EDGE COMPUTING
Embedding AI into connectivity A key element of NTT DATA’ s strategy is embedding AI directly into network environments. Edge AI agents operate on infrastructure platforms where data is generated, allowing systems to interpret and respond to events without routing information to central cloud environments.
This architecture supports use cases that require immediate action. In manufacturing, systems analyse sensor and vision data to identify faults or deviations. In transport and logistics, vehicle and asset data feeds into routing and safety systems. In energy and mining, remote operations depend on continuous monitoring and automated responses. The integration of AI with connectivity changes how enterprises design systems. Rather than separating data collection, transmission and processing, these functions operate within a unified environment. This reduces delays and simplifies system design.
“ Private 5G is the backbone for scaling AI in production, where autonomous systems must operate reliably and at scale, but integration complexity often remains the final hurdle,” says Alejandro Cadenas, Associate Vice President of Worldwide Telco Research & Consulting, 5G, IoT and Mobility at IDC.
“ The combined expertise of NTT DATA and Ericsson seamlessly integrates edge AI and physical AI with enhanced connectivity, overcoming operational, scalability and accountability challenges and accelerating the deployment of AI with confidence.”
“ As enterprises adopt AI at the edge, they need partners who can bring connectivity, intelligence and security together”
Shahid Ahmed Global Head of Edge Services NTT DATA
Real-time processing in data centres NTT DATA applies real-time processing within its own data centre operations. At its Rhine-Ruhr 1 facility in Bonn, the company has deployed an AI-driven system to manage cooling infrastructure. The system replaces static control methods with dynamic optimisation based on live operational data.
The platform, developed with etalytics, uses a digital twin of the cooling system to simulate and adjust performance. It processes inputs such as ambient temperature, system load and flow rates, updating control parameters in real time. This allows the system to respond to changing conditions without manual intervention.
The digital twin combines data models with physical representations of system components. This ensures that predictions remain aligned with operational constraints. The system evaluates scenarios at short intervals, modelling performance across extended periods while maintaining real-time responsiveness.
112 April 2026