I visited a winery in the Grampians wine region a few weeks ago — I won’t name them because they asked me not to, but they’re a mid-sized operation producing about 8,000 cases a year. Cellar door, online sales, a handful of restaurant accounts in Melbourne and Ballarat. Family-owned, three full-time staff plus seasonal workers.

What caught my attention wasn’t the wine, though the Shiraz was excellent. It was the way they were using AI tools I’d normally associate with city startups. Simple stuff, nothing flashy, but quietly transforming how they run the business.

The inventory problem

Wine inventory is deceptively complicated. You’ve got different vintages, different varietals, different formats (bottles, magnums, cases), product aging in barrel versus in bottle, cellar door allocation versus wholesale versus online. And seasonal demand swings are dramatic — you sell more Shiraz in winter, more Riesling in summer, and everything spikes around Christmas.

“I used to manage it on a spreadsheet,” said the owner, who I’ll call James. “It worked fine when we were doing 3,000 cases. At 8,000, I was making mistakes. Over-ordering bottles for one wine, running out of labels for another, not realising we were almost out of a vintage until someone tried to order it online.”

James started using a combination of Airtable for his database and the AI features built into it to forecast demand. Nothing custom-built. Nothing expensive.

He loaded three years of sales data into Airtable, broken down by product, channel, and month. The AI extension analyses the patterns and generates monthly demand forecasts for each product. It’s not perfect — James estimates it’s accurate to within about 15% — but that’s a massive improvement over gut feel.

“Last year I would have ordered 200 cases of labels for the 2023 Shiraz. The AI suggested 140. I was sceptical, went with 160 as a compromise. Ended up needing 145. It was right, and I was going to waste money.”

Customer management without a CRM team

The winery has about 4,000 people on their mailing list, accumulated over a decade of cellar door visits, online purchases, and wine club sign-ups. Until recently, everyone got the same emails. Same promotions, same frequency, same content.

“I knew we should be segmenting,” James said. “But I’m a winemaker, not a marketer. I didn’t know where to start.”

His daughter, who’s studying business at Federation University, set up a system using Mailchimp’s AI-powered segmentation. It analysed their purchase history and automatically created customer groups: frequent buyers, lapsed customers, cellar-door-only visitors, online-only purchasers, high-value customers, and wine club members.

Then she used the AI content tools in Mailchimp to draft different email versions for each segment. The lapsed customer email had a “we miss you” tone with a special offer. The high-value customer email previewed an exclusive small-batch release before it went public. The cellar-door-only email promoted the online store.

The results after three months: email open rates went from 18% to 34%. Click-through rates doubled. Online sales from email campaigns increased by about 45%.

“I couldn’t believe the difference,” James said. “Same product, same people, just talking to them differently. I felt silly for not doing it sooner.”

Wine club retention

The winery runs a wine club with about 300 members. Churn had been a problem — they were losing 8-10 members a month and only gaining 5-6. At that rate, the club was slowly shrinking.

James’s daughter built a simple churn prediction model using a no-code AI tool. It flagged members who were at high risk of cancelling based on signals like reduced engagement with emails, no cellar door visits in the past year, and declined or failed payment attempts.

They started reaching out to at-risk members personally. A phone call from James — “Just wanted to check in, see how you’re enjoying the wines, and ask if there’s anything we can adjust in your next shipment.” Simple, human, but prompted by AI.

Churn dropped from 8-10 members a month to 3-4. In dollar terms, that’s roughly $30,000 in retained annual revenue.

The cost of all this

I asked James what he’s spending on these tools. His answer surprised me.

• Airtable Pro plan: $20/month
• Mailchimp Standard plan: $45/month (for their list size)
• No-code AI tool for churn prediction: $30/month

Total: about $95/month. Call it $1,100 a year.

The return? Conservatively, the inventory improvements save $5,000-8,000 a year in overstock and waste. The email segmentation has driven about $15,000 in additional online sales. The churn reduction is worth $30,000 in retained revenue.

That’s a return of 40-50x on the tool investment. Even accounting for the time James’s daughter spends managing the systems — maybe five hours a week — the ROI is extraordinary.

What other regional businesses can learn

I tell this story because I think it’s representative of where regional businesses should be with AI in 2026. Not building custom models. Not hiring data scientists. Just using the AI features that are already baked into affordable, accessible tools.

The barrier isn’t technology or cost. It’s awareness. James didn’t know these capabilities existed until his daughter showed him. And he’s a smart, successful business owner. He just hadn’t been exposed to what’s available.

Three principles from James’s experience that apply broadly:

Start with a pain point, not a technology. James didn’t say “let’s implement AI.” He said “I keep running out of labels.” The technology was the solution, not the starting point.

Use tools that already exist. Every tool James uses is a mainstream SaaS product with AI features built in. No custom development. No technical expertise required beyond basic computer literacy and a willingness to experiment.

Let the younger generation lead. This isn’t patronising — it’s practical. Younger people who’ve grown up with these tools have an intuitive understanding of what’s possible. James had the domain knowledge. His daughter had the tech literacy. Together, they built something neither could have alone.

If you’re running a regional business and you haven’t explored what AI tools can do for your specific challenges, it’s worth an afternoon of experimentation. The tools are there. They’re cheap. And as James put it: “I wish I’d started two years ago.”