Is Your Data Ready for AI? Assessment Guide

​The Make-or-Break Factor: Why 73% of AI Projects Fail Due to Poor Data

​AI is only as good as the data you feed it, and that’s where most companies fall short. In fact, a staggering 73% of AI projects fail due to messy, incomplete, or siloed data that was never ready for automation in the first place.

​In this strategy session, we’ll walk you through a practical assessment guide to evaluate whether your data is AI-ready, before you invest time and money in automation or agent deployment.

​What you'll learn:

• ​Why even the best AI fails without clean, structured data

• ​What “AI-ready” data actually looks like, across documents, APIs, and workflows

• ​How to assess your existing systems and identify data blockers

• ​How Beam uses task mining and tool instrumentation to automate messy processes anyway

• ​A step-by-step checklist to prepare your team for agent deployment

​​About the Host

• ​​​​​​Derya Firat (GTM Lead, Beam AI): Derya draws on enterprise experience at Microsoft and Oracle and a founder’s perspective from Antler to scale AI agents into market-ready solutions.