Source: SiliconANGLE
Enterprise AI deployment has shifted from a model problem to a data problem. Organizations can access capable foundation models relatively easily, but lack the clean, labeled, production-ready datasets required to fine-tune and validate them for real business outcomes. Early AI pilots often stall because companies have GPT access but no coherent strategy for data governance, lineage tracking, and quality assurance at scale. The competitive advantage belongs to organizations that can systematize data curation and validation faster than they can adopt new model architectures.