Edge technologies were developed years ago and continued to grow as a buzzword in the IT industry, but many organizations have been prioritizing cloud infrastructure instead. However, more recent improvements in edge technologies have lowered the barrier to entry for companies across nearly every industry. 

As a result, the number of sensors and devices deployed to collect data at the edge, and the software deployed to the edge to generate value from this data, are both growing rapidly. In fact, the 2024 Gartner CIO and Technology Executive Survey found that 19% of respondents have already deployed edge computing, and an additional 32% expect to deploy during the next three years.  

AI and low-latency data analytics will continue to accelerate the demand for optimized and efficient edge computing solutions, with Gartner predicting that 75% of enterprise-generated data will be created and processed outside a traditional centralized data center or the cloud by 2025. This means the remaining companies without existing edge plans may rethink their strategies in the near future as well. 

Read on to learn more about the trends and technologies enabling edge growth, the leading areas of edge adoption, and AHEAD’s perspective on the future of edge computing. 

Technologies Enabling Edge Growth 

Technical innovations in both hardware and software have drastically lowered the cost and reduced the complexity of developing and deploying edge systems. Here are some of the major technology improvements and industry trends that are enabling edge growth. 

Hyperscaler Distributed Cloud Solutions 

Although many organizations are approaching adoption from an edge-in perspective rather than a cloud-out perspective, there is a growing need for hyperscalers to support physical edge deployments. Gartner predicts approximately 5% of large enterprises will deploy a hyperscaler distributed cloud solution for edge computing workloads outside data centers in the next few years. 

For most organizations, the diversity of use cases, compute requirements, and other factors reduce the efficacy of hyperscale distributed cloud solutions at the edge. However, these hyperscalers solutions are valuable for applications and data that cannot migrate to centralized hyperscaler data centers. This means distributed cloud solutions and cloud-based applications can complement edge deployments by providing back-end services for enterprise integration, IoT management, and streaming analytics and ML. 

Edge Management & Orchestration 

Another technology area that has continued to mature is edge management and orchestration software. It’s challenging to remotely operate an edge fleet consisting of thousands of individual devices and components across different deployment sites. That’s why a growing set of vendors are developing edge management and orchestration (EMO) solutions, and Gartner predicts 20% of large enterprises will have deployed an EMO solution by 2027.  

As part of this trend, AHEAD has seen increasing interest in its Hatch IT lifecycle management platform. Hatch transforms the way IT teams manage multi-site infrastructure, whether it’s just a few distributed data centers or thousands of edge devices. The platform provides complete asset-to-site visibility and consolidated data for all AHEAD project implementations, streamlining the planning, execution, and support of edge initiatives.  

Edge Application Platforms 

Finally, application platforms have evolved to support the unique requirements of edge-native applications and architectures. For example, Gartner now predicts 80% of custom software running at the physical edge will be deployed in containers. This shift towards containers at the edge — as well as new tools and operational processes to manage large fleets of clusters with Kubernetes — is leading to more consistent and predictable deployments, which has been a challenge with remote and embedded runtime environments in the past. 

In addition, newer machine learning inference platforms and pre-defined software solutions have accelerated the development of edge AI applications. One example of an innovative edge application framework is NVIDIA Clara Guardian, which provides pre-trained models, deployment SDKs, analytics tools, and other capabilities to make new edge use cases possible in the medical industry. 

Leading Areas of Edge Adoption  

AI & Machine Learning 

A leading area of edge adoption is for AI and machine learning use cases. In fact, Gartner predicts at least 50% of edge computing deployments will involve machine learning (ML) by 2026. Edge AI can collect and analyze data to unlock insights that drive efficiency, reduce safety and security risks, and help organizations deliver more value to customers.  

At AHEAD, we’ve been helping clients deliver edge AI solutions ranging from predictive maintenance in the rail industry and manufacturing to medical AI use cases related to patient monitoring and diagnostics. Edge AI is growing across nearly every industry, and we believe organizations that fail to operationalize edge AI will fall behind their competition. 

Low-Latency Data Processing 

The second major area of edge adoption is low-latency data processing for advanced automation and real time intelligence. Many edge use cases have requirements for lower latency, data gravity (processing data where it is created to reduce bandwidth costs), and greater resilience when disconnected, which are driving the demand for on-location processing. 

AHEAD has seen a range of use cases requiring low-latency data processing, including physical security systems that use computer vision to detect safety and security incidents as well as data-driven clinical decision support (CDS) solutions for healthcare providers. As the demand for real time analytics continues to grow, organizations will require more data processing and inferencing at the edge. 

The Future of Edge Computing 

While retailers have been early adopters of edge computing, organizations across nearly every industry are also recognizing its potential. For example, asset-intensive industries, such as manufacturing, utilities, and transportation and logistics, have shown significant value from adopting industrial edge technologies for AI. 

Looking ahead, additional advancements in distributed cloud solutions, device orchestration and management software, edge application platforms, and other related technologies  — combined with the demand for real time analytics — will drive further adoption of edge computing. But many organizations will still face challenges related to edge hardware procurement and management. 

If your organization is looking to roll out a new edge system, AHEAD can help you deploy, orchestrate, and manage large-scale edge fleets. Our Edge Advantage program dramatically reduces deployment times, quality control problems, and asset traceability headaches for multi-site edge systems. We also provide AHEAD Hatch to customers so that they can optimize every aspect of planning, executing, and supporting their edge projects. 

Contact AHEAD to learn more about our Edge Advantage program and Hatch IT Lifecycle Management platform.