AI Isn’t Failing-Your People Systems Are - 4/15/2026 - Conversation with Jill Delgado of Kyndryl and Jason Todd Wade of BackTier and NinjaAI - AI Visibility and SEO, GEO and AEO

Connect with Jill:

https://www.linkedin.com/in/jilldressen

https://www.kyndryl.com/us/en

https://podmatch.com/guestdetail/1775579614787917dfce1a580

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Episode Summary
AI isn’t failing—companies are. More specifically, their people systems are. In this conversation, Jill Delgado breaks down why most AI transformations stall: not because of bad tools, but because organizations underestimate human resistance, overload their teams, and destroy trust during rollout. The result is predictable—fake adoption, shadow workflows, and zero real ROI.

Key Themes

• AI replaces tasks, not jobs—but companies implement it like it replaces people
  That mismatch is where most failure starts.

• No time + no trust = guaranteed failure
  You can’t mandate adoption while overloading people and expect anything real to happen.

• Most AI adoption is performative
  Teams use it just enough to say they are, while real work stays unchanged.

• Middle management is the choke point
  Strategy says “yes,” leadership decks say “go,” but execution quietly dies in the middle.

• Disengagement is the real red flag
  Negative feedback means people care. Silence means you’ve already lost them.

Notable Insights

• “Time is investment—if you don’t give people time to learn AI, they won’t adopt it.”

• “AI replaces tasks, not roles—so you have to map the work, not the job.”

• Companies are cutting jobs for AI, then rehiring because they removed critical human capability

• Employees don’t trust internal tools → they go external → loss of control + data risk

• If AI output isn’t trusted, adoption collapses immediately

Frameworks

• Adoption Path:
  Clarity → Confidence → Commitment

• Behavior Signal Model:
  Invite → Attend → Engage → Sentiment

• Cultural Buoyancy:
  Not bouncing back—staying stable while everything keeps changing

Practical Takeaways

• Start at the task level, not “AI strategy”

• Remove fear before pushing adoption

• Give protected time to experiment or expect zero uptake

• Don’t position AI as cost-cutting if you want trust

• Train people to question AI—not just use it

• Fix your data before layering AI on top

Closing Line
AI transformation isn’t a technology problem. It’s a trust and behavior problem—and most organizations are structurally incapable of solving it the way they’re currently operating.

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