Despite a surge of optimism and experimentation in Gen AI, the majority of organizations remain stuck in proof-of-concept purgatory. A recent BCG study found that 74% of companies struggle to create real business value from AI, even as others report tens of millions of dollars in impact. The gap is about execution. The difference between aspiration and adoption lies in the operating model, the data foundation, and, above all, the discipline to scale deliberately.
This tension between rapid experimentation and enterprise-grade implementation was at the center of a recent panel at MachineCon New York, featuring Sudip Chakraborty, Principal and Head of AI and Gen AI at Axtria; John Tucker, Director of Enterprise Data at McDonald’s; Diego de Aragão, SVP of Balance Sheet Management & Analytics at Citi; and moderator Ozgur Dogan, President, Americas at Blend360.
The Cost of Getting Data Wrong
Without data quality, lineage, and observability in place, even the most promising models are destined to fail. But fixing data infrastructure is a business problem that demands business ownership. Embedding stewards from the start ensures that context and accountability travel with the data, improving both utility and adoption.
