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Cracking The Code: Navigating The Edge AI Development Life Cycle
Cracking The Code: Navigating The Edge AI Development Life Cycle

Forbes

time24-07-2025

  • Forbes

Cracking The Code: Navigating The Edge AI Development Life Cycle

Rajesh Subramaniam is Founder and CEO of embedUR systems. How many intelligent devices are running in your home right now? I bet it's more than you think. The current average is 25 devices per household, and the number is only going up every year. What's more, many of these devices, from fridges to fans, now come equipped with AI accelerators tucked into their chipsets. Whether or not you're aware of it, your thermostat may be learning your habits, and your washing machine may be whispering to the cloud. This quiet evolution marks a new frontier in technology: Edge AI. It's the convergence of embedded systems and AI, designed to run efficiently right where the data is generated: on the edge. But getting from an idea to a working AI-enabled product is anything but straightforward. The development process is fragmented, the talent pool is bifurcated and the hodgepodge of tools were all designed for AI development in the cloud, not the edge. I've spent the last two years focused on one central question: How do we make edge AI easier? Edge AI Development Pain Points Let's start with the development workflow itself. Building an AI solution for an edge device is a series of deeply interdependent challenges. You start with model discovery: finding a neural network architecture that might solve the problem you're working on. Then comes sourcing and annotating relevant data, fine-tuning the model, validating its accuracy, testing it on real devices, optimizing it for specific chipsets and finally deploying it into production. That's a lot of moving pieces. That's where engineers get stuck. Using the output from one step, as the input to the next, hoping they are compatible, and discovering they mostly are not. A lot of jerry-rigging is needed to string dev pipelines together, because until now there has not been a unified dev environment for Edge AI. The challenge is that most developers are forced to stitch this pipeline together from scattered tools. You might use one platform to find a model, a separate one to label data and something entirely different to benchmark your results. There are constant handoffs, and each transition brings the risk of versioning problems, performance degradation or flat-out failure when trying to get a model to run on resource-constrained hardware. On top of that, most embedded engineers aren't AI experts, and most AI experts don't come from embedded systems. Bridging this language and tooling divide is one of the core problems we're trying to solve. A New Mindset And A New Toolchain Traditionally, embedded software followed a familiar pattern: Write the code, compile it, test it and ship it. Now, though, you have to fit an AI model into that life cycle. But AI doesn't behave like conventional software. You need to train AI models with a large amount of high-quality data. You also need to make sure they're accurate, secure, upgradeable and able to run efficiently on limited hardware—and they still need to integrate cleanly with the rest of the software stack. What's really needed is a toolset that allows embedded developers to stay in their comfort zone while unlocking the power of AI. Think of it like a sandbox: You identify the type of application you're building and get model recommendations from a curated library. Then the system walks you through fine-tuning, validating and benchmarking the model. It should also help with things like security and upgrade paths. This is where I see us heading: tools that abstract the complexity of AI while integrating seamlessly with existing embedded workflows. That means packaging up best-in-class models, simplifying the training process and making on-device validation dead simple. Standardization And The Path Forward Our goal is to bring some structure to the edge AI development lifecycle. Right now, there are too many tools and frameworks and no common standards for building, testing or deploying AI models in an embedded context. By pushing for standardization, we're trying to make it easier for traditional developers to adopt AI. Once the life cycle is defined and toolchains are aligned, more engineers will feel confident jumping in. Consistency will help build trust and reduce friction in the process. It's hard to overstate the implications of this shift to embedded edge AI. Think about the early days of the internet or the rise of smartphones—we're at that kind of inflection point. The number of embedded clients per household is only going to continue to soar, from smart doorbell cameras that recognize family and friends to voice assistants that control everything from lighting to entertainment with natural commands. That means it's essential to solve the issue of integration. The sheer scale and reach of edge AI applications are staggering, maybe even a little scary, but mostly it's exciting. Because what we're really talking about is democratization. AI was once limited to massive data centers and elite development teams. Now it's finding its way into everyday devices at a price point that's accessible to everyone. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?

Embedded AI Market Size to Surpass USD 25.68 Billion by 2031
Embedded AI Market Size to Surpass USD 25.68 Billion by 2031

Yahoo

time26-06-2025

  • Business
  • Yahoo

Embedded AI Market Size to Surpass USD 25.68 Billion by 2031

NEW YORK, June 26, 2025 /PRNewswire/ -- 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. The embedded AI market is expected to reach US$ 9.87 billion in 2024 and is projected to reach US$ 25.68 billion by 2031; it is expected to record a CAGR of 12.4% from 2025 to 2031. The embedded AI market is experiencing rapid growth, driven by increasing demand for real-time data processing, edge computing, and intelligent decision-making in devices. 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. To explore the valuable insights in the Embedded AI Market report, you can easily download a sample PDF of the report – 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 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 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. Stay Updated on The Latest Embedded AI Market Trends: 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. Trending Topics: Edge AI, Neural Processing Unit (NPU), AIoT (Artificial Intelligence of Things), Autonomous systems, AI-enabled devices, AI-driven automation, etc. Global Headlines on Embedded AI Market Safran launches ACE embedded AI solution Qlik Expands Embedded AI Capabilities for Smarter Decisions and Faster Intelligence Quvia Launches Q: Embedded AI Tools to Maximize Productivity and Efficiency Artificial Intelligence Meets Embedded Development with Microchip's MPLAB® AI Coding Assistant Purchase Premium Copy of Global Embedded AI Market Size and Growth Report (2025-2031) at: 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. Trending Related Reports: About Us: The Insight Partners is a one stop industry research provider of actionable intelligence. We help our clients in getting solutions to their research requirements through our syndicated and consulting research services. We specialize in industries such as Semiconductor and Electronics, Aerospace and Defense, Automotive and Transportation, Biotechnology, Healthcare IT, Manufacturing and Construction, Medical Device, Technology, Media and Telecommunications, Chemicals and Materials. Contact Us: If you have any queries about this report or if you would like further information, please contact us: Contact Person: Ankit MathurE-mail: +1-646-491-9876Home - Logo: View original content to download multimedia: SOURCE The Insight Partners Error in retrieving data Sign in to access your portfolio Error in retrieving data Error in retrieving data Error in retrieving data Error in retrieving data

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