Tronel’s 2025 Embedded Trends Outlook

28 January 2025

Technology evolves faster than ever, and embedded systems are right at the heart of this transformation. From the rapid development of AI and machine learning to the growing demand for real-time data processing, embedded systems are the driving force behind today’s most transformative technologies. The surge in connected devices has made these systems indispensable across industries, powering everything from smart homes to critical infrastructure.

Their impact is particularly evident in the automotive sector, where embedded systems enhance technologies like ADAS (Advanced Driver Assistance Systems), driving the creation of smarter, safer vehicles and shaping the future of mobility.

But these changes aren’t just worth observing – they’re worth being part of. That’s why, as every year, we’ve prepared a roundup of the most significant trends that will define the future of embedded systems in the upcoming year. What does the future hold for embedded systems, and what should you keep an eye on in 2025? Let’s dive in and find out!

Curious what was trending in 2024? Explore 5 trends in embedded systems that shaped the industry last year.

Increased integration of AI and machine learning

Artificial Intelligence is a topic that’s been dominating the tech world. With more and more companies investing in AI, it has become one of the fastest-growing technologies, with private investment in GenAI reaching $25.2 billion in 2023 – nearly eight times more than in 2022.

Integrating AI and machine learning in embedded systems brings numerous benefits, such as predictive maintenance and anomaly detection that can improve system reliability. AI algorithms can detect unusual data patterns, flagging potential issues before they become real problems. Moreover, by leveraging historical data, they can predict equipment failures and optimize maintenance schedules.

Generative AI is also transforming the embedded software development process. In 2023, over 82% of developers used AI tools for code writing, and nearly 49% for debugging. While AI-assisted programming may raise some concerns about whether AI can potentially take away jobs from embedded engineers, this scenario is very unlikely. But it will certainly impact the way programmers work, taking over repetitive tasks and accelerating the development process.

Another trend gaining traction is agentic AI. Unlike GenAI, which typically requires to be prompted by the user, agentic AI can act autonomously, based on predefined goals and parameters. Gartner predicts that by 2028, 33% of enterprise software applications will include agentic AI, up from less than 1% in 2024. This technology could significantly enhance real-time decision-making, allowing devices to adapt and respond autonomously to changing conditions. From smarter IoT devices to more autonomous industrial systems, agentic AI promises to reshape industries by creating more self-sufficient systems that can operate independently in complex environments.

AI-powered edge computing

In time-critical systems, real-time decision-making is not just an option – it’s a necessity. With the increasing demand for faster data processing, edge computing seems like a logical next step.

Edge computing moves data processing closer to the source, reducing latency and lowering costs associated with bandwidth and storage. However, the true potential of edge computing lies in its integration with AI. With advances in AI algorithms and specialized hardware, such as neural processing units (NPUs) in microcontrollers, it is now possible to run AI models directly on embedded devices. This allows even resource-constrained devices to perform autonomous tasks and make real-time decisions.

Of course, implementing edge AI in embedded systems does come with its challenges. AI models must be compressed to prevent performance compromises, and hardware must be energy-efficient, especially for battery-powered devices. Additionally, AI solutions usually need to be customized to match the specific capabilities of each embedded device.

Despite these challenges, the potential of edge AI in embedded systems is undeniable. It overcomes the limitations of cloud computing, efficiently manages the vast amount of data generated by IoT devices, and opens up new opportunities for autonomous applications.

In 2025, we can expect even greater integration of edge computing in industries such as automotive and healthcare – from the development of Advanced Driver Assistance Systems (ADAS) and autonomous vehicles to AI-powered diagnostics analysing tools that enhance medical imaging precision. As technology continues to evolve, edge AI is set to become a cornerstone of our increasingly connected world.

Security is no longer an option

The development of technology brings not only opportunities but also new threats. Cyberattacks are becoming more frequent and expensive. In the past two years, data compromises have risen, and the global cost of a data breach reached 4.88M USD in 2024 – a 10% increase over last year and the highest total ever. The most common attack vectors in 2024 were DDoS attacks and ransomware. DDoS attacks pose a significant threat both to mobile networks and IoT devices. Meanwhile, organisations that extensively used security AI and automation in prevention saved an average of $2.22M.

Ironically, the same AI that is used to protect systems can also be weaponised by cybercriminals. While real-time data analysis and machine learning improve threat detection and allow for proactive action before an attack escalates, hackers can use techniques like adversarial machine learning to manipulate AI algorithms and compromise sensitive systems.

In response to these evolving threats, new legislation is being introduced, such as the EU Cyber Resilience Act. The regulation came into effect at the end of 2024 and applies to all products with digital elements, including embedded devices. It aims to enhance security in all products connected directly or indirectly to another device or network. Manufacturers have until December 2027 to comply with the new standards, marking the beginning of a new era in embedded security.

Gone are the days when security in embedded systems was optional. Engineers must now consider it from the very beginning and throughout the entire device lifecycle, using methods like secure boot, cryptographic processing, attestation, random number generation, and physical tamper monitoring.

With the implementation of the EU Cyber Resilience Act and the growing interconnectivity of devices, security continues to be one of the top trends in embedded systems – and will likely remain so even beyond 2025.

Racing towards a more connected future: 5G and beyond

If we were to describe the direction modern technology is heading, we couldn’t overlook the significance of speed. Alongside security and efficiency, it is shaping the trajectory of the latest technological advancements, such as the next generations of mobile networks.

As for now, 5G remains the dominant mobile technology, but 6G is already on the horizon, anticipated to become commercially available around 2030. Year after year, this technology becomes more and more widespread. Approximately 45% of global networks now support 5G, projected to rise to 85% by the end of the decade.

5G offers significantly lower latency and higher bandwidth compared to previous generations. It is revolutionizing IoT by providing faster, more reliable and efficient connectivity. Its ability to support up to one million devices per square kilometre makes 5G an ideal choice for applications such as smart cities, industrial IoT, and healthcare.

The synergy between 5G, AI, and edge computing brings even more benefits. Together, they enhance the speed of operations and enable faster decision-making in critical applications like autonomous driving or smart manufacturing. The adoption of 5G-enabled edge computing is becoming the standard for enterprise data processing.

However, it is only a matter of time before 5G will have to make way for its successor. Though still in the research and development phase, 6G promises to revolutionize connectivity with data transfer speeds reaching up to 1 Tbps, ultra-low latency, and unprecedented reliability. The first technical specifications for 6G are expected to emerge from the 3GPP around 2028, with pre-commercial trials starting as early as that year.

More energy-efficient computing

The rise of energy-intensive technologies like AI calls for more energy-efficient solutions. New computing technologies, such as neuromorphic and optical accelerators, are emerging to deliver lower energy consumption, meeting the demands of AI-driven applications while reducing carbon footprints. Neuromorphic computing, which mimics brain-like processing, is particularly suited for embedded systems needing real-time decision-making.

There is also a growing demand for low-power embedded systems that can operate with minimal power consumption, while still maintaining a high performance. Of course, balancing those two is no easy task, yet it’s a necessity, especially in IoT and portable devices that depend on battery to function. That’s why companies are focusing on creating more sustainable solutions such as ultra-low power integrated circuits (ICs) designed to reduce energy usage.

Efficient power management is becoming essential for meeting the demands of next-generation connected devices as it reduces the environmental impact of IoT and is crucial for prolonged battery life. This involves not only the design of low-power hardware but also the implementation of software strategies that optimize energy use during operation.

By adopting a low-power mindset in design and development processes, engineers can create solutions that not only meet performance standards but also align with global sustainability goals.

Learn more about how IoT solutions can drive sustainable development.

Get ready for the future

Last year was undoubtedly marked by AI, which is significantly shaping our reality –whether we like it or not. Its influence is felt across nearly every trend we’ve discussed, from empowering edge computing and mobile networks to accelerating data processing and supporting real-time decision-making. It also impacts security, acting as both an ally and an adversary.

While AI brings incredible advancements, its hefty energy demands and carbon footprint present significant environmental challenges. Fortunately, the rise of low-power embedded systems is paving the way for more efficient and sustainable solutions.

The future is shaping up to be faster, more connected, and undeniably AI-driven – and we’re excited to see where it will take us. Stay ahead of the curve and discover how these trends can drive both the growth and sustainability of your business. Not sure where to start? Get in touch, our experts will be happy to support you in finding the best solution for your company.

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