AI Adoption Starts With Data Foundations
AI Adoption Starts With Data Foundations
One thing I've noticed across analytics projects over the last year is that many organizations are talking about AI, but the biggest challenge is still getting the data foundation right.
In several analytics projects I've worked on, discussions often start with AI and predictive capabilities, but quickly shift to topics such as:
Data quality
Data governance
Consistent business definitions
Integration across multiple platforms
Without those foundations, it becomes difficult to move AI initiatives beyond pilots and into production.
From your experience, what has been the biggest barrier to moving AI from experimentation into production?
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