In the era of Industry 4.0, companies are being forced — in a good way! —to change. Particularly in sectors that rely heavily on operational technology (OT) such as manufacturing, transportation, energy and healthcare, enterprises are progressing along their transformational journeys faster, thanks to the exciting promises of emerging technologies like artificial intelligence (AI).
Defining AI
Data is everywhere today, as smart devices, sensors, and machines generate a wealth of valuable information for enterprises in all sectors. However, one of the biggest barriers these days, for companies, are the lack of the tools and resources, as well as the necessary skill sets, to decode and make sense of it all.
Additionally, sectors that are reliant on OT can face legacy and interoperability challenges. They may be using software or systems to collect pertinent device data from physical systems, but it could still be a Herculean task to integrate that information with corporate IT systems and create one coherent view.
This is where artificial intelligence comes in – a broad term that encompasses natural language processing, deep learning, speech recognition, and machine learning technologies. But the essential concept is simple: AI algorithms can be used to train machines and computers to analyze information.
The benefits of flowing data from the Industrial Internet of Things (IIoT) — both real-time and historical — into AI models are substantial, and ones that can affect all industries. For example, AI can help organizations better understand why a piece of equipment has failed while also offering potential remedies, or assist doctors with real-time diagnostics. It can help mining companies locate minerals to extract, or even provide cities with immediate traffic and road-condition analysis.
All of these examples translate to tangible business impacts: implementing AI can improve cost savings, service speed, customer satisfaction, efficiency and use of existing resources.
AI at the Network Level
But there’s a hitch. As companies seek the advantages of AI technology, they must not underestimate the value of a key element in infrastructure management: having a high-performance network.
Consider all of the data constantly captured by IIoT devices. It takes significant compute power and capacity to handle the necessary processing speed and volume to make intelligent decisions, meaning that the network must be designed to deliver along dimensions of connectivity, elasticity, and reliability. For example, in the case of smart algorithms helping doctors during operations, those AI-assisted surgeries would also require reliable and scalable connectivity – it could even be a matter of life and death.
To solve for this, organizations must build business-critical industrial networks that are capable of delivering AI functionality. The “must-haves” of this hyper-connected network include:
- Mission-critical connection requirements
- Edge and multi-cloud connectivity
- Intelligent provisioning for scalability, availability, and performance
- Seamless, secure connections across devices, network, and application layers
- Performance, latency, bandwidth, and manageability
Having well-built connective networks ensures that enterprises are prepared for Industry 4.0 and can make the most of emerging technologies like AI that are already changing the game – for everyone.