CELab: The Customer Education Lab

The episode contains some valuable insights about AI resistance and the role of customer education in adoption, particularly the four faces of resistance framework and the argument for CE ownership of AI rollout. However, significant portions are devoted to casual rapport-building, sponsor reads, and repetitive discussion of general principles (empathy audits, outcomes-driven approaches) that listeners familiar with modern L&D likely already know. The concrete actionable insights per minute are diluted by meandering conversation.

“the four faces of um, AI resistance...people have the essential fear like will this replace me...there's the other thing, like pride. Like, I was the expert and now suddenly, I don't know...there's some scar tissue about initiatives that don't get carried out...people that are super valuable actually to you...it's like they're the resistors that say the output is wrong”

“if you're rolling out an AI initiative, you want to have the enablement team look at what's helping true adoption. Not the vanity figures of they logged in, but actually is it in their workflow?”

While the four faces of AI resistance framework is a useful categorization, it primarily repackages known change management challenges (fear of replacement, pride/status, initiative fatigue, quality assurance) under an AI lens. The core argument that CE should own adoption is sensible but not deeply counterintuitive. The playground/sandbox metaphor for safe AI experimentation is somewhat novel but not fully developed. Most other discussion recycles familiar frameworks (outcomes-driven learning, KPI mapping, iterative design).

“AI adoption should be owned...by sales enablement...and customer education...Because all of the go to market...they're not interested...Is their resistance, is there human resistance to actually using these tools?”

“people have the essential fear like will this replace me? Much like when Elearning first came out”

Eve Kadar is a credible and relevant guest with substantial operational experience building enablement and CE programs at scale across multiple organizations (Insight, Apple, Gainsight, Seagate, PayPal, Cisco, etc.). She has doctoral studies in education and leadership for change, lending academic rigor. However, she is positioned more as a consultant and thought leader than as a current operator actively executing AI adoption at a major enterprise today, which limits the freshness and real-time applicability of her perspective.

“I did work with Dave in the day...I've built enablement and customer education programs at companies that didn't have one, like, at Insight...at Apple...at Gainsight...at Seagate...at PayPal...at Cisco”

“I did my doctoral studies on education and leadership for change”

The episode lacks specific data, metrics, timelines, and named examples to ground its arguments. The Seagate story about transitioning sellers to system selling is somewhat detailed but remains relatively anecdotal. Discussion of AI adoption, resistance, and learning frameworks stays at a general level without referencing specific companies' AI rollouts, adoption rates, failure modes, or measurable outcomes. No concrete numbers, revenue impacts, or time-to-value figures are provided.

“At Seagate, I worked and built their, um, system selling academy, taking or rather dragging sellers from selling just boxes to their new journey of being system sellers and consultative selling...they saw from their peers that the few champions that adopted it...the people that had started leveraging the training...were really exponentially being more successful”

“But what does that mean in terms of the workflow afterwards?”

The host asks reasonable opening questions and allows Eve space to develop thoughts, but follow-ups are often soft and exploratory rather than challenging or probing for deeper specificity. When Eve makes claims (e.g., about playgrounds being essential, or AI replacing roles), the host rarely pushes back or asks for evidence. Conversely, the host frequently goes on long monologues (e.g., the calculus metaphor, the Outreach story, the Vast Data use case) that consume airtime and reduce genuine dialogue. The tone is warm and collaborative but lacks the kind of intellectual sparring that would sharpen arguments.

“Can I pause you for just one?...I have some relatable experience sets that I want to use to prompt you”

“um, you touched on change management, you touched on how you're selling things”