Getting AI-Ready with Reliable Data (Barr Moses)

Everyone is talking about building AI agents. But is your foundation even ready?

In this episode, Andreas Welsch speaks with Barr Moses, CEO of Monte Carlo, about the hidden risks behind the push for Agentic AI and what business leaders are missing when they rush to scale.

Barr shares what she’s hearing from data and AI leaders: The majority is building or deploying AI this year, but only 1 in 3 believe their data is ready, and even few have a reliable way to ensure agent outputs are correct.

What does that mean for your AI roadmap? A strong model or prompt isn’t enough. If the data is wrong, the agent’s action will be wrong and in some cases, costly. From AI chatbots selling cars for $1 to data platforms returning flawed recommendations, the risks are real.

Barr also breaks down three areas data and AI teams must prioritize:

• Productivity by using AI to accelerate their own workflows
• Readiness to build a foundation of high-quality, trustworthy data
• Realiability by ensuring AI agents produce outputs that align with business goals

If you’re working on an AI strategy or are leading teams expected to deliver on it, this conversation makes the case for why AI success is built upon operational readiness.

Don't miss out on the conversation – tune in now to learn how to turn AI hype into business outcomes.

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Disclaimer: Views are the participants’ own and do not represent those of any participant’s past, present, or future employers. Participation in this event is independent of any potential business relationship (past, present, or future) between the participants or between their employers.

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