Real AI Tools That Actually Save Professionals Time

By Imran Khan (AI Tech Safar)

Every AI tool ships with a pitch: this is what it's for. The more interesting stories are the ones where someone ignored the pitch — pointed the tool at a problem nobody sold it for, and got a better answer than the one they were looking for.

The Vending Machine That Turned Into a Merchandising Consultant

Manuel Mojica's smart coolers were bought to solve one problem: checkout. Computer vision watches what customers grab and bills them automatically when the door closes. That's the whole pitch. What he found afterward wasn't in the sales deck.

"The coolers track exactly which items get grabbed and when, down to shelf position. I started cross-referencing that with peak traffic times and realized certain products were underperforming purely because of where they sat in the cooler, not because people didn't want them."

Moving high-margin items to eye level, on the side of the cooler people reach first during busy hours, lifted their pull rate — without changing the product, the price, or anything else. The insight didn't come from the AI being clever. It came from Mojica asking a different question of data the system was already collecting for an unrelated reason.

"The AI was sold as a checkout replacement, but the behavioral data it generates is actually a merchandising intelligence engine. If you have any smart retail tech already deployed, stop just using it for payments and start treating the data as your silent store manager."

The Coding Assistant That Learned to Read, Not Write

Girish Songirkar, a delivery manager in enterprise software engineering, found an equally unintended use case at the opposite end of the tech stack — inside legacy ERP systems nobody wanted to touch. Generative AI coding tools are built and marketed for writing new code. His team used one to do the reverse: decode old code that nobody left alive to explain.

"We shifted the paradigm of using AI to come up with a new program code, and instead used it to feed thousands of lines of messy legacy SQL and stored procedures and then obtain a functioning business logic outline."

Systems whose original developers had long since moved on, with documentation missing or years out of date, got translated into plain-English explanations of what they actually did — including undocumented tax overrides and discount logic that existed only in the code itself.

"The AI was asked to elucidate what it was doing in plain English, which aimed to reveal undocumented business practices such as local tax overrides and complicated discounts. This established a basis for company leaders regarding what the functional system comprised before starting plans for the new one."

A task that would have taken days of manual auditing turned into a two-week discovery exercise that gave leadership an actual foundation to plan on. His broader advice for anyone sitting on old, undocumented systems: "Stop using AI for faster systems development only, and start making use of it for understanding purposes as well."

Quick Summary & Key Takeaways

• The pattern: Neither use case needed a different AI tool — just a willingness to point an existing one at data or code it was never advertised for.
• Retail tip: If you have any AI-powered checkout or monitoring hardware, the behavioral data it's already collecting is often more valuable than the task it was bought for.
• Engineering tip: Code-generation AI can be pointed backward — feeding it legacy, undocumented code to get a plain-English explanation of what it actually does.

About the Contributors

• Manuel Mojica — Owner, MM Healthy Vending / Vending Circle
• Girish Songirkar — Delivery Manager, Enterprise Software Engineering