AI is a top priority for businesses, but without a strong data foundation, AI efforts fail. This session highlights why data maturity must come before AI adoption, ensuring organizations don’t waste time and money on unreliable models.

Problem Statement & Relevance
Many organizations rush into AI without properly managing their data. Poor data quality, inconsistent governance, and siloed information lead to flawed AI outputs, legal risks, and lost trust.

Solutions & Insights
This talk provides a structured approach to AI readiness, covering:

- Data governance, quality, and accessibility as prerequisites for AI success
- How to assess AI readiness based on data maturity
- Real-world case studies of AI failures caused by poor data management

Key Takeaways
- Why AI adoption fails without a solid data foundation
- A step-by-step guide to building AI-ready data ecosystems
- Actionable strategies to fix data quality and governance issues before investing in AI