ai:sight sat down with Vikrant Bhan, Nestle’s group head of analytics, data and integration, to uncover how the CPG giant harnesses AI to connect with the universal everyman. Here, Bhan explains how Nestlé bridges global reach with local impact by breaking silos and democratizing data and AI tools.

In the spirit of Nestlé's ‘Take a Break’ campaign, what's your favorite Nestlé product that you and your family can't live without?

With over 2,000 global brands, it’s tough to pick one. Growing up in South Asia, India, I loved Maggi noodles – a common favorite among students from that region. Currently, I enjoy Maggi culinary products and Nespresso. But beyond products, the purpose behind Nestlé Health Science’s Resource products, especially for oncology and nutritional needs for those with serious diseases like cancer, stands out. I’ve seen firsthand how these improve lives, making them particularly meaningful.

Nestlé touches nearly 8.2 billion people, and your role involves managing that scale. As one of the largest food and beverage companies, what unique AI opportunities and challenges come with scaling at speed?

To use a food analogy, the challenge is getting the recipe right. Three key ingredients are business alignment, end-to-end processes, and data.

In a complex matrix organization like Nestlé, aligning P&L entities, zones, categories, and group functions is critical. But we also need the right processes, change management, adoption, capabilities, people, and culture. We design to leverage services rather than embedding AI in every geographic entity.

When it comes to data, we have abundant data in some areas, thanks to our single ERP solution. However, in areas with observational or external data, fragmentation poses issues with coverage, consistency, and quality, impacting AI scalability.

To overcome these challenges, we pilot initiatives in mature markets with robust data, capabilities, and culture, creating ‘lighthouses’ that are designed for scalability. We leverage partners and services to automate and reduce costs as we scale across the group, ensuring faster, more efficient expansion.

That ties into balancing global and local needs in a fragmented world. With regional products like unique Maggi flavors in India, how does AI help incorporate local and global needs?

In the CPG industry, especially in the food and beverages segment, you need to be very localized. You need to understand different sentiments, different consumer touchpoints, and journeys, and differentiate accordingly. We use a hybrid operating model, where shared services are managed centrally but local AI expertise addresses market-specific needs. Anywhere we are making decisions on commercial investment, on marketing investment, or consumer promotions, they need to be managed super-locally. 

Nestlé bridges global and local incredibly well. How do you tailor strategies for emerging markets, where data, technology and needs differ?

We divide the business into archetypes based on trade routes and channels. Emerging markets, like those in Asia or Latin America, often involve mom-and-pop shops, fragmented trade, or rapidly growing e-commerce, sometimes skipping traditional retail. Our data fabric accounts for these differences, integrating competitor, syndicated, and retailer data. AI solutions, like sales recommendation engines, vary by channel – traditional, e-commerce, or modern grocery trade. Data richness, veracity, and variety also differ, so our data models adapt to these realities to ensure effective solutions.

With 70% of your brands being number one or two in their categories, what’s next for creating the next billion-dollar product? How does AI help with your portfolio expansion?

Previously, we spread innovation too thin, but now we focus on big bets and growth platforms, ‘billionaire brands’, that we scale through the launch process.

AI drives efficiency in procurement, logistics, manufacturing, and commercial spend, freeing up funds to invest in these platforms. For big bets, like our viral coffee concentrates that are proving incredibly popular with younger audiences today, AI optimizes launch forecasting, capacity planning, production, and marketing investments. AI models help allocate spending across channels for maximum ROI, embedding AI across end-to-end processes to drive innovation and growth.

Where is AI delivering the biggest productivity gains across your value chain?

Classical AI is delivering gains in revenue growth management, marketing spend optimization, supply chain control towers, logistics, predictive maintenance, and enterprise business planning. These are measurable, with clear value reported to stakeholders.

Generative AI, meanwhile, is enhancing our existing capabilities and improving the user experience. It’s certainly improving productivity too, but it’s very difficult to measure that gain. We’re on a learning journey, much like we first started experimenting with machine learning. There’s a real opportunity to reimagine some of our ways of working and end-to-end processes. As we venture further down this path, I expect we will further improve the employee experience and maximize value.

That ties into breaking silos. How are you using AI to replace data, functional, and technology silos with a unified digital engine?

End-to-end processes are the vehicle. Take our idea-to-launch process: it spans marketing, R&D, supply chain, sales and regulatory functions. If you use AI in buckets or silos, you will only optimize one of these parts of the chain. At Nestlé, we are seeing a paradigm shift by focusing on outcomes like time-to-market or launch success. This means leveraging AI cross-functionally. It’s not easy, but when you get it right you are able to serve customers and consumers far better.

Nestlé prioritizes health, nutrition, and wellness. How do you embed these values into AI systems?

One of the indicators we’ve committed to, which started in Australia many years back and has now become more of a global norm, is the health star rating (HSR). So how does that integrate into our AI systems? Think about product lifecycle management: when we’re working on recipe formulation, we’re selecting key purchasing specifications and ingredients. In AI terms, this is a recipe optimization use case. We have around 120,000 recipes in a single system, which is already a strength, but then we layer AI on top. In the past, the drivers for optimization might have focused mainly on cost, or later on procurement ease and sustainability KPIs. Now we also consider impacts on climate and packaging. And of course, we include nutritional value and HSRs to see how different ingredients affect the overall nutritional profile of a product. That’s a very real demonstration of how our values are embedded in AI systems. By getting it right at the base of the end-to-end process at the formulation stage, we influence company-wide KPIs and deliver on the commitments we communicate to external stakeholders and consumers.

How does AI help navigate market shifts like sustainable farming and rising cacao prices?

Change is constant – COVID-19, geopolitics, and inflation have disrupted models. Our operational master plan, revised annually and quarterly, aligns AI with business risks and drivers like market share. AI optimizes SKUs, brands, consumer tastings, order fulfilment, and availability in growing channels like convenience, value retailers, and e-commerce. It evolves marketing ROI models, pricing algorithms, and product recommendations to adapt to dynamic market realities.

You’ve worked with Fractal for years. What stands out about our partnership, and how do you see us co-developing solutions?

Fractal’s partnership started in specific units but grew globally, beginning with revenue growth management. I appreciate Fractal’s passionate leadership, domain expertise in CPG, and focus on industry-specific innovation rather than commoditized solutions. Its subject matter expertise and practice advancements help the CPG industry grow, making it a valuable partner in driving innovation.

How do you make the most of AI for boosting growth and empowering your workforce instead of automating them away?

We’re at a point now where nearly 100,000 employees here at Nestlé are using AI co-pilots. And now we’re adding even more capabilities like research and learning agents. Employees creatively use these tools daily. It’s amazing to see how it boosts effectiveness and really empowers our workforce, without needing ROI metrics.

For leaders just starting on their AI journey, what’s your advice?

Understand your organization’s maturity and benchmark it. Don’t be disheartened, because if you’re not working in a digitally native company, then there are likely to be cynics. Be smart about picking your first set of capabilities to run with so you can build credibility and trust quickly. Surround yourself with the right people, good, trusted platform partners, and internal experts who may not know everything, but who have extremely high learning agility and a bias toward outcomes that are right for the company. It doesn’t need to be a lot of people, but it needs to be the right people. And if you have the right team and the right sponsors to kick off your journey, you will be successful.