Nestlé Connects Farm to Fork with NesGPT and Singtel Cube, Unifying 1,700 Sites and 80,000 Employees

Nestlé's next operating system: AI at the edge, visibility at the core

Nestlé is accelerating its shift into an intelligent enterprise built on real-time data and AI. At the user level, more than 80,000 employees are now using NesGPT, a secure generative AI platform that cuts an average of 45 minutes per person each week in content creation and information retrieval.

For operations leaders, that time compounds into throughput: faster decisions, tighter cycles, fewer handoffs. The bigger unlock is what sits beneath it-a network and observability layer that keeps factories, fields, and offices in sync.

Why this matters for operations

Nestlé runs 1,700 sites and over 350 factories, many located near agricultural sources. That footprint needs consistent connectivity, data flow, and AI assistance that doesn't break when the terrain gets rough or the tech stack changes.

Previously, a legacy MPLS network was dependable but slow to adapt-rolling out campaigns or supporting remote teams took time. To move faster, Nestlé selected Singtel to rebuild its digital backbone.

From MPLS to a unified network layer

Singtel's Cube acts as the orchestration layer for unified network management. From one platform, teams can monitor performance, security, and traffic across private 5G, MPLS, and satellite links.

Cube reduces complexity, improves visibility, and enables automation-surfacing health signals from remote plantations to central data centers. It goes beyond dashboards to help keep business processes running even when underlying technologies face disruption.

NesGPT: AI that pays for itself in minutes

NesGPT's value shows up in simple use cases: drafting SOPs, summarizing field reports, retrieving specs, and standardizing communications. At scale, 45 minutes per week per employee converts into thousands of reclaimed hours for higher-value work.

The lesson for ops teams: embed a secure, governed AI assistant into daily workflows, measure time saved, and expand into tasks with clear ROI. Pair it with strong access controls and policy checks to keep data safe. For a governance reference, see the NIST AI Risk Management Framework here.

What operations teams can apply now

• Unify network visibility: one orchestration layer across private 5G, MPLS, broadband, and satellite.
• Instrument the edge: telemetry, SLOs, and health checks at plantations, warehouses, and factories.
• Deploy a secure AI assistant: start with content creation, knowledge retrieval, SOP drafting, and Q&A over approved knowledge bases.
• Engineer for last-mile reality: multi-path connectivity, offline-first patterns, and local processing where links are unstable.
• Automate incident response: policy-based routing, zero-touch site turn-ups, and templated failover playbooks.
• Close the skills gap: run targeted enablement for network automation, data literacy, and prompt quality.

Practical KPIs to track

• Average minutes saved per user per week with AI; monthly aggregate hours reclaimed.
• Mean time to detect and resolve network issues (MTTD/MTTR).
• Percentage of sites with end-to-end visibility and policy-based controls.
• Time to bring a new site live (from request to productive traffic).
• Change failure rate tied to network updates or policy pushes.
• Service availability at remote locations (including satellite/5G links).

90-day implementation checklist

• Days 0-30: Map transports (MPLS, 5G, satellite), define SLOs, and select an orchestration layer. Stand up a governed AI assistant on a safe knowledge base. Baseline current MTTD/MTTR and time-to-site-live.
• Days 31-60: Onboard 10-20 representative sites (urban, rural, and remote). Roll out role-based access, data controls, and prompt policies. Automate top three network workflows (e.g., site provisioning, policy pushes, incident triage).
• Days 61-90: Expand to priority regions, add synthetic monitoring, and implement auto-failover policies. Publish KPI improvements and reinvest saved hours into throughput or quality initiatives.

Risk and mitigation

• Shadow AI usage: enforce approved tools, data access tiers, and audit logs.
• Data exposure: apply red-teaming, prompt injection checks, and strict retrieval scopes.
• Provider concentration: keep multi-path connectivity and exportable configs.
• Last-mile volatility: design for degraded modes and cache critical workflows locally.
• Skills gap: certify site leads on network automation and safe AI use. Explore role-specific training here.

The takeaway

Nestlé shows what happens when AI productivity meets unified network control: people move faster, sites come online sooner, and processes keep flowing from farm to factory. For operations teams, the blueprint is clear-centralize visibility, automate the backbone, and put a safe AI assistant in everyone's hands.