From Wearables to $100M: How Kinetic Comp Learned to Listen to Its Customers
Four years ago, Kinetic was a wearable device company that had just launched a workers comp MGA. The thesis was straightforward: take injury prevention technology that had been proven in large self-insured environments and bring it to mid-market employers through an insurance product. The technology worked. The adoption did not.
What happened next is one of the clearest examples of product-market fit discovery in recent InsurTech history. Kinetic did not double down on the thesis. It asked its customers what they actually needed. And the answer was not prevention data — it was help with the claims that were already in the system.
Today Kinetic is north of $100 million in premium, writing 25% of all workers comp flowing through Nationwide Mutual's programs, and operating as a software-based claims oversight platform that happens to still have a few thousand wearable devices in the field. In Episode 155 of InsurTechTalk, CEO Adam Price joined me for a conversation about the pivot, the data opportunity in workers comp, what MGA investors actually want to hear, and an honest AI self-assessment that every InsurTech founder should read.
About Adam Price
Adam Price is the CEO of Kinetic Comp, a monoline workers comp MGA he joined in 2020. Kinetic was founded out of a wearable device company that had been selling injury prevention technology to large self-insured employers. Adam led the company's pivot from hardware to software, its rebranding from Wear Kinetic to Kinetic Comp, and its growth from an early-stage MGA to over $100 million in premium — all while maintaining profitable underwriting results and deepening a full-stack capacity partnership with Nationwide Mutual. He is based in Mexico City, where he has lived since 2022.
The Wearables Problem — and Why It Was Never About the Technology
The story of Kinetic's pivot starts with a distinction that most prevention technology companies miss: the technology works. The change management does not.
Why Prevention Technology Struggles in Mid-Market Workers Comp
Wearables, computer vision, and safety apps are genuinely effective when properly implemented — the problem is the word "when"
Large self-insured employers have the infrastructure, data fluency, and internal resources to implement prevention programs and act on the data they generate
Mid-market employers — 50 to 2,000 employees, $500K in premium — typically do not have those structures
Prevention technology asks the policyholder to change behavior, retrain staff, and respond to leading indicators that predict future injuries — before anything has actually gone wrong
The result is what Adam calls "selling phantom benefits": at the end of the policy year, the best case pitch is "trust us, you avoided two injuries"
That is not a compelling sales argument. And it is extremely difficult to maintain adoption and usage over time when the value is invisible
The Political Capital Problem
Adam's framing of why some prevention technology works and some does not was one of the sharpest insights in the conversation. The differentiator is not ROI — it is political capital.
Flood sensors on a pipe require zero political capital. The sensor detects a leak. Nobody argues with the pipe
Telematics on a truck requires a truck driver to accept monitoring — and the truck driver's incentive is speed and throughput, not safety scores
Wearables on a warehouse worker require continuous training, employee acceptance, and ongoing change management — all of which cost time, attention, and internal goodwill
Computer vision in a warehouse requires employees to accept being recorded — a "big brother" concern that never fully goes away
The higher the political capital cost, the lower the sustained adoption — regardless of how good the underlying technology is
The Pivot: From Hardware to Claims Oversight Software
Kinetic did not abandon wearables overnight. The transition took years and was driven by a deliberate decision to let customer feedback — not internal conviction — determine the direction.
How the Shift Happened
Kinetic's internal account management team had been 100% focused on hardware adoption and usage metrics
As questions started to emerge about whether hardware was delivering the value customers needed, the team began shifting its focus toward claims oversight and data impact
The incentive structure for account managers was gradually reoriented: less focus on device utilization, more focus on claims outcomes and software engagement
Customer conversations confirmed the direction: policyholders consistently said they understood what the wearables were doing, but what they actually needed help with was the claims already in the system — is this claim being managed well, are reserves accurate, can we close faster, can we close cheaper
The rebranding from Wear Kinetic to KineticComp.com was the external signal of a shift that had already happened internally
What Kinetic Actually Does Now
The core of Kinetic's current value proposition is data fusion applied to claims oversight — and it is more specific than the generic "data company" positioning that every insurance organization uses.
The Claims Oversight Model
Mid-market workers comp claims are a significant financial event for a $500K premium business — employers care about them, want visibility into them, and want someone actively managing them
The insurance industry's dominant strategy for managing expense load is to maximize adjuster throughput — 300 claims per adjuster at large carriers, 100 at best-in-class
Kinetic's software-based approach asks a different question: what would claims management look like if one adjuster had only one file?
Software makes that model scalable. By fusing data from claims systems, adjuster notes, AI agent communications, loss control activity, and policyholder engagement into a single account-level view, Kinetic can surface the insights and actions that get missed when adjusters are managing hundreds of files simultaneously
The result is a claims experience that feels like dedicated attention — at the expense load of software, not headcount
AI at Kinetic: An Honest "5 or 6 Out of 10"
Adam's self-assessment of where Kinetic sits on the AI spectrum was one of the most grounded answers to this question I have heard from an InsurTech CEO.
On a scale of 1 (using ChatGPT to improve emails) to 10 (AI agents running everything, no employees needed), Adam placed Kinetic at a 5 or 6
Specific AI use cases in production: claims note digestion, account-level visibility dashboards, AI agent bots touching aspects of the claims workflow, and rapid internal tool development — a working hosted platform built in a weekend using AI development tools
Hard limits: all underwriting is done by humans, AI-assisted communications escalate to a person after three unresolved back-and-forths, and broker relationships at commission levels that matter to real people are handled by humans
The honest assessment: brokers writing $500K in workers comp premium are not interested in talking to a chatbot
Building an MGA: What Investors Actually Want to Hear
Adam's advice for founders building MGAs — drawn from Kinetic's own fundraising journey and current conversations with growth investors like FTV Capital — was direct and practical.
Tips for MGA Founders Raising Capital
The pool of investors who genuinely understand MGA underwriting is small — probably countable on two hands for early-stage, fewer for risk-bearing businesses
Do not waste time with generalists who cannot analyze underwriting advantage; find the specialists early
The MGA investor's central question is always the same: what is your underwriting advantage and how does it show up in loss ratio results
MGA enterprise value does not trade on premium. It trades on capacity relationship health — driven by profitability — and cash flow. Both are direct outputs of underwriting performance
Drop the $6 trillion TAM slide. Replace it with: how many nursing homes are in your target market, how many can you realistically write, and what does your unit economics look like when you do
At seed, show a credible thesis. At Series A, show early maturity. At Series B, show that your dollar-in to dollar-out ratio is known and scalable
Key Takeaways
Prevention technology's failure in mid-market workers comp is not a technology problem — it is a change management and political capital problem
The most durable InsurTech pivots are driven by customer listening, not internal conviction — Kinetic's shift from hardware to software came directly from asking policyholders what they needed
The opportunity in workers comp is not expense ratio optimization — it is delivering a claims experience that feels like dedicated attention, at software scale
MGA enterprise value is built on underwriting profitability and capacity relationships — not premium volume or TAM
AI at a 5 or 6 out of 10 is honest, functional, and probably more accurate than most companies claiming a 9