Drinks is an odd industry to put AI into. The product is legal, loved and age-restricted. It is regulated for safety, taxed by the litre and faked by the case. A model that is fine for recommending shoes can do real harm when it recommends a fourth bottle of whisky to someone who already buys one a week.
This series is about the guardrails. Each part takes one Responsible AI principle and turns it into something you can build: a register table, a policy file, an eval set, a release gate, a ledger. The beer, whisky and wine examples are there to make the principle concrete. None of it is legal advice, and the regulatory dates are as they stood in September 2026.
The series
- What Responsible AI actually means for a drinks business: six principles, the frameworks behind them, and a use-case register in SQL.
- GenAI marketing for a product you cannot sell to minors: policy-as-code, a pre-publish classifier and a red-team eval set against the age codes.
- Responsible recommenders: harm caps, consent-aware features and why the heaviest drinkers should not get the best offers.
- When the model touches food safety: flag-only release gates, abstain thresholds and model cards.
- Whose palate did the model learn?: bias in sensory AI, and evals that catch hallucinated tasting notes.
- Provenance, fakes and synthetic labels: a hash-chained cask ledger, C2PA and label claims you can prove.
- Accountability when an agent drafts the decision: owners, audit logs, red teams and safety cameras that are not surveillance.
- A Responsible AI playbook for a small beverage business: the lightweight version of ISO/IEC 42001, a 90-day plan, and where it breaks.
Start with part 1: the register it builds is used by every later part. Every post is tagged #rai-beverage. For the agents and plant-floor guardrails underneath parts 4 and 7, see AI for Operational Excellence.