The insurance industry is running out of people. Underwriters are aging out, fewer young professionals are entering the field, and carriers have been trying to solve a workforce problem with workflow tools. Meanwhile, submission volumes keep climbing. The gap between what the industry needs and what it can deliver is widening rapidly.
AI has arrived at a convenient moment. However, convenience and strategy are not the same thing.
Founded in 2020, Pibit.AI is helping push the sector into its next phase. The company has expanded its proprietary CURE underwriting platform, which automates data extraction, enrichment, and risk flagging while giving underwriters a single consolidated workspace. Customers now process billions of dollars in submissions annually without increasing headcount, a meaningful shift for an industry long defined by manual review. The company has also announced a $7 million Series A led by Stellaris Venture Partners, with participation from Y Combinator and Arali Ventures.
Founder Akash Agarwal watched his father slog through paperwork and assumed for years that insurance was unfixable. Ultimately, he came to understand that, “The issue was never the industry. Insurance is incredibly unstructured, and for years everyone assumed it couldn’t be automated. Back then I thought this industry couldn’t change. Now the technology exists to fix problems carriers have been facing for decades.”
The tech has finally caught up. The workforce has not. To move forward without hollowing out its own pipeline, the industry needs a different approach.
Here are the three steps carriers need to take now.
1. Redesign underwriting roles
The industry has to shift junior underwriters from data janitors to risk analysts. Historically, new talent learned through volume. They reviewed documents, researched exposures, enriched submissions, and built intuition through repetition. Today, AI systems are beginning to take over much of that front-end work: parsing documents, gathering data, and flagging early signals before a human even touches the file.
Platforms like Pibit.AI’s CURE illustrate how quickly this shift is happening. If core tasks move upstream to automation, the role itself has to evolve. Junior underwriters should be spending more time on decisions, rationale, and portfolio thinking, not clerical review. The work has to become judgment-first from day one.
2. Build a structured training path
AI should accelerate learning, not eliminate it. Carriers need structured models for exposure to real underwriting complexity: edge cases, ambiguous risks, contradictory signals, and scenarios where human reasoning is essential. Automation can compress the time it takes to understand data, but judgment still comes from guided repetition.
Training programs should explicitly pair AI output with human review, mentor-driven decision walkthroughs, and case-based learning. The next generation of underwriters cannot become passive validators of machine recommendations. They need to be students of risk, not auditors of automation.
3. Make the work aspirational
Young professionals are not avoiding insurance because it is difficult. They are avoiding it because it looks monotonous. From the outside, underwriting appears slow, procedural, and disconnected from impact. AI can change that, but only if the industry actively reframes the work.
Pibit.AI’s customers across the ecosystem include HDVI, Shepherd Insurance, RMS Insurance Brokerage, Kinetic, and Method Insurance Company. They are already seeing the operational impact of adopting Pibit.AI: reported results include meaningfully faster underwriting cycles, stronger productivity per underwriter, and improvement in loss performance. For carriers and MGAs, these types of gains show up in greater capacity, steadier growth, and clearer risk visibility.
Adam Price, CEO at Kinetic, noted that the platform has helped his team manage a very high volume of annual submissions without adding overhead, supporting material growth in premium as they are able to respond more quickly and consistently. “Pibit.AI helps us handle more than a billion dollars in submissions … because we’re able to get those looks and quotes up and running,” he noted
What’s next?
The industry is at a crossroads. MGAs and new-generation brokers have moved faster because specialized risk requires specialized tools. Larger carriers are now following, not because they want to, but because combined ratios have squeezed margins and inefficiency is no longer sustainable. AI is becoming the standard.
But adoption and evolution are not the same. AI can make underwriting faster, more accurate, and more scalable. It cannot build a talent pipeline. And it cannot convince the next generation to choose a profession that still looks like it belongs in another era.
Stellaris Venture Partners, which participated in this round, has tracked this shift closely. As Partner Alok Goyal puts it, “Underwriting has long been constrained by manual reviews, inconsistent data and tools that haven’t kept pace with rising submission volumes.” Early results from platforms like Pibit.AI show that accuracy can improve, costs can decline, and quotes can move faster at scale.
As Agarwal puts it, “The technology is ready. The real question is whether the people will be.”