Expert Task Businesses To Leverage Edge AI to boost Productivity
Oluchi Chibuzor
A Nigerian network professional has tasked businesses in the country to leverage edge AI to boost their productivity due its vast application and implication across sectors.
This is because Edge AI uses AI algorithms on edge devices such as the Internet of Things (IoT) sensors, drones, cameras, and smartphones to allow processing in real time and autonomy, particularly in latency-sensitive scenarios.
Speaking with THISDAY virtually recently, Oluwatosin Aramide, said the rise of Edge AI marks a significant turning point in how artificial intelligence systems are developed, deployed, and experienced.
He explained that as Artificial Intelligence (AI) transforms various sectors of industry and all spheres of everyday life, there is more necessity to make intelligence relocate towards the place where data is being generated at the edge.
In his research work titled, ‘Edge AI and its Impact on Resilient AI Fabric Design:
Distributed Intelligence and Data Locality’, Aramide, noted that in the past, application of AI models and analytics has been trained and run on high-performance, centralized cloud resources where extensive compute capacity and large volumes of data can be tapped.
He noted that with the proliferation of the connected devices it is starting to be questioned as the requirement to make real-time decisions becomes more essential, adding it has adopted a new wave of innovation that can take the capabilities of AI where the data is produced at the edge.
According to him, “This shift is termed Edge AI, and it allows gadgets, including smartphone, sensors, and cost-automated machines, to determine decisions on the ground, devoid of the continuously reliant connection to geographically distant distributed cloud servers. It is expected that it would augment response rates, data security as well as autonomy, but it also raises profound issues on how we would be able to construct resilient systems that are made of distributed agents with AI capabilities that could work effectively under real world conditions,” he said.
He noted that his recent research set out to explore not just the technical underpinnings of Edge AI, but its broader implications on the design of a resilient AI fabric, an interconnected system of distributed intelligence that can learn, adapt, and operate reliably in dynamic, decentralized Environments.
According to him, “As we move computation closer to where data is generated on devices, sensors, vehicles, or eld equipment we open up new opportunities for real-time decision-making, improved data privacy, and reduced dependency on central infrastructure. However, we also introduce new challenges: from resource limitations at the edge, to intermittent connectivity, inconsistent data, and growing cybersecurity risks.
“The proposed framework developed in this research offers a modular, adaptive blueprint for deploying Edge AI systems in the real world. It integrates local intelligence, hybrid learning models, resilient data infrastructure, and orchestration tools while always keeping human and contextual needs in focus. This human-aware design ensures that the system is not only technologically sound but also socially responsible and sustainable.
“At its core, this study argues that the true value of Edge AI lies not simply in decentralizing computing power, but in reshaping the relationship between data, intelligence, and autonomy. By enabling devices and systems to think and act locally while staying connected globally we can build AI infrastructures that are more robust, responsive, and equitable, especially in underserved or infrastructure-challenged regions.”
Explaining the key significance of the edge AI, he said that it radically minimizes latency, maximizes both the user privacy and promotes functional operation in an environment with limited or interrupted connectivity.
“Edge AI also introduces independence and stability in the setting with a lack of internet connectivity or unstable and unreliable internet. This is in the form of the rural, oshore platforms, space missions, disaster zones and military deployments. In these situations, capability to make devices smart enough to work in autonomous mode without real time cloud connection is highly important. As an example, Edge AI-powered drones or rescue robots may make in-time life-saving decisions during natural disasters when the cellular networks are out, and keep working during the situation.
“In rural or underserved regions with limited internet access, edge AI devices such as diagnostic tools or wearable monitors allow local analysis of medical data. A portable ultrasound device using AI can analyze images on the spot, giving immediate feedback to healthcare workers without needing to upload data to the cloud. This not only improves patient outcomes but also ensures continuity of care during connectivity outages,” he said.
On the future outlook, he noted that the evolution of Edge AI will intersect with advancements in 5G/6G networks, neuromorphic computing, and decentralized governance models such as blockchain.
He explained that these innovations will further push the boundaries of what’s possible at the edge, adding that however, “as the technical capabilities expand, so too must our frameworks for ethics, resilience, and human-centric design.
“In conclusion, resilient Edge AI fabrics are not merely technological constructs, they are foundational enablers of the next generation of smart, adaptive, and inclusive systems.
Whether in healthcare, transportation, education, or public safety, the ability to deploy AI closer to where it matters most will redefine how we design for intelligence, trust, and resilience in the digital age.”
