MSP Mindset with Damien Stevens

The episode delivers a handful of genuinely useful ideas - the concentric circles of trust for AI governance, the QBR/MBR automation saving 18-25 hours, and the hypothesis-testing agentic loop model - but large stretches are occupied by AI-enthusiasm generalism, motivational framing, and conversational filler that dilutes the payload.

“it used to take somewhere between 18 and 25 hours to prepare an MBR or a QBR...we have fully automated the preparation of those business reviews now through AI”

“looking for signal in what used to be a pile of noise becomes a really powerful application...automating things that you didn't know existed”

The MSP 1.0/2.0/3.0 periodisation is a clean and useful framework, and the 'SaaS-ification of AI' disintermediation warning is a sharp observation, but the bulk of the AI content follows well-worn paths about automation removing toil while humans do creative work, with no real contrarian or first-principles arguments.

“MSP 1.0 was, I believe the term back then was called your mess for less”

“we need to be better than our customers are at AI today”

Jerry Miller is a genuine practitioner - healthcare cloud MSP CEO, early cloud adopter, HITRUST AI controls committee member, 40-plus years in tech - who has actually built and operated the systems he describes, not a thought-leader recycling frameworks from a stage.

“actually was on the committee that developed those rules”

“we've been high trust R2 certified coming on almost a decade, uh, SOC 2 type 2”

Real numbers appear - 18-25 hours per QBR, one-third headcount reduction over 18 months, 20-plus SaaS integrations, ~50 new HITRUST AI controls - but the episode lacks hard financial outcomes, specific named client case studies, and dollar-level impact figures that would push the score higher.

“it used to take somewhere between 18 and 25 hours to prepare an MBR or a QBR”

“we're probably down in the past 18 months, probably down about a third in headcount”

The host structures the conversation well and asks useful follow-up questions about adoption pace, culture, and business model, but never challenges any claim - including the one-third headcount reduction or the churn-prediction assertions - and breaks flow with a lengthy mid-episode product advertisement.

“How are you able to do that kind of at the speed that might be needed in kind of an AI era?”

“What was impossible before? That now may not be.”