Rick W / Monday, February 16, 2026 / Categories: Business Intelligence Why AI Fails without Data Engineering Industry reports suggest that as many as 80% of AI projects fail to deliver anticipated value. This failure rarely stems from the AI models themselves, but from fundamental issues such as poor data quality, integration challenges, or scalability bottlenecks. In the landscape of Artificial Intelligence, transformative opportunities promise everything from enhanced predictive capabilities to automated […] Previous Article DPO vs PPO for LLMs: Key Differences & Use Cases Next Article ServiceNow Series E214: 'Maximizing IT Efficiency with ServiceNow' with MultiCare Health System's Laurie Wheeler Print 3 Tags: ModePredictModeldataArtificial IntelligenceAIAI ModelsIntel