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Muhammad Zulhusni
26th February 2026
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Nestlé is embedding AI into its marketing and sales operations, reshaping how teams plan campaigns and manage workflows. The company is rebuilding the infrastructure beneath its marketing systems to support artificial intelligence.
Over the past year, Nestlé has taken steps to prepare its global teams to use AI in routine, revenue-related tasks and creative production. The change offers a case study in how large brands are approaching AI in marketing and e-commerce.
Nestlé recently completed the first phase of a global upgrade to SAP S/4HANA Cloud Private Edition, its core system for handling operations in finance, sales, and more. The rollout covered more than 50,000 users in 112 countries, and the broader deployment is expected to finish over the next two years.
The upgrade gives teams a single, shared data platform that can serve AI tools. The new system embeds SAP’s AI copilot directly into core business systems, which can help employees pull insights and automate routine tasks in ways that were harder to do before.
This can change what’s possible in a digital-first marketing and sales context. According to Nestlé’s IT leadership, the upgrade should help the company build platforms to support digital marketing and sales activities that span geographies and channels.
In other words, the work is about making the data and workflows behind marketing and commerce ready for AI-driven abilities, like automated analysis and execution.
One of the more tangible marketing tech advances at Nestlé lies in content creation. In 2025, Nestlé rolled out an AI-powered content service that uses digital twins (detailed digital 3D models of real products) to generate product visuals for e-commerce and digital media.
Digital twins are created using tools like NVIDIA Omniverse and OpenUSD, which let the company’s teams build virtual replicas of physical products. A product twin can be adapted in many ways: packaging variants, localised images, backgrounds or lighting that match campaign needs, without physical photography.
Nestlé says the service supports brands like Purina, Nescafé Dolce Gusto, and Nespresso by giving marketing teams the ability to produce images faster and at lower cost.
Instead of planning dozens of separate photoshoots, teams can produce and adapt assets in a digital environment that reflects the real product. That can speed up campaigns and let marketing teams act closer to the moment when trends or seasonal events arise.
At first glance, ERP upgrades and digital twins might sound like internal tech talk. But the implications stretch into how brands plan and deliver marketing content.
Here’s how:
1. Faster content production
With models that can be reused and adapted, teams don’t need to start from scratch for every campaign or variation, useful if brands sell in many markets and need tailored visuals for local audiences.
2. Lower costs over time
Traditional creative work: photoshoots, retouching, editing for format variants, can be expensive and slow. Digital twins convert that work into a reusable asset. Teams can produce visuals in different sizes or contexts without repeating costly production stages.
3. Data-ready marketing workflows
The company’s SAP upgrade was important as data driving campaigns and customer interactions sits on a common platform. That makes it easier to apply AI in functions that matter to marketing, from customer behaviour analysis to inventory insights that inform promotional timing.
4. Automation for routine work
Tools like SAP’s AI copilot can help employees finish routine tasks faster. In marketing or sales operations, that could mean less time spent pulling reports or updating campaign plans by hand, and more time focused on strategy or creative decisions.
Nestlé’s moves show what large brands are doing to make AI useful beyond pilot projects: fix the data foundation first, then build tools that integrate into everyday work. For marketing leaders evaluating AI investment, this offers two lessons:
What Nestlé is making marketing operations more predictable and flexible; an approach that many large brands may soon need to match if they want AI to do more than solve narrow tasks.
(Photo by inma santiago)
See also: Walmart expands Scintilla In-Store to link store data and marketing
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Muhammad Zulhusni
Journalist
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