Why AI Leaders Must Get Their Hands Dirty ft. Allison Sagraves
There are no easy answers. Just ask your finance, marketing and operations teams what “sales” means, and you’ll see.
In this episode of Bringing Data and AI to Life, host Amy Horowitz (GVP, Informatica) sits down with Allison Sagraves, Chief Data Officer and Carnegie Mellon CDAO faculty. They break down the real barrier holding enterprise AI back: agreeing on what your data actually means.
Learn why taxonomy trumps simple data quality, how to close the executive AI confidence gap, and why real AI leadership demands getting into the trenches.
What does "sales" really mean in your business? Why does every department give a different answer?
In this episode of Bringing Data and AI to Life, host and GVP Solutions Sales and Business Development at Informatica, Amy Horowitz, sits down with Allison Sagraves, a leading AI, Data & Tech Advisor and faculty member in Carnegie Mellon’s Chief Data and AI Officer Executive Program. They unpack the massive disconnect between C-suite AI ambition and operational execution.
Allison argues that many companies may be misdiagnosing their biggest data problem. Data quality still matters, but the harder issue is often data meaning: whether people across the organization agree on what critical business terms represent in the first place.
What You’ll Learn
- Why data meaning can be a bigger barrier to AI than traditional data quality issues
- How executive alignment and clear ownership can build confidence in critical data domains
- Why taxonomy becomes increasingly important as AI moves beyond simple pilots
- How the Chief Data Officer mandate has shifted from defense to offense, and, with AI, partly back toward defense
- Why executives need direct, hands-on experience with AI to ask better questions and lead with greater confidence
- How creating room to admit what you don’t know can lead to more realistic AI decision-making
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