A lot of discussions about the role of AI today circle around digital products – and, arguably, how they can improve productivity and cut costs for businesses. 

That said, it’s also worth shedding light on how AI is revolutionizing embedded systems. High chances are, the first tech that comes to mind is Edge AI. And while it plays a significant role, it’s not our focus today.  

This article centres on the integration, cloud capabilities, and software-driven aspects of AI-powered embedded systems and their growing significance in modern businesses. This includes factors such as system optimisation, software design, and the use of cloud and analytics. Let’s see why this is the case. 

Business prospects with AI enabled embedded systems 

Enhancing product capabilities 

While a worker could wander from gauge to gauge, readout to readout, recording data, identifying trends, and tuning a system (e.g. an HVAC or food processing plant), this old-fashioned approach means time wasted for assimilation and interpretation. It could take literal months or years to achieve an efficient harmonious system.  

AI, on the other hand, could be instructed that “this” is the ideal outcome.  

For example, the task could be defined as: “Monitor the system’s inline sensors for excessive vibrations, slowing processes, and other changes. Analyse that information in the cloud in real-time; develop a comprehensive overview; and then, feed back into the system with any optimisations. Note any inefficiencies, potential failures, and then manage downtime scheduling and maintenance”.  

The operative phrase here, however, is “real-time analysis”. It predicts failures before they happen, reducing repair costs, limiting the impact on production schedules, and makes the entire process more efficient. Cloud-based AI models can teach themselves, which is one of their most attractive features. But they can also be updated with human optimisations, as well as utilising global datasets to incorporate further efficiencies discovered elsewhere.  

Efficiency 

AI powered automation does all of the above things, with better resource use, reduced waste, and lowered costs. It also frees up those metre-readers to be more productive in your company, doing the things only humans can do—being creative and innovative. Get the humans out of the monitoring process equation, where the flu or insufficient rest can cause them to miss important factors. Let the AI do the drudge-work and get the full benefit of those human brains elsewhere in your organisation. 

Let’s take farming as an example of an industry that’s becoming increasingly automated. Drones apply carefully measured fertilisers and insecticides where they are needed, not simply covering everything “just in case”. This saves immense amounts of resources and money, far outweighing the technology costs. Such drones have embedded AI for collision avoidance, but use cloud-based AI directives to tell them where they really need to go. It’s the perfect synergy. 

Similarly, AI can analyse output for irregularities and defects on a production line, assuring a quality product without the need for constant manual checking. Spotting six defects and kicking them out of the process while simultaneously modifying the ongoing process to eliminate the error is far better than making 5,000 unusable, defective items before a human notices. 

AI enabled embedded systems – examples from healthcare and automotive industry  

Examples of this sort of predictive maintenance can be found in the modern-day automotive industry. Real-time AI monitoring and analysis checks inline sensors and tells us it’s time for new brakes, lubrication, or repairs. This can be handled entirely locally, but to greater effect in the cloud when combined with a much larger dataset that provides much deeper insights. 

Your typical electric car informs you when you need to stop for a brief charge to reach your destination. It generally also recommends the best place along your route to acquire that charge based on urgency, cost, or efficiency—this is cloud interaction. 

Similarly, in medical monitoring, medical imaging is significantly better in the care of, or combined with, AI analysis. Shown time and again, AI imaging can spot disease indicators months or years before such would be recognisable to human eyes.  

These systems are ideal for pattern recognition because that is what they do. Whether MRIs, X-rays, Ultrasound, CT scans, or PET scans, AI uses vast databases to identify problems, reduce diagnostic errors, plan surgical interventions, and much more.  

Main challenges in adopting AI enabled embedded systems and how to overcome them 

Challenge 1: selecting the right programming language 

Unsurprisingly, many issues with scaling AI-driven embedded systems start in the preparatory stage, i.e., when you’re choosing the programming language. Selecting the wrong one makes it challenging – if not impossible – to create lightweight software that aligns with the constraints of embedded environments.  

That’s not to say you can’t replace a suboptimal coding language on an existing project – as I’ll explain below. 

Solution

To best illustrate how issues with programming languages can be solved, I’d like to refer to a project we completed for Heimgard Technologies, a Nordic 

smart home solutions provider. Their app manages multiple sensors, switches, and cameras, but the original coding language, Python, wasn’t up to par. Instead, we identified that C++ would be a better option. The primary motivation for this transition was to address performance issues and limited space for adding new features.  

Replacing the code with C++ brought significant improvements. One example, shown on the screen below, shows how the pairing process – which consumes a lot of CPU and RAM – is now 3 times shorter. Additionally, we also reduced the app’s “hard disk” size slightly above the 50% mark. 

Similar optimisations are essential for supporting AI-driven functionalities in embedded systems, ensuring they operate efficiently even with limited resources. 

The real challenge with AI-driven embedded systems isn’t just about integrating AI models—it’s about making them work efficiently within resource-constrained environments. The key is striking the right balance between on-device processing and cloud computing while ensuring scalability. Companies that master this approach will not only optimize performance but also future-proof their embedded solutions.

Łukasz Janek – Competence Unit Manager 

Challenge 2: building partnerships 

Finding and working with an embedded software development partner for AI systems is complex due to the specialised nature of these projects. Misalignment on AI requirements, hardware compatibility, and project scalability often leads to inefficiencies. Moreover, many development partners lack deep expertise in integrating AI models into resource-constrained environments. 

Solution

Work with cloud experts and system architects who have proven success in your industry. Bringing them on early in the project can make a big difference. Focus on creating clear project plans and start with early prototypes to test if everything works together smoothly. I highly suggest using collaboration tools to keep communication open. Also, while working with a partner, make sure that everyone treats system scalability as a top priority, like running AI models on different hardware without losing performance. 

Challenge 3: the cloud-first approach 

AI models need a lot of training and processing power, which can be tough for embedded devices to handle directly. Using the cloud can help with these tasks, but being overly dependent on it can cause delays, use too much bandwidth, or create privacy concerns. Industries like healthcare and automotive face even bigger challenges because of strict rules on data management. 

Priyansh Kothari, Founder of Stargazer, said that while cloud-based AI has many advantages like minimal upfront costs and no hardware requirements it also has a few downsides.  

“You’re sharing your data with cloud providers and become dependent on their platform. Costs can become substantial over time. We have used Azure and Google Cloud but both have similar practices, and I believe all of them have these “locking” practices”. 

The company used Azure to train a custom Mistral model and now, they’re locked into Azure AI to run it. A few days ago, Azure discontinued a legacy model they were using, forcing them to upgrade to a new model and retrain their AI, which was a major challenge. 

“Another issue is latency. For real-time applications, relying on the cloud can be too slow. If we had an AI model in our backend office, we can get things done faster. Plus, if your internet connection goes down (that is common in India), your whole system stops working,” added Kothari.  

Solution

To answer that, it’s necessary to use a mix of cloud and on-device processing. Let the cloud handle big tasks like training AI models or managing large datasets, while smaller, pre-trained models run on devices for quick decisions. Save money by using pay-as-you-go cloud services and tools.  

For sensitive data, follow rules like GDPR or HIPAA by using encryption and techniques like federated learning, which process data locally while securely updating models through the cloud. 

Challenge 4: optimise software development for AI integration 

Creating AI-powered embedded systems means building software that combines smart AI features within the limited resources of small devices, like low processing power, memory, or battery life. Standard software usually isn’t efficient enough for real-time tasks, such as running autonomous vehicles, industrial robots, or medical equipment.  

Solution

Some of the techniques that can help you optimise existing AI projects include shrinking and simplifying AI models (quantisation and pruning). This lets you make them run faster without losing accuracy. Design the software in small, flexible pieces so it’s easy to update AI features or work with new hardware. As you do, work closely with hardware makers to ensure everything runs smoothly.  

Finally, test the system in real-world scenarios to make sure they’re reliable and efficient. 

Challenge 5: develop software that aligns with the constraints of embedded systems  

Functionalities like AI-based operations rely on software that is optimised, lightweight, and adaptable. One of the biggest challenges in adopting AI enabled embedded systems that I regularly come across is developing software that aligns with the constraints of embedded environments. 

Embedded systems are resource-constrained – they have limited processing power, memory, and energy efficiency. Developing software that fits these limitations while still allowing AI algorithms to work effectively requires careful planning. 

Solution

I believe the key to overcoming this challenge is designing software with flexibility, efficiency, and scalability at its core, supported by the right tools and strategic partnerships. 

• Prioritizing modular and flexible software architectures. Modular designs allow developers to adapt and refine specific components without redesigning the entire system. This is especially important when integrating AI models that may evolve over time or need to be updated to align with new business requirements or advancements in AI technology. 

• Leveraging tools and frameworks that enable resource-efficient AI applications (such as AWS IoT Greengrass, TensorFlow Lite, TinyML, ONNX Runtime and others). These applications allow for building lightweight AI models that perform well even on devices with constrained resources. 

• Constant testing and optimisation. Real-time monitoring tools allow for adjustments that improve software performance in different scenarios

AI enabled embedded systems offer huge benefits for enterprises  

AI-powered embedded systems offer transformative potential across industries by improving efficiency, reducing waste, and enabling real-time monitoring. Unlike traditional methods, AI can analyse sensor data, predict failures, and optimise processes in real-time, leading to better performance and lower costs. 

Adopting AI enabled embedded systems is challenging, requiring specialized partners, clear planning, and early prototyping. Balancing cloud and device processing is crucial, as the cloud handles heavy tasks, but over-reliance risks delays and privacy issues. A hybrid approach—offloading big tasks to the cloud while devices handle quick decisions—is the solution. 

Optimizing software for small devices is another challenge. Lightweight AI tools combined with techniques like model pruning, make AI integration efficient. Testing systems in real-world scenarios ensures robustness and reliability, and positions businesses to fully harness the benefits of AI-driven embedded systems. 

What’s next for AI in embedded systems? 

AI-driven embedded systems are already reshaping how modern enterprises operate. They streamline processes, cut costs, and boost efficiency by enabling real-time monitoring and intelligent automation. The potential is huge—but so are the challenges. 

Choosing the right programming language, optimizing software, and balancing cloud and edge computing are just a few of the hurdles companies face. Success depends on careful planning, hands-on testing, and working with the right experts to ensure scalability and performance. 

Those who get it right will see major gains—lower operational costs, fewer failures, and smarter, more responsive systems. AI isn’t just a trend; it’s a competitive advantage that businesses need to embrace sooner rather than later. 

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