You have to digitalize to grow
Danone commercials now use iPad-based AI image recognition to analyze product placement and shelf space in supermarkets, enabling data-driven negotiations with retailers. The company reduced repetitive financial tasks by 20% while increasing strategic analysis time by 35%, freeing staff from mechanical work to focus on value-creation decisions.
- €20 million invested over three years in Iberia digital transformation
- Sales reps use iPad-based AI image recognition to analyze shelf placement and negotiate with retailers
- 20% reduction in repetitive financial tasks; 35% increase in strategic analysis time
- Approximately 1,000 employees (half the Iberia workforce) trained in data and AI literacy
- Innovation lab with five dedicated professionals prototypes disruptive projects in weeks
Danone has invested €20M over three years to establish Iberia as its digital transformation laboratory, deploying AI for retail analytics, demand prediction, and process automation while training half its workforce in data skills.
Danone's sales representatives now arrive at supermarkets with iPads in hand. They photograph the shelves—the placement of yogurt containers, the spacing of milk cartons, the real estate occupied by competing brands. Artificial intelligence reads these images instantly, analyzing whether Danone products occupy the shelf space the company negotiated with the retailer, and whether they're positioned where customers can find them. The system cross-references this visual data with demand patterns and macroeconomic indicators, giving the sales rep concrete ammunition for the next conversation with store management. It is a small, concrete example of how Danone has begun to remake itself as a data-driven company.
The company has invested twenty million euros over the past three years to transform its Iberia division into a laboratory for digital transformation. Nicolás Cosqueric, Danone's chief information officer for the region, describes the strategy plainly: "You have to digitalize to grow." The investment is not theoretical. Projects are already running in production. The shelf-scanning system is one. Machine learning models that predict demand with far greater accuracy than the company could achieve five years ago are another. The underlying conviction is that better data leads to better decisions, and better decisions drive growth.
Iberia holds strategic weight within Danone's global structure. The French multinational reported consolidated revenues of 27.3 billion euros in 2025 and net profit of 1.8 billion euros. Cosqueric positions the Iberia region as a proving ground—a place where the company tests the full integration of digital transformation and demonstrates its value to other business units around the world. "We position ourselves as a beacon," he says, "where we test the integration of all digital transformation and show the value it can bring to a business unit, to inspire the rest of the countries."
The work has required roughly three hundred people across the organization. The company established a small innovation lab modeled on startup culture, with five dedicated professionals tasked with incubating and accelerating disruptive projects in analytics, automation, and artificial intelligence. The lab operates on a different cadence than traditional corporate development. Rather than demanding exhaustive business cases before proceeding, the team builds prototypes in weeks. If a concept shows promise—if it offers potential for growth, efficiency gains, or better data for decision-making—it gets scaled. If it fails, it gets discarded. Speed matters more than certainty.
The efficiency gains are measurable. In Danone's financial operations, automation and process simplification have reduced the time spent on repetitive mechanical tasks by twenty percent. That freed-up capacity has been redirected toward analysis, decision-making, and business improvement—work that has increased by thirty-five percent. The net effect is not layoffs but redeployment: people moved from routine work to strategic work, from executing tasks to solving problems.
Parallel to the technological shift, Danone launched a training program in data and artificial intelligence for the entire Iberia workforce, not just technical specialists. The curriculum adapts to each role. Approximately one thousand employees—roughly half the regional staff—have completed the program. The goal is straightforward: teach people across the organization to recognize where artificial intelligence can create value. A marketing manager should understand what machine learning can do. A supply chain coordinator should see opportunities for automation. The company is building a culture in which data literacy is expected, not exceptional.
What emerges from this three-year investment is a company in transition. Danone Iberia is learning to operate at the intersection of human judgment and algorithmic insight. The sales rep still negotiates with the store manager, but now carries data that changes the conversation. The demand planner still makes forecasts, but now draws on machine learning models that have absorbed years of historical patterns. The organization is not being replaced by algorithms; it is being augmented by them. The question Danone is answering, through this expensive, sustained experiment, is whether that augmentation translates into the growth Cosqueric claims it will.
Citas Notables
We position ourselves as a beacon where we test the integration of all digital transformation and show the value it can bring to a business unit, to inspire the rest of the countries.— Nicolás Cosqueric, CIO of Danone Iberia
We don't present a super-detailed business case because it slows us down too much. In weeks we already see if it works and we scale it, or it doesn't work and we discard it.— Nicolás Cosqueric, on the innovation lab's approach to prototyping
La Conversación del Hearth Otra perspectiva de la historia
Why does Danone need an innovation lab with just five people when the company has three hundred people working on digital transformation?
Because those five are freed from the constraints that slow down the rest of the organization. They can prototype in weeks without needing board approval or a fifty-page business case. They're testing ideas, not executing strategy.
The shelf-scanning system sounds useful, but is it really artificial intelligence, or just image recognition?
It's image recognition, yes, but that's a form of AI. The power isn't in the photography—it's in what happens next. The system analyzes the image, compares it to what was negotiated, pulls in demand data and economic indicators, and surfaces insights the sales rep can use. That's where the intelligence lives.
You mentioned that financial teams now spend thirty-five percent more time on analysis. How do you know that's actually happening and not just a number someone calculated?
Fair question. The company measured time allocation before and after automation. Whether those hours translate into better decisions is harder to prove. But the logic is sound: if a person spends less time on data entry, they have more time for thinking. Whether they use that time well depends on the person and the culture.
Why train half the workforce in AI if most of them will never build an algorithm?
Because the people closest to the work—the sales reps, the supply chain coordinators, the store managers—are the ones who see where AI could solve a real problem. You can't innovate from the top down. You need people throughout the organization who understand what's possible.
Is Danone actually going to become a tech company, or is this just a way to sell more yogurt?
It's the latter, but that doesn't diminish it. Danone is a food company. The goal isn't to become Google. The goal is to use data and algorithms to understand customers better, predict what they want, and get products to them more efficiently. That's not a betrayal of what Danone is—it's an evolution of it.