Business Context in AI Initiatives: Why It Matters More Than You Think

AI initiatives depend on data—but data without business context can lead to inconsistent answers, unreliable insights, and decisions that miss the mark.

What does “customer,” “revenue,” or even “active” actually mean across your organization? The answer can vary by team, system, or business function.

While these inconsistencies have long created challenges for data and analytics teams, the rise of AI makes the stakes even higher.

In this expert panel, moderated by Jennifer Jackson, Chief Marketing Officer at Actian, we’ll explore why business context has become a critical foundation for trusted AI. Hear perspectives on how organizations can align definitions, metrics, ownership, and relationships across their data environment—and make that context accessible to the AI experiences employees and leaders increasingly rely on.

The session will also feature a live walkthrough demonstrating how modern data platforms can activate business metrics and context with less manual effort, along with practical questions to consider when evaluating whether your data and AI foundation is ready to scale.

What You'll Learn:

1️⃣ Why business context matters beyond traditional data and technical teams

2️⃣ How incomplete, inconsistent, or siloed business definitions can impact AI initiatives

3️⃣ How trusted and up-to-date business context can improve the relevance and reliability of AI-driven insights

4️⃣ What it looks like to operationalize business context without creating additional manual overhead

5️⃣ Key questions to ask when evaluating your organization's data and AI readiness

Who Should Attend

Business, data, analytics, and technology leaders responsible for developing, scaling, or operationalizing AI initiatives.

Register here