Generative AI in transportation infrastructure is no longer a speculative topic. State agencies are running pilots, federal research programs are actively developing guidance, and professionals across the industry are finding practical applications in their day-to-day work.

This webinar takes stock of where the technology stands, grounded in recent agency experience and published research, and traces its progression from today’s conversational and document-based applications toward the emerging agentic capabilities that will define the next phase of infrastructure decision-making. This session maps generative AI capabilities directly onto the Transportation Asset Management pipeline, identifying where in the workflow these tools are ready to deploy today, where they require human validation, and where professional judgment and accountability must remain in the loop. It covers the four-stage progression from conversational AI through retrieval-augmented generation, tool-augmented workflows, and autonomous agentic systems, with transportation-specific examples at each stage.

Attendees will leave with a practical framework for evaluating generative AI applications in their own agency context, an understanding of where the federal research community is heading and what it means to operate ahead of formal guidance, and a clear picture of the entry points available today regardless of agency size, technical capacity, or existing AI policy. The session is designed for transportation professionals at all levels of AI familiarity.

What Participants Will Learn:

1. How generative AI extends the capabilities transportation agencies already use and where it fits within established asset management and performance management frameworks
2. The four-stage progression from conversational AI to agentic systems, with transportation-specific examples and honest performance benchmarks at each stage
3. Which steps in the Transportation Asset Management pipeline are ready for AI-assisted workflows today, which require human validation, and which require human judgment and accountability without exception
4. Practical entry points for generative AI in transportation work, including document summarization, network-level data analysis, condition reporting, and grant development
5. What the current federal and cooperative research landscape says about trustworthy AI deployment in infrastructure contexts, and what it means for agencies operating ahead of the formal guidance.