Database and analytics companies are rolling out text-to-SQL tools that let users query databases in natural language, but experts warn non-specialists should be cautious. Toronto University computer science professor Nick Koudas said systems can generate queries that are grammatically correct but not what business users intended. He put current accuracy at about 80 percent, below human experts at around 93 percent, and said company-specific data and jargon are hard for LLMs without extra training.

Han Sun-ho, chief AI officer at Bespin Global, said many companies fail to feel the impact of AI investment because they take a technology-first approach, overlook data complexity and build systems from a vendor perspective. He said AI is a management issue and requires internal capability-building alongside partners who understand the technology. Han cited text-to-SQL accuracy limits and vector database constraints, and urged firms to define problems first and start small based on business impact.