Sommelier
Build and maintain the wine list
What You Do Today
Curate wines by region, style, and price point. Balance the list across varietals, appellations, and margins. Rotate selections seasonally and manage the mix of by-the-glass versus bottle offerings.
AI That Applies
Wine program AI analyzes sales data, margin performance, and inventory turnover to identify which wines sell, which sit, and where the list has gaps in style or price point.
Technologies
How It Works
For build and maintain the wine list, the system analyzes sales data. The analytics engine aggregates data across sources, applies statistical analysis to identify significant patterns and outliers, and presents the results through visualizations that highlight what needs attention. The results integrate into the practitioner's existing workflow — presenting recommendations, flags, or automated outputs alongside their normal working context.
What Changes
List decisions are data-informed. AI shows that your Burgundy section turns slowly while the by-the-glass Albariño outsells everything — quantifying what your instincts already suspected.
What Stays
Curation is taste and vision. Choosing the wines that tell a story, that complement the chef's menu, that surprise and delight guests — this is artistic expression, not data optimization.
What To Do Next
This section won't tell you what your numbers should be. It will show you how to find them yourself. Every instruction below produces a real, verifiable result in your organization. No benchmarks, no projections — just the steps to build your own evidence.
Establish Your Baseline
Know where you are before you move
Before adopting AI tools for build and maintain the wine list, understand your current state.
Without a baseline, you can't measure whether AI actually improved anything. You'll adopt tools without knowing if they're working.
Define Your Measures
What to track and how to calculate it
Time per cycle
How to calculate
Measure how long build and maintain the wine list takes end-to-end today, then after AI adoption.
Why it matters
The most visible improvement is speed. If AI doesn't save time, question whether it's adding value.
Quality of output
How to calculate
Track error rates, rework frequency, or stakeholder satisfaction scores before and after.
Why it matters
Speed without quality is just faster mistakes. Measure both.
Start These Conversations
Who to talk to and what to ask
your VP Operations or COO
“What data do we already have that could improve how we handle build and maintain the wine list?”
They're prioritizing which operational processes to automate
your process improvement or lean lead
“Who on our team has the deepest experience with build and maintain the wine list, and what tools are they already using?”
They understand the workflow dependencies that AI tools need to respect
a frontline supervisor
“If we brought in AI tools for build and maintain the wine list, what would we measure before and after to know it actually helped?”
They see the daily reality that AI tools need to fit into
Check Your Prerequisites
Confirm readiness before you invest
Check items as you confirm them.