2 Unexpected AI Use Cases Nobody Sold These Companies

By Imran Khan (AI Tech Safar)

Ask ten people which AI tool saves them the most time and you'll get ten different answers — because the honest answer usually isn't a chatbot, it's whatever narrow, unglamorous task they stopped doing by hand. We asked professionals across two very different fields exactly that, and skipped the marketing-speak.

The Inventory System That Became a Full-Time Employee

Manuel Mojica runs a seven-figure automated retail and vending operation, managing nationwide route deployments. For him, the tool that actually moved the needle wasn't software he opens on a laptop — it's computer vision baked directly into his hardware.

"The AI tool that has saved our team the most time daily is Sandstar's computer vision AI platform, which we integrate directly into our grab-and-go smart coolers."

His team uses it to track product removals, process cashless payments the moment a door closes, and monitor live inventory remotely — which has eliminated manual on-site stock audits entirely. But the part worth stealing is how he uses the data afterward, not just the fact that he collects it:

"Link the AI's real-time inventory alerts directly to your route dispatching process so restocks are triggered dynamically by product turnover rather than fixed calendar schedules."

In practice, that means drivers only show up where they're actually needed instead of running a fixed loop regardless of demand — a small change that compounds into real time saved every week.

The Copilot That Does the Reading So You Don't Have To

Udaya Bhaskar Venum, a senior application security analyst, solves a completely different version of the same problem: too much manual groundwork standing between him and the actual work. He uses an LLM-based assistant as what he calls a "security copilot."

"I use it to review application documentation, summarize lengthy security findings, identify recurring patterns, and prepare focused questions before secure design reviews or developer discussions."

The time savings, he says, come almost entirely from the front end of the job:

"The biggest time savings come from reducing the amount of manual reading and organization I have to do before I can start the deeper security analysis. Instead of spending time pulling information together from multiple sources, I can use the assistant to create a structured starting point and then focus my attention on validating higher-risk areas and making the actual security decisions."

His rule for using it well is easy to state and easy to skip: give it as much context as possible, and never treat its output as final. "I validate recommendations against the application evidence, architecture, and business context before using them in a security decision," he said.

💡 AI Tech Safar Insight

Put Mojica's vending machines next to Venum's security reviews and a pattern emerges that neither of them mentioned explicitly, but both of their answers point straight at: the AI isn't replacing their judgment in either case — it's replacing the unpaid, invisible labor that used to happen before their judgment could even start. Mojica didn't need AI to decide where to send a restock truck; he needed it to stop manually checking inventory levels so a decision could get made at all. Venum didn't need AI to make a security call; he needed it to stop burning hours assembling the information a security call requires.

That's a meaningfully different story than the one most AI coverage tells. The dominant narrative is usually "AI does the thinking now" — but neither professional here handed over a single actual decision. Mojica still decides which routes matter; Venum still validates every recommendation against real evidence before it counts. What both of them actually automated was the *friction before* the decision, not the decision. If there's a lesson buried in these two very different jobs, it's that the AI tools genuinely worth adopting right now aren't the ones promising to think for you — they're the ones quietly clearing the runway so your own thinking can start sooner.

Frequently Asked Questions (FAQs)

Q1: Is computer vision AI like Sandstar's only useful for vending machines, or does it apply more broadly?
The same core idea — using cameras and sensors to automatically track physical inventory changes — is increasingly used in retail checkout systems, warehouse management, and any operation where manual stock counts currently eat up staff time.

Q2: How do you know when to trust an AI security copilot's output versus double-checking it yourself?
Venum's approach is a useful rule of thumb: treat AI output as a structured starting point for lower-risk, information-gathering tasks, but always independently validate anything feeding into an actual risk or security decision.

Q3: What's the risk of connecting AI inventory alerts directly to automated dispatching, like Mojica did?
The main risk is over-trusting sensor data without spot-checks — a faulty camera or sensor could trigger unnecessary restocks or missed ones, so operations that automate dispatching this way typically still run periodic manual audits as a backstop.

What Do You Think?
Which unglamorous, repetitive part of your own job would you most want an AI tool to quietly take off your plate? Share your take in the comments below!

About the Contributors

• Manuel Mojica — Owner, MM Healthy Vending / Vending Circle
• Udaya Bhaskar Venum — Senior Application Security Analyst

Related Reading:

• How to Use Claude Code: A Complete Beginner's Guide (2026) — Another look at AI tools that speed up the groundwork before real technical decisions get made.
• AI Infrastructure Stocks Beyond Big Tech: Suppliers Getting Paid in 2026 — The less-glamorous side of the AI boom, this time in hardware supply chains instead of daily workflows.