PERNOD RICARD: 2025-2026

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Pernod Ricard is a multinational corporation and the world’s second-largest producer of wines and spirits, after Diageo. The Perfect Store scoring framework was a global programme aiming to baseline brand strategy out in the field with clear actions for sales teams at retail level ensuring the right product was perfectly displayed at the right price.

Challenge

Pernod Ricard’s third-party Trax AI image recognition tool automatically recognised products from photos captured in retail and hospitality environments. Using machine learning models, the AI was trained to identify, tag, and segment products displayed on drinks menus, back bars, shelves, and in coolers before delivering SKU-level data for reporting and insights in minutes. This enabled field sales teams to speed up off-trade and on-trade retail from manual compliance audits to algorithmic audits and recommendations, telling the sales teams exactly where to go and what specific actions to prioritise during and after their visits. 

Their main challenges were primarily a delayed pilot launch due to poor stakeholder and vendor management, incoherent governance processes, data quality, pipeline and technical issues and a misalignment of the product strategy with local market needs. One size or approach did not fit all. 

Task

Provided paternity cover as Product Manager for the global rollout starting with bringing India pilot back on track for delivery. Then use what was being learned from India to inform balancing other local market requirements within the wider programme.

Actions

• Established a new governance structure for the failing pilot.
• Embedded agile ways of working to stabilise delivery and set up future phases for success.
• Reset the product strategy to align with local market needs.

Results

• Course corrected and delivered phase 1 rollout for India
• Kicked off discovery phases for USA, Turkey, South Africa & Spain within the global Perfect Store framework