Nestlé makes AI a key ingredient

The global food and beverage industry is undergoing significant transformation, driven not only by master chefs and marketers of all stripes, but also by systems based on AI. This being the case, technology is no longer a futuristic reality for food companies as it influences so many areas in the sector, including recipe development, waste reduction, sustainability initiatives, and ways to anticipate supply chain disruptions.

Nestlé is no exception with its use of gen AI in many of its business areas. But despite all the enthusiasm behind it, any breakthrough tech can’t perform miracles, and very promising use cases also carry risks of overextending themselves or making serious mistakes, especially when it comes to ethical data use or automation.

For IT and tech decision-makers, the challenge isn’t so much about realizing the added value of AI first, but more that they must develop the culture, skills, and  systems  that enable the technology to be scaled responsibly.

What Nestlé optimizes with AI

The food industry has always had to contend with uncertainties, like fluctuating harvest yields, logistical bottlenecks, and changing consumer preferences that make planning difficult. That’s particularly the case at Nestlé now that it increasingly uses AI to better and more accurately anticipate demand patterns than previously possible, and dynamically adjust production and inventory levels. Specifically, the technology is used to predict the arrival time of containers at destination ports, create statistical forecasts, and improve the accuracy of demand planning.

Apart from that, AI is also used in formulation development. To accelerate idea generation and product development, ML models analyze historical research and development data. These aren’t experiments or pilot projects, but productive pipelines that already deliver results.

But operational efficiency alone isn’t enough. For AI to have a real impact, it must also support overarching goals including sustainability, which is one of the biggest challenges for the food industry, where regenerative agriculture and innovative packaging play key roles. And with AI, it’s possible to further amplify these effects.

“We’re currently exploring how AI models can optimize carbon tracking across complex agricultural supply chains to enable smarter procurement decisions and reduce emissions at scale,” says Luca Dell’Orletta, global head of tech innovation and enterprise architecture at the Nestlé Group. “And in manufacturing, AI-powered vision systems can minimize waste and improve energy use, which also help Nestlé meet its environmental commitments.”

But even as AI is integrated into the core structure of operations, the company doesn’t lose sight of how technology shouldn’t replace or displace the values ​​that define the craftsmanship and humanity of high-quality food.

No free pass for AI

One of the most enticing features of gen AI is its speed to allow campaign content, copy, or product concepts to be generated almost instantly. But faster doesn’t necessarily mean better. Food is culturally influenced and evokes emotions, and what resonates in one region can completely backfire in another. That’s why Nestlé has human-in-the-loop systems that enable local teams to adapt global solutions. “And we can only warn against leaving algorithms to manage creative tasks without supervision,” says Dell’Orletta.

Added to this are concerns about bias when it comes to product testing and consumer research, especially when datasets don’t fully reflect the diversity of global consumers. For multinational food companies like Nestlé, responsible scaling of AI should also include a commitment to diversity at the data level.

In this new era, IT decision-makers are no longer just a technology partner but growth drivers and data guardians that coordinate between various transformation enablers. AI success, therefore, doesn’t depend on deploying the latest models, but acquiring talent, governance, and encouraging experimentation in ways that align with a brand’s purpose and risk-taking. This involves an integrated level that consistently extends across all functional areas, departments, and the entire business value stream.

“We believe food companies that embrace AI as a core competency rather than an add-on will ultimately win,” he says. “It’s not about who automates fastest, but the ability to rethink things and embrace new ways of working.”