About this session

Are you ready to learn how global leaders blend cutting-edge technology with real-world business needs?

In today’s fast-paced world, where logistics is the backbone of global commerce, innovation is the key to staying ahead. The big players are leading the charge by integrating artificial intelligence and generative AI into its operations, but with a carefully considered approach.

In this session, Sunzay Passari will share his journey into AI and machine learning, revealing how he identifies areas for the most disruptive and valuable applications of AI. You will gain insights into how qualitative and quantitative research informs machine learning projects, and how he balances innovation with privacy and compliance concerns — especially when deploying technologies like chatbots.

Additionally, Sunzay will offer a behind-the-scenes look at why he takes a more conservative stance on generative AI, highlighting specific use cases that he finds worthwhile. Whether you are interested in operational efficiency, customer experience, or the future of logistics, this session will provide concrete examples and actionable insights.

Discussion Points:

• AI in Your Role: How has artificial intelligence and machine learning evolved within your role over the past few years?
• Identifying Disruptive Opportunities: How did you and your team determine where AI could be most disruptive and beneficial in your massive operations?
• Conservative Approach to Generative AI: Why do you have a cautious approach to generative AI, and what are the specific key use cases?
• Research-Driven Development: What kind of qualitative and quantitative research drives AI development, and how does this inform strategy?
• Customer Benefits: In what ways are these AI advancements designed to directly benefit your customers, improving efficiency and experience?
• Balancing Innovation with Compliance: How do you ensure privacy and data compliance when implementing new tech like chatbots?
• Data Engineering and Data Science: How do you merge these teams and how do you strategize together as part of the Gen AI innovation goal?