AI adoption is outpacing AI value. Most organizations are already using AI in at least one business function, yet only a fraction are seeing the productivity gains they expected — and BCG has found that only 5% of organizations are achieving AI value at scale. The reason usually isn’t the model, it’s the data behind it. The right analytics foundation lets agentic AI reason differently: evaluating the full data landscape at once rather than just what’s explicitly asked for, and signaling whether the data behind an answer is actually trustworthy before an agent acts on it. As agents start acting autonomously — drafting summaries, triggering workflows, making recommendations — that’s what turns “an answer” into “a trustworthy answer”.

In this session, we’ll dig into what it actually takes to close that gap: how governed, quality-scored data changes what an AI agent can safely do; why associative, in-memory analysis surfaces context and exceptions that query-based approaches miss; and how enterprise access controls can survive the move from dashboards to autonomous agents. Attendees will leave with a practical framework for evaluating whether their own AI/agentic initiatives are standing on a trustworthy data foundation — and where the biggest blind spots tend to hide.

Key Takeaways:

• Why most AI programs stall on the data layer, not the model — and the questions to ask to find out if that’s happening to you (data trust, governance survival, context completeness).

• How data quality signals (e.g. completeness, validity, lineage, discoverability) can be surfaced directly inside an AI/agent conversation instead of requiring a separate BI tool.

• Why “associative” analysis surfaces hidden correlations and exceptions that single-query retrieval typically misses — and what that unlocks for faster, more confident decisions.