality decisions. The outcome? Superior risk results, scalable growth, and a commanding market position.
Connect with us to turn your working setup into a winning system.
References
1 www.oecd.org/en/publications/global-insurance-market-trends-2025_0d11ecf4-en.html
2 www.gartner.com/en/documents/5409263
3 www.gartner.com/en/documents/6678434
4 www.insurancebusinessmag.com/reinsurance/news/breaking-news/ai-raises-the-bar-for-reinsurance-underwriting–without-replacing-judgment-567980.aspx
5 www.insurancebusinessmag.com/us/news/technology/only-30-of-insurer-ai-projects-make-it-past-pilot-stage-report-finds-564367.aspx
6 www.guidewire.com/resources/blog/technology/how-ai-and-ecosystem-innovation-are-transforming-underwriting
