NTT DATA Expands Global Network of AI Factories
The company launched a new AI Factory facility in Munich on October 7, 2026, to support enterprise AI deployment.
Updated on Oct. 27, 2026 in Data Centers
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NTT DATA expanded its global network of AI Factory facilities with the opening of a site in Munich, Germany, on October 7, 2026. These facilities provide enterprise environments for testing AI workloads and infrastructure requirements.
Why it matters
Enterprise leaders often require verified performance and proven business value before scaling their AI investments. This ecosystem offers a structured space to test these workloads against specific infrastructure and cost requirements.
The AI Factory ecosystem now spans five locations, including facilities in Riyadh, Paris, Singapore, Plano, and Munich. These sites leverage partnerships with Cisco and NVIDIA to support technical deep dives and proof-of-concept planning.
The players
NTT DATA
An international information technology services and consulting company that provides cloud, AI, and data center infrastructure solutions.
Cisco
A multinational digital communications technology conglomerate that develops networking hardware and enterprise security software.
NVIDIA
A technology company widely known for designing graphics processing units and hardware essential for advanced AI and machine learning workloads.
The details
The facilities offer hands-on environments where businesses can conduct discovery workshops and solution design to optimize their AI governance. By utilizing these localized centers, enterprises can align their specific technical needs with broader infrastructure capabilities.
Timeline
The Riyadh AI Factory launched in September 2026.
The Munich AI Factory launched on October 7, 2026.
The Tech Race
The expansion follows the industry pattern of deploying standardized hardware environments to facilitate the scaling of the NVIDIA enterprise AI computing infrastructure. It marks a shift toward dedicated testing facilities designed to replace legacy ad-hoc AI implementation models.
Enterprise clients can now utilize these regional labs to reduce the risks associated with scaling AI workloads before moving to production environments. This ensures that technical teams have access to verified infrastructure specifications without needing to build custom testing labs.
The takeaway
Businesses looking to integrate AI should prioritize pre-production testing to align infrastructure costs with actual performance metrics. Utilizing established factory ecosystems can provide a clearer roadmap for long-term technological governance.
Further reading
For more information on the infrastructure supporting these deployments, visit the Data Centers section.
Live Poll
Do you trust that businesses are successfully balancing AI innovation with responsible, cost-effective deployment?
