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As Artificial Intelligence continues to scale new frontiers, enterprises are grappling with a central question: where should AI reside? Should it be in the centralized comfort of the cloud or at the agile, responsive edge? On the latest episode of the Analytics Insight Podcast, host Priya Dialani speaks with Shrihari Bhat, CEO of Orient Technologies Ltd., to explore the evolving dynamics of AI deployment. The conversation revealed how businesses can leverage both paradigms to achieve speed, scale, and strategic agility in today’s digital-first world.
Orient Technologies Ltd., under the leadership of Shrihari Bhat, stands as a three-decade-strong IT transformation firm. Specializing in IT infrastructure, cybersecurity, cloud, and application services, and digital transformation, the company champions a unified technology experience. “We simplify complex IT ecosystems through a single SLA-driven engagement,” Mr. Bhat noted. This approach ensures seamless delivery, business continuity, and operational accountability for clients across industries.
With a global background that includes leadership roles at FIS, NCR, and Fiserv, he brings a transformative vision focused on long-term client relationships, innovation-driven growth, and sustainable enterprise practices. His leadership is grounded in a foundation of integrity, empathy, and accountability—principles he considers non-negotiable.
During the episode, Shrihari Bhat broke down the fundamental contrast between Edge AI and Cloud AI. Cloud AI operates in centralized environments, offering massive computational power, ideal for training large language models and managing data lakes. “Think of cloud as the brain—centralized and powerful,” he explained.
Conversely, Edge AI brings intelligence closer to the data source—on devices like IoT sensors, autonomous systems, and smartphones. It delivers low-latency decision-making, enhanced privacy, and resilience, especially in scenarios requiring real-time insights. “If the cloud is the brain, the edge is the reflex,” Mr. Bhat emphasized, highlighting how localized intelligence is critical in scenarios like healthcare wearables or autonomous vehicles.
Edge AI is already making a measurable impact across multiple sectors. In manufacturing, it’s enabling predictive maintenance, quality inspection, and real-time automation on shop floors. In healthcare, it supports diagnostics and monitoring in remote areas, even when internet access is limited.
Autonomous systems—such as self-driving vehicles and delivery drones—rely on edge processing to make millisecond decisions, where latency could mean the difference between safety and failure. In retail, edge technology powers smart shelves and real-time analytics while maintaining local data privacy, a growing concern among consumers.
One of the emerging challenges of Cloud AI is the mounting cost of data transfer. Bandwidth, egress fees, and long-term storage create substantial financial burdens. To counter this, organizations are shifting to edge-based data handling.
“Edge computing enables local data filtering, sending only relevant information to the cloud,” Mr. Bhat explained. This significantly reduces storage and bandwidth costs. However, the shift brings its own trade-offs—edge devices are often limited in power, requiring model optimization techniques like pruning and quantization. While there might be slight drops in model accuracy, the operational efficiency gained usually outweighs the compromises.
As generative AI tools like ChatGPT and Midjourney dominate cloud infrastructure, the industry is now exploring ways to bring them closer to the user. Shrihari Bhat described an emerging trend of “smart offloading,” where edge devices pre-process or contextualize inputs before sending them to the cloud. Smaller, compressed generative models are also being designed to run directly on edge hardware.
“Generative AI at the edge will not replace the cloud—but it will enhance experiences in real-time, privacy-sensitive, and offline environments,” Mr. Bhat stated. Applications like on-device summarization, AR/VR context interfaces, and personalized assistants are already pushing boundaries.
The conversation highlighted that the future lies in Hybrid AI—a model that combines the strengths of both cloud and edge computing. Cloud provides the compute-intensive infrastructure for model training, while the edge handles real-time inference.
For highly regulated sectors, this approach ensures compliance by keeping sensitive data local while still benefiting from centralized processing power. “The ideal architecture is one that’s context-aware and adaptable,” Mr. Bhat said, underscoring the need for a balanced strategy.
Shrihari Bhat shared key enablers that are accelerating edge adoption. Hardware innovations, such as NVIDIA Jetson, Google Coral, and Apple Neural Engines, now offer compact yet powerful inference capabilities. Connectivity enhancements via 5G and Wi-Fi 6 are bridging the latency gap between the edge and the cloud.
Software frameworks like TensorFlow Lite, ONNX, and Edge Impulse are democratizing AI deployment. Tools for federated learning and low-code platforms are further expanding access, while enhanced edge security ensures compliance and trust.
Hybrid architectures deliver tangible business outcomes. Local processing enhances application speed and privacy, while selective cloud engagement reduces costs. This adaptability enables businesses to achieve faster response times, higher customer satisfaction, and strategic resilience.
“In just 2–3 years, we’ll see pilot Edge AI solutions mature into full-scale deployments,” Mr. Bhat predicted. From remote healthcare procedures to globally synchronized manufacturing, the possibilities are transformative.
The podcast concluded with a compelling insight from Shrihari Bhat: “The future isn’t about cloud vs edge—it’s about orchestration.” Companies that master this orchestration will lead in performance, security, and innovation.
As AI becomes more ubiquitous, businesses must embrace hybrid strategies that align with both technical demands and user expectations. With leaders like Mr. Bhat at the helm and platforms like Orient Technologies driving scalable solutions, the enterprise AI landscape is poised for unprecedented evolution.
