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DESCRIPTION:Click for Latest Location Information: http://dgiq-aigov2026.da
 taversity.net/sessionPop.cfm?confid=166&proposalid=16872\nAI readiness usua
 lly gets measured by tools, use cases, and adoption plans. That misses the 
 harder question. When AI influences a real decision, can the organization p
 rove what happened?\n\nA candidate is screened out. A claim is denied. A fr
 aud alert moves forward. A customer is flagged. A patient is routed. After 
 the decision, teams often discover the evidence is scattered across source 
 data, vendor outputs, workflow logs, business rules, human review notes, ex
 ception records, and approvals.\n\nThis session examines the evidence probl
 em inside AI readiness. It shows why data governance, privacy, security, ri
 sk, and AI governance teams need to work from the same decision record befo
 re AI becomes part of daily operations.\n\nAttendees will learn how to:\n\n
 Identify what evidence should exist before an AI-influenced decision is que
 stioned.\n
 Connect data quality, lineage, metadata, and stewardship to AI accountabili
 ty.\n
 Recognize where privacy, security, and governance reviews miss decision-lev
 el risk.\n
 Build clearer records for audit, compliance, legal, and risk review.\n
 Prepare data teams for a stronger role in AI readiness and responsible AI a
 doption.\n\n
DTSTART:20261117T100000
SUMMARY:The Data Behind the Decision
DTEND:20261117T104459
LOCATION: See Description
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