By Anant Adya, EVP and Service Offering Head at Infosys
Cloud computing is on a steady growth trajectory in the United States, with the market projected to expand from $246.8 billion in 2025 to $907.9 billion by 2034. The edge computing market is relatively smaller at $8.4 billion (2024), but is forecasted to grow very rapidly to cross $76 billion by 2033. Converging together, edge and cloud are driving real-time intelligence to unlock enormous possibilities for U.S. industries.
A bunch of trends are creating a natural need for edge and cloud convergence: hybrid solutions are connecting hyperscale clouds with edge micro-data centers, enterprise data processing is rapidly shifting from the data center to the edge, and deployments in remote or inaccessible locations are demanding reliable edge computing options that can function without constant cloud connectivity. Artificial intelligence is fulfilling this need by powering the convergence of edge and cloud computing, building a synergistic ecosystem and driving instant intelligence across the enterprise.
It’s a match made in heaven.
Acting as first responders, edge devices, such as IoT sensors, cameras and autonomous vehicles, process time-sensitive computations close to the data source to minimize latency and allow instantaneous actions and decisions: this is critical for applications such as collision avoidance systems that cannot afford to wait for data to travel to and from a remote cloud server. On the other hand, cloud is the core intelligence that provides scalable compute and storage for complex, resource-heavy applications. It aggregates massive data from various edge sources to perform analytical computations, and to train and refine AI and machine learning algorithms, which are pushed back to the original edge sources to create a continuous learning and optimization loop.
Other responsibilities are also shared – for example, edge AI filters and pre-processes raw information to transfer only the most useful or important insights to cloud, which handles larger analytical workloads. Sensitive data is processed locally and stored on edge devices to safeguard privacy and comply with data protection mandates. On the other hand, orchestration, updates and backup happen on cloud to ensure business continuity and operational resilience.
When it comes to AI, algorithms are deployed both on cloud and at the edge to enable complex analysis and real-time intelligence respectively. Together, cloud, edge and AI form a balanced system that provides in-depth analyses and instant intelligence as required.
Let us examine these varied and substantial benefits of edge and cloud convergence more closely.
- Ultra-low latency: Certain applications require real-time computations because even the tiniest delay can compromise quality, safety or user experience. Think drones, which need to make instantaneous decisions to detect obstacles and plan flight paths, smart city traffic management systems that have to optimize traffic lights and public transport based on real-time conditions or surveillance cameras that need to monitor video feeds for threats and raise alarms without delay. The addition of edge computing to cloud can reduce latency from 500-1000 milliseconds to 100-200 milliseconds, to enable optimal performance.
- Scalability and Efficiency: When edge and cloud computing converge, both scalability and efficiency improve. As resource utilization goes up, U.S. enterprises can scale technology capabilities in a dynamic and cost-effective manner. Because preliminary data processing tasks are handled at the edge, there is less pressure on the network. This reduces dependence on costly, high-bandwidth connections. A good example is an automated manufacturing system, where sensors, PLCs, robots and other AI-based applications produce continuous streams of data. By processing this information at the edge – on the shop floor itself – rather than on a faraway cloud, manufacturers can drastically reduce bandwidth consumption and associated data transfer costs.
- Resilience, Reliability and Compliance: Edge computing also improves resilience, reliability and compliance. By processing sensitive information at source, edge devices enhance data security and privacy. In financial services, this translates to real-time threat detection and containment, while in industrial manufacturing, it enables real-time quality control, predictive maintenance and proactive anomaly detection. In healthcare, wearable devices and on-site medical equipment leverage edge AI to monitor patients and diagnose conditions without compromising patient privacy. Edge computing can facilitate compliance with data sovereignty regulations, by processing and storing data within specified jurisdictions.
Devices with edge processing capabilities can also maintain continuity of operations by functioning autonomously in case of poor connectivity. This is proving immensely valuable in remote installations, such as oil fields, where internet connectivity could be limited and intermittent. By deploying edge computing devices to monitor flow rates, inline pressure, temperature and other factors, oil and gas companies can detect and even prevent leaks to avoid potential accidents or environmental contamination.
- Continuous Improvement: Data from the edge is used to train, optimize and refine cloud-based AI models in a continuous improvement loop, to create self-learning and self-optimizing systems. For example, U.S. retailers can use edge cameras analyzing customer behavior to iteratively adjust inventory and prices in real-time, while leveraging cloud to optimize the supply chain.
Cloud and edge computing are enabling real-time intelligence to help organizations become more agile, efficient and customer-centric. By leveraging this powerful combination, companies across industries can enhance competitiveness and further accelerate business value.
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ABOUT THE AUTHOR
Anant Adya is Executive Vice President & Head of Americas Delivery for Cloud, Infrastructure and Security Services (CIS) for Americas and APAC at Infosys. He and his team are responsible for designing solutions to help customers in their digital and cloud journey. They use a combination of AI-led solution sets combined with capabilities from partner and startup ecosystem to design best solutions for customers. Cloud and Infrastructure Service line include infrastructure operations, security, data center and network transformation, cloud (public, private and hybrid), workload migration and service experience.
