As businesses accelerate their AI adoption, CIOs are faced with a critical challenge—how to deploy AI inferencing at scale without overhauling their existing IT infrastructure. With AI inference becoming a game-changer in real-time decision-making, organizations are exploring strategies to optimize both edge and PC-based AI deployment to unlock business value.
In a knowledge exchange session, curated by CORE Media in association with Lenovo, CIOs and digital leaders discussed how AI inferencing is transforming IT operations, enabling real-time insights, and driving innovation—without the need for heavy infrastructure investments.
Anoop Mathur, Founder, CORE Media, Sanjay Sharma, Sr. Presales Solution Architect, Lenovo ISG, and Ruchi Sethi, Global Manager - Public Sector, Health & Education - India, Intel Corporation, shared their perspectives on AI inferencing revolutionizing business operations.
Some of the key points highlighted by CIOs and digital leaders who attended the discussion were:
- AI Inferencing Without Heavy Infrastructure
CIOs discussed how AI inferencing can be implemented effectively without requiring massive IT overhauls. By optimizing existing infrastructure and leveraging efficient AI models, organizations can achieve significant performance gains without the need for high-cost hardware upgrades. Lenovo’s AI-optimized solutions were highlighted as a way to maximize inferencing capabilities with minimal infrastructure expansion. - Edge AI for Real-Time Decision-Making
Processing AI workloads at the edge allows businesses to analyze data closer to the source, reducing latency and enhancing security. This is especially critical for industries like healthcare, manufacturing, and retail, where real-time insights can drive better customer experiences and operational efficiencies. The discussion emphasized how AI at the edge enables organizations to act on data faster while ensuring compliance with data sovereignty regulations. - Balancing Cloud and Edge AI for Scalability
With AI models requiring varying levels of computing power, CIOs explored strategies for balancing cloud-based AI with edge inferencing. A hybrid approach—leveraging both edge and cloud—ensures enterprises can scale AI capabilities while optimizing costs. The conversation also touched on how organizations can dynamically allocate workloads between cloud and edge environments based on business needs and real-time data processing requirements.
