Enterprises are increasingly distributed — from the digital architecture they rely on, to the human workforce that powers their business daily. Across industries, this change has altered where data is generated, collected, delivered and consumed. And as factories increase their reliance on robotics, vehicles become autonomous, and the number of devices on the grid increases, there is a greater need for localised computer processing power managing large language models (LLM) for Gen AI with near-instantaneous response times.
This is where edge computing comes in. Placing processing power at the edge of the enterprise network, edge computing enhances a wide range of use cases, such as mobile robotics, autonomous machines, AR, remote management, and predictive maintenance, to revolutionize processes in multiple industries. Manufacturing, transportation and logistics, and healthcare organizations are the current leaders in edge adoption with a particular focus on applications in employee safety, experience, and operational efficiency.
Where organizations are seeing the real value for edge computing is in its ability to take advantage of security, low latency, predictability, and high bandwidth for real-time data collection to perform according to industry-specific needs.
On the edge of the factory floor
Imagine a manufacturer. Today’s factories embrace cloud computing, machine learning, AI, and automation technologies. Since connected devices create a significant amount of data, it has become a challenge to extract, aggregate and leverage actionable information in a scalable way. Having the data processed locally removes the backhaul stress and strain to move it from where it is generated to the cloud.
A manufacturing firm shipping approximately 1.4 million SKUs to 900 destinations might use edge processing with IoT sensors to assist with the automation of physical asset security, audit goods processing, and improve efficiency. Through edge processing, the organization is not only able to reconcile actual and expected shipping weight of packaged goods, but to alert teams against errors and stolen goods through real-time LLM and AI tools.
EVP New Ventures and Innovation, NTT Ltd
According to a recent study Omdia conducted with NTT, although motivations will differ from industry to industry, operational efficiency, and data security are top drivers behind edge investment and adoption. Other drivers include automation and integration of AI into business processes, gaining real-time data access, and managing IoT device growth.
Whatever the motivating factors, 93% consider edge to be a competitive advantage. Here’s a breakdown of the industry’s top priorities and how edge is, well, giving businesses an edge:
- In manufacturing (52%) and in healthcare (63%), both industries find that edge compute and edge AI improved agility and speed of decision-making.
- 54% said edge AI yields environmentally responsible business practices in transport and logistics.
- In energy and utilities, 61% said it improves quality control.
- In financial services, 60% said the edge helped grow the business and develop new revenue streams.
The hurdle at the edge
Enterprises need to modernize their networks to reap the rewards of the edge. The movement of data requires reliable connections. And low-latency environments are required for real-time data processing and analysis – this cannot be your ‘grandfather’s network’. Consistent and secure edge application performance depends on your organization’s network connectivity. This means that future edge deployments strongly correlate to a campus network overhaul.
This inflection point was another key finding in the study I mentioned above, where almost 40% of enterprises planning edge deployments acknowledge a need to upgrade their network to support the expected spike in connected devices and the demands of new applications. In addition, nearly two-thirds of enterprises already deployed edge must coordinate a WAN network refresh.
In addition to ensuring the network is optimised, an edge deployment requires tight orchestration of hardware, platforms, systems, and devices; consistent operational performance without compromising security; and overcoming legacy infrastructure and technical debt. Management of computing, connectivity and IoT devices must become more unified and seamless. Otherwise, they are a hindrance in reaching the edge’s full potential.
Most organizations will need help to ensure their legacy systems and protocols don’t get in the way and that is where a managed services provider can step in to ensure a seamless journey.
Partner up for a competitive edge
The growing need for faster processing and Hybrid AI (combining AI that runs on the cloud with AI that runs on the edge) creates increased pressure on networks and infrastructure capabilities, driving both accelerated adoptions of private 5G and edge.
For edge deployments, ultimately, enterprises want service partners who help bring everything at the edge together – infrastructure, devices, operating environments, connectivity, and AI applications. Organizations that choose to go it alone risk a DIY edge that may not scale or be able to support business needs as new use cases are developed.
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