AI Governance in Transportation
Why Governance Matters
AI is changing how transportation agencies operate — from predicting traffic patterns to automating maintenance alerts. But deploying AI responsibly requires more than just the technology itself. It requires governance: the people, policies, and processes that ensure AI works as intended, stays accountable, and earns public trust.
Without governance, agencies risk deploying AI systems that overlook certain communities, fail unexpectedly, put travelers in harm's way, or expose sensitive data. With effective AI governance, agencies can deploy responsibly, confidently, and with public trust.
NOTE
AI governance isn't about slowing innovation — it's about making sure innovation sticks.
What Is AI Governance?
AI governance is the organizational framework that guides how an agency adopts, oversees, and uses AI. Think of it as the "rules of the road" for AI: it defines who makes decisions, how projects get approved, and what guardrails are in place to protect the public and the agency.
At its core, AI governance answers four questions:
- Who is responsible for AI decisions?
- How are AI projects reviewed and approved?
- What policies and tools support responsible use?
- When should AI be used and when shouldn't it?
Who's Involved: Filling the Right Roles
Effective AI governance is a joint effort. No single part of a transportation agency can manage AI alone. Agencies that succeed typically bring together:
Agency Leadership
Sets the strategic vision and ensures Al aligns with the agency's mission and priorities.
Program Staff
Engineers, planners, and specialists who understand the real-world problem Al is meant to solve.
Legal & Procurement
Navigates contracts for Al tools and services, which often look very different from traditional infrastructure purchases.
IT & Data Roles
Ensures Al systems are secure, compatible with existing infrastructure, and powered by reliable data.
WHY IT MATTERS
When these groups work in silos, AI projects stall, fail, or create unintended risks. Cross-functional collaboration is the foundation for responsible AI.
How It Works: Governance in Practice
Agencies are putting governance into action through several proven structures:
1 AI Steering Committee
A cross-agency leadership group that reviews and approves AI projects. Committees may evaluate proposals using a readiness scorecard — a checklist that assesses data quality, safety considerations, ethical implications, and expected value before a project moves forward.
Note: Agencies with fewer staff can 1) designate a single "AI Point Person" to evaluate readiness and safety and 2) integrate AI discussions into existing meetings instead of establishing new committees.
2 AI Champions
Identifying "AI Champions" in each program area creates a network of informed advocates. These individuals surface opportunities for AI to solve real transportation challenges and serve as a bridge between technical roles and program staff.
3 AI Knowledge-Sharing Groups
Informal communities of practice where practitioners share what's working, troubleshoot challenges, and build AI literacy. These groups offer opportunities to connect with others who are piloting AI solutions, both within and outside their agencies, to accelerate learning.
Note: Agencies without their own AI Knowledge-Sharing Group can participate in external communities of practice or partner in discussion with their state DOT.
What Agencies Are Building: Key Focus Areas
Transportation agencies developing AI governance strategies may create a core set of documents and tools. Here's what they can look like in practice:
| FOCUS AREA | WHAT IT IS |
|---|---|
| Acceptable Use Policy | Sets clear rules for how employees can (and cannot) use AI tools — for example, prohibiting the entry of sensitive data into public AI chatbots. |
| AI Strategy & Roadmap | Outlines how AI will support the agency's strategic goals, such as reducing congestion or improving infrastructure maintenance. |
| Data & AI Dictionary | Establishes shared definitions so agency staff and vendors are speaking the same language. |
| AI Risk Management Framework | Provides a structured process for agencies to identify, assess, and prioritize potential harms from AI, with clear protocols to mitigate risks throughout the system's lifecycle. |
| AI Incident Playbook | Provides step-by-step guidance for responding when an AI system fails or behaves unexpectedly. |
GETTING STARTED TIP
You don't need to focus in all five areas on day one. Depending on agency type, agencies may start with an Acceptable Use Policy — it's the fastest way to establish guardrails while longer-term planning continues.
Small agencies can borrow Acceptable Use Policies from larger agencies, without implementing the same level of detail. When adapting from examples, agencies of all sizes need to review templates, including AI specific clauses like data ownership, system monitoring, and definitions.
Real-World Examples
Transportation agencies developing AI governance strategies may create a core set of documents and tools. Here's what they can look like in practice:
Texas DOT (TxDOT)
TxDOT has developed a comprehensive AI Strategic Plan (released in Dec. 2024, updated in Jan. 2026) that includes TxDOTˇs Governance Framework and AI Acceptable Use Policy grounded in seven core principles security, transparency, accuracy, accountability, trustworthy, privacy, and safety.
California DOT (Caltrans)
Based on Research Notes , Caltrans is evolving its AI governance structure by establishing new leadership roles and processes to support responsible AI adoption. Caltrans is also developing new supporting materials, such as an AI Readiness Assessment, Use Case Prioritization Rubric, and Responsible Use
Learn More
This page is part of an ongoing series on AI in Intelligent Transportation Systems. Additional resources, case studies, and tools will be added as the program develops.
References
- Donaldson, G., & Rosica, M. (2022, June 13). ATCMTD AI for ITS Interview with the Delaware DOT [Virtual].
- Karanikolas, C., Gaisser, T., & Wadsworth, J. (2025, February 25). FY22 SMART Stage 1 AI for ITS Interviews with the RTC of Southern Nevada [Virtual].
From the Field
Practitioners share experiences from real AI deployments.
The Delaware DOT emphasized developing relationships with stakeholders — internal meetings every week with key user groups to discuss how things are working and what needs to change.
DOT of Delaware DOT [1]
The RTC of Southern Nevada improved their legal templates to include data ownership, safeguards, testing requirements, vendor support requirements, and a data dictionary so agency and vendor staff use the same language.
RTC of Southern Nevada [2]