Whom is this article is for: This article is intended for individuals concerned about the efficient control and management of embedded software and Internet of Things (IoT) devices throughout their lifecycle—from development to sales and eventual decommissioning. This audience includes Product Owners, Asset Managers, Developers, Architects, Technical Managers, and other stakeholders impacted by associated costs, risks, and intellectual property (IP) rights.

Most devices created today have some form of electronic functionality. They are designed to perform real-world tasks using digital processes, often storing the resulting data for further use. Many of these devices are interconnected—whether locally or globally—with others of the same or entirely different design.

Examples include factory robots, drones, modern electric cars, deep-sea exploration tools, fitness trackers (like smartwatches and rings), smart fridges, surveillance equipment, simulators, and interactive kiosks, to name just a few.

Here is an important distinction: IoT devices are generally thought of as systems that communicate with one another and share data online. In contrast, embedded systems tend to function autonomously or in small, localized groups. Embedded systems often perform specific tasks without being continuously or openly connected to the internet.

The diagram below (Picture 1.1) aims to highlight some commonly accepted differences, usefull shared traits, and the grey zone where IoT and embedded systems overlap.

Picture 1.1 Comparison IoT Vs Embedded Systems

In the words of Bob Blumenscheid: ‘As embedded systems applications appear in every industry and sector today, embedded devices and software play a crucial role in the functioning of cars, home appliances, medical devices, interactive kiosks, and other equipment we use in our daily lives’.

When we expand this definition to include industrial embedded systems and IoT devices—such as complex simulators, logistics systems, and even factories capable of turning raw materials into finished, packaged goods—the potential business impact (and opportunity) is enormous.

Adding artificial intelligence (AI) capabilities to these devices, systems, and cloud entities makes it imperative to control them effectively for security, sustainability, cost management, and optimization. According to Open PP:
*”Embedded AI is expected to see its applications expand, particularly in real-time data processing, autonomous systems, and edge computing solutions. By 2030, the global market is projected to reach approximately USD 42.5 billion.”*
(Note: This figure only accounts for the embedded AI market, not all embedded and IoT devices currently in use or those being developed.)

In summary, the growth of embedded systems and IoT devices—with or without AI—continues at an extraordinary pace. But what does this mean from an IT Asset Management (ITAM) perspective? Commit that question to memory, as we need to examine the architecture and creation process for these devices before addressing it.

The diagram below (Picture 1.2) illustrates a simplified architecture of an embedded system or IoT device. Although many systems are far more complex in reality, this basic example uses a drone to explain key functionalities.

Picture 1.2: Simplified Architecture of an IoT Device

From this illustration, we can conclude that the basic components of embedded systems are similar to those in computer/server architecture.

– Hardware: Sensors and actuators perform tasks such as accelerating, turning, or detecting angles.
– Operating System: Facilitates communication between hardware and software components.
– Storage and Networking: Translate inputs and outputs into actionable data.

These components fulfill the primary purpose of the IoT device—in this example, a drone flying to a specified location and filming a river, producing high-quality footage that can later be edited, enhanced with music, and uploaded for others to enjoy.

The software components may be consumer off-the-shelf (COTS), open-source, self-built, or some combination thereof. Licensing rules can vary based on the device type, intended usage, and geographic location of both purchase and operation.

Now, let’s shift from the drone example to a food factory. Imagine a system where raw food ingredients enter one end and emerge as cooked, inspected, weighed, priced, and packaged products on the other. Although this is an embedded system rather than IoT, the principle remains the same: licensing rules must be clearly understood and adhered to—especially when selling the system or its services.

Additionally, as devices and systems change ownership or responsibilities, they must be managed as monetized assets. This means that every embedded system or IoT device has a lifecycle, requiring systematic management much like any other IT asset.

With the projected growth of embedded systems and IoT devices, the volume of data transmitted between devices and stored (whether in the cloud or on-premises) will also grow. Protecting this data, while managing the increasing number of data consumers (both human and machine), adds complexity to this rapidly expanding ecosystem.

The lifecycle of these devices is another key consideration. Complex embedded systems are often expensive, necessitating cost-efficient management from creation to decommissioning. Achieving full visibility of the asset, its configuration, usage, and governing rules is vital. Leveraging IT asset management tools can make this process more coherent and systematic, though discipline and effort will always play a significant role.

Finally, the rise of AI introduces new challenges. Embedded AI systems, connected devices, and applications processing large datasets will further increase ownership complexity, legal requirements, and costs—all of which must be carefully managed.

Based on my understanding of the domain and the data referenced in Section 4, we advocate the following insights:

Continued Growth of Embedded Systems and IoT Devices:
Embedded systems and IoT devices are poised to grow significantly in numbers, importance, and business impact. This growth will increasingly affect the asset management ecosystem, demanding new strategies and tools to ensure effective governance.

Expansion of AI in Embedded Systems and IoT:
The integration of AI into or alongside embedded systems and IoT devices will expand their capabilities, driving further growth in significance and relevance across industries.

Ripple Effects on Hardware, Software, and Cloud Consumption:
The combined growth of embedded systems, IoT devices, and AI will lead to higher demand for hardware, software, and cloud solutions. This expansion will also drive increased global data traffic and storage needs, both directly and indirectly.

Necessity for Robust Tools, Processes, and Management:
Managing this growth effectively will require the right tools, professional asset management practices, and solid processes—supported by dedicated effort and attention to detail. Only by maintaining control over these assets and the data they generate can organizations achieve cost-efficient outcomes.

Opportunities for Asset Managers:
Developing the skills, knowledge, and experience to manage embedded systems and AI-driven assets represents an invaluable opportunity for asset managers. Those who rise to the challenge will likely reap significant professional and organizational benefits.

Closing Statement

Now is the perfect time to dive into the embedded systems and IoT ecosystem. This fast-moving domain spans major industry leaders such as Microsoft, IBM, Linux, Oracle, MySQL, Veeam, and Broadcom, among others. The pace of innovation is rapid, but it’s far from too late to get involved and seize the opportunities this sector presents.

Thank you for reading this far—and congratulations on taking the first step toward understanding this exciting and evolving field!