Wednesday, December 24, 2025

AI Won’t Transform Mobility Without Unprecedented Collaboration, Study Reveals

First comprehensive analysis of real-world AI applications exposes critical gap between technology’s promise and scaled deployment, according to the MIT Mobility Initiative and Kearney Advanced Mobility Institute

RIYADH, Saudi Arabia, December 08, 2025–(BUSINESS WIRE)–Artificial intelligence is quietly reshaping global transportation systems, but most deployments remain isolated pilots that haven’t achieved scale – and the gap between AI’s promise and its execution is widening, according to a landmark study launched today at CoMotion GLOBAL in Riyadh.

The Envisioning the Future of Mobility Powered by AI report, developed by the MIT Mobility Initiative and Kearney Advanced Mobility Institute, analyzed input from 55 leading global organizations, including Google, Lyft, Uber Freight, Deutsche Bahn, and NEOM. The study mapped real-world AI applications across mobility systems in Europe, the Americas, APAC, and the Middle East – revealing both breakthrough potential and fundamental challenges in bringing AI-powered mobility to scale.

The report identifies a critical dynamic reshaping the industry: AI doesn’t simply replicate human intelligence – it excels dramatically in some functions while failing unpredictably in others. This “jagged frontier” creates both opportunity and risk in safety-critical transportation systems.

“The industry sees enormous opportunity – but also fragmentation,” said John Moavenzadeh, Executive Director of the MIT Mobility Initiative. “Our study reveals a paradox: the bigger the potential for AI to deliver safe, clean, and inclusive mobility, the tougher the execution becomes. Delivering on this promise will require governments, regulators, operators, and technology leaders to collaborate across borders and share a common vision of impact, safety, and trust.”

Key Findings

  • AI adoption remains fragmented: Most applications across network planning, autonomous driving, demand simulation, and crowd monitoring are pilots, not scaled systems

  • System-level coordination delivers exponential value: AI’s potential increases dramatically when deployed across entire systems – optimizing fleets, infrastructure, energy use, and passenger flows simultaneously – but requires unprecedented public-private cooperation

  • Human-AI pairing is safety-critical: In some applications, combining AI with human operators makes systems safer. In others, human intervention actually reduces reliability. Understanding this balance is now a strategic imperative

  • The execution gap is widening: Without shared data infrastructure, interoperable standards, and coherent governance frameworks, regions risk fragmenting into competing and potentially incompatible AI futures

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