Enterprise AI Projects Stalled by Data Issues
Many enterprise AI projects spend 6 months and $1M without results due to lacking data foundations, according to David Dokes on X.
Data readiness is often the biggest hidden cost in AI. Many companies underestimate the work needed to clean, transform, and integrate data before AI models can deliver value. A solid data foundation includes well-defined data governance policies and robust data pipelines. Without these, AI projects risk being built on unreliable or incomplete data, leading to inaccurate results. Organizations should invest in data quality assessments and data engineering expertise early in the AI project lifecycle. This helps avoid costly rework and ensures that AI initiatives are aligned with business goals.
