In 2023, enterprises invested heavily in generative AI POCs, but by 2025, 30% may abandon projects due to issues with data quality and governance.
The primary challenge in moving AI initiatives from prototype to production is ensuring high-quality data, which is often overlooked in planning stages.
As AI systems evolve, prioritizing data quality has become crucial—errors in large data sets can skew outcomes and reduce the effectiveness of models.
Organizations must balance sufficient quality data against quantity; excessive data can dilute insights, slow iteration cycles, and increase operational costs.
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