HomeData AnalyticsSmart manufacturing will drive 10-fold jump in AI-based IoT services – to $10bn by 2026

Smart manufacturing will drive 10-fold jump in AI-based IoT services – to $10bn by 2026

The IoT market for artificial intelligence (AI) and machine learning (ML) services will reach $1.09 billion in 2020 and grow to about 10-times the size, to $10.6 billion, by 2026, according to market advisory firm ABI Research.

The massive growth of so-called ‘advanced analytics’ within the IoT technology market is down, in large part, to the industrial sector moving their AI and ML programmes on to edge-based networking architectures, found a new report by the firm. The emergence of out-of-the-box and off-the-shelf AI and ML solutions is also helping, notably as they are available as application plugins in cloud IoT platforms.

Kateryna Dubrova, research analyst at ABI Research, commented: “The IoT edge advanced analytics market is essentially operationalised AI and ML products and services targeted at operational technology (OT) teams to understand and extract insights. AI and ML frameworks are also enabling advanced analytics in the cloud, where algorithmic models are deployed on pre-processed and organised datasets.”

She added: “Edge AI/ML is more prevalent in manufacturing and industrial segments, where there is an immediate need to assess, transform and augment data as it is being generated through functions of quick pattern recognition, labelling, and protocol optimisation.

The likes of Amazon Web Services (AWS), Microsoft Azure, Google, SAS, and C3.ai are dominating with end-to-end IoT portfolios and combined native and third-party AI/ML toolkits. But ABI notes the likes of Seeq, DataRobot, Noodle.ai, and Dataiku will enable “greater democratisation of IoT ML technologies, with more powerful AI engines and low-to-no-code solutions.”

It also notes “steady and robust” development by edge-centric software and platform as-a-service vendors like Crosser, Swim.ai, and FogHorn.

Dubrova said: “While the vendors have clear positions on deployment choice, edge and cloud are merging into a singular edge-cloud paradigm. However, the increasing value of edge AI/ML solutions within the IoT has unveiled a gap in the accessibility of these solutions.

“The scalability and productisation of an edge solution are fundamentally dependent on cloud vendors expanding their marketplace portfolios toward the edge. The IoT edge marketplace will take off within a couple of years and become an integral part of the IoT ecosystem.”

Greater availability of off-the-shelf AI/ML solutions will lessen the need for professional analytics services, said ABI. “Fortunately, IoT is a growing market so custom analytics engagements will still see demand.  The real upside is that more people can apply advanced analytics to their IoT data expanding its usefulness to a broader cross-section of the enterprise,” said Dubrova.

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