Bryan Mistele, Co-founder and CEO of INRIX.gettyFor years, autonomous vehicles (AVs) have dominated AI conversations. But transportation’s most significant use of AI today isn’t self-driving cars. It’s the streets, intersections and highways on which we drive.Tech leaders are constantly looking for real-world use cases for successfully operationalizing AI. They don’t need to look any further than the transportation sector’s AI-powered intelligent infrastructure.While many industries are still determining how to deploy AI effectively, in transportation, AI is delivering real-world results, making drivers and pedestrians safer and moving people, vehicles and goods more efficiently through traffic networks.Intelligent infrastructure offers three practical lessons for implementing AI systems in high-stakes environments.Intelligent Infrastructure As A Road MapToday’s cities and transportation agencies are using AI to identify congestion patterns and predict bottlenecks before they form and respond to potential problems with real-time insights. Intelligent infrastructure also finds intersections and corridors with a higher risk of crashes, giving traffic engineers the insights needed to address risks before crashes occur. Using predictive analysis, cities and agencies have adopted a proactive approach to traffic management using forecasting and real-time analysis. In each case, the value comes not from the technology alone but from connecting data to a decision an agency already needs to make.The transportation sector’s adoption of AI can serve as a map for almost any other industry. This experience highlights three priorities: the importance of reliable data, the need to design for integration and the importance of human judgment in AI decision making.AI Success Needs The Right DataTransportation’s intelligent infrastructure runs on probe vehicle data generated by GPS-enabled devices such as cell phones and connected vehicles. Collecting this data doesn’t require roadside infrastructure. It’s continuous data that’s processed through a secure pipeline.AI systems are only as good as the data powering them. With all the hype surrounding potential uses of AI tools, this is a truism that too often gets forgotten. Without reliable, representative data, AI can’t generate real-time insights or recommend real-world solutions.Successful AI adoption is predicated on high-quality data and data management. As Rohit Sehgal, the CEO of a data-focused tech company, has said, “At the core of every AI system lies a fundamental truth: The quality and quantity of data it ingests are paramount to its effectiveness.” Losing sight of this fundamental idea can undermine even a well-designed AI initiative.Design AI For IntegrationWhile searching for ways to use AI to understand and act on data faster, it can be easy to overlook the importance of integrating these AI-powered tools with existing systems. Deploying AI tools from scratch requires significant investments of time and resources, as well as technical expertise.Designing AI tools with integration in mind, then, is key to smooth and successful adoption. The transportation sector has already discovered this. Some systems are able to integrate the probe vehicle data into existing transportation management systems to deliver solutions based on years of industry best practices.Using AI As A Tool For Human JudgmentAI doesn’t deliver useful answers simply because it’s applied to a problem. AI can be transformative, but only when it’s used intentionally.AI’s use in intelligent infrastructure highlights this key principle. Transportation is a high-stakes sector. Decisions about traffic signals, speed limits and congestion solutions often come with life-and-death implications.This is a principle we all must take forward as we adopt AI systems. AI doesn’t replace humans. AI helps our analysts, planners and engineers make better use of their expertise, and make better decisions faster. But we must keep in mind its limitations. This means applying AI to the right questions with the right data.This last principle is perhaps the most important. AI supports decisions. It’s never a replacement for professional judgment.The Road AheadWhile many of us have focused on the flash of AVs, the transportation sector has adopted AI tools to create safer, more efficient streets, intersections and interstates. Transportation gives us a valuable use case for operationalizing AI.​ Its focus on reliable and representative data, AI integration and the importance of human judgment lights a path forward for other industries’ adoption of AI.Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?