Why Enterprise AI Fails at Scale

A data readiness framework for leaders preparing generative and agentic AI for real-world autonomy

Enterprise AI ambition is high.
Enterprise readiness often isn’t.

Organizations are moving from pilots into generative and agentic AI, only to see systems stall, misfire or introduce new risk in production. The issue isn’t model performance. It’s the data foundations those systems depend on.

This white paper examines why enterprise AI fails at scale and why data readiness is the single strongest predictor of success for advanced AI.

Drawing on research from the Reworked State of the Digital Workplace, it explores:

Where enterprise AI breaks down as autonomy increases
How weak data, governance and integration undermine reliability
Why agentic AI amplifies security, compliance and trust risk

You’ll come away with a clearer understanding of what it takes to support autonomous AI responsibly and at scale.

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Presented by

Founded in 2006, RBA is an award-winning national digital and technology consultancy. They are a team of strategists, designers and engineers who have a proven track record of delivering successful solutions for organizations throughout the U.S. Consistently named a Best Place to Work and Top Workplace, RBA attracts the industry’s top technical and creative talent to help their clients deliver on the promise of technology in today’s digital landscape.