Explore the practical application of generative AI, particularly within enterprise settings using platforms from some of the leaders in the space. It critically examines the prevailing trend of deploying excessively large language models (LLMs), arguing that a "one-size-fits-all" approach may not optimize value creation. Instead, it advocates for a shift towards customized generative AI strategies, emphasizing the importance of tailoring LLM scalability and customization to specific organizational needs. By questioning the default "XL" approach, this work highlights the potential pitfalls of over-engineered AI and underscores the necessity of nuanced, personalized solutions for achieving optimal performance and alignment with unique enterprise objectives.
Generative AI in Action: Enterprise Value Creation
Executive Event
Visionaries
Badrish Prakash
Global Head of Alliances
Tiger Analytics
About Me
Seena Ganesh
VP, Engineering
Staples
Jason Weinstein
Director Business Development
Tiger Analytics
About Me
Gagan Singh
Regional Sales Head
Tiger Analytics
About Me
Shadaab Kanwal
MD of Digital, Data, & Analytics
Charles Schwab
About Me
Marc Mackey
Global IT Director
Nike
About Me
Ting Zou
Director, Global Tech Operation
TikTok
Shubham Kulshrestha
Generative AI Partner Go-To-Market
AWS
About Me
Jimmy Shah
Title: Principal GTM Specialist, SageMaker AI
AWS
About Me
EVENT DETAILS
April 24, 2025
Agenda
10:00 AM-10:45 AM
Welcome & Registration
10:45 AM-11:00 AM
Opening Remarks
11:00 AM-11:30 AM
Keynote
Generative AI in Action: Enterprise Value Creation
Panelists
11:30 AM-11:45 AM
Break
11:45 AM-12:30 PM
Panel
Beyond the Hype: Navigating the Challenges and Opportunities of Generative AI Implementation
Generative AI offers huge potential, but successful implementation requires navigating key challenges. Data bias, computational costs, ethical concerns, and workflow integration are significant hurdles. However, automation, personalization, accelerated R&D, and enhanced creativity present transformative opportunities. Organizations must prioritize data quality, responsible AI, and strategic use cases to move beyond the hype and realize generative AI's true value.