Short answer: after years applying AI to beer, I’m taking the same data-driven, anti-hype approach into wine, whiskey, and mead. The core methods — fermentation forecasting, quality monitoring, demand prediction — carry over directly. What changes is the constraints each drink brings. This is where my journey goes next, and where I think AI takes the whole drinks industry. Here’s the road ahead.
The principles travel; the details don’t
Everything this series covered — collect clean data first, prefer simple tools, verify outputs, keep the human in charge — applies to any fermented or distilled drink. What differs is the specific hard part:
- Wine — vintage variability and terroir make quality hard to predict, even when yield and ripeness aren’t. (Can AI predict wine quality?)
- Whiskey — maturation runs for years, so labeled data arrives a decade late, keeping AI an assistant to the master distiller. (AI and whiskey maturation)
- Mead — honey varies enormously and batches are tiny, leaving thin, noisy data. (Mead meets AI)
Same toolbox, different walls. A brewer’s instinct for process and measurement is exactly what makes crossing over feel natural.
Mead, and the future of an old craft
Mead — humanity’s oldest fermented drink — is where the contrast is sharpest: ancient craft, modern data. I’ve been thinking and talking about where mead goes from here (including in Mid-Day’s “Mead of the Future”). My honest view: AI won’t reinvent mead, but it can make a fickle, honey-driven ferment more predictable and less wasteful. The future of mead is still made by meadmakers.
Where I think this all goes
Not toward replacing makers. Toward accessible, honest tools for producers of every size — grounded in real sensor data, aimed at cutting waste and supporting decisions. The frontier I’m most excited about is sensor-grounded tasting (electronic-nose arrays, GC-MS), which could finally give AI something real to “perceive” instead of guessing.
Closing the loop
I started this series as a brewer who fell into data. I’m ending it as someone using that data across beer, wine, whiskey, and mead — still thinking like a brewer the whole way. If there’s one takeaway from the whole journey: the technology is only as good as the honesty you bring to it.
Thanks for reading. If you want to follow where this goes, the archive and topics are the best place to start, and you can always reach out.
From Brewer to AI — Part 8 of 8. Back to the start of the series
Frequently asked questions
Can the same AI used for beer work for wine, whiskey, and mead? The core methods transfer — fermentation forecasting, quality monitoring, demand prediction — but each drink adds its own constraints: vintage variability in wine, years-long maturation in whiskey, and honey variability with tiny batches in mead.
What’s the future of AI in the drinks industry? More accessible data tools for producers of every size, grounded in real sensor data, used to reduce waste and support decisions — not to replace makers. Sensor-grounded tasting (electronic nose, GC-MS) is the most promising frontier.
Why expand from beer to other drinks? Because the data principles are shared. A brewer’s discipline around process, measurement, and honest limits applies just as well to winemaking, distilling, and meadmaking.