{"id":9841,"date":"2026-06-12T12:20:09","date_gmt":"2026-06-12T16:20:09","guid":{"rendered":"https:\/\/www.softwareag.com\/?post_type=resource&p=9841"},"modified":"2026-06-12T12:20:18","modified_gmt":"2026-06-12T16:20:18","slug":"why-enterprise-ai-fails-the-canonical-truth-problem","status":"publish","type":"resource","link":"https:\/\/www.softwareag.com\/de\/resources\/data-integration\/why-enterprise-ai-fails-the-canonical-truth-problem\/","title":{"rendered":"Why Enterprise AI Fails: The Canonical Truth Problem\u00a0"},"content":{"rendered":"
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Summary <\/h2>\n\n\n\n

Enterprise AI struggles to scale not because of model limitations, but because it relies on incomplete or outdated data. This paper explains the gap between the data AI typically uses and the real-time, authoritative data in systems of record\u2014and why that gap leads to failure in high-stakes use cases. Solving it requires rethinking how AI accesses, governs, and acts on enterprise data. <\/p>\n\n\n\n

What You\u2019ll Learn <\/h3>\n\n\n\n
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  • What it takes to make enterprise data truly \u201cAI-ready,\u201d including governance and audit requirements<\/li>\n\n\n\n
  • Which type of data can safely drive critical business decisions<\/li>\n\n\n\n
  • Why most AI systems fail in production, even after successful pilots<\/li>\n\n\n\n
  • How data timelines shape AI architecture and outcomes<\/li>\n\n\n\n
  • Why real-time access to systems of record is essential for agentic AI<\/li>\n\n\n\n
  • How stale or replicated data leads to costly errors in automated decisions<\/li>\n<\/ul>\n<\/div>\n\n\n\n
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    Download your free copy<\/h3>\n\n\n\n \n
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