AI’s Transformative Potential in Mobility Hinges on Unprecedented Global Collaboration, Landmark Study Finds
Artificial intelligence (AI) is quietly revolutionizing transportation systems around the world, yet most deployments remain in pilot phases and have not reached large-scale implementation. The disparity between AI’s theoretical promise and its practical execution is growing, according to a landmark study unveiled today at CoMotion GLOBAL 2025 in Riyadh.
“Cities are waking up to the reality that AI is not just a smarter algorithm—it fundamentally changes the dynamics of how humans and machines share control,” said Dr. Christian Gasparic, Partner at Kearney and Global Head of the Kearney Advanced Mobility Institute. “Getting this balance right is now a strategic and safety-critical decision that will determine which cities successfully implement next-generation mobility systems.”
The report, titled “Envisioning the Future of Mobility Powered by AI,” was developed through a collaboration between the MIT Mobility Initiative and the Kearney Advanced Mobility Institute. Drawing on insights from 55 leading global organizations—including Google, Lyft, Uber Freight, Deutsche Bahn, and NEOM—the study provides the most comprehensive analysis to date of AI applications across mobility networks spanning Europe, the Americas, Asia-Pacific, and the Middle East. It highlights both the transformative potential of AI and the significant challenges that impede its widespread adoption.
The Jagged Frontier of AI in Transportation
A key insight from the study is the concept of AI’s “jagged frontier.” Unlike traditional automation technologies, AI does not consistently replicate human intelligence. Instead, it excels in certain tasks while performing unpredictably in others. This irregular performance presents both immense opportunities and significant risks, particularly in safety-critical transportation systems.
“AI can be extraordinarily effective in predictive traffic management, route optimization, and autonomous fleet coordination, but its inconsistencies can pose challenges if humans are not appropriately integrated into decision-making processes,” explained John Moavenzadeh, Executive Director of the MIT Mobility Initiative. “The bigger the potential for AI to deliver safe, clean, and inclusive mobility, the harder the execution becomes. Delivering on this promise requires unprecedented collaboration across governments, regulators, operators, and technology leaders.”
- Fragmented AI Adoption:
The study finds that most AI applications—ranging from autonomous driving and demand forecasting to crowd monitoring and network planning—remain pilots rather than system-wide deployments. This fragmentation limits the ability of AI to generate meaningful, city-scale impact. - System-Level Coordination Drives Exponential Value:
AI’s transformative potential increases dramatically when applied across entire transportation systems. Optimizing fleets, infrastructure, energy use, and passenger flows simultaneously can create substantial efficiency gains, but doing so requires public-private cooperation on an unprecedented scale. - Human-AI Pairing is Safety-Critical:
The effectiveness of AI is highly context-dependent. In some applications, human operators enhance safety; in others, human intervention may inadvertently reduce reliability. Understanding when and how humans should collaborate with AI is now a strategic imperative for urban planners and operators alike. - The Execution Gap is Widening:
Without shared data infrastructure, interoperable standards, and coherent governance frameworks, regions risk fragmenting into competing and potentially incompatible AI ecosystems. This fragmentation could undermine safety, efficiency, and equity in global mobility systems. - High-Impact Applications Near the Frontier:
Applications such as autonomous fleets, adaptive traffic control, and automated enforcement sit closest to AI’s jagged frontier. These areas offer enormous potential but also present the greatest implementation challenges.
Voices from Industry Leaders
Dr. Gasparic emphasized the growing recognition among cities that AI is more than a tool—it changes the fundamental human-machine relationship within transportation networks. “Striking the right balance between human oversight and AI autonomy is critical. Cities that master this balance will define the future of mobility,” he said.
Kristin White, Head of Transportation Strategy and Partnerships at Google Public Sector and a contributor to the report, noted: “Placing humans at the center of problem-solving and mission-focused AI deployment is critical. For AI to scale successfully and safely, public agencies and technology providers must collaborate closely to ensure transportation systems are resilient and inclusive.”
From a public transport perspective, Dr. Axel Sondermann, Executive Director at Deutsche Bahn, emphasized AI’s system-wide potential. “AI already supports our agenda of providing safe, reliable, and environmentally friendly railway and bus services in Germany and beyond,” he said. “This study confirms that AI delivers its highest value when deployed across entire mobility systems. Unlocking this potential requires shared standards and industry-wide collaboration. We are committed to working collectively to create better, safer, and more resilient mobility for all.”
Collaboration Over Isolated Innovation
A central message of the report is that the future of AI in mobility depends less on isolated technological breakthroughs and more on coordinated ecosystem execution. Governments, private operators, and technology providers must work in concert to scale solutions effectively. Without this cooperation, the promise of AI-powered mobility—safer streets, cleaner air, and equitable access—may remain unrealized.
“Launching this research at CoMotion GLOBAL in Riyadh is a milestone for the industry,” said John Rossant, Founder and CEO of CoMotion. “The next leap in global transportation won’t come from individual breakthroughs; it will come from collaboration. Our role is to unite governments, operators, technologists, and cities to ensure that this future can be built, not just imagined.”
Global Implications and the Road Ahead
AI’s potential to transform urban mobility is immense, particularly as cities face growing congestion, rising emissions, and increasing demands for accessibility. When implemented effectively, AI can optimize traffic flow, reduce energy consumption, improve public transport reliability, and enable the safe deployment of autonomous vehicles. However, the study warns that realizing these benefits requires overcoming systemic challenges:
- Data Infrastructure: Shared platforms and interoperable data frameworks are critical to avoid fragmented systems.
- Standardization: International and cross-industry standards are essential for safe and reliable AI integration.
- Public-Private Collaboration: Close cooperation between municipal authorities, transport operators, and technology developers is necessary to coordinate policies, safety protocols, and deployment strategies.
- Human-Centric Design: AI must complement human decision-making rather than replace it, ensuring safety and operational reliability.
The report highlights that cities already deploying AI in limited pilots often see promising outcomes, such as improved traffic predictions, optimized fleet dispatch, and enhanced safety monitoring. Yet these successes are typically constrained by siloed systems and fragmented data. Scaling these applications across entire networks exponentially increases both the potential benefits and the complexity of implementation.
The Envisioning the Future of Mobility Powered by AI report underscores a crucial insight: AI alone will not revolutionize mobility. Its full potential can only be realized through unprecedented collaboration across governments, technology providers, operators, and cities worldwide. Achieving safer, cleaner, and more equitable mobility systems depends on building interoperable frameworks, scaling pilot projects, and integrating human oversight with AI-driven automation.
As urban populations grow and demand for efficient, sustainable transport intensifies, the stakes for successful AI deployment have never been higher. The study provides both a roadmap and a call to action for stakeholders worldwide: the next era of mobility will not emerge from isolated innovation, but from a shared commitment to collaboration, coordination, and the judicious integration of AI into our transportation ecosystems.
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