Arun Prakash Asokan, an award-winning AI thought leader and Intrapreneur, has been awarded and honoured as Top Gen AI Leader 2024 by Analytics Vidhya, Scholar of Excellence from ISB Hyderabad, and Grand Global Winner of the Tableau International Contest. He holds a Master’s in Computer Science Engineering from BITS Pilani, a B.Tech. in Computer Science & Engineering, and has completed the Advanced Management Program from Indian School of Business, Hyderabad. He has also been honored at global enterprise forums for his groundbreaking AI innovations.
With close to 17 years of experience across all fields of AI, including 7 years at Novartis, Arun currently leads global AI programs driving enterprise-wide adoption of cutting-edge AI and GenAI solutions. Known as a strategic AI translator, he bridges business vision with technical execution, delivering transformative, scalable solutions that have won global recognition.
He has pioneered reusable GenAI accelerators that have reduced development time by up to 80%, developed enterprise-grade AI security frameworks, and created award-winning AI platforms for anomaly detection and risk mitigation — delivering measurable business impact across finance, legal, compliance, and HR.
As a passionate AI thought leader, Arun has spent the last decade democratizing AI through community contributions, talks, keynotes, hack sessions, and sustained efforts to bridge the academia–industry gap. He has mentored thousands of AI enthusiasts worldwide, delivering several hundred talks, including 75+ on GenAI in just the past two years at ISB, IIMB, IIITH, Analytics Vidhya, and other global forums.
Retrieval-Augmented Generation has rapidly evolved, from basic RAG pipelines to sophisticated agentic workflows. Yet, as GenAI systems are applied to real-world problems, a critical limitation becomes clear: flat retrieval and isolated agents struggle to reason over interconnected, contextual, and evolving data.
This full-day workshop takes participants beyond Agentic RAG into the next frontier of GenAI: Graph-Powered, Knowledge-Driven systems.
Designed for intermediate GenAI practitioners, the workshop explores how graphs fundamentally transform retrieval, reasoning, and orchestration. Participants will learn how to move from document-centric pipelines to relationship-aware AI systems, where entities, connections, hierarchies, and context become first-class citizens.
The workshop covers how graphs enhance agentic reasoning, enable multi-hop and contextual retrieval, and unlock deeper intelligence across complex data landscapes. Through architecture patterns, real-world problem scenarios, and hands-on design approaches, participants will learn how to build knowledge graphs, design graph-based retrievers, and implement Agentic Graph RAG architectures that scale beyond traditional approaches.
By the end of the workshop, participants will be able to design GenAI systems that do not just retrieve information, but reason over knowledge, connect data meaningfully, and solve complex, real-world problems with greater accuracy, explainability, and impact.
*Note: These are tentative details and are subject to change.
Read MoreManaging and scaling ML workloads have never been a bigger challenge in the past. Data scientists are looking for collaboration, building, training, and re-iterating thousands of AI experiments. On the flip side ML engineers are looking for distributed training, artifact management, and automated deployment for high performance
Read MoreManaging and scaling ML workloads have never been a bigger challenge in the past. Data scientists are looking for collaboration, building, training, and re-iterating thousands of AI experiments. On the flip side ML engineers are looking for distributed training, artifact management, and automated deployment for high performance
Read More