Embedded systems industry trends in edge AI, security and software-defined design

The industry is moving from invisible controllers to strategic platforms

The embedded systems industry is no longer defined only by microcontrollers hidden inside appliances, meters, vehicles and factory machines. More products now sense, decide, connect, update and need to prove their security over a long service life. Market estimates vary by methodology, but published forecasts point to steady growth through 2030. At the same time, regulation and buyer expectations are raising the cost of weak security.

For engineers and product teams, the practical impact is significant. Choices around MCUs, SoCs, real-time operating systems, connectivity modules and update mechanisms now affect more than performance and bill of materials. They also influence compliance, serviceability, field risk and the ability to support products after shipment. For ongoing topic coverage, see our embedded systems section. (grandviewresearch.com)

What the embedded systems industry includes now

An embedded system is still a purpose-built computing system inside a larger product, but the category has widened. At one end are small battery-powered nodes running bare-metal firmware or a compact RTOS. At the other are Linux-based gateways, automotive domain controllers, industrial edge computers, and medical or energy devices that combine sensors, actuators, connectivity and remote management.

The important change is that more embedded products now operate inside a service ecosystem. A smart meter, robot controller or connected camera may depend on local firmware, cloud-enabled functions, mobile apps, certificates, update servers, diagnostic tooling and data retention policies. The EU Cyber Resilience Act uses the broader term “products with digital elements” and explicitly covers hardware and software products, including certain remote data processing solutions needed for product functions. That definition shows why embedded design can no longer be treated as a purely local hardware problem. (digital-strategy.ec.europa.eu)

Market signals point to steady growth, not a single exact number

Research firms do not all define embedded systems in the same way, so their numbers should be read as directional rather than directly interchangeable. Grand View Research valued the global embedded system market at USD 112.3 billion in 2024 and projected USD 169.1 billion by 2030, with a 7.1% CAGR from 2025 to 2030. ResearchAndMarkets estimated the 2024 market at USD 100.11 billion and projected USD 146.65 billion by 2030, with a 6.57% CAGR. The useful signal is the same in both views: connected hardware, industrial automation, smart infrastructure and automotive electronics are keeping demand on a growth path. (grandviewresearch.com)

For suppliers, growth will not be evenly distributed. Commodity control boards remain price-sensitive, while higher-value opportunities are moving toward secure connectivity, edge inference, functional safety, deterministic networking and fleet lifecycle management. In many projects, the supplier that reduces integration risk may be more valuable than the one offering only a faster processor.

Edge AI is changing board-level design decisions

Edge AI is one of the clearest forces changing embedded design. The aim is not to replace every cloud function, but to move selected inference tasks closer to sensors and actuators. This can reduce latency, lower bandwidth requirements, improve privacy and keep systems operating when connectivity is intermittent. In practice, that changes what engineers need from silicon: more memory bandwidth, better power efficiency, optimized accelerators, secure model storage and tooling that can turn trained models into reliable embedded workloads.

A joint European roadmap on edge AI describes specialized circuits such as tensor engines and neural processing units as important for efficient AI computation. It also notes that MCUs still have a role alongside ASICs or SoCs for low-power and real-time responsiveness. That mix reflects the real industry pattern: the future is not only large AI chips, but heterogeneous embedded architectures where an MCU, DSP, NPU, sensor hub and connectivity processor may each have a defined role. (smart-systems-integration.org)

What changes in the bill of materials

Traditional embedded selection often started with pin count, peripherals, temperature range and unit cost. Those factors still matter, but edge AI adds new questions. Can the memory subsystem feed the accelerator without wasting power? Is there enough nonvolatile storage for models and rollback images? Can the device update models safely without breaking certification or safety assumptions? Does the toolchain support quantization, profiling and debugging on the target hardware, not only in a desktop environment?

These questions make software and hardware selection more interdependent. A low-cost processor may become expensive if it requires custom optimization for every model revision. A higher-priced SoC may be justified if it reduces engineering time, enables secure over-the-air updates and supports a longer product roadmap.

Security and compliance are becoming product features

Security is becoming a design requirement, a procurement filter and, in some markets, a regulatory obligation. The EU Cyber Resilience Act entered into force on December 10, 2024. Its main provisions apply from December 11, 2027, while conformity assessment body provisions apply from June 11, 2026, and certain reporting obligations apply from September 11, 2026. For embedded manufacturers selling into the EU, that timeline makes secure development, vulnerability handling, technical documentation and update support part of product planning rather than after-sales cleanup. (digital-strategy.ec.europa.eu)

In the United States, the policy direction is also moving toward clearer IoT security expectations. The FCC’s voluntary Cyber Trust Mark program is designed for qualifying consumer IoT products and uses a label plus QR code to communicate cybersecurity information. NIST’s IoT cybersecurity work supports this shift; NIST IR 8259A defined a core baseline of IoT device cybersecurity capabilities, and NIST IR 8259 Revision 1 was published on April 20, 2026, with manufacturer activities spanning pre-market through post-market support. (docs.fcc.gov)

The engineering implication is direct: embedded security has to be designed into device identity, secure boot, protected storage, access control, update mechanisms, vulnerability intake and end-of-support communication. A device that cannot be patched, identified or decommissioned safely may become a liability even if its core function still works.

Software-defined products are reshaping architecture

Automotive is the most visible example of the software-defined shift, but the same pattern appears in industrial equipment, smart buildings, energy systems and connected healthcare devices. Instead of placing each product function in a separate hardware island, more designs are consolidating compute, separating hardware abstraction from application logic and enabling features through software updates. See also: BUYING GUIDES.

McKinsey analysis projects that the automotive software and electronics market could reach USD 519 billion by 2035, with growth driven by zonal and central compute architectures, over-the-air updates, advanced driver assistance and more connected features. This is a source viewpoint rather than a guaranteed outcome, but it explains why embedded developers are being asked to think beyond firmware images and toward platform architecture. (mckinsey.com)

For industrial devices, the same logic appears in condition monitoring, robotics, programmable automation controllers and edge gateways. A product may need deterministic control loops at the machine level, analytics at the edge and integration with enterprise systems. That pushes teams to separate safety-critical code from updatable functions, define stable APIs and maintain test pipelines that catch regressions before deployment.

Hardware choices are becoming ecosystem choices

Processor selection is no longer only an electrical decision. It is also a tooling, operating system, compiler, security, certification and talent decision. Arm remains deeply established across MCUs, application processors and industrial SoCs. RISC-V is gaining attention because it offers an open instruction set architecture and customization potential. RISC-V International’s 2025 annual report describes accelerating adoption across embedded, automotive, AI, space and other markets, supported by real silicon and software activity. As an industry organization, it naturally presents an ecosystem-positive view, but the trend is still relevant for long-life embedded planning. (riscv.org)

The practical question is not whether one architecture replaces another. The better question is which ecosystem reduces risk for a specific product. A safety-critical motor controller, a consumer wearable, a gateway running containers and a battery-powered sensor node have different constraints. Engineers should evaluate long-term availability, tool maturity, security IP, functional safety support, power profile, software libraries and second-source options before committing to an architecture.

What product teams and buyers should watch

The next phase of the embedded systems industry will reward teams that manage the full device lifecycle. That does not mean every product needs a premium processor or a complex cloud stack. It means the architecture should match the device’s risk profile, operating environment and expected support period.

• Lifecycle support: Define update policy, support period, vulnerability response and end-of-life communication before release.
• Security evidence: Maintain documentation for secure boot, access control, encryption choices, dependency tracking and vulnerability handling.
• Edge-to-cloud balance: Keep time-critical, privacy-sensitive or offline functions local, while using cloud services where fleet learning, dashboards or remote maintenance add value.
• Architecture modularity: Separate safety-critical functions from updatable applications whenever practical.
• Supply resilience: Qualify critical components carefully and avoid designs that depend on a single hard-to-replace device when lifecycle risk is high.
• Toolchain quality: Evaluate debugging, profiling, model optimization, RTOS support and security tooling as part of component selection.

Buyers should adjust procurement questions as well. Asking only for unit price and performance misses a large part of the risk. More useful questions include: How long will security updates be provided? What happens if a vulnerability is reported? Can the device be securely onboarded at scale? Is there a documented software bill of materials? Can firmware be rolled back if an update fails? These questions are becoming normal because embedded devices increasingly operate as connected infrastructure.

Frequently asked questions

What is driving growth in the embedded systems industry?

Growth is being driven by connected products, industrial automation, software-defined vehicles, smart infrastructure and the need to process more data near sensors and machines. Forecasts vary, but multiple market estimates point to continued expansion through 2030 rather than a flat market. (grandviewresearch.com)

Is edge AI replacing cloud computing in embedded products?

No. Edge AI is usually a complement to cloud computing. Time-sensitive inference, privacy-sensitive processing and offline operation may move to the device, while cloud platforms remain useful for fleet analytics, long-term storage, remote management and model development.

How does cybersecurity regulation affect embedded device makers?

Cybersecurity regulation raises the importance of secure development, vulnerability handling, software updates and technical documentation. The EU Cyber Resilience Act is especially relevant for products with digital elements made available on the EU market, with major obligations phasing in through 2026 and 2027. (digital-strategy.ec.europa.eu)

Will RISC-V replace Arm in embedded systems?

That is not the most useful way to frame the market. RISC-V is gaining ecosystem momentum, but Arm has a large installed base, mature tooling and broad supplier support. Product teams should choose based on risk, software maturity, power, cost, safety needs and long-term availability.

Which sectors will feel these changes first?

Automotive, industrial automation, energy equipment, smart buildings, consumer IoT and connected medical devices are among the sectors most exposed to the shift. They combine long product lifecycles, connectivity, safety or reliability expectations, and growing pressure for secure updates and data-driven features.