For many companies, AI adoption starts with technology. For Pernod Ricard, it started with a business problem.
“We never launch AI for the sake of it,” said Milagros Talabucon, VP of Technology for North America. “It always begins with a case: what are we trying to solve?”
That mindset has quietly placed Pernod Ricard ahead of the curve. Recently named the most advanced European company in AI adoption by AlixPartners, the French spirits group, home to brands like Jameson and Absolut, has taken a pragmatic but expansive approach to embedding AI across its operations. From pricing models to marketing spend optimization, the use cases are multiplying. But the real shift, as seen in a Lead Summit session this week, isn’t technological. It’s cultural.
AI Is Not a Project. It’s a Practice.
Talabucon and Thomas d’Aboville, the company’s Deputy CFO in North America, unpacked how the company has built an AI strategy not around one team or discipline but around the idea that every business function can and must engage with it.
It started, in typical Pernod Ricard fashion, with data. With a vast global footprint and consumer-facing products, the company had no shortage of information. But the challenge wasn’t access. It was activation.
How do you turn fragmented data into decisions? How do you enable local teams to act on insights without becoming dependent on a central function? And how do you govern that process, ethically, securely, and at scale?
The answer wasn’t a single platform. It was a structure. A steering committee, built with representatives across digital, architecture, and technology, ensures that experimentation is balanced with accountability. A network of over 150 global experts supports frontline teams. And a set of principles governs how and where generative tools can be used.
In short: AI isn’t treated as a finite project. It’s a living capability that evolves with the company.
Experimentation Without Recklessness
The conversation struck a careful balance between enthusiasm and restraint. “AI works for us,” Talabucon noted. “We don’t work for AI.”
That means guardrails. Not just around model outputs or enterprise tools but around behaviour. As employees across disciplines become more curious (and more vocal) about generative AI, the company has implemented internal education programs and guidance frameworks. The goal isn’t to stifle innovation. It’s to prevent it from happening in isolation.
There are also structural realities. In finance, for instance, experimentation is welcome but data integrity is non-negotiable. According to d’Aboville, even experimentation needs to happen within a process that protects accuracy, traceability, and trust. Particularly in public companies, the stakes are higher and so is the scrutiny.
What AI Really Changes: Entry-Level Roles and Talent Signals
When asked how AI is reshaping hiring, both speakers emphasized that there is no such thing as an “AI role.” Instead, they’re looking for mindset particularly among younger hires.
Candidates who demonstrate curiosity, literacy in experimentation, and openness to change are already standing out. Technical fluency is helpful, but not essential. In some teams, traditional entry-level tasks like spreadsheet prep or rote reporting are vanishing forcing a redefinition of what junior roles actually mean.
Rather than replacing jobs, AI is changing what jobs start as. That requires new thinking in HR, onboarding, and career design.
The Cultural Compact: Curiosity and Trust
One of the sharpest moments came late in the discussion. AI adoption, Talabucon explained, isn’t just about tools or projects. It’s about mutual trust.
Employees must trust that experimenting with AI won’t lead to obsolescence. Employers must trust that employees won’t jeopardize data security with a rogue ChatGPT prompt. In between lies education, policy, and a clear articulation of AI’s role: to enable, not displace.
It’s also a reminder that the fear of AI is often self-fulfilling. “If you assume it’s coming to replace you, you won’t engage with it,” said Talabucon. “And then it probably will.”
Ongoing, Not Afterthought
Where some organizations treat AI as a layer, something to be “added on”, Pernod Ricard sees it as an evolving core. Consumer models shift. Markets change. New data arrives. That means the work is never finished.
And that, perhaps, is the point. AI transformation isn’t linear or even measurable in traditional ROI terms. It’s a recalibration of how companies learn, decide, and scale. And for Pernod Ricard, it’s no longer a future state. It’s the present.
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