u/Boring-Metal-7672

As a BI developer, I recently spent time stress-testing a company’s LLM implementation and "AI Data Analysis" tool to see where the actual value lies. It confirmed what most of us working in the trenches already know: the marketing is completely divorced from the reality of data architecture. Yes it can tell you if you made a purchase at Safeway this month, but it can’t tell you your trends for spending groceries over months or YTD.
Here is the unvarnished truth about what these tools actually do, and where they completely collapse:
Any chatbot or agent can spit out sql queries but that’s not business analysis. You need visualized analysis with cold hard facts about your business or budget or personal finances.
AI Does Not Build the Backend
The biggest lie in the "AI Data Analysis" pitch is that it replaces the need for data engineering. AI sits at the penthouse level; it cannot pour the concrete. These tools do not design the backend. They cannot look at a messy, unnormalized flat file and build a proper dimensional model or a Star Schema. If you don't have the discipline to properly stage the data first, handing it to an AI just gives you wrong answers, faster.
The Speed and Scale Bottleneck
Databases are built to scan millions of rows in milliseconds. LLMs process data sequentially via tokens. Trying to feed a massive, granular dataset through an AI tool is incredibly slow and ponderous. It’s like trying to drain a swimming pool with a cocktail straw.
Sorting is Not Analysis
Finding the "Top 10 Highest Expenses" is a basic ORDER BY ... DESC query. Spitting out a top-ten list is not true analysis. When you actually need deterministic math—like month-over-month growth, year-over-year variance, or understanding the context behind a trend—the LLM starts guessing. It does not perform hard calculations; it is a Large Language Model.
Conclusion:
AI is fantastic at language tasks. It is great for summarizing meeting notes, structuring emails, or formatting boilerplate code. But there is zero value added in having an AI read numbers that have already been calculated for it by a real database.
Right now, executives are throwing massive budgets at these tools simply because they are the new shiny object, expecting them to do the job of a relational database.
What is the most expensive or ridiculous example you’ve seen of a company trying to force an LLM to replace a proper database model?

One of the critiques of the pipeline was the fact that I used Merge…instead of Insert and Update. I was wondering if anybody else ran into the same situation? Or knew why? I find that Merge TSQL statements are very easy to read and setup if I wanted to Insert then Update which basically does the same thing I would have written it that way. Is there some sort of memory buffer or leak or rowcount limitation when using Merge? Just trying to find out why Copilot stated this. (Should’ve thrown in upsert logic of update then insert!) (thanks to the users who pointed me in the right direction) (if it were me I wouldn’t ship something so buggy)