[Blog] Edge AI Opportunity Will Come to Life in 2026

When it comes to making predictions, sometimes you really have to go out on a limb and sometimes, well, it’s pretty easy. In 2026, there’s zero doubt that AI, generative AI, and agentic AI will continue to be the key buzzwords driving the tech industry forward.

Perhaps a bit less obvious is this year should also mark the beginnings of a growing opportunity in Edge AI, where AI-focused workloads can run in disconnected environments and/or locally on client type devices. The combination of dramatically improved AI performance on the edge—thanks to the introduction of NPUs (Neural Processing Units) and improved GPUs and CPUs—along with more capable SLMs (Small Language Models) is opening up the possibility of doing things locally that used to be only possible with cloud computing resources.

Equally important are several critical software developments. First, there are better tools for abstracting the various types of xPU architectures found in modern SOCs. This allows AI application developers to leverage all the elements of these powerful chips in a much more straightforward way and without having to write to company-specific architectures. Second, advancements in model quantization and distillation techniques are enabling the creation of small AI models that are significantly more powerful than their predecessors and equal to the capabilities of early cloud-based models. Plus, we’re starting to see the emergence of a much larger number of small, specialized models that are specifically tuned for certain applications/and or industries. Finally, AI-powered coding tools are making it much easier for developers and even non-developers to create the wide range of often very-specific edge applications that will be necessary to drive the category forward.

Combine all this with the rapidly growing support for technology protocols such as MCP (Model Context Protocol) and A2A (Agent-to-Agent) which allow AI workloads to be easily distributed across a heterogeneous combination of different computing resources, and the stage is set for edge devices and environments to play a critical role in future AI applications and across increasingly common Hybrid AI deployments.

Moving AI workloads to the edge brings with it several important benefits and aligns with critical needs that recent research has shown companies are trying to address. Key drivers include spiraling costs for cloud-only options, and growing concerns around data security and privacy. As organizations have learned, the best way to leverage new GenAI models and tools is to train or fine tune them with their own data, and in many situations, that comes from edge devices. Plus, in environments where latency concerns are critical—such as reading and interpreting large amounts of sensor data—edge computing deployments offer the only realistic option.

The implications of this shift are profound for any organization looking to create and deploy AI solutions in the new year. Most notably, it suggests a modernization and rethinking of corporate computing resources. Not only will it likely drive the need for more sophisticated AI computing in on-prem data centers, this shift will also encourage the purchase and usage of other types of AI-capable compute devices. A fascinating data point that Intel recently shared at their CES keynote speech was that the combined AI computing resources of all the AI PCs it has shipped so far are equivalent to over 40 major AI data centers. That’s a vast amount of power that enterprises can tap into and should encourage the development of more applications and tools that can leverage those capabilities.

In addition to focusing on more raw computing capability, the increase in edge AI applications will also shine a spotlight on the increased security requirements that this will entail. As a result, the hardened security capabilities that are enabled by FPGAs from companies such as Lattice Semiconductor are going to be increasingly important. Control systems for network switches and other elements of a more sophisticated AI hardware infrastructure are also going to be the subject of more direct focus. Finally, as we head towards the post-quantum era, companies will be more dependent on solutions that can enable post-quantum cryptography (PQC), within these new AI environments.

As a result, products like the Lattice MachXO5™-NX, which is designed for secure control applications in these types of deployments, will play an increasingly important role in these new buildouts and edge devices. Similarly, companies are likely to be investing in more AI-enabled edge devices as part of these initiatives and low power FPGAs built on the Lattice Nexus™ small FPGA platform, Lattice Avant™ mid-range FPGA platform and other lines can be an important factor here. Not only do these devices offer hardened security capabilities, but they also feature the ability to provide high-speed connectivity to sensors, perform sensor fusion, and even run small, specialized AI models on their own.

The speed of developments in the AI world can often make it difficult to predict exactly where some specific trends will move to, but at a higher level, there are numerous signs pointing to the rapid growth of Edge AI. Because of that, it’s also clear that FPGAs will continue to play an important—though perhaps not always fully appreciated—role in the growing opportunity for AI computing infrastructure across multiple environments. The specific solutions may vary, but the consistent story of flexibility, security, and customizability that FPGAs enable will continue to be an important force moving into 2026 and beyond.

Bob O’Donnell is the president and chief analyst of TECHnalysis Research, LLC a market research firm that provides strategic consulting and market research services to the technology industry and professional financial community. You can follow him on Twitter @bobodtech.