The AI execution gap: Why nearly half of enterprises struggle to deliver AI success

Enterprises are struggling with AI implementation due to delays, underperformance, and failures, despite pouring billions into data centralization. The issue lies in the gap between strategy and execution, as AI requires fully centralized, governed, and ready data to deliver results. A new report highlights poor data readiness as a leading factor in undermining enterprise AI implementation, emphasizing the long-term need for AI-ready data. The consequences of poor AI execution extend beyond IT, negatively impacting business growth, operational costs, and customer satisfaction, with 38% of enterprises citing increased operational costs due to AI project failures. To achieve greater AI success, organizations must eliminate data silos, embrace automation, and invest in modern data integration tools that automate pipeline management.

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