Russ Fradin, CEO & Co-Founder of Larridin – Interview Series

Russ Fradin, CEO and Co-Founder of Larridin, is a seasoned entrepreneur and executive with a remarkable track record in building and scaling successful technology ventures. He co-founded Larridin in August 2024 following over a decade of leadership at organizations such as Carbon Health, where he currently serves as Vice Chairman, and Dynamic Signal, which he founded and led as CEO for nearly ten years. Fradin has also held key board positions with innovative companies including Leanplum and CoachArt, reflecting his dedication to advancing technology, healthcare, and social impact initiatives.

Larridin is a next-generation technology company dedicated to transforming how organizations engage with data and decision-making. The platform leverages advanced artificial intelligence and automation to streamline complex business workflows, helping enterprises gain actionable insights faster and more intuitively. With a focus on efficiency, scalability, and intelligent integration, Larridin empowers companies to make smarter, data-driven decisions while unlocking new levels of operational excellence across industries.

You’ve helped build measurement platforms in digital advertising and analytics before launching Larridin — how did your earlier career and experiences lead you to see the need for a company dedicated to enterprise AI measurement and governance?

As a serial entrepreneur, I wanted my next venture to bring together my deep background in measurement and analytics with the transformative potential of AI. In countless conversations with enterprise leaders, one theme kept coming up: despite massive investments, companies had no way to track, measure, or optimize their AI initiatives. There were simply no tools giving them visibility or control over AI adoption, and that gap is exactly what inspired the creation of Larridin.

You often describe Larridin as an “intelligence layer” above all AI tools — how did that positioning come about, and what risks did you see that made it essential?

For enterprises, it’s becoming increasingly challenging to understand how AI is actually being used and whether it’s delivering real ROI. Many leaders are asking the same questions: Are employees truly using the AI tools we’ve invested in? How can we measure their impact? Which use cases are delivering the most value and worth scaling? Recognizing this visibility gap, we built Larridin as an intelligence layer across all AI tools, giving business leaders the ability to map, measure, and optimize AI usage, and ultimately turn it into tangible business outcomes.

Scout collects metadata (rather than content) to uncover AI usage across the organization — in which situations do those signals become ambiguous, and how do you validate or refine them?

Scout doesn’t just collect metadata; it also analyzes prompts to understand proficiency levels and usage patterns. Think of this platform as Google Analytics. It doesn’t track individual prompts or content; it actually aggregates and interprets trends that are updated in real-time across the organization. This flow of data helps refine insights over time, ensuring the visibility companies get is accurate, contextual, and actionable.

Nexus centralizes prompts, policies, and integrations — how do you persuade teams to shift workflows into Nexus rather than continuing with their ad hoc AI tools?

The key to adopting new technologies lies in driving behavioral change. In this case, building AI competency. People are eager to use AI, but how do you help them do it responsibly? It is likely that most employees don’t know about the risks of using unsanctioned tools or whether they’re staying compliant when interacting with certain apps.

From your vantage point, what are the most deceptive metrics or indicators that leaders tend to misinterpret as real AI maturity?

What every company should be asking today is whether the AI tools available across the company are delivering real value. One thing is the number of people using ChatGPT, for example. But how many of them are maximizing its use to the point that it brings a real competitive advantage and shows real value? Having the latest or most expensive AI tools does not mean everyone is going to use them and maximize them. Companies that learned how to benefit from AI are tracking and measuring its usage to ensure real ROI, scaling successful internal use cases, and creating a culture that fosters AI literacy for everyone.

When sanctioned and unsanctioned AI tools coexist, how do you isolate which usage truly delivers business impact?

The first step would be gaining visibility and understanding which tools are being used, by whom, how are they affecting performance, are they safe? Once companies map out the AI assets that are running in their ecosystem, it is time to dig into the metrics and measure the ROI of these tools. It is possible that an unsanctioned tool delivers more value than an authorized one, while others will just add noise and risk. When organizations have a clear landscape and real-time insights into what’s working and what’s not, they can build on their tech stack and keep moving forward as a whole.

 How do you balance governance (rules, monitoring, limits) with giving teams the flexibility and freedom to experiment and innovate?

Governance and innovation must walk hand in hand in the era of AI. The best way to accomplish this balance is by giving employees the tools and the opportunity to experiment with AI safely and responsibly. When governance becomes a blocker or is perceived as a bottleneck, creativity and experimentation will stall, but when it is an enabler, it empowers people to push boundaries and build new solutions confidently.

As new models, agents, and self-service tools emerge rapidly, how do you keep Larridin’s detection, connectors, and policies current and robust?

Adaptability and flexibility are two tenets for Larridin. New AI tools emerge every day, and people are willing to test them, whether they’re sanctioned or not. Our platform automatically detects new AI assets and their usage trends and has an ever-evolving connector library, ensuring that visibility is never lost. This dynamic framework allows organizations to reorganize their rules for safety and compliance in real-time.

Since Larridin’s founding, which of your early assumptions have surprised you most — particularly around adoption, resistance, or measuring value?

Today, companies live in an AI-divided world. Some people adopt it instinctively and naturally work with this tech, continuously experimenting and pushing innovation. Others may not have the time or inclination to climb the learning curve, which doesn’t necessarily mean they are resistant. Understanding that people are motivated by different things and promoting a change of behavior around AI by bridging these two worlds is essential for adoption and growth. Some organizations may not have realized this yet.

Also, the visibility issue is intriguing. In today’s fast-paced AI world, losing sight of the AI stack happens more frequently than anticipated, even with companies constantly conducting internal audits. In a recent survey we conducted, over 70% of IT and Finance enterprise leaders admitted they have lost visibility. This makes it difficult to measure the true value of AI, and many organizations lack the tools and processes needed to maintain up-to-date tracking and measurement of their AI applications.

Over the next 3–5 years, what is your ideal vision for enterprise AI governance and adoption — and how do you see Larridin’s role evolving toward that future?

Going forward, governance, safety, compliance, and adoption must operate in harmony. This integrated environment allows companies to thrive by empowering employees to create their own AI agents to support workflows and solve problems, freeing them to focus on strategic initiatives. Larridin is opening the path to this next-generation approach for the collaboration of AI and humans.

Thank you for the great interview, readers who wish to learn more should visit Larridin.