This article is part of Cio Applications Europe's Innovation Insights series featuring expert contributions nominated by our subscribers and reviewed by our editorial team.
Rise of the industrial IoT platform
The Industrial Internet of Things (IIoT) poses unique challenges to the IoT platform market. IIoT technologies need to operate within asset-intensive, highly regulated environments that involve thousands of connected IoT devices, massive volumes of data, as well as infrastructure management, security, and compliance support. Yet the prospects are high: Gartner research has uncovered that by 2025, 50% of industrial enterprises will use IIoT platforms to improve operations and 25% of large global industrial companies will acquire or invest in an IIoT platform. In offering a singular convergence of information technology (IT) and operational technology (OT), the industrial IoT platform works towards rapid decision-making and enhances process transparency.
Growing AI adoption in IIoT edge scenarios
Even today, industrial organizations use AI algorithms for real-time decision-making, monitoring and control of manufacturing processes, and many predictive maintenance scenarios. In 2021, this development will go a step further as AI is increasingly shifting towards the edge of the IIoT network, all the way down to the IIoT device.
AI will become more relevant in IIoT edge settings as it ensures swift reaction times and greater autonomy through decentralization. And while data scientists will continue to train machine learning models in data centers or in the cloud, we will get to see more and more ML models trained locally, right at the industrial IoT edge.
A shift towards combined IoT & AI products
IoT & AI can work together in a variety of scenarios where IoT generates massive volumes of data requiring big data analytics. And even though IoT & AI projects are exceedingly more technically challenging than standalone IoT initiatives, products and services that combine IoT & AI will become more accessible in 2021. Further, products featuring both big data analytics and IoT capabilities are expected to deliver better outcomes than isolated IoT offerings. The reason is that combining IoT & AI encourages collaboration across the entire value chain and thus helps us overcome the shortage of technical expertise. With the increase of combined IoT & AI adoptions, vendors will broaden their offerings to streamline IoT & AI implementation as we shift towards AIoT, the artificial intelligence of things.
IoT collaboration platforms become the norm
As IoT implementation scenarios become more complex and the sheer number of actors involved in IoT development efforts continue to rise, traditional approaches to IoT collaboration come to their limits. The unprecedented nature of the challenges posed by large-scale IoT initiatives calls for a radical rethinking of the entire structures within which IoT development and implementation take place. This is where the IoT collaboration platform steps in to bring together an increasing number of decision-makers, experts, and non-technical staff in the IoT development process. Well into 2021, collaborative IoT platforms will become established as the common ecosystems for the specialists within an organization to interact and co-develop.
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At Record Evolution, we have built a collaborative IoT development platform that is inclusive at its core and fosters non-hierarchical interaction between the various actors across the value chain. With our IoT development platform, we bring IoT and data science together in one ecosystem that extends from the edge to the cloud. Stakeholders benefit from shorter decision-making paths and increased transparency across processes.
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The articles from these contributors are based on their personal expertise and viewpoints, and do not necessarily reflect the opinions of their employers or affiliated organizations.
