It’s the most common reason enterprise AI gets delayed — and one of the least justified.
In this article, Nikhil Prakash, Associate Vice President of AI & Analytics, and Raghavendra Dikshit, Associate Vice President of Data Transformation, challenge the long-held assumption that AI requires perfect data to be effective. Instead, they show how Agentic AI can operate across fragmented, inconsistent, and incomplete data — without waiting for cleanup, harmonization, or centralization.
Download the article to learn:
- The four most common data readiness blockers — and why they no longer apply
- How Agentic AI extracts, validates, and acts on data in the flow of work
- Real-world examples from gaming, agriculture, media, and energy
- Why enterprises deploying now are gaining a massive head start
Don't wait for perfect data. Deploy anyway.
Get the full article now.