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NTT Data unveils Edge AI platform for industry, manufacturing

Infrastructure and services company claims to be breaking down IT/OT silos with what it says is industry’s first fully managed Edge artificial intelligence offering, enabling advanced AI use cases for industrial and manufacturing applications

NTT Data has unveiled its Edge AI platform to accelerate IT/OT convergence by bringing artificial intelligence (AI) processing to the edge.

The infrastructure and services company said that by processing data when and where it is generated and unifying diverse internet of things (IoT) devices, systems companies can enable real-time decisions, enhanced operational efficiencies and secure AI application deployment across industries to drive advanced Industry 4.0 technologies.

NTT Data also cited research from IDC in March 2024 calculating that worldwide spending on edge computing will reach $232bn in 2024, an increase of 15% compared with 2023. IDC said the growth will be perpetuated by the growing number of connected IoT devices worldwide, expected to exceed 41 billion by 2025.

It added that while the current spotlight has been on GenAI and large language models (LLMs), these technologies are impractical for industries requiring real-time and local decision-making. NTT Data said its Edge AI service addressed this challenge by processing massive data sets on compact computing platforms, using smaller, more efficient machine learning models to deliver real-time AI insights.

Supported by NTT Data’s consulting data scientists, managed services and global technical resources, the Edge AI platform is claimed to be able to address the shadow IoT challenge and AI infrastructure requirements, offering data discovery, collection, integration, computation power, seamless connectivity and AI model management.

It is said to do this by auto-discovering, unifying and processing data from IoT devices and IT assets across the organisation, simplifying AI deployment and management. 

Designed to support industry-specific requirements, the Edge AI platform is said to use lighter, cost-effective AI models, enabling it to run within a small compute box. Edge AI will perform specific tasks, such as supporting safety or operational efficiency, by collecting data from disparate devices across a network environment, enabling instantaneous and secure data processing and analytics.

Read more about industry 4.0

NTT Data said a key attribute is solving industry-specific challenges with AI-driven insights. For example, the company said manufacturing operations could benefit from improved predictive maintenance by accessing IT/OT data from sensors, machinery, cameras and applications to plan and address failures. In addition, NTT Data’s Edge AI could see use in monitoring and optimising energy consumption in real time, predicting energy spikes and optimising machine usage, reducing costs and CO2 emissions with renewable energy.

Noting that it was offering the industry’s first fully managed IT/OT convergence platform, NTT Data said Edge AI can transform physical assets into software assets for data-driven insights, regardless of the manufacturer. Operating at the edge, managed services integrate OT assets with IT applications, boosting operational efficiency. Edge AI also provides a view of the firmware version of all connected devices to promote vulnerability patching and overall device security.

“Computing and AI must happen where they create the most value for the enterprise; for many industrial enterprises, this is where the data is generated,” said Pablo Tomasi, principal analyst of private network at Omdia. “By ingesting IT and OT data and leveraging AI models to drive use case-specific results, the NTT Data solution takes another step towards realising the industry 4.0 vision.

“Additionally, using task-specific small AI models will help drive AI democratisation by making it is easier for the enterprise to introduce AI where and when is needed, without the need for an extensive overhaul of their whole infrastructure.”

Read more on Internet of Things (IoT)

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