According to a new comprehensive report from The Insight Partners, the global embedded AI market is observing healthy growth owing to expansion of IoT ecosystem, and Integration of AI to enhance defense capabilities.

Embedded AI integrates artificial intelligence directly into hardware systems, enabling faster responses, reduced latency, enhanced data privacy, and greater reliability. It plays a crucial role in sectors like automotive, industrial automation, consumer electronics, healthcare, and IoT. As advancements in low-power AI chips and edge technologies continue, the market is poised for significant expansion, supporting smarter, more autonomous systems across various industries and applications.

The report runs an in-depth analysis of market trends, key players, and future opportunities. Embedded AI refers to the integration of artificial intelligence capabilities directly into hardware devices, enabling them to process data and make decisions locally without relying on cloud connectivity. It is widely used to provide real-time, low-latency responses in applications such as autonomous vehicles, smart cameras, industrial automation, and IoT devices. By operating on-device, embedded AI improves speed, enhances privacy, reduces bandwidth use, and ensures continuous operation even without internet access.

Overview of Report Findings

Integration of AI to Enhance Defense Capabilities: With the increasing complexity of modern combat environments, defense forces require advanced tools that improve situational awareness and streamline decision-making. Addressing this need, Safran Electronics & Defense unveiled its Advanced Cognitive Engine (ACE) at Eurosatory 2024 in June. This innovative artificial intelligence system is designed to be integrated into all Safran Electronics & Defense products. ACE enables enhanced situational awareness, real-time decision support, and significantly reduces the cognitive burden on military personnel in high-pressure scenarios. By embedding AI directly into its systems, Safran aims to deliver smarter, faster, and more autonomous capabilities to forces on the ground. This launch marks a major step forward in the company's strategy to harness the power of AI for next-generation defense operations and battlefield efficiency.

Growing Demand for Real-Time Data Processing: Embedded AI enables devices to process data locally without relying on cloud connectivity. This real-time processing is critical in applications like autonomous vehicles, industrial automation, and healthcare, where immediate decision-making is essential. To cater the growing demand for real-time data processing, market players are launching solutions, which contributes to the market growth. For instance, in March 2025, Quvia, the AI-powered QoE platform formerly known as Neuron, announced Q, a suite of embedded AI tools designed to maximize productivity and efficiency for connectivity, digital operations, and customer experience teams in the aviation, maritime, and enterprise sectors. With Q, customers can easily access network performance and QoE insights using natural language queries. In addition, new AI tools will help them unlock next-level productivity, boost operational efficiency, and elevate end-user experiences.

In May 2025, Qlik, a global leader in data integration, data quality, analytics, and artificial intelligence, announced an expanded set of capabilities coming soon in its Qlik Cloud Analytics solution — equipping enterprises with tools to detect anomalies, forecast complex trends, prepare data faster, and take immediate action through embedded decision workflows.

Expansion of the IoT Ecosystem: The rapid proliferation of IoT devices across smart homes, industrial systems, healthcare, agriculture, and smart cities is significantly driving demand for embedded AI. As billions of connected devices generate vast amounts of data, there is a growing need for real-time, low-latency, and energy-efficient processing at the edge.

Embedded AI enables these devices to process data locally, reducing reliance on cloud computing, minimizing bandwidth usage, and enhancing privacy. This intelligent on-device processing allows IoT systems to operate autonomously, respond faster to changing conditions, and support more advanced applications, ultimately making IoT ecosystems more responsive, efficient, and scalable.

Geographical Insights: In 2024, North America led the market with a substantial revenue share, followed by Europe and Asia Pacific, respectively. Asia Pacific is expected to register the highest CAGR during the forecast period.

Market Segmentation

Based on component, the embedded AI market is segmented into hardware, software, and services. The software segment held the largest share in the embedded AI market in 2024.

Based on data type, the embedded AI market is segmented sensor data, image and video data, numeric data, categorial data, and others. The numeric data segment held the largest share in the embedded AI market in 2024.

Based on vertical, the embedded AI market is segmented into healthcare, BFSI, IT and ITES, retail and ecommerce, telecom, manufacturing, and others. The qualified electronic signature segment held the largest share in the embedded AI market in 2024.

Based on end-user, the embedded AI market is segmented into manufacturing, BFSI, pharmaceuticals, government agencies, legal, and others. The manufacturing segment held the significant share in the embedded AI market in 2024.

Competitive Strategy and Development

Key Players: A few of the major companies operating in the embedded AI market are Oracle, Microsoft, IBM, AWS, NVIDIA, Google, LUIS Technology, Siemens, AMD, and Salesforce.

Conclusion

The embedded AI market is poised for substantial growth as demand for intelligent, real-time processing at the edge continues to rise across industries. By enabling devices to operate autonomously with low latency, enhanced privacy, and reduced dependence on cloud infrastructure, embedded AI is transforming applications from automotive and industrial automation to healthcare and smart cities. Advances in specialized hardware and algorithms further accelerate adoption, driving innovation and efficiency. As technology evolves, embedded AI will become increasingly integral to next-generation devices and systems, solidifying its role as a critical enabler of the digital and connected future.