By: Ben Pietrabella

5G and the Internet of Things (IoT) will completely revolutionize our lives in the coming years. According to a recent study from Juniper Research, industrial IoT connections are projected to jump from 17.7 billion in 2020 to 36.8 billion in 2025. Along with connectivity, the number of IoT devices is poised to grow massively. According to data gathered by Statista, the number of IoT devices is expected to more than double to 21.5 billion in the next five years. While 5G is currently being introduced, there’s been plenty of bandwidth available within 4G and Wi-Fi technology to keep rolling out devices.

With this in mind, it bears exploring how can enterprises make the most of the IoT in 2021 and beyond. The answer lies in artificial intelligence (AI) and Wi-Fi 6, the next evolutionary technology for Wi-Fi. Why Wi-Fi 6? The reason is that 5G is not quite ready to shoulder the global burden of 21.5 billion devices just yet.

It’s a myth that future infrastructure will be solely dependent on wireless mobile data. Rather, it will need a blend of both 5G coverage and Wi-Fi 6 to attain the gold standard of seamless connectivity and interoperability the industry has been eagerly anticipating. With gigabit speeds like 5G, Wi-Fi 6 also offers enhanced security upgrades to deliver a comprehensive connectivity solution. And Wi-Fi 6 is already here.

Bridging the gap with AI and workflows

Rolling out connectivity isn’t simply about increasing bandwidth so more devices can be added. It’s about creating targeted bandwidth for IoT use cases. Think of connectivity as the arteries that connect all the different IoT devices to their uses. Now consider the staggering amount of data that IoT generates. According to market research firm IDC, IoT devices will create nearly 80 ZB of data in 2025. From smart cities to smart energy and from aerospace to automotive, data from IoT devices will provide a treasure trove for enterprises to train their AI applications. AI in IoT will be worth billions over the next few years, adding value across the IoT ecosystem. ReportCrux Market Research expects it to produce around $15.72 billion in value by the end of 2027.

AI can be harnessed to add real value to IoT. It’s done by introducing cognitive workflows that integrate machine learning (ML) and AI, automation and operational processes that continuously learn and are self-aware. Thanks to cognitive workflows, IoT sensors and devices will provide enterprises and operators with a lot more intelligence – from, say, the size, position and speed of a moving vehicle to the precise location and depreciation of a piece of factory equipment or the type of leak on a remote oil pipeline. This level of intelligence will lead to faster, more informed decision-making, all thanks to cognitive workflows.

DevOps will play a critical part in 2021 as enterprises ramp up their cognitive workflows. According to IDC, over the next few years, DevOps teams will look to reduce complexity by adopting toolkits and workflows focused on cloud services and infrastructure automation. New DevOps toolkits and workflows will enable application developers to easily deploy and integrate software systems into 5G-enabled IoT solutions. Of course, app developers cannot become overnight experts in AI- and 5G-specific features like network slicing or intelligent 5G RAN and core automation. Smart middleware entities like IBM or VMware are likely to bridge the skills gap between developers and operators – especially when it comes to leveraging technologies such as edge computing.

Driven to the edge in 2021

Infused with AI, edge is what will really energize IoT. By integrating and orchestrating AI and ML technologies to analyze data locally at the edge, applications will make decisions and react even faster. This need for speed requires ultra-fast connectivity. According to IBM research on speed and latency at the edge, 5G reduces device-to-cell-tower latency to around 4 milliseconds, compared to 9 milliseconds for 4G. What’s more, IBM found that moving IoT workloads to the edge can reduce latency to 10 to 20 milliseconds. This dramatic cut in latency is what makes use cases such as automated and connected driving possible; in them, every millisecond counts.