AI in IoT Market Set to Reach USD 172.8 Billion by 2035 with 6.4% CAGR Amid Rising Digital Transformation

The AI in IoT market is witnessing rapid expansion as enterprises across industries increasingly integrate artificial intelligence with Internet of Things (IoT) systems. Valued at USD 92.9 billion in 2025, the market is projected to reach USD 172.8 billion by 2035, representing a compound annual growth rate of 6.4%. The integration of AI into IoT infrastructure enables predictive analytics, real-time monitoring, and automated control across multiple applications, transforming how businesses collect, process, and act on data.

Advances in 5G, edge computing, and cloud platforms have bolstered scalability, connectivity, and responsiveness, further accelerating adoption. Organizations are leveraging AI-powered IoT solutions to optimize supply chains, enhance operational efficiency, and provide personalized customer experiences. In addition, regulatory emphasis on data transparency and sustainability goals across sectors such as manufacturing, healthcare, and energy is driving investment in intelligent IoT systems.

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Component Analysis: Platforms Driving Market Expansion

The platform segment is the leading component within the AI in IoT market, expected to account for 44.9% of revenue in 2025. Platforms provide integrated frameworks for device management, connectivity, and data aggregation, enabling enterprises to unify complex IoT ecosystems. They allow for efficient deployment and monitoring of connected assets, supporting predictive analytics, cloud integration, and real-time insights. By acting as the foundation for AI-enabled IoT systems, platforms are crucial for enterprise digital transformation, allowing organizations to manage hardware, networks, and applications seamlessly.

Technological Advancements: Machine Learning and Deep Learning

Machine learning (ML) and deep learning technologies dominate the AI in IoT market, expected to hold 69.2% of revenue in 2025 within the technology category. These technologies empower IoT devices with self-learning capabilities, enabling predictive and prescriptive decision-making. ML and deep learning facilitate anomaly detection, image recognition, natural language processing, and automation, essential for predictive maintenance, personalized services, and autonomous operations. Their integration with IoT allows companies to harness massive data volumes for actionable insights, making them indispensable tools in smart industrial and consumer applications.

Vertical Insights: Manufacturing Leading Adoption

The manufacturing vertical is projected to capture 26.4% of market revenue in 2025, driven by the need for operational efficiency, equipment monitoring, and resource optimization. AI-enabled IoT solutions help manufacturers streamline production, improve product quality, and minimize downtime through predictive maintenance. The push for Industry 4.0 and sustainable practices accelerates adoption of intelligent devices that connect machines, humans, and processes. Smart factories benefit from automated workflows, enhanced safety, and cost reduction, establishing manufacturing as the leading sector in embracing AI in IoT.

Market Evolution and Emerging Applications

The increasing deployment of AI-enabled IoT systems is reshaping industries by providing real-time visibility, improved customer experiences, and optimized operations. In retail, cloud AI enhances customer engagement through IoT-based solutions, while manufacturing and supply chains gain efficiency through automated data management systems. IoT-driven monitoring of equipment and production processes enables faster product delivery, better quality control, and enhanced human-machine collaboration.

The BFSI sector is also adopting AI in IoT for secure transactions and fraud prevention. As IoT devices and AI algorithms evolve, businesses can leverage predictive insights to anticipate customer needs, optimize networks, and automate decision-making. The combination of deep learning, edge computing, and machine learning is increasingly crucial in managing the massive datasets generated by connected devices.

Technological Challenges in AI in IoT

Despite rapid growth, the AI in IoT market faces several challenges. Edge devices contend with power consumption and size limitations, particularly in automotive and consumer electronics applications. High costs and limited consumer understanding of AI and IoT technologies can slow adoption. Privacy and security concerns over personal data also create barriers, while automation may reduce employment in certain low-income-level jobs. Addressing these challenges requires innovation in compact, energy-efficient AI chips, and enhanced cybersecurity measures.

Regional Market Insights

North America is the largest AI in IoT market, with the United States contributing significantly. The region benefits from strong technology adoption, cloud infrastructure, and presence of key global players, with projected growth at a 5.8% CAGR through 2035. By that year, the market in North America is expected to reach USD 49 billion.

Asia-Pacific is expected to experience robust growth, driven by China and India. In China, industrial automation, IT investments, and a growing IoT ecosystem are expected to yield a 5.6% CAGR. Japan and South Korea also demonstrate steady growth, fueled by industrial production expansion and technological advancements. The region is leveraging AI and 5G networks to accelerate digital transformation and smart infrastructure development post-COVID-19.

Europe, particularly the United Kingdom, Germany, and other industrialized nations, is experiencing steady AI in IoT adoption due to increasing demand for IoT and machine-to-machine technologies. Industrial expansion, research capacity enhancements, and smart city initiatives are significant growth drivers.

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Competitive Landscape and Key Players

The AI in IoT market is highly competitive, with established tech giants and emerging innovators shaping the landscape. Google leads with Vertex AI, a managed ML platform that simplifies AI model deployment across IoT systems, reducing coding requirements by up to 80% and supporting MLOps for end-to-end management. Microsoft, IBM, AWS, Oracle, SAP, PTC, GE, Salesforce, and Hitachi provide comprehensive AI-powered IoT solutions targeting enterprise digital transformation.

Emerging players, including Uptake, Autoplant Systems Pvt Ltd, Kairos, Softweb Solutions, Arundo, C3 IoT, Anagog, Imagimob, and Thingstel, are innovating with niche AI solutions, edge computing applications, and specialized industry platforms. Recent developments include Bosch's Phantom Edge platform for real-time industrial monitoring, Johnson Controls' acquisition of Foghorn for edge AI integration in smart buildings, Synaptic's edge AI evaluation kits for human recognition and motion detection, and Gupshup's acquisition of Active.AI to enhance conversational AI in fintech.

These strategic partnerships, technological advancements, and product innovations are enabling both established and emerging manufacturers to expand their market presence, drive adoption, and deliver real-world AI-driven IoT solutions.

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