AI in IoT Market Trends

Growth Drivers

• Explosion of connected devices & data generation: The increase in IoT-connected devices is a primary catalyst boosting the AI in IoT market development. Moreover, the large number of sensors and endpoints generates volumes of data that require intelligent processing, analytics, and automation, efficiently fueling the demand for AI-based IoT solutions. In October 2025, Hyundai Motor Group announced that, in collaboration with NVIDIA and the Korea government, it is building an AI factory powered by 50,000 NVIDIA Blackwell GPUs to advance in-vehicle AI, autonomous driving, smart factories, and robotics.  In addition, this initiative also supports Korea’s physical AI ecosystem, combining large-scale IoT data, advanced AI infrastructure, and talent development to create an interconnected, intelligent mobility and manufacturing ecosystem.
• Need for real-time processing: This, coupled with the requirement of advanced analytics, is also fostering a profitable business environment for AI in IoT market. These capabilities are highly essential in smart manufacturing, autonomous systems, and real-time monitoring applications. In August 2025, Siemens reported that it had implemented edge AI-based predictive maintenance on its industrial production lines, embedding Armv9-based AI sensors to monitor vibration, temperature, and energy usage in real time. It also mentioned that these systems can automatically adjust machine parameters, balance loads, and trigger targeted interventions to prevent any type of equipment failures, reduce energy use, and extend asset life. Further, by integrating AI at the edge within Siemens’ MindSphere and industrial edge ecosystems, manufacturers minimize downtime and optimize operational efficiency across smart factories.
• Demand for automation and operational efficiency: Businesses across the globe are opting for AI IoT with a prime focus on automating workflows, particularly in the sectors of manufacturing, logistics, energy, and utilities. Also, Predictive maintenance results in cost savings and productivity improvements, which in turn improve uptake in this sector. In June 2025, Siemens announced that it had partnered with Sachsenmilch to implement its AI-based Senseye predictive maintenance system in the dairy producer’s Leppersdorf facility, which enables early detection of equipment issues and also reduces unplanned downtime. The system analyzes sensor data such as vibration, temperature, and frequencies, and it also allows proactive maintenance. Building on the pilot’s success, Sachsenmilch plans to integrate Senseye with SAP plant maintenance to automate maintenance notifications across its highly automated production lines, benefiting the overall AI in IoT market.

AI-Driven IoT Market Updates from Leading Companies 2025

Source: Company Official Press Releases

Challenges

• Data security and privacy: One of the biggest challenges in the AI in IoT market is ensuring strong data security and privacy. IoT devices collect large volumes of data, which includes personal, industrial, and operational information. When the AI systems analyze this data, the risk of cyberattacks, data breaches, and unauthorized access increases, causing an obstacle to widespread adoption in this field. Most of the IoT devices have minimal processing power and weak built-in security, making them vulnerable targets for hackers. In addition, data is often transmitted across networks and cloud platforms, readily expanding the attack surface. In this context, organizations need to make investments in terms of encryption and regular updates, which increases both cost and complexity for AI-enabled IoT solutions.
• Interoperability and standardization: Interoperability is considered to be yet another major challenge hindering the expansion of the artificial intelligence in IoT market due to the variety of devices, platforms, and communication protocols. IoT ecosystems mostly include hardware and software from multiple vendors, each using different standards. Therefore, integration of AI models across the fragmented systems is very complex and time-consuming. In addition, the absence of common standards makes data sharing and system coordination difficult, limiting the effectiveness of AI-driven insights. The existence of this fragmentation also slows down innovation and increases development costs due to the necessity for custom solutions.  Furthermore, establishing universal standards and improving cross-platform compatibility are highly essential for enabling smoother integration and broader adoption of AI in IoT environments.