Aug 15, 2025

Even as dashboards and query tools become easier to use, asking for skills in querying languages (i.e., minus the layman-friendly interfaces), most people can't do it. Natural language interfaces based on LLMs provide promise, but most Text-to-SQL tools do not satisfy the requirements for enterprise-level operations. Analysts from leading firms have highlighted this “unused data crisis,” noting that well over half of enterprise data is never leveraged for analytics or decision-making. For example, an IDC survey of global companies (commissioned by Seagate) found only 32% of available data is put to work—meaning 68% remains untapped. Why Text-To-SQL Is More Important Than You Think Text-to-SQL is one of the most useful and direct LLM applications in the industry. It links the chasm between decision-makers and databases, translating English into code and minimizing data team dependencies. But adoption has been slow—not because the concept is flawed, but because most systems were not designed for the data challenges we face now. The Reality: Most Tools Come Up Short • Single-Database Specificity: Most agents are bound to a single RDBMS (PostgreSQL or SQLite, for instance). At scale, even large organizations have to deal with dozens of databases, each using its own special schema. According to AWS , enterprise topology typically involves 100 or more tables, and dynamic schema discovery combined with strong error handling is a must. • Schema Blindness: LLMs from schema context to schema-less context can either hallucinate or output wrong SQL. K2View highlights the issues: "LLMs need to be aware of relevant database schema ... it's challenging for databases with hundreds or thousands of tables." • Security And Stability Vulnerabilities: LLM agents, which are stateless, can be used for formulating unsafe or invalid queries. Risks to accuracy and compliance. Those risks can multiply when the messages are sent without both validation and retry logic. Both AWS and K2View stress the need for query auditing and results tracking. • Latency And Cost Without Caching: Repeatedly invoking an LLM to produce the same SQL query can be costly and slow. To work efficiently at an enterprise scale , you need memory, you need caching and you need an offline mode. • Schema Awareness In The Real World: In the wild, ask any engineer on Reddit's /r/LangChain about their experiences and gripes with understanding a schema, interpreting column names loosely or joining tables to make it to the lights. The Rise Of Open-Source Innovation Thankfully, this problem has a response from the open source world. There are not many tools that offer a complete solution, but a few are addressing important chunks of the problem: DB-GPT is designed to enable conversational queries over multi-table relational databases with minimal metadata awareness, targeting a structured backend. LlamaIndex connects structured and unstructured data for hybrid retrieval in the context of metadata graphs. MindsDB connects LLMs with SQL and ML workflows from over 30 data sources, focusing on ease of use. TextQL, a YC-backed company, translates programs over business hypotheses to SQL queries for experiments. mcp_ohmy_sql is an open-source Text-to-SQL system developed for deployment in enterprise production. It is not about specific features as much as about integration, scale and reliability: It supports multiple SQL-compatible backends, including Redshift, with Snowflake, OpenSearch and MongoDB (coming soon). It supports local Parquet cache and contains an embedded DuckDB engine for offline queries. It has SQL memory, so it's not generating the same again and again and ensures consistency. It allows resolving user intents to schema-aligned queries by incorporating a business term dictionary (MCP). The endgame isn't just generating SQL; it's generating SQL that is usable, auditable and deployable at scale. Conclusion: From Possibility To Practice The true potential for Text-to-SQL is not mere technical feasibility but rather in the ability to develop robust, contextually-aware and scalable systems that free enterprise users to genuinely democratize access to their data. There are tools like mcp_ohmy_sql that do this for us: It is the link between natural language and structured data, which operationalizes the promise of AI-driven analytics. Here's the prevailing argument: In order to succeed, these sorts of problems need infrastructure-level solutions that are deeply integrated with real business problems. As organizations invest in generative AI, sustained value will come from tools that can convert more than just language to SQL but do so with an understanding of business rules, governance and real-world schemas. The winning solutions will go way beyond shimmering demos; they will be trusted, industrial-grade platforms that extract enterprise data and turn it into a universally accessible resource—realizing the essential promise behind this technological shift. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?