『224: Stop Using AI to Do More with Victoria Mensch』のカバーアート

224: Stop Using AI to Do More with Victoria Mensch

224: Stop Using AI to Do More with Victoria Mensch

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 Listen & Subscribe:[Apple Podcasts] | [Spotify] | [YouTube] | [Simplecast]Key Discussion Points and Insights1) AI transformation ≠ “add a tool”; it’s “redesign the work”Victoria frames a common failure mode: organizations bolt AI onto existing workflows instead of questioning whether those workflows make sense in the first place, especially in knowledge work.Practical reframing for leaders:What is the purpose of this workflow?What decision is a human actually making here?What can be simplified or deleted before we automate anything? 2) The barrier is dropping so the “who can do this?” conversation changesThe episode highlights how quickly AI is moving from specialist territory to broad accessibility (no-code tools, conversational interfaces), which raises the stakes for shared understanding, norms, and guardrails. 3) Hidden adoption is real and it creates risk (and a leadership opportunity)We discuss the reality that many people are already using AI quietly, which means schools and organizations need clarity and psychologically safe training environments. 4) Burnout won’t be solved by “more output”; it’s solved by “better use of human time”A central point: if AI removes routine tasks but leaders refill that time with more routine tasks, nothing improves. The higher-order shift is using reclaimed capacity for work that builds culture and learning (coaching, reflection, feedback, relationship-rich instruction, better decisions). 5) Start small: experimentation is a strategy, not a side questVictoria repeatedly returns to “run the reps” thinking: pick a small use case, test it quickly, learn, and stack wins as data points. 6) Education lens: advance the mission because AI is not going awayYou explicitly connect the conversation to school realities: the goal is not to “win AI,” but to move the mission forward in a world where AI is embedded into everything. Actionable Takeaways for Teachers and LeadersRun a 2-week “AI workflow audit”Pick one recurring task (newsletter, family comms, lesson resource creation, feedback bank).Map the current steps.Ask: Which steps are “human judgment” vs “human labor”?Create a “safe sandbox” normOne protected time block/week for staff to try a use case and report back.Focus on learnings, not performance. Name and support champions (formal or informal)Champions are “self-appointed” and momentum makers; don’t wait for a committee. Reinvest reclaimed time into the most human workStudent conferencing, richer feedback loops, community-building routines, coaching conversations. Resources and LinksSilicon Valley Executive Academy (SVEA) — program model centered on immersion and experience-based knowledge sharing. Silicon Valley Executive AcademyVictoria Mensch (LinkedIn) — leadership and AI transformation writing. LinkedInMicrosoft / LinkedIn Work Trend Index (AI at work + BYOAI) — useful framing for why hidden adoption and governance matter. MicrosoftSuggested Past Episodes216: Designing Trustworthy AI in K-12: NASA, Ethics, and Teacher Voice (David Lockett) — direct complement on governance, ethics, and implementation realities in schools. Podcasts222: From Burnout to Better Questions – Human-Centered AI Adoption (Jackie Celske) — closely aligned with the burnout → redesign theme and the “people/process over tools” framing. Podcasts218: Teaching What Can’t Be AI’d (John “Camp”) — matches the “reinvest in what’s human” thread (presence, discourse, competency-based learning). PodcastsSupport the ShowIf you found this episode valuable, please share it with a colleague and leave a review. Your support helps other educators and leaders discover the show.
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