How to Build a Data Team Ready for AI

AI can make teams faster, but it can also expose every weakness in the data underneath it.

Elizabeth Stanford, VP of Data at PandaDoc, joins The Tech Trek to talk about what it takes to prepare a growing company to actually execute on AI. That means more than giving engineers access to Claude or Cursor. It means getting the data foundation, team skills, stakeholder expectations, and ownership model right.

Elizabeth explains how PandaDoc is preparing its data organization for AI while keeping a small team from becoming the company’s quality control department. She also shares how AI is changing what she looks for when hiring data professionals, and why expertise, problem framing, and judgment may become more valuable as coding gets easier.

What you’ll take away

• AI readiness starts with reliable data, shared definitions, and systems that can provide consistent context.

• Giving stakeholders easier access to data creates a new problem when the data team becomes responsible for checking everyone else’s AI generated work.

• Technical execution is becoming easier, which puts more value on knowing what questions to ask and whether an answer is actually correct.

• Hiring standards are changing. Candidates need to show how they think with AI, not simply that they can use it.

Best Line

“It’s not whether you know today’s technology, it’s whether you can figure out tomorrow’s technology.”

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