AI assistants use language models, data retrieval, and reasoning to automate tasks. Explore how they work and what to consider before adoption.
by Databricks Staff
AI assistants use language models, data retrieval, and reasoning to understand requests and take action on behalf of users. For enterprise data teams, this means generating SQL, building dashboards, troubleshooting pipelines, and automating repetitive work without requiring everyone to write code.
The market reflects how quickly organizations have moved from experimentation to production: the global AI assistant market was valued at $19.1 billion in 2025 and is projected to reach $114.1 billion by 2035.
What separates a useful assistant from a novelty is depth of integration: one that understands your data catalog and respects your governance policies is fundamentally different from one that generates generic text in isolation.
