Is Your Data Ready for AI? A Practical Roadmap for Enterprises

Ask most enterprise teams if their data can support AI, and you'll get a confident yes. Dig one layer deeper and the answer usually falls apart. Duplicate records, siloed systems, no clear ownership over who governs what. The AI conversation tends to skip straight past this and go to models and use cases instead.

This session doesn't. It starts where the real problem sits.

Thirunavukkarasu RM walks through what it actually takes to get enterprise data into shape for AI, analytics, and automation. Not theory. The kind of assessment and cleanup work that determines whether an AI initiative six months from now succeeds or quietly gets shelved.

A few things tend to come up in almost every enterprise environment:

• Inconsistent or duplicated data across systems
• Governance that exists on paper but not in practice
• Legacy systems that were never built to talk to each other
• Leadership asking to "trust the AI" before anyone's checked what's feeding it

None of this gets fixed by switching vendors or buying a better model. It gets fixed by doing the unglamorous work first.

• How to actually assess your current data estate, not just assume it's fine
• What "AI-ready" modernisation looks like on the ground, beyond the buzzword
• Where governance needs to sit so AI output can be trusted, not just fast
• A roadmap that takes an organisation from scattered data to something usable

The enterprises that get AI right aren't the ones with the flashiest models. They're the ones who did the boring data work first.