AI has made guarantees. Large ones. When you’ve ever watched a keynote or demo, you’ve seemingly heard some model of: “Simply give it your knowledge—and watch the magic occur.”
As a enterprise operator who works with knowledge each day, I made a decision to check that concept the laborious method: might I exploit GPT to create constant, built-in operational experiences throughout a number of retailer areas…with out a formal knowledge layer?
Fast reply: No. And I misplaced my weekend studying why.
My aim appeared simple:
– Combination each day knowledge from two QSR areas
– Layer in key operational metrics—gross sales, labor, process completion
– Generate each day summaries that would information decision-making
Easy, proper? Copy, paste, immediate. However nearly instantly, I bumped into issues.
To supply a significant report, I needed to first normalize and construction all my uncooked knowledge—line by line. GPT couldn’t make sense of inconsistent codecs, column names, or lacking context. Every session turned a battle to re-explain the total image.
Even after fine-tuning elaborate prompts, outcomes various wildly. If I modified the enter barely or reopened the session later, the output would lose coherence. The AI couldn’t “keep in mind” what I wanted in the best way a reporting system would.
I discovered myself manually recalculating summaries, checking formulation, rewriting prompts, and correcting errors—defeating the whole level of automation. The phantasm of AI simplicity fell aside with out a stable infrastructure beneath it.
Right here’s what 20+ hours of trial and error taught me:
- Clear knowledge is non-negotiable: No AI can repair disorganized inputs. Structured, normalized knowledge is the prerequisite for constant outputs.
- Context home windows are fragile: GPT’s reminiscence isn’t persistent. You’re not feeding a system — you’re explaining issues from scratch, time and again.
- Immediate engineering isn’t a technique: Manually crafting multi-step directions simply to simulate primary reporting isn’t scalable—or environment friendly.
This experiment deepened my appreciation for the worth of a real knowledge orchestration layer— one which integrates disparate programs (like POS, labor, and process administration), normalizes inputs, and automates reporting with repeatable precision.
It’s not about plugging uncooked spreadsheets into an AI chat. It’s about creating a sensible basis the place AI can really do what it guarantees: speed up choices, not add friction.
My recommendation to restaurant operators:
AI generally is a highly effective companion. However with out the infrastructure to help it, it is simply one other handbook process in disguise.
When you’re contemplating AI for operational insights, don’t begin with the AI. Begin together with your knowledge. Construct the programs that make intelligence doable. Then let AI do what it’s good at— as soon as it is arrange for fulfillment.
When you’ve tried wrangling uncooked knowledge into AI—or are occupied with it— I’m at all times pleased to speak store about make operations smarter (and weekends rather less irritating).
