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Why Data, Not Models, Determines AI Success

Why Data, Not Models, Determines AI Success

Strong models alone are not enough, and this article shows why data readiness, accessibility, and governance often determine whether AI succeeds in production.

Proving the ROI of Enterprise AI: From ESG Insights to Business Outcomes

Proving the ROI of Enterprise AI: From ESG Insights to Business Outcomes

Enterprise leaders are under pressure to show that AI investments deliver more than experimentation, and this piece explores how to connect initiatives to measurable business outcomes.

Where Should AI Workloads Run? Rethinking Workload Placement in a Hybrid AI World

Where Should AI Workloads Run? Rethinking Workload Placement in a Hybrid AI World

Because placement decisions affect cost, performance, and control, this piece examines how data gravity and latency shape where AI workloads should run.

Dell's Vrashank Jain on the Data Problem That Could Break Your AI

Dell's Vrashank Jain on the Data Problem That Could Break Your AI

In this eSpeaks conversation, Vrashank Jain explains why fragmented environments, pipeline complexity, and data bottlenecks continue to slow enterprise AI progress.

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Top Products AI Developer Payroll Security Events Project Management Accounting CRM Academy Cloud Innovation Cheat Sheets Big Data View All Topics

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The Enterprise Guide to Scalable AI The AI-Ready Workplace Hub The IRIS Reality Check TechRepublic Premium TechRepublic Academy Newsletters Resource Library Forums Sponsored

marklefcowitz

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Why does process improvement fail?

It’s clear, both anecdotally and objectively, that process improvement efforts have failed far more often than they have succeeded. This blog talks about why this is true.

Jun 17, 2008

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