Advances in computing techniques, processing capacity, and data collection are enabling artificial intelligence applications in myriad real-world settings. Algorithms at the heart of artificial intelligence can provide decision support, ease labor-intensive operations, perform predictive analysis, and inform targeted outreach. In the transportation sector such applications could reduce the administrative burden at public agencies such as TxDOT, the Department of Motor Vehicles, and other state agencies with oversight of infrastructure, vehicles, and transportation services. The combination of improved hardware engineering and manufacturing and machine learning methods for image processing has enabled the collection of higher resolution traffic data with less infrastructure, thus enabling more detailed transportation planning models and improved traffic incident management. Artificial intelligence is also being used in a new wave of traffic control devices, and preliminary deployments have been promising. However, with the advent of advanced models and the significantly higher quantity of data they typically consume and produce, key challenges for stewards of data and technology will include managing complex data sources, ensuring their ethical application in decision-making, protecting the privacy of the public, and reducing cybersecurity risks.