Winning in IoT: How the enterprise IoT market is evolving

 According to our January 2023 IoT market update, IoT enterprise spending reached $201 billion in 2022, up from below $100 billion in 2018. For comparison, this represents roughly 5% of the global IT market in 2022. While we had to lower the outlook for the forecast period of 2023–2027 in our January 2023 update, the market is still expected to grow by +19% annually and reach $483 billion in spending by 2027.

IoT technology in 2023 adds value to organizations in all verticals, from manufacturing (e.g., factories) to retail (e.g., warehouses) and transport (e.g., cars). The IoT market has moved past headlines such as the infamous “3/4 of all IoT projects fail” (Cisco 2017).

87% of all IoT projects meet or exceed expectations

In 2023, 87% of all IoT projects met or exceeded expectations, based on a 2023 survey of 300 IoT decision-makers that will be published in an upcoming IoT Analytics adoption report. Some companies have connected millions of connected IoT devices (e.g., Walmart, Tesla, and Hapag-Lloyd) and are looking to expand with more sophisticated software tools. Despite several key challenges remaining related to interoperability, skills and know-how, and chipset supply, companies do not question if they should do IoT but rather how it will be scaling from here.

IoT technology maturity framework

To understand how the IoT tech stack is changing and where the growth opportunities in IoT are going forward, one needs to consider a typical technology-focused maturity curve an IoT adopter goes through:

Stage 1: Enabling the asset

In the first stage, whether it is a smart washing machine, a heavy asset in a factory, or a ship at sea, companies need to invest in sensors and local controllers/gateways to be able to process IoT data. Despite a renewed edge computing investment cycle that is seeing many companies invest into more powerful and flexible hardware, many companies at this point have passed the first hurdle of ensuring they have basic IoT data to work with. We expect the spending for IoT hardware/devices to be the lowest growth category at 14% until 2027.

Stage 2: Establishing connectivity

In the second stage, enterprise end users establish and simplify the connectivity to their IoT hardware. While some technologies used have been around for decades (e.g., certain field buses), companies in recent years have invested heavily into higher bandwidth connectivity (like ethernet), wireless connections (e.g., 4G/5G and LPWAN), and more modern and lightweight protocols (e.g., OPC-UA and MQTT). Spending on connectivity is expected to grow by 18% until 2027.

Stage 3: Creating the software backbone

Data normalization and analysis are key to the third stage of IoT maturity. Companies invest in the software backbone that allows them to access various IoT data sources and build valuable services, e.g., using cloud storage and platform services, centralized data lakes, containerization, and modern databases. Many companies are currently in a major investment phase in this part of the maturity curve. That is why we expected spending related to IoT Platforms and middleware to grow 30% and 34% for Infrastructure as a Service (IaaS) until 2027.

Stage 4: Building value adding IoT applications

In the fourth stage of IoT maturity, IoT end users build cloud native or edge-based applications that make use of IoT data at scale. The ability to connect to any asset (stage 1) in a standardized fashion (stage 2) and having those data easily accessible (stage 3) enables a number of IoT use cases. Some of the early innovators (e.g., several automotive OEMs) have reached this stage and are building all kinds of internal (e.g., for their factories) and external (e.g., for their cars) IoT applications. We expect more companies to reach this stage of maturity in the coming years, which is why we expect the spending on IoT applications to exhibit a CAGR of 29% until 2027.

Stage 5: AIoT = infusing AI into IoT

Enabling business with AI is the fifth stage of IoT maturity. This is where companies explore ways to augment existing applications and build new applications by embedding AI. Machine vision and predictive maintenance are two of the most common AI-enabled IoT use cases today. Recent breakthroughs in generative AI may add a new dimension and are a driver for companies to rapidly adopt AI further.

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