Pierre-Yves Calloc’h, Global Chief Digital Officer, Pernod Ricard, speaks with Julian Schirmer, Co-Founder of OAO, in a video interview about the company, pioneering AI in customer engagement, adapting to the diversity in the global market, balancing global with local, prioritizing high-value AI projects, and navigating B2B2C with AI.

An inside look at Pernod Ricard’s transformation

Founded five decades ago, Pernod Ricard has grown into a global leader, widely recognized for its expertise in crafting and distributing a diverse range of spirits and champagne brands, says Calloc’h.

With over 250 brands in its portfolio, Pernod Ricard’s name may not be as familiar to the public as the products it brings to life. He adds that Pernod Ricard operates in over 60 countries, supported by a network of dedicated professionals across multiple functions.

Calloc’h’s own journey within the group spans more than 20 years, with experiences across various continents and leadership roles. Speaking of Pernod Ricard’s digital transformation, Calloc’h shares how the company began its transition more than a decade ago — well before AI became mainstream. This early shift was driven by a strategic focus on building more personalized connections with consumers.

According to Calloc’h, the company showed foresight in recognizing the potential of AI even before the widespread adoption of generative technologies. One such initiative involves optimizing the performance of Pernod Ricard’s field sales force using AI-powered recommendations, he adds.

By leveraging data-driven insights, Calloc’h emphasizes that the company is empowering its teams to be more effective and focused in their day-to-day operations.

Adapting to a diverse global route-to-market landscape

Moving forward, Calloc’h highlights the complexity and diversity of Pernod Ricard’s global distribution network. He notes that the route to market varies significantly across countries. For example, in France, 90% of the sales happen through large retail chains for at-home consumption and the rest is distributed through wholesalers that distribute to bars, restaurants, etc.

In contrast, the Spanish market shows a much more balanced dynamic between in-home and out-of-home consumption, says Calloc’h.

Markets like China illustrate even more complexity, with a highly fragmented distribution model, he adds. This variability requires a tailored approach to data sourcing and project execution in each region.

In some markets, such as Canada or Sweden, government-regulated distribution simplifies the data landscape. Ultimately, Calloc’h states the importance of understanding and adapting to the distinct routes to market around the world.

Balancing global efficiency with local adaptation

Navigating a global operation means constantly managing the balance between standardization and local responsiveness, says Calloc’h. He further states the importance of tailoring systems and practices to local needs while optimizing what can be done collectively.

“So like any company, it’s about finding the right balance and optimizing so that you can operate and reach consumers in the right way.”

Some functions naturally lend themselves to centralization, especially when efficiency and consistency are essential, says Calloc’h. In addition to finance, technical infrastructure, and support are globally unified, he adds.

However, not all systems follow a one-size-fits-all approach. Calloc’h points out that flexibility is crucial, particularly in markets with specific legal or operational requirements. The U.S. market is a prime example of the need for tailored solutions due to regulatory constraints, he maintains.

“There are cases, for example, like in the U.S., we cannot distribute products ourselves; we need to go through a distributor. And so, we need to make sure that our systems are adapted to provide the information to a third-party salesforce that will visit the outlets.”

Meanwhile, in countries like Germany, the setup is entirely different. This variation underscores what Calloc’h describes as a complex global reality. “We need to make sure that things are adapted to each of the markets,” he affirms.

How Pernod Ricard prioritizes high-value AI projects

For Calloc’h, who has overseen Pernod Ricard’s digital evolution for years, the challenge with AI is not the abundance of possibilities but identifying what truly delivers value.

“We can do hundreds or thousands of things with AI today. And the biggest complexity for me is to identify the right use cases that will provide more value than the cost to actually design, train people, implement, and scale them.”

Rather than chasing every new innovation, Pernod Ricard has adopted a disciplined and ROI-focused approach to AI. Calloc’h explains that the company is deliberately limiting the number of AI initiatives to avoid overextending resources.

It collects the different ideas internally from people who are close to customers, vendors, etc. Once ideas are collected, the company rigorously evaluates them. The assessment process is focused on business impact and is repeated regularly.

Calloc’h further stresses the importance of transparency, even for the ideas that do not make the cut. “For a for-profit organization, if a project does not deliver some P&L value, it’s going to be difficult to sustain, finance, and have people working on it as a priority.”

When Pernod Ricard began rolling out its AI strategy, projects were prioritized for areas such as optimizing marketing mix, optimizing pricing and promotion, and segmenting consumer demand.

Navigating B2B2C complexity with AI precision

Furthermore, Calloc’h outlines several unique challenges Pernod Ricard faces in its operations, ones that make the smart use of AI not just helpful, but essential.

Firstly, as a B2B2C business, Pernod Ricard primarily sells to intermediaries — retailers, wholesalers, or venues — who then sell to the end consumer. This indirect model makes it more difficult to directly access and analyze consumer data.

Secondly, the company’s extensive brand portfolio of over 240 products adds another layer of complexity. Not every brand fits every venue, and the placement strategy must be highly tailored.

Calloc’h gives a concrete example: a high-end cocktail bar may demand specific brands like Absolut or Kahlúa for an Espresso Martini, while mainstream venues may prioritize affordability and broader appeal.

This is where AI makes a significant impact. It enables Pernod Ricard to process massive combinations of brand, outlet, consumer type, and occasion — and recommend optimal brand placements.

In conclusion, Calloc’h says that AI helps the company fine-tune its brand strategy at scale, matching the right product to the right place at the right time, despite the complexity of its B2B2C model and vast product range.

CDO Magazine appreciates Pierre-Yves Calloc’h for sharing his insights with our global community.