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.
AI for drinks producers, from someone who has run the brewhouse
Ankur Napa spent a decade in breweries (AB InBev, SABMiller, United Breweries) before building AI and data tools for beer, wine, and spirits. Select consulting engagements in demand forecasting, quality and process analytics, dashboards, and production-grade GenAI.
Talk to a brewing data scientist →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 AIThe honest journey
- The Brewer's AI RoadmapStep-by-step, free resources
- Asking Better QuestionsAI for new brewers
- Vibe-Coding for BrewersBuild tools with Claude Fable 5
- Seeing Your BeerData visualisations brewers underuse
- The Brewer's Chart Field GuideEvery chart type and its beer use cases
- AI Foundations for DistillersAI, ML & GenAI explained
- Process Intelligence for DistilleriesThe four analytics, distillery edition
- Process Intelligence for WineriesThe four analytics, winery edition
- The Cellar LedgerData engineering and GenAI for wineries
- The Still and the ModelGenAI and data engineering for distilleries
- The Brewer's AgentGenAI and data engineering for brewing
- AI for Operational ExcellenceFrom AI basics to agents on the plant floor
- Responsible AI for Beer, Whisky and WineGuardrails, evals and governance for drinks AI
- Beer NPD with DataNew product development
Core reading on AI in drinks
Latest posts
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A Responsible AI Playbook for a Small Beverage Business, and Where It Breaks
Part 8 of Responsible AI for Beer, Whisky and Wine. A lightweight version of ISO/IEC 42001 for a craft brewery, small distillery or winery with no AI team: a use-case register, a one-page impact assessment, an eval harness, monitoring, an incident log and a quarterly review, mapped to the NIST AI RMF. Plus AI's own energy and water footprint against your ESG claims, the regulatory calendar as of September 2026, and a 90-day plan.
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Accountability When an Agent Drafts the Decision: Owners, Audit Logs, Red Teams and Safety Cameras That Are Not Surveillance
Part 7 of Responsible AI for Beer, Whisky and Wine. When an AI agent drafts the work order, the batch note or the supplier query, who answers for it? A named owner for every tool, an append-only audit log of every tool call, a query that catches rubber-stamp approvals, red-teaming against prompt injection from operator notes and supplier PDFs, and computer-vision safety cameras that detect events, not people.
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Provenance, Fakes and Synthetic Labels: A Hash-Chained Cask Ledger, C2PA and Label Claims You Can Prove
Part 6 of Responsible AI for Beer, Whisky and Wine. GenAI makes a convincing fake label, bottle photo or heritage story in seconds, and it can just as easily draft an illegal age statement for your own product. How an append-only, hash-chained traceability ledger from cask or tank to bottle, label claims derived from that ledger, and C2PA Content Credentials on your images give you provenance you can actually prove.
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Whose Palate Did the Model Learn? Bias in Sensory AI, and Evals That Catch Hallucinated Tasting Notes
Part 5 of Responsible AI for Beer, Whisky and Wine. Flavour models and LLMs learn a Western tasting vocabulary and a narrow panel, then write confident notes that invent casks, vineyards and awards. How to find the bias in descriptor data and panels, how to ground note drafting in the batch record, and how to build an eval set that checks every claim in a tasting note against its source.
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When the Model Touches Food Safety: Flag-Only Release Gates, Abstain Thresholds and Model Cards for Beer, Whisky and Wine
Part 4 of Responsible AI for Beer, Whisky and Wine. Reliability and safety in practice: AI may rank risk and raise flags, but a person releases the batch and validated methods stay on the HACCP critical control points. Worked through soft sensors, mycotoxin and gushing risk in malt, methanol and the spirit cut, and SO2 and Brett in wine, with a model card, an abstain threshold, drift monitoring and a release gate where the model can block but never release.
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Responsible Recommenders: Harm Caps, Consent-Aware Features and Why the Heaviest Drinkers Should Not Get the Best Offers
Part 3 of Responsible AI for Beer, Whisky and Wine. A recommender tuned for clicks and volume quietly over-serves the top decile of drinkers. How to fix it with harm-aware re-ranking, frequency caps, low and no-alcohol swaps, age gating, a consent-filtered feature view with purpose tags, and an offline eval that measures who the model is really talking to. With India's DPDP Act and Rules and GDPR in view.
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.