The fastest way to discover that your analytics stack is broken is to put a natural-language interface on top of it. Suddenly every business user is asking questions your data model cannot answer, and every contradictory answer ends up in a Slack thread with the CFO copied.
Why Generic LLMs Fail in the Enterprise
A general-purpose model does not know your chart of accounts, your cost-center hierarchy, or what your organization means by 'active headcount.' It will approximate. Sometimes it will hallucinate. The output will look authoritative and will be wrong in ways you cannot trace.
If your AI cannot cite the table, the SQL, and the time period behind an answer, Finance cannot use it and Audit will not accept it.
Fix the Foundation First
Before you turn on any AI agent, three things have to be true: your data is in one governed layer, your hierarchies are canonical, and your access controls are enforced at the row level. Skipping any of these makes the AI layer worse than no AI at all.
What Good AI Analytics Looks Like
Every answer cites the source. Every query is logged. Tenant data is isolated. The model never trains on customer data. If the AI cannot answer from your governed data, it says so instead of guessing. This is the baseline SplashAI was built against.
The SplashBI Editorial team covers enterprise analytics, AI-driven decision-making, and the future of Finance and HR intelligence.
Frequently Asked Questions
Will AI replace our BI team?
No. AI moves your BI team out of report-building and into data modeling, governance, and curation — the work that makes AI answers trustworthy.
Can we use ChatGPT on top of our warehouse?
You can. It will give you ungoverned answers with no citation trail, no row-level security, and no audit log. Fine for exploration, unusable for finance, HR, or compliance.
