The reality of building a text-to-SQL solution in-house

AI’s impact on enterprises is undeniable. Generative AI adoption rates are spiking in 2024, especially in data management through large language models (LLMs) and SQL generation. 

Enterprises have vast amounts of data and can unlock significant value using BI tools and AI to turn complex data warehouses into self-serve insight engines. However, many companies struggle to access effective solutions despite heavy investment in business intelligence. In-house builds are common but often bring unexpected challenges and regularly fail to launch. 

This whitepaper will explore current rates of AI adoption, the difficulties of building AI solutions in-house, and emerging alternatives that better serve enterprise ambitions for self-serve data insight.

Find out more about: 

• The current landscape of AI adoption among enterprises 
• The rates and reasons for failure of in-house corporate IT projects
• A rough roadmap on how to build an enterprise-level text-to-SQL solution
• The alternative routes to solution adoption