The Texas Department of Transportation (TxDOT) has published a January update for its Artificial Intelligence Strategic Plan (AISP), adapting to rapid changes in the artificial intelligence (AI) sector over the past year and delivering a strategic approach to implementing these innovative technologies efficiently and effectively.
In the department’s efforts to elevate operation and management practices of the state’s transportation systems, TxDOT builds upon four core pillars to optimize the transition to an integrated AI infrastructure network. Notably, the department will take a human-first approach to AI deployment, only relying on AI initiatives to support operations and streamline processes. These pillars are:
- Developing a governance framework for professional oversight and guaranteeing data privacy and security.
- Creating a data foundation provided by a robust and scalable data platform.
- Conducting an AI project intake and readiness evaluation and scorecard to prioritize and manage a portfolio of over 200 AI use cases.
- Maintaining foundational capabilities, including human and technical capacity to expand AI technical capabilities.
The state has already made tremendous progress in taking experimental AI pilots and transitioning them to fully integrated components of governance. TxDOT’s AI roadmap leans heavily on the Enterprise Data Platform (EDP), consolidating dozens of unique solutions to maintain data maturity, support scalable infrastructure and prioritize the democratization of AI. AI integration has already saved TxDOT employees thousands of hours annually, accelerated communication times and increased productivity in administrative procedures.
As the department moves forward, it will evolve its current usage of AI to develop an AI-native enterprise anchored by digital infrastructure and operational intelligence. TxDOT will follow three strategic priorities to drive this evolution:
- Augment the entire project pipeline to accelerate schedules and administrative workflows while maintaining rigorous technical accuracy and optimize efficiency.
- Transform fleet, facility and infrastructure operations through data-informed models that anticipate maintenance needs, optimize asset lifespan and maximize taxpayer value.
- Leverage real-time sensor fusion and computer vision to proactively identify roadway anomalies and safety risks before they pose a danger to the public.
TxDOT’s AI adoption success will require strategic technical deployments, broad workforce engagement and operational readiness to see full integration fulfilled over the course of the next year and beyond. Developing and refining a governance framework to serve as the department’s primary approach to ensuring the responsible, ethical and effective deployment of AI technologies comes first.
Keeping in line with the Texas Responsible Artificial Intelligence Governance Act (TRAIGA) passed in 2025, TxDOT has a directive to safeguard public safety, individual rights and privacy through all AI development and usage. The Information Technology Division (ITD) administered the process creating a framework that ensures all AI initiatives aligned with TxDOT’s goals while preserving technical integrity through a clear acceptable use policy with risk management.
TxDOT established an AI Risk Management Workgroup to thoroughly ingratiate a risk management mindset into every AI project. Represented by multiple divisions under the TxDOT umbrella, the group developed and implemented a management framework based on National Institute of Standards and Technology (NIST) guidelines to effectively manage all risks that may occur throughout a project’s life cycle.
The framework involves:
- Identifying and documenting potential risks associated with AI systems or projects.
- Integrating risk assessments into ITD’s intake and governance process.
- Prioritizing and mitigating risks, developing action plans and escalating high-risk projects to review boards.
The department’s AI data foundation plays a crucial role in ensuring AI is successfully implemented, leveraging well-managed data to inform and optimize AI models. TxDOT’s EDP functions as the cornerstone of these efforts, ensuring the state has access to high-quality data, uses standardized processes for data organization and delivery and offers the highest-level protection possible for all data.
A major update included in the AISP targeting future EDP success revolves around a roadmap built on three key principles. These principles will help position the state as a leader in data-driven decision making and AI innovation.
- Accelerating data assembly
- Harmonizing data for interoperability.
- Automating data integrity.
Alignment with these principles will help consolidate operations through real-time integration, support seamless operability through institutionalized standardized data formats and metadata and improving data quality through AI-driven cleansing and validation processes.
Currently, TxDOT’s AI project portfolio contains more than 200 use cases, with more to be added as the state expands its investments and identifies new opportunities. The portfolio outlines several key successes accomplished through completed projects and has dozens of initiatives in-progress. The AISP includes a catalogue of planned AI projects in line for implementation in the near future.
These initiatives cover three main sectors: engineering, asset management and business productivity. A selection of upcoming projects featured in the portfolio include:
- Using machine learning to streamline and improve decision making while managing construction projects ready for public bidding.
- Developing advanced machine learning models to forecasts key properties of construction materials.
- Streamlining and modernizing employee travel and expense reporting at TxDOT through automation.
- Streamlining and improving the review process for Texas’ Statewide Transportation Improvement Program (STIP) projects.
The future of AI innovation across all TxDOT division relies on establishing foundational capabilities and fostering and environment conducive to progress and development. This includes building an AI talent pipeline to sustain an ongoing skilled workforce by investing in AI talent resources, AI literacy, workforce development and continued development of the AI and Automation Community of Practice (CoP). Core technologies essential for securing this development include:
- Scalable machine learning platforms.
- Low-code/no-code enablement by implementing the Copilot Studio platform.
- Resilient cloud infrastructure through “Cloud Runways” that effectively deploy AI applications.
- Emerging applications including automated systems maintenance, AI-augmented development and distributed intelligence.
Photo by Stephen Leonardi from Pexels
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