In this second part of our conversation with Abhishek Prasad, we shift the focus from governance models to the often-overlooked factor that determines AI success or failure: data quality. We explore how poor-quality data can silently derail even the most promising AI initiatives, leading to bad predictions, missed opportunities and costly compliance risks. Abhishek shares real-world examples, actionable best practices and the key metrics every organization should track to keep their data clean, consistent and AI-ready. Abhishek walks us through the most common pitfalls in AI projects, and why Data & AI literacy thinking are often missing from the equation. We also challenge the idea that AI is always helpful, asking whether it’s a blessing or a curse for data quality initiatives, depending on how it’s applied. This episode is both a practi ...