Article

Right-Sizing AI for Organizations: Why Edge Computing Beats the Cloud

Christine Dunbar

October 2, 2025

Is Edge AI the right-sized artificial intelligence solution for organizations? How big should your agency’s AI system be to improve efficiency, security, and citizen services?

This decentralized approach significantly reduces latency, enhances real-time decision-making, and reduces dependence on network infrastructure.

ROCIMG Executive Summary

A simple definition for edge AI: A combination of edge computing, which captures, processes, and analyzes data at the farthest reaches of an organization’s network, and artificial intelligence enabling the deployment of AI applications in devices of the physical world, where the data is located, such as “Internet of Things” (IoT) devices, devices on the manufacturing floor, healthcare devices, or a self-driving car. Edge AI integrates AI into edge computing devices for quicker and improved data processing and smart automation.

The main benefits of edge AI include:

• Real-time data processing capabilities to reduce latency and enable near real-time analytics and insights.
• Reduced cost and bandwidth requirements as there is no need to transfer data to the cloud for computing.
• Increased data security as the data is processed locally, on the device, reducing the risk of loss of sensitive data.
• Improved automation by training machines to perform automated tasks.

The Issue

However, there are key challenges (Scalecomputing, 2025) businesses face when it comes to data and where it is contained and processed.

Key Challenges:

• Latency and real-time processing: Unlike cloud-based AI, edge devices have limited processing capabilities, making it difficult to run sophisticated AI algorithms effectively. Furthermore, real-time processing requires significant tuning to ensure models operate efficiently without consuming excessive energy.
• Connectivity and bandwidth limits: Organizations often rely on legacy infrastructure that may not be readily compatible with Edge AI solutions, necessitating customized integrations. Additionally, reliable network connectivity across distributed edge locations can be a hurdle in industries like manufacturing, where connectivity is often inconsistent.
• Scalability: Managing large-scale Edge AI deployments across multiple locations can introduce significant logistical issues. Ensuring that models are updated and synchronized across a distributed infrastructure requires robust orchestration tools. Without centralized control, organizations may struggle with inconsistent AI performance, leading to reduced efficiency and increased operational costs.

These challenges and more underscore the significance of edge computing and addressing critical business needs related to real-time processing, latency, and compliance that is essential for many industries.

Our Insights

Initiating your company’s edge computing journey is recognizing that success hinges on comprehensive planning.

Before diving into implementation, invest time in:

• Understanding your specific use cases
• The technical requirements for the edge AI system your company actually needs
• The ecosystem you’re operating within

This includes considering factors like hardware selection, software frameworks, data security measures, and integration with existing systems.

In Conclusion

By defining your edge computing technology in terms of readiness and value, you will be able to make a well-defined analysis of each technology. Selection will then come from a short list of potential solutions.

A “Strengths, Weaknesses, Opportunities, and Threats” (SWOT) analysis can also give you great insights into the pros and cons of each edge technology, providing a clear understanding of what each technology brings to the table based on value and cost.

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About the Author

Christine Dunbar

CEO

We believe in listening to our clients and facilitating robust dialogue to learn the full picture of the project from multiple perspectives. We craft solutions that are tailored to our client’s needs, emphasizing a robust process that engages the correct stakeholders throughout the project so that once it’s complete, our clients can continue to manage it successfully.