Laying a Strong Data Foundation for AI
As AI adoption accelerates with predictions of 90% of companies globally having generative AI workforce partners by 2025, organizations face significant data foundation challenges that prevent successful AI implementation. Only 20% of enterprises report their data fabric supports GenAI effectively, while 72% of leading organizations identify data management as a top obstacle to scaling AI use cases. To achieve AI success, data must meet six critical attributes: diverse, timely, accurate, secure, discoverable, and consumable. Black-box AI approaches present risks in transparency, security, validity, and governance that organizations must address through proper data management strategies.
In this guide, you'll explore
- By 2025, generative AI will be a workforce partner for 90% of companies globally
- Only 20% of enterprises say their data fabric supports GenAI very or extremely well
- Among companies that have adopted AI/ML, 74% have reported a positive impact
- Data for AI must be diverse, timely, accurate, secure, discoverable, and consumable
- Black-box AI creates risks in transparency, security, validity, and governance