---
id: "entity-d-star"
type: "entity"
entityType: "product"
canonicalName: "D-STAR"
aliases: ["DSTAR", "D‑STAR"]
source_timestamps: ["§ Employees Are Initially Skeptical", "§ How the Company Gained Buy-In"]
tags: ["ai-tool", "sales-optimization"]
related: ["entity-pernod-ricard", "entity-matrix", "question-matrix-adoption-gap"]
sources: ["adoption"]
sourceVaultSlug: "hbr-seg-adoption"
originDay: 9
articleStem: "hbr-edu-41-french-spirits-employee-buy-in"
sourceUrl: "https://hbr.org/2025/12/how-a-french-spirits-company-created-employee-buy-in-for-ai"
sourceTitle: "How a French Spirits Company Created Employee Buy-In for AI"
---
# D-STAR

An AI-powered system developed by [[entity-pernod-ricard-d9]] that uses machine learning to optimize sales representatives' store visits and product recommendations. It achieved an **85% adoption rate** across deployed markets by 2023.

**Enrichment context.** D-STAR provides real-time, tailored recommendations for each sales representative on what SKUs to push, which stores to prioritize, and how often to visit; value shows up in improved conversion rates, coverage, and stronger retailer relationships. Roughly **80% of D-STAR's code is reportedly tailored per market**, underscoring the importance of local data quality and conditions. It is the higher-adoption counterpart to [[entity-matrix]]; the gap between them is an open question ([[question-matrix-adoption-gap]]). Its measured success was the proof point in the localized A/B tests ([[action-run-local-ab-tests]]) that seeded the [[concept-pull-vs-push-adoption]] dynamic, and its usage-gated value realization is the basis of [[claim-value-requires-usage]].