219: "Why You Cannot Shortcut The Human Part Of AI Implementation" ft. Justin Coats

Justin Coats breaks down the idea of “super apps” for AI and what that really means for corporate adoption, change management, security, and tokenomics. 🧭 Conversation Highlights Justin defines super apps as AI workspaces that combine model access, agents, and computer or browser use in one interface. Erik pushes on the human side of adoption and asks whether waiting for the tech to mature actually reduces the real cost. They discuss how change management needs education, executive buy-in, an

🧠 Erik’s Take Erik treated this as a compact leadership debrief, not just a recap. The uncomfortable through-line for him was the idea that forward movement can come from releasing control, even when his natural instinct is to define the result, set the plan, and solve the problem. He keeps circling back to three tensions Jaclyn named: letting go is a skill, specificity can become a ceiling, and early goals can manufacture clarity without actually creating it. He also noticed something personal

🧠 Erik’s Take Erik treats this “short review” as a real question, not a tech demo. The core shift for him is moving from AI personalization as memory, to AI personalization as understanding: a predictive model of how a specific person learns, gets stuck, adapts, and responds. He also feels the conversation complicate the regulation instinct. It is not just “should we regulate,” but “who gets to decide what these tools can do” and what values get embedded. Finally, he leaves with a more intellect

Jaclyn Orent joins Erik to unpack her path from legacy sales success to a mission focused on culture change. She explains “selling from force vs. from listening,” the science language behind her work on surrender and consciousness states, and how identity-based marketing helps cultural catalysts opt in, align, and execute systemic change from the top down. 👤 About the Guest Jaclyn Orent is CEO and co-founder of the Cultural Catalyst Network. After nearly 20 years in sales and sales leadership,

Gavin Lorenzo explains why AI struggles to truly understand people, not just store facts. He shares how Tellbloom builds a “third-party”-like model of a user by learning from passive interactions, then uses that context to improve responses. The conversation also covers testing, privacy, and the governance challenges raised by frontier models like Fable. 👤 About the Guest Gavin Lorenzo is the founder of Tellbloom, working from Georgia with support from the Alabama Entrepreneurship Institute. A

Erik and Justin tackle viewer-submitted AI questions in a practical Q&A focused on trust, compliance, meeting recording boundaries, and how to scale an AI pilot without losing control. 🧭 Conversation Highlights Trust AI at work by treating it like human trust: start slow, use tasks you can verify, and measure over repetition. For compliance constrained data, begin with low-risk admin work, then use governance, vendor requirements, and existing industry frameworks to guide decisions. Decide wh