OTM AI Evolution: Predictive ETAS to AI Agents
Artificial Intelligence within OTM/GTM has progressed significantly in recent years, but many organizations are still determining how to interpret and apply these capabilities in a meaningful way. This session provides an educational overview of that evolution and its practical implications for OTM/GTM users.
We begin with a grounded look at OTM’s earlier use of machine learning, including capabilities such as predictive ETAs. From there, we explore the shift toward the introduction of agent-based frameworks and Oracle AI Agent Studio. The focus is not on features alone, but on how these changes reflect a broader shift in how transportation decisions can be supported and automated.
A key component of this session is helping attendees translate AI concepts into terms that resonate within a logistics organization. Many teams face challenges in articulating the relevance of AI to leadership or aligning it with operational priorities. This session provides practical guidance and examples to help bridge that gap.
Attendees will leave with a clearer understanding of the progression of AI within OTM, along with tools to evaluate its applicability and communicate its potential value internally. The goal is to support informed decision-making and help organizations approach AI adoption in a thoughtful, business-aligned manner.
Learning Objectives
1. Understand how AI capabilities in OTM/GTM have evolved and what distinguishes newer agentic features from earlier machine learning.
2. Identify practical use cases where OTM/GTM AI can drive measurable business value in transportation and trade compliance operations.
3. Develop a clear, business-focused approach to explaining and advocating for AI adoption within a logistics organization.
Educational Tracks:
Cloud SCM
Presenter:
Tom Meisinger, Senior Director, Portfolio & Innovation, Redwood Logistics, Oracle ACE Associate
Chirag Patel, CIO, Penske