Five Signs Your Data Is (Actually) Ready for AI

Many enterprises believe they’re ready for AI once models are selected, platforms are deployed, and pilots are underway. But in practice, AI initiatives often stall before reaching production—or they underperform once they do. The root cause? The data isn’t actually ready.

In this thought-leadership session, Naveen Punjabi from Google Cloud and Jake Bengtson from Striim unpack five critical signs that your data foundation can actually support AI at scale. Drawing from real-world customer patterns, we’ll explore why centralized data isn’t enough, how invisible data errors derail AI outcomes, and why stale data threatens use cases like RAG and AI agents.

What we'll cover:

• What Google Cloud is seeing across enterprise AI adoption—and where data readiness tends to break down
• Five practical indicators your data is ready for trustworthy, production-grade AI
• Why validation and accuracy matter before any models get involved
• How real-time pipelines give AI access to operational truth
• How Google Cloud and Striim help enterprises move from AI experimentation to enterprise impact

This session is built for data, analytics, and AI leaders ready to move beyond AI experimentation—and build foundations that truly support scale.

What You'll Learn:

1️⃣ Spot the Warning Signs: Know when your data might silently sabotage your AI initiatives.

2️⃣ Go Beyond Centralization: Why a data lake isn’t the same as a data strategy.

3️⃣ Enable Live AI: How real-time pipelines can unlock true operational intelligence.

4️⃣ Validate Early, Scale Confidently: Why data quality must be addressed before model deployment.

5️⃣Learn from the Field: What Google Cloud and Striim have seen across real enterprise AI journeys.

Register here