Bad data, not bad AI, is what’s stalling enterprise deployments
Nobody is talking about this. While everyone obsesses over model capabilities, enterprise AI deployments are stalling on something much more boring: data quality.
Why it matters
Enterprise AI adoption is hitting a wall — not because models lack capability, but because organizations lack the data infrastructure to feed them. This shifts the bottleneck from compute/models to governance/ops, and signals a structural challenge for the next wave of AI ROI.
The key facts
7 to knowData quality, availability, and governance identified as top blockers to scaling agentic AI
Research from Qlik Technologies and Enterprise Technology Research
Deployments stalling before delivering returns due to lack of trusted data foundation
Question has shifted from 'should we deploy AI' to 'why are deployments failing'
Data quality, availability, and governance cited as top blockers to agentic AI scaling (Qlik/ETR research)
Enterprise deployments stalling before ROI realization — a systemic infrastructure gap, not a model gap
Implication: Data foundation is now the competitive moat in AI deployment
Go to the source
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Publisher excerpt: The question is no longer whether to deploy AI — it’s why so many deployments stall before delivering returns. The answer usually comes down to a lack of trusted data foundation. As research from Qlik Technologies Inc. and Enterprise Technology Research shows, data quality, availability and…