1600_OBrien.pptx

1、AI-Readiness in Enterprise Data architecture2026 Q1 MARKET STUDY,Data Summit Closing KeynoteMay 7th,2026John OBrienPrincipal Advisor&Industry AnalystData&AI Strategy and Architecture,Survey SectionsAI Maturity and OutcomesAgentic AI and OrchestrationAI-Enabling Data InfrastructureAI Trust and Govern

2、anceOutcomes,ROI&MeasurementChallenges and Strategic Needs,Survey DetailsMarch 2026259 Qualified Respondents31 QuestionsSponsored by:,AI Readiness in Enterprise Data Architecture,2026 MARKET STUDY,AI-Readiness,INSIGHT#1,3,AI-Readiness Does Not Predict AI Success,INSIGHT 1,74%,52%,77%,rate themselves

3、mostly or fully AI-Ready,had half or fewerAI initiatives succeed,of AI failurestrace backto data,Outcomes Tell a Different Story,Self-assessed AI-Readiness does not correlate with AI outcomes,74%of organizations rate themselves mostly or fully AI-Ready.Yet 52%report that half or fewer of their AI in

4、itiatives succeed.77%of failures trace back to data.,INSIGHT 1,Confidence is high.Delivery is not.,False Confidence in AI-Readiness?,Self-assessments for Mostly&Fully Ready,INSIGHT 1,“,If data quality,lineage,and governance are disciplines weve practiced for thirty years,why are AI initiatives faili

5、ng on exactly those dimensions?”,The disciplines are not new.What they are being asked to support has changed.A data quality framework built for monthly reporting is not the same as one supporting real-time inference.A governance framework for audit trails is not the same as one supporting model exp

6、lainability.A semantic layer designed for BI consumption is not the same as the one feeding context to agents.,What I Expect You Are Thinking,INSIGHT 1,RADIANT PERSPECTIVE,A Better Way to Define AI-Readiness,The difference is understanding what AI needs to be successful,INSIGHT 1,AI Pilots vsAI Prod