Nestlé's AI push puts marketing ops on a shared, faster track
Nestlé is baking AI into the core of its marketing and sales work. The company is rebuilding the plumbing beneath campaigns and content so teams can plan faster, automate routine work, and ship assets at scale.
This is a blueprint for enterprise marketers: fix the data foundation, then plug AI into the daily workflow - not as a pilot, but as the default.
The foundation: shared data with AI inside core systems
Nestlé completed the first phase of a global upgrade to SAP S/4HANA Cloud Private Edition, covering 50,000+ users across 112 countries, with broader deployment set to continue over the next two years. The move creates a single data platform for finance, sales, and marketing - and brings SAP's AI copilot into the systems people already use.
For marketing teams, that means insights and automations where work actually happens: faster reporting, cleaner handoffs, and fewer manual updates across regions and channels.
- One source of truth for customer, product, and channel data
- AI-assisted analysis and execution tied directly to campaign and sales workflows
- Better timing decisions with inventory and commercial signals in the same stack
Content at scale: digital twins replace repeat photoshoots
In 2025, Nestlé launched an AI-powered content service built on digital twins - detailed 3D models of real products created with platforms like NVIDIA Omniverse and OpenUSD. Teams can render product visuals for e-commerce and media, then adapt them for local packaging, lighting, backgrounds, and formats - without rebooking crews or studios.
Brands such as Purina, Nescafé Dolce Gusto, and Nespresso are already using it to speed up delivery and reduce costs across markets.
- Faster production: Reuse models, swap variants, and localize on demand - no need to start from zero for each campaign.
- Lower costs over time: Replace repeat shoots and retouching with reusable assets that render into any format or channel.
- Data-ready workflows: With SAP as the base, campaign triggers and performance signals flow into the same system that runs operations.
- Automation for routine work: AI copilots handle reporting, updates, and basic planning so teams spend more time on strategy and creative.
What marketers should copy
Nestlé shows how to make AI useful beyond experiments: fix the data, then make AI the easy path inside everyday tools. Two takeaways:
- Start with shared data and workflows. If teams don't use the same source of truth, AI adds noise instead of clarity.
- Treat content as a platform asset. Build once, adapt endlessly. Digital twins make reuse real.
Execution checklist (start here)
- Unify customer, product, and asset metadata into one ID system; set rules for freshness and access.
- Pick a copilot that sits inside ERP/CRM and define guardrails for prompts, approvals, and publishing.
- Build a 3D asset library for priority SKUs; document naming, materials, variants, and usage rights.
- Pilot in one category and one market; measure cycle time, cost per asset, and reuse rate before scaling.
- Wire inventory and pricing signals into campaign planning so promos match product availability.
- Set brand QA: color accuracy, pack compliance, claim checks, and alt-text standards for accessibility.
KPIs that prove it works
- Asset cycle time (brief to live) and cost per asset
- Reuse rate across markets and channels
- Campaign lead time and on-time launch rate
- AI-assisted task share (reports, briefs, variations) per FTE
- Promo timing accuracy vs inventory (reduced stockouts/overstock)
- Time-to-market for localizations
Resources
If you're building the same muscle across teams, this practical path helps: AI Learning Path for Marketing Managers
For ongoing playbooks and case studies, explore: AI for Marketing
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