Beer, Wine, Whiskey & AI
How artificial intelligence, data science, and BI tools — Power BI, Tableau, and the Power Platform — are transforming the drinks industry: beer, non-alcoholic beer, wine, whiskey, and mead. From the brewhouse and the still to fermentation, quality control, sensory tasting, demand forecasting, and ESG. Practitioner-grounded, honest about what works and what doesn't, and free of hype.
Start here
New to the blog? Begin with the flagship series on how a brewer becomes an AI and data practitioner — the real path, the failures, and what actually works.
Explore by track
Two kinds of deep dive. Business tracks — executive, data-driven analytics across the beverage business, applied to beer and the fast-growing non-alcoholic segment. And production & science tracks — the technical process itself, from grain and grape to glass, through AI, data science, and generative AI:
Explore by series
Prefer a guided, multi-part read? Each series builds in order — start to finish on one theme:
- From Brewer to AI — the honest journey
- The Brewer's AI Roadmap — step-by-step, free resources
- Asking Better Questions — AI for new brewers
- Vibe-Coding for Brewers — build tools with Claude Fable 5
- Seeing Your Beer — data visualizations brewers underuse
- The Brewer's Chart Field Guide — every chart type and its beer use cases
- AI Foundations for Distillers — AI, ML & GenAI explained
- Process Intelligence for Distilleries — the four analytics
- Process Intelligence for Wineries — the four analytics
- Beer NPD with Data — new product development
Core reading on AI in drinks
Latest posts
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Where Your Extract, Water and Energy Go: Sankey and the Brewhouse Balance
A brewery's biggest losses hide in plain sight across a dozen separate reports. A Sankey diagram puts the whole mass and energy balance in one picture — extract from grain to glass, water in to effluent out, energy in to heat lost — so the leaks become impossible to miss.
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Shelf-Life Trajectories: Visualizing Forced-Ageing and Flavour Stability
A beer's flavour stability is a story over time, not a single number — yet most breweries record it as a pass/fail. How to visualize forced-ageing and shelf-life trials as trajectories and small multiples, so staling becomes a path you can see, compare and predict.
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SPC That Actually Catches Drift: Capability, CUSUM and EWMA for Brewers
The Shewhart control chart catches sudden spikes but is blind to slow drift — exactly how brewing quality usually fails. How process capability (Cp/Cpk), CUSUM and EWMA charts visualize the slow creep a basic control chart misses, in brewing terms.
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True-to-Target: Visualizing Whether a Batch Matches Its Brand Profile
True-to-target (TTT) tasting asks a different question than 'is it in spec' — does this batch still taste like the brand? How to visualize brand conformance with radar profiles, target bands and deviation tracking, so a panel's verdict becomes a picture the whole brewery reads.
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Beyond the Control Chart: Data Visualizations Brewers Underuse
Most brewery dashboards stop at the line chart and the control chart. This opens a series on the visualizations brewers rarely reach for — radar brand-profiles, CUSUM drift charts, shelf-life trajectories and Sankey balances — and how to match the chart to the brewing question.
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Vibe-Coding a Fermentation Tracker: A Brewer's Build Log
A concrete, step-by-step build log of vibe-coding a small fermentation tracker with Claude Fable 5 — the actual prompts, the verify steps, the bug it introduced and how it got caught, so you can copy the loop for your own brewery tool.
Frequently asked questions
How is AI used in the drinks industry?
AI and data analytics help breweries, wineries, and distilleries forecast fermentation, predict demand, control quality, optimize ESG metrics like water and carbon, and sharpen marketing — while human judgment and taste stay essential.
Can AI improve brewing and winemaking?
Yes, where clean data exists. AI reliably helps with forecasting, monitoring, and demand planning, but it cannot taste or replace a brewer's or winemaker's craft, and it can produce confident-but-wrong outputs if its results aren't verified.
Who writes Beer, Wine, Whiskey & AI?
Ankur Napa, a brewer turned brewing data scientist who spent about a decade at breweries including AB InBev, SABMiller, and United Breweries before building AI and data tools for the drinks industry.