Value-First Platform: AI Data Readiness - Mar 31, 2026

Your AI metrics are probably lying to you. Every organization deploying AI tracks something. Adoption rates. Prompts per user. Time saved per task. Usage dashboards that light up green and make everyone feel like progress is happening. But here's the uncomfortable question nobody wants to ask out loud: Is any of this actually making your business better? Trisha Merriam and special guest Erin Wig...

Your AI metrics are probably lying to you. Every organization deploying AI tracks something. Adoption rates. Prompts per user. Time saved per task. Usage dashboards that light up green and make everyone feel like progress is happening. But here's the uncomfortable question nobody wants to ask out loud: Is any of this actually making your business better? Trisha Merriam and special guest Erin Wiggers (Geekeri) join Chris Carolan for Part 1 of an exploration into the metrics gap — the space between knowing AI tools are being used and knowing whether they're producing outcomes worth the investment. The episode opens with a raw conversation about AI resistance. Trisha shares her experience being publicly called out for AI-assisted documentation — work that was objectively better but rejected because it was visibly AI-generated. Erin offers a practical solution: give people what they say they want (four bullet points) and leave the comprehensive document as an escape hatch they'll use more than they admit. The conversation evolves into a live demonstration of a multi-agent platform Erin and Chris have been building — an AI observability operating system that solves the measurement problem by putting everything in one place. Three dimensions of AI ROI emerge: 1. AI-Assisted Revenue Closed — tracking which agents touched which deals, for how long, in what capacity 2. Revenue Protected — health scores that trigger proactive measures to retain or expand business 3. Hours Saved — conversations grouped by task type, compared to human equivalents Chris challenges the conventional ROI framework: when a team uses AI to build product requirements and functional requirements docs from a one-hour conversation, the value isn't "20 hours saved" — it's that they actually made an informed vendor decision instead of showing up unprepared. The episode also explores the emerging role of the "AI Imagineer" — someone who designs systems for AI to build, not just plugs AI into existing systems. This is transformation, not optimization. Trisha confirms this is Part 1. Follow-up questions are already forming. Erin will return. Bring your measurement challenges to the next episode.