Estimated reading time: 5 minutes

I’ve been spending a lot of time thinking about AI through the lens of my corporate work. How do we introduce it responsibly? How do we help people learn to use it? Where does it actually make the work better?

But a story from my neighborhood brought that conversation a lot closer to home. It involves my corner liquor store, an expansion, and a plan for temporary storage pods. No big corporate strategy presentation required.

A small business with a very practical problem

This is a small operation—the kind of business run by one person or a couple of people, where the people making the decisions are also doing the everyday work. They wanted to figure out how to expand the store and transition into a larger business.

Getting more space is one part of that. Working out what happens to the inventory while the building work is going on is another. There is a lot to think through between deciding to grow and being ready to use that bigger space.

They used AI to help work through it.

The plan they described involves putting temporary storage pods out front, moving inventory into them during the build, and moving things back once the expanded space is ready. AI helped them think through how that transition could work.

To be clear, I’m describing a plan, not reporting a completed expansion or a measured business result. What caught my attention was the way they used the tool to get from a difficult question to a possible way forward.

Sometimes the value is a different way of looking at things

Storage pods aren’t a new invention. Neither is moving inventory during construction. The interesting part, to me, is having a way to turn a problem around, explore the pieces, and consider an approach you might not have put together on your own.

When you’re close to a problem, it can be hard to see beyond the first obstacle. We need more room. But where does everything go while we build? Each answer creates another question, and you still have a business to run.

AI can be useful in that back-and-forth. You can describe the situation in ordinary language, add the constraints, ask for alternatives, and push back when a suggestion doesn’t fit. An initial idea can become something more practical as you work through it.

That doesn’t mean every suggestion will be useful. Sometimes the value is simply realizing which questions you haven’t asked yet.

The people still have to make it work

There are real limits here. A convincing plan on a screen doesn’t establish that something is safe, affordable, or permitted. In a situation like this, the owners would still need to check issues such as local approvals, alcohol licensing requirements, insurance, security, storage conditions, and access with the appropriate people. Those are questions to verify, not details I’m claiming this store has already resolved.

AI doesn’t know the business the way the owners do. It can miss a constraint, make a bad assumption, or confidently suggest something that won’t work. And feeding sensitive business or customer information into a tool deserves some thought before anyone starts typing.

The owners still carry the responsibility for the decisions, the spending, and the outcome. Their experience matters. So does the advice of the contractors and other professionals involved.

I can be excited about what AI makes possible and still expect people to question its answers. Both belong in the conversation.

This reaches beyond the office

What I like about this story is how ordinary the starting point is. A neighborhood business wants to grow. The people running it need help thinking through a transition. They find a tool that helps them explore their options.

That’s a useful way to think about AI in everyday life, too. You don’t have to begin with a grand strategy. You might begin by organizing a complicated household project, comparing ways to approach a task, or asking what you’ve overlooked in a plan. Those are possibilities, not promises that AI will always give the right answer.

The same lesson I keep coming back to at work applies here: people need practical examples and a chance to learn. Access to a tool is a starting point. Understanding how to explain a problem, question the response, and check the important details is what makes that access useful.

The liquor store story stayed with me because it puts a human face on a conversation that can get very abstract. AI is becoming part of how people think through the decisions right in front of them—even at the business down the street.

Where have you seen AI help with an everyday problem—in your own life, at work, or at a local business—and what did a person still need to figure out?

Until next time, Rob.