Four Big Questions Enterprises Are Asking About AI and Modern Data Platforms

Enterprises are increasingly focusing on operationalizing AI to deliver tangible business value, as AI transitions from experimental phases to production. Despite a high failure rate of AI projects in delivering measurable returns, mainly due to task-specific pilots that were hard to integrate, organizations are learning that success hinges on data foundation, governance, and workflow integration. Key areas where data-driven solutions are proving effective include fraud detection, supply chain optimization, and customer engagement, which require blending structured and unstructured data. As enterprises strive to make data AI-ready, they face challenges such as data discoverability, fragmentation, unstructured data, governance, and tool sprawl, which can be addressed through AI-assisted documentation, federated queries, vector searches, and robust governance models. Privacy and governance remain crucial, demanding that AI governance extends beyond data tables to include models and prompts, with strategies like minimizing data movement and leveraging open standards. Recent breakthroughs in data platforms, such as AI SQL functions, vector search, model governance, and agent-assisted discovery, are shifting the focus from experimentation to scalable, governed AI implementation. The future success of AI in enterprises will depend on unifying data, consistent governance, and designing for real-world action, with platforms like Starburst addressing these challenges through federated queries and Iceberg-based data access.