CELab - Ep 184 - Your Biggest AI Problem Isn’t Skills - It’s Belief: Customer Education in the Age of AI

In this kickoff to Season 8 of the CELab podcast, host Dave Derington sits down with Anya Eydman to explore a provocative idea: our biggest AI problem in Customer Education isn’t a skills gap or a technology gap - it’s a belief gap. Anya draws on her background in corporate finance, small business and startup operations, coaching, municipal and nonprofit work, and energy/civic tech to show how AI is becoming a new learning style and a genuine learning partner, not just another productivity tool. Anya connects the dots between AI, underserved communities, workforce shifts, and what it means to design learning experiences that people actually want to engage with.

Across the conversation, they unpack the role of prompt engineering as a core literacy, not just a niche technical skill, and examine how AI can level the playing field while also exposing new inequities. Anya shares how she’s using AI with interns, cities, and universities (including a forthcoming “AI Without Hype” course at Vassar College) to build trust infrastructure, redesign workflows, and bring more people into complex conversations about energy, affordability, and technology. If you’re in Customer Education or Customer Success and wrestling with how AI changes your strategy, your stakeholders, and your learners, this episode will challenge how you think about outcomes, belief systems, and what “meeting learners where they are” really means in 2026.

Key Topics:

• The “belief gap” vs. skills gap
  • Why resistance to AI is often about belief systems, not capability.
  • How this parallels learner motivation and adoption challenges in CE/CS.
• AI as a new learning style and learning partner
  • Treating AI like a sharp intern or co-worker rather than a magic answer machine.
  • Designing learning journeys that assume AI is in the loop.
• Prompt engineering as a core literacy
  • Why prompt engineering should be taught like a fundamental communication skill.
  • How better questions drive better learning outcomes and better customer outcomes.
• Non-linear, outcome-focused learning journeys
  • Moving beyond “I took a course” to “we changed a workflow and a behavior.”
  • Aligning CE programs with measurable participation and impact, not just completions.
• Cross-pollination beyond SaaS/B2B silos
  • Lessons from civic tech, energy, and community engagement that apply directly to CE/CS.
  • How to bring community-style thinking into customer communities and academies.
• Underserved learners and access to AI
  • What “underserved” means when AI accelerates knowledge but not everyone can use it well.
  • How CE can help customers navigate fear, overload, and trust issues with AI.
• Trust infrastructure and stakeholder mapping
  • Building trust frameworks inside organizations and with customer stakeholders.
  • Rethinking stakeholder mapping now that decision-making paths and gatekeepers are changing.
• Team dynamics, titles, and shared ownership of knowledge
  • Shifting from heavy titles to everyone as a “lead” owning a slice of the mission.
  • How this mirrors modern CE/CS teams that must be flexible, cross-functional, and AI-augmented.
• Practical AI use cases in learning and community engagement
  • Using AI to co-create plans (e.g., interns drafting a 3‑month strategy) then iterating as a team.
  • Designing pilots and learning experiences that feel like meaningful participation, not checkbox training.