Like many business owners intrigued by the promise of AI, I recently embarked on an experiment to see firsthand what it takes to manually replicate sophisticated, integrated business reports using powerful third-party AI tools like GPT.
I thought, “How hard could it be? I’ll just copy, paste, and prompt my way through this.” I thought, I’ll just wake up early on Saturday morning, knock this out and go enjoy the weather with my family, maybe even go to Bed Bath & Beyond, you know if I had time. That’s for you Old School fans reading this.
What I discovered was eye-opening—and frankly, a bit miserable.
My Experiment: Can You DIY Complex Business Reports Using AI?
At first glance, AI tools like GPT seem magical. Feed it some data, ask for a detailed report, and voilà—actionable insights appear. But I quickly realized there’s a massive gap between that illusion and the reality of dissecting complex business data.
My goal was seemingly straightforward:
- Aggregate and summarize daily data from two store locations.
- Clearly overlay key operational dimensions like sales, labor, and task completion.
- Generate consistent, replicable daily reports to support strategic decision-making.
Simple enough, I thought, for AI.
The reality was data chaos and prompting nightmares.
To even begin generating useful reports, I needed to organize and normalize massive amounts of raw data first. Without that as a foundation, every prompt I wrote became a confusing mess—long context windows, complex instructions, and painful iterations.
Each report took hours of meticulous prompting. Even then, GPT frequently misunderstood my intentions. For example, every time I opened a new conversation thread, it felt like starting over entirely from scratch because the context and subtle formatting details weren’t retained.
- Context Windows were a nightmare. GPT struggled to consistently retain the granular details needed for accurate reporting. Every session felt like starting from scratch.
- Prompts became complex instructions. The instructions required to get GPT to accurately reproduce daily summaries were cumbersome, lengthy, and difficult to replicate consistently.
- Constant Manual Checks: Ironically, I found myself manually verifying totals and correcting data, defeating the purpose of automating reports in the first place.
In short: It wasn’t feasible.
Spending over 20 hours on this exercise, I learned a few critical lessons:
- Data Organization Isn’t Optional: Without a structured, normalized database, AI is effectively blind. It needs clean, consistent, structured inputs to yield consistently valuable outputs.
- Context is Fragile: Generating reliable, consistent reports through GPT alone requires extraordinary effort because context windows are limited and difficult to maintain across sessions.
- Aggregation and Normalization are Essential: Attempting to manually aggregate complex business data—sales, labor, task management, operational KPIs—highlighted just how much expertise, planning, and infrastructure goes into data orchestration.
Despite AI’s incredible potential, manually orchestrating this data was frankly miserable. I burned an entire weekend copying, pasting, and attempting to refine my prompts, hoping for consistency that simply never came. Each time I opened a fresh chat or started anew, my progress evaporated, leaving me frustrated and exhausted.
Doing this manually simply isn’t practical or scalable for business owners.
What I’ve truly come to appreciate is the immense value of a dedicated orchestration layer—one that pulls data from standardized, normalized databases, integrates multiple dimensions of operations (sales, labor, tasks), and provides reliable, automated insights.
This much is clear:
- Yes, AI is powerful.
- But, without the proper infrastructure and data orchestration, it becomes another headache—another manual task in disguise.
- The future of operational intelligence isn’t about randomly dumping raw data into AI; it’s about meticulous data management, integrated orchestration, and leveraging AI as a trusted companion—precisely calibrated by structured systems—to guide smarter, clearer business decisions.
Trust me, after the weekend I spent trying to do this manually: you don’t want to DIY this.
There’s a reason organized data infrastructure exists. Use it.
If you’ve tried wrangling AI with raw data—or are thinking about it— I’m always up for a good conversation about data, operations, and where AI actually makes life easier. Feel free to get in touch.
Mary Pillow Thompson is the founder of foh&boh, a company revolutionizing hiring for restaurants and retailers. With a 20-year career spanning both sides, wholesale to storefront, Mary Pillow has a deep understanding of how hiring impacts every corner of a business. Mary Pillow’s engaging, witty, and storytelling-driven style makes complex ideas relatable and actionable for business owners. Passionate about helping leaders embrace the realities of a changing workforce, Mary Pillow inspires them to overcome their fears of trying new strategies. As an expert in Gen Z workforce trends, foh&boh provides actionable insights into what this next generation values, how they communicate, and where they spend their time online.
