The Hidden Cost of AI Delays: How Slow Deployment Erodes ROI
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Enterprises are eager to deploy agentic AI, but most underestimate the cost of waiting. New research from FeatureByte, Databricks, and CIO.com shows that it takes most organizations up to 6 months to move an agentic AI use case from concept to production. That lag is costly, and getting costlier.
What’s Slowing AI Down
Top culprits include model inaccuracies, data readiness issues, and compliance or security concerns. These aren’t just technical hiccups, they’re signals of weak data foundations and fragmented workflows.
Meanwhile, 92% of leaders express concern about these delays. More than half are concerned about higher costs caused by delays, and nearly half see diminished ROI as a direct consequence. The longer AI sits in pilot mode, the more value leaks from the system.
The Compounding Impact
In a fast-moving AI landscape, delays don’t just hurt project timelines, they stall competitive advantage. When predictive models go stale or data pipelines break down, agentic systems lose their ability to act with confidence and speed.
The Path to Acceleration
Accelerating deployment isn’t about taking on more work, it’s about automating smarter. By using a solution like FeatureByte and automating the data science process from raw data to model deployment, organizations can ensure that predictive insights are always production-ready.
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