Is Your Data Ready for AI? Common Pitfalls and Practical Solutions

About The Webinar

GenAI is only as powerful as the data it runs on—but many enterprises aren’t ready. 42% report that half of their AI projects stall or fail due to poor data readiness, and Gartner predicts that 60% of initiatives without AI-ready data will be abandoned by 2026.

So how to know if your data is GenAI-ready—and close the gaps before they hit your strategy and ROI?

Join this 30-minute session to learn where most enterprises break down, how AI-era data needs are evolving, and how to upgrade your stack for sustained success. Our expert—who’s led enterprise data builds for ML, GenAI, and RAG—will share proven, real-world strategies to make your data not just AI-friendly, but AI-future-proof.

Who Should Attend?

This session is ideal for CTOs, data leaders, AI strategists, and product heads who want to ensure their data is ready for the Generative AI era. If you’re preparing to integrate GenAI into enterprise systems, tackling data silos and governance issues, or looking to build scalable, trustworthy AI-ready pipelines, this session is for you.

Key Takeaways

Why data readiness is non-negotiable for GenAI

Shifts in data needs from ML to Agentic AI

7-point checklist to assess data infrastructure

A roadmap for implementing future-proof data foundation for GenAI

Real-life case studies

Live Q&A with practical next steps

Meet the Speakers

Ratnesh Parihar

Principal Architect

Ratnesh Parihar is the Principal Architect at Talentica Software and an alumnus of National Institute of Technology, Surat. With years of experience designing large-scale architecture, he has helped organizations move from siloed, inconsistent datasets to AI-ready infrastructures that power real business outcomes. Ratnesh has led implementations across industries, from financial services to SaaS,integrating tools like Databricks, vector databases, and real-time pipelines to make AI adoption seamless and scalable. His approach blends technical rigor with practical business impact, ensuring teams not only deploy AI—but deploy it successfully.