AI Sensor Market Dynamics - Global Forecast to 2028

The AI sensor market is projected to reach USD 22.1 billion by 2028, from USD 3.0 billion in 2022, at a CAGR of 41.6%.

Smart infrastructure and digitalization to facilitate edge computing growth.
Rapid urbanization and digitalization significantly impact the growth of smart infrastructure. Edge computing is a critical technology increasingly used in smart infrastructure. It enables real-time data processing and analysis from smart sensors and other IoT devices. By processing data near the source, edge computing can reduce latency, increase reliability, and improve overall system performance. Smart infrastructure utilizes data and technology to optimize the performance of buildings, transportation systems, energy networks, and other essential infrastructure. With the help of smart sensors, IoT devices, and other digital tools, smart infrastructure can manage and monitor anything from traffic patterns to energy usage, enabling cities to make more informed decisions and provide better services to their residents.

Download PDF:
https://www.marketsandmarkets.com/pdfdownloadNew.asp?id=122565258

Restraint: High installation and maintenance cost
Cost plays a critical role in determining the growth of the AI sensor market. The huge capital requirement for the deployment and maintenance of integrated AI sensors can pose challenges for market players. Furthermore, additional expenditure on AI sensor software solutions, related software compatibility compliances, and software maintenance services make the deployment of AI sensors costly, limiting the adoption of these sensors. Artificial intelligence is still under development in some sectors as it cannot be applied to specific applications due to technical and implementation constraints. This, along with the high cost of ownership, may limit the growth of this market. End users also tend to be slightly more skeptical about the cost benefits of AI sensors across emerging use cases.

Opportunity: Growing preference for AI-sensor enabled wearables
Wearables based on AI sensors form one of the fastest-growing markets. Smartwatches and ear-worn devices are the leading market segments. This market is projected to account for USD 265.4 billion by 2026, registering a CAGR of 18.0% during the forecast period. Spending on hearables increased by 124% in 2020, and global shipments of wearable technology reached 570 million units in 2021 at a CAGR of 16.6% (Source: MarketsandMarkets). This growing preference for wearables will likely boost the market for AI sensors. The adoption of wearables is growing rapidly as users increasingly focus on improving their overall health and fitness, driving the demand for fitness and medical tracking equipment. This, in turn, drives the demand for AI sensors. These sensors are used in applications such as smart glasses/goggles; ring/fingerworn scanners; footwear such as athletic, fitness, and sports shoes; wristwear such as advanced electronic watches and wristbands; smart textiles; and headbands & neckwear. Bosch Sensortec (Germany) has started actively working on the wearable technology market.

Challenges: Lack of skilled workforce
The AI sensor market is growing rapidly, and there is a significant shortage of trained professionals with the skills and expertise necessary to develop and maintain AI sensor systems. This shortage of skilled workforce is a major challenge for companies operating in this market and can limit their ability to innovate and compete effectively. One of the main reasons for this shortage is the relatively new nature of the field, due to which few educational programs and training opportunities are available for those interested in pursuing careers in AI sensors. Additionally, the complexity of these systems requires a broad range of skills, including expertise in cognitive computing, machine learning, deep learning, image recognition technologies, data analytics, computer vision, and signal processing. Integrating AI solutions into existing systems requires extensive data processing to replicate the behavior of a human brain.