2026 Virtual NAEP Workshop

 Welcome, introductions, and Workshop Framing
 Moderator: Lauren Schramm, Senior Transportation Planner, Environmental Science Associates

 Why artificial intelligence, technology, and permitting timelines matter now. 

This opening session will welcome participants, review the workshop goals and agenda, and establish a shared foundation for the day. Lauren Schramm will frame the current state of technology and AI in environmental review, including why these tools matter as agencies seek to manage complex information, improve coordination, and reduce permitting timelines. The session will also introduce key themes that will carry through the workshop: practical agency applications, data-backed case studies, implementation challenges, and lessons from federal, state, laboratory, and policy perspectives.

 Federal Strategy and Modernization
 Invited: Jordan Eccles, Deputy Director for Innovation, Council on Environmental Quality

 Technology Action Plan, Technology Showcase, CEWorks Update.

Moderated Q&A

 Break

 Using Technology and Data to Reduce Permitting Timelines - 
 Evidence Case Study 1
 Speaker: Matthew Aumeier, Environmental Policy & Systems Innovation Specialist, Idaho National Laboratory

 Evidence-backed example of using technology, process data, and implementation tracking to reduce environmental review and permitting timelines at Idaho National Lab.

Environmental compliance programs are under increasing pressure to deliver faster, more consistent reviews while managing growing workloads and limited resources. This session demonstrates how Idaho National Laboratory has approached that challenge through the modernization of environmental compliance workflows, enterprise system design, and the practical application of artificial intelligence. Attendees will see several custom-developed tools supporting NEPA and environmental review processes, including examples of how structured workflows, intelligent automation, and AI-assisted drafting have improved consistency, reduced administrative burden, and enhanced the user experience. The session will also discuss the measurable efficiencies realized through modernization, lessons learned during implementation, and realistic expectations for organizations considering similar initiatives. Whether beginning a digital transformation or refining existing processes, participants will leave with practical insights into designing scalable, user-focused systems that improve both regulatory outcomes and organizational efficiency.

 Using Technology and Data to Reduce Permitting Timelines -
 Evidence Case Study 2
 Speaker: Reeve Bull, Policy Director, Fulcrum Foundation

 Evidence-backed example of using technology, process data, and implementation tracking to reduce environmental review and permitting timelines via the Virginia Permit Transparency tracking system.

The Virginia Permit Transparency (VPT) platform is a first-of-its-kind statewide permitting dashboard, which allows you to track permit applications much as you would an online order.  It uses a series of Gantt charts to show the overall steps in each permit application, how long they’re supposed to take, and how long they’re actually taking.  It lets both the public and the agency staff pinpoint delays with precision and find ways to speed up the process.  A backend dashboard provides even more granular information for agency staff.  VPT has proven to be a game-changer for Virginia agencies.  The Department of Environmental Quality has reduced processing times by 65%, and several agencies achieved reductions of 20% or more.  This presentation will highlight VPT’s successes and offer ideas for future reform, including dashboards integrated across all levels of government and interactive permitting chatbot tools.

 Joint Discussion - Data-Backed Case Studies
 Moderator: Pam Danko, Owner, Danko Engineering
 Speakers: 
 Matthew Aumeier, Environmental Policy & Systems Innovation Specialist, Idaho National Laboratory
 Reeve Bull, Policy Director, Fulcrum Foundation

 What the data shows, what worked, and what could transfer to other agencies.

 PermitAI and Implementation Insights
 Speaker: Anastasia Bernat, Data Scientist, Pacific Northwest National Laboratory

PermitAI: Agentic Discovery and Review for Federal Permitting   Permitting undergoes constant updates and changes – this evolving and historic context needs to be captured, retrievable, and actionable for federal (and AI) agents to complete high-quality reviews for faster and cheaper. After transforming decades of fragmented permitting, legal, and regulatory records into structured, agent-ready databases, PermitAI has equipped 25+ federal agencies with a suite of AI agents to streamline permitting fact-finding and compliance processes. The result is a multi-agentic discovery and review layer: 1) a research agent that can report on trends across 200k+ permitting documents and 2) screening agents that assess projects and recommends CE/EC determinations. Together, this layer is not only pluggable to other technologies, but it also increases interagency coordination, enables data federation, and ensures overall auditability and decision traceability for the future of permitting documentation.

11:50 - 12:15 PM

 Lunch Break

12:15 - 1:15 PM

 Federal Agency Implementation - Department of Energy
 Invited: Pranava Raparla, Senior Advisor, U.S. Department of Energy

 Digital transformation, National Environmental Policy Act modernization, artificial intelligence applications.

1:15 - 1:40 PM

 Federal Agency Implementation - Air Force
 Speaker: Danny Rieke, Senior Environmental Scientist, U.S. Air Force

 Creating RAG pipelines and NEPA and AI research efforts.

AI RAG, or Retrieval Augmented Generation, is a method that strengthens large language models by pairing them with an external knowledge source at the moment a question is asked. Instead of relying only on what the model learned during training, the system retrieves relevant documents from a curated knowledge base—often using vector search—and feeds those documents into the model’s context. The model then generates an answer grounded in real, authoritative information. The process begins with ingesting and preparing documents, converting them into embeddings, and storing them in a vector database. When a user submits a query, the system embeds that query, retrieves the most relevant pieces of information, and augments the model’s prompt with those retrieved documents. The model uses this grounded context to produce a response that is more accurate, more specific, and more trustworthy than what it could generate on its own. The advantages of AI RAG are substantial. It dramatically reduces hallucinations because the model is anchored to verified content rather than guessing from memory. It keeps answers current by pulling from live or regularly updated data sources, avoiding the limitations of a model’s training cutoff. It allows organizations to inject domain-specific knowledge—policies, manuals, product documentation—without the cost or complexity of retraining the model. This makes RAG both flexible and cost-effective. It also improves transparency and user trust, since responses can reference the exact documents they came from. And because the knowledge base can grow or change over time, RAG systems scale naturally as an organization’s information evolves.

 Moderated Q&A

 Break

 State Implementation 
 Speaker: Nicole Moon, Strategic Communications Artificial Intelligence Lead, HDR

 Applied artificial intelligence for public comment analysis, Utah Department of Transportation’s Environmental Public Involvement AI Project

Artificial intelligence is changing how transportation agencies plan, deliver and evaluate public involvement. This presentation highlights two agency-focused applications currently being developed and deployed to support public engagement and environmental planning. First, attendees will learn about the Commentator, a machine learning-based comment analysis tool developed to help categorize public comments by topic, sentiment, and type. The tool has reduced comment-coding time by approximately 60 percent while maintaining required human review. The session will also highlight work underway on the Utah Department of Transportation’s Environmental Public Involvement AI Project, which is developing an AI-assisted planning resource to improve consistency, leverage institutional knowledge and support public involvement decision-making. Through these case studies, participants will see how agencies can responsibly integrate the power of AI into public involvement programs while maintaining transparency, governance and human oversight.

 State Implementation 
 Speaker: Carla Benton-Hooks, Environmental Program Manager (Districts 3 and 6), Georgia Department of Transportation

 Using applied AI to win the USDOT modernizing NEPA challenge.

This presentation explores how AI can improve public involvement in the NEPA process through a case study of the SR 22/SR 24 Roundabout project in Milledgeville, Georgia. The project team developed a custom ChatGPT tool that allowed users to ask questions about project documents and receive immediate, plain-language responses in English or Spanish. The chatbot aimed to make technical information easier to navigate, expand accessibility, support more meaningful engagement, and reduce response delays. The case study also identified challenges, including inaccurate AI-generated responses, technology barriers, and limitations in document integration and chat-log retrieval. The case study concluded that AI can strengthen transparency, accessibility, efficiency, and public trust when agencies pair it with human oversight.

 Governance and Policy Considerations
 Speaker: Boon Sheridan, Permitting Technology Lead, Environmental Policy Innovation Center

 Policy innovation, governance, and responsible implementation of artificial intelligence

 Closing
 Moderator: Lauren Schramm, Senior Transportation Planner, Environmental Science Associates