What you're probably doing wrong with AI: Failures, Lessons, and capturing 60 years of data

What you're probably doing wrong with AI: Failures, Lessons, and capturing 60 years of data

From CARE Failing Forward by Emily Janoch

February 19, 2026 · 27 min · Episode 129

About this episode

Lindsey Moore discusses common mistakes in AI and emphasizes the importance of context, research methods, and understanding organizational culture.

Lindsey Moore was working in AI before most of us knew what it was, and she can tell you the most common mistakes to avoid. Ignoring context, building ever more precise models that provide terrible answers, and assuming that AI will replace smart strategy and human decision-making are three on the top of her list. If you're looking to do more with AI, she recommends you invest in learning good research methods, double-down on your data architecture, find ways to counteract bias, and stay skeptical. Developmetrics' Large Language Model with was trained on 60 years of USAID documents, and taps into a wealth of expertise that doesn't exist anywhere else. It can tell you what has worked, and what hasn't, over decades of work in dozens of countries. Here's what it tells us: we often repeat the same failures over and over again. Why? Because failures are as much about organizations as they are about tactics. The newest widget won't solve an organizational culture that drives people away from spending time understanding the local context.

People in this episode

Host: Emily Janoch

Guest: Lindsey Moore

Topics covered

  • AI
  • failures
  • data architecture
  • bias
  • organizational culture
  • research methods

Keywords

  • AI mistakes
  • data architecture
  • bias in AI
  • organizational culture
  • research methods
  • USAID

Mentioned in this episode

Organizations: USAID

Products: Developmetrics' Large Language Model

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