I Tried to Get AI to Read My Reports — and Wasted My Weekend

AI has made promises. Big ones. If you’ve ever watched a keynote or demo, you’ve likely heard some version of: “Just give it your data—and watch the magic happen.”

As a business operator who works with data daily, I decided to test that idea the hard way: could I use GPT to create consistent, integrated operational reports across multiple store locations…without a formal data layer?

Quick answer: No. And I lost my weekend learning why.

My goal seemed straightforward:
– Aggregate daily data from two QSR locations
– Layer in key operational metrics—sales, labor, task completion
– Generate daily summaries that could guide decision-making

Simple, right? Copy, paste, prompt. But almost immediately, I ran into problems.

To produce a meaningful report, I had to first normalize and structure all my raw data—line by line. GPT couldn’t make sense of inconsistent formats, column names, or missing context. Each session became a battle to re-explain the full picture.

Even after fine-tuning elaborate prompts, results varied wildly. If I changed the input slightly or reopened the session later, the output would lose coherence. The AI couldn’t “remember” what I needed in the way a reporting system would.

I found myself manually recalculating summaries, checking formulas, rewriting prompts, and correcting errors—defeating the entire point of automation. The illusion of AI simplicity fell apart without a solid infrastructure beneath it.

Here’s what 20+ hours of trial and error taught me:

• Clean data is non-negotiable: No AI can fix disorganized inputs. Structured, normalized data is the prerequisite for consistent outputs. 
• Context windows are fragile: GPT's memory isn’t persistent. You’re not feeding a system — you’re explaining things from scratch, over and over.
• Prompt engineering isn’t a strategy: Manually crafting multi-step instructions just to simulate basic reporting isn’t scalable—or efficient.

This experiment deepened my appreciation for the value of a true data orchestration layer— one that integrates disparate systems (like POS, labor, and task management), normalizes inputs, and automates reporting with repeatable precision.

It’s not about plugging raw spreadsheets into an AI chat. It’s about creating a smart foundation where AI can actually do what it promises: accelerate decisions, not add friction.

My advice to restaurant operators:

AI can be a powerful partner. But without the infrastructure to support it, it's just another manual task in disguise.

If you're considering AI for operational insights, don’t start with the AI. Start with your data. Build the systems that make intelligence possible. Then let AI do what it’s good at— once it's set up for success.

If you’ve tried wrangling raw data into AI—or are thinking about it— I’m always happy to talk shop about how to make operations smarter (and weekends a little less frustrating).