How can businesses make smart AI bets when they cannot even see the full picture of what is already happening inside their own organization?
In this episode of Business Tech Perspectives, I sit down with Russ Fradin, CEO of Larridin, for a conversation about one of the biggest blind spots in enterprise AI right now. While many leaders are focused on adoption, experimentation, and speed, Russ argues that a more fundamental issue is being overlooked. Companies are investing in AI at scale, but many still lack a clear view of which tools are being used, who is using them, and whether any of it is delivering measurable value.
What made this conversation so timely for me was Russ’s perspective as someone who has lived through several major waves of technology change. From digital advertising and mobile to cloud and now AI, he has seen what happens when innovation moves faster than the systems designed to manage it. In this case, the challenge is what he calls the AI visibility gap, where tools are spreading across teams faster than IT, finance, and leadership can track. That creates questions around governance and cost, but it also raises a more practical business issue. If you do not know what is being used, how do you know what is working?
We also get into why Russ believes experimentation is not the problem. In fact, he makes a strong case that organizations should be trying lots of tools right now. The issue is when those experiments happen without measurement, without accountability, and without a framework for understanding productivity and return on investment. I particularly liked his point that this is not about shutting innovation down. It is about building the right measurement, governance, and data foundations so businesses can experiment with confidence instead of chaos.
Another part of the conversation that stayed with me was the idea of identifying the people inside an organization who are already becoming dramatically more productive with AI. Russ talks about how some employees are already figuring out what great looks like, while others are still staring at a blank prompt box unsure where to begin. That creates an opportunity for leaders to stop treating AI adoption as a vague aspiration and start turning real employee behavior into repeatable playbooks that can help the wider workforce improve.
This episode is really about the gap between AI excitement and AI accountability. If AI is now moving into every corner of the enterprise, leaders need more than enthusiasm. They need visibility, they need measurement, and they need a way to connect spending with outcomes in real time. So as AI use continues to spread across your own business, do you actually know what is happening under the surface, and what do you think companies should be measuring first? Share your thoughts.
The link Russ mentioned during the podcast can be found here: