Hastimal Jangid

expert panel

Artificial intelligence may be transforming the enterprise, but behind every successful AI initiative is something far less glamorous: disciplined data engineering.As organizations race to deploy generative AI, agentic systems and increasingly sophisticated analytics, many are pouring resources into new models, cloud platforms and AI applications. Yet time and again, ambitious projects fail to deliver meaningful business value—not because the technology falls short, but because the underlying data is inconsistent, poorly governed or difficult to trust.According to McKinsey's latest State of AI research, organizations seeing the strongest returns from AI distinguish themselves not by the models they choose, but by the maturity of the data, governance and operating foundations supporting those models. In other words, AI success begins long before a prompt is entered or an algorithm is deployed.Members of the Senior Executive AI Think Tank, a curated community of executives specializing in machine learning, generative AI and enterprise AI applications, have witnessed this firsthand across industries ranging from healthcare and financial services to manufacturing, retail and cloud computing. Below, they outline the foundational data engineering capabilities they believe consistently deliver the greatest business value and why leaders should take more notice.