71% of transportation companies surveyed in the 2025 Gartner CIO and Technology Executive Survey reported having already adopted GenAI or planning to do so by 2026. The sector -- which includes aviation, logistics, public transit and automotive -- has gone through a consolidation phase over the past year: the most mature pilots have transformed into operational deployments, while some applications that seemed promising showed concrete limitations once tested at scale.
The updated assessment of 20 GenAI use cases for the transportation sector reflects this selection process.
Use cases that have reached operational maturity
Predictive maintenance is now one of the most solid use cases across the entire sector. Generative AI creates synthetic data for rare failure scenarios -- situations where real data is insufficient to train robust models -- enabling more accurate anomaly detection and more reliable maintenance predictions. Adoption is widespread across fleets and public transit systems, with documented benefits in reduced downtime and maintenance costs.
Route planning and optimization has become a common tool among fleet and public transit operators. GenAI systems generate and simulate alternative routing scenarios based on real-time data -- weather, traffic, road closures -- providing planners with rapid decision support for dynamic situations.
Passenger service and support -- multilingual virtual assistants, automated response systems for high-volume requests, personalized travel information -- has demonstrated strong operational value with solid technical feasibility. The reduced workload on human operators for routine requests is measurable, and perceived service quality improves when systems are well calibrated.
Public transit model planning has moved from the calculated-risk category to the "likely win" category in the latest update: GenAI's data integration and scenario simulation capabilities have proven to be an effective tool for optimizing routes and schedules more quickly than traditional approaches.
Training content generation -- safety materials, route simulations, maintenance tutorials -- reduces manual development time and ensures up-to-date, consistent content for drivers, technicians and logistics staff. Dynamic resource allocation -- optimal strategies for vehicle allocation, crew scheduling and spare parts management in complex, rapidly changing environments -- has also entered the scope of cases with a strong feasibility profile.
Use cases that fell short of initial expectations
Irregular operations management -- disruption handling, delays, real-time rerouting -- was among the highest-expectation use cases. In practice, it encountered significant obstacles: poor real-time adaptability, lack of standardized data across different systems, and difficulty integrating with legacy systems. The expected value has been revised downward.
Wayfinding -- AI-assisted navigation in major transportation hubs -- ran into limitations in accuracy, personalization and user adoption. Promising in theory, it demonstrated lower practical value than expected in most deployments.
Voice of customer analysis -- sentiment analysis on passenger feedback -- showed difficulty in extracting actionable outcomes from nuanced feedback and integrating insights with service improvement workflows. The operational value is there, but more limited than anticipated.
Use cases with potential but high complexity
Digital twin augmentation -- digital models enriched with GenAI capabilities to simulate complex operational scenarios -- has high strategic value but requires a level of data and infrastructure maturity that many transportation organizations have not yet achieved.
AI-assisted cargo scheduling -- load plan optimization, shipment routing, capacity management -- sits in an intermediate position: medium feasibility, significant value, but with integration requirements against existing operational systems that increase implementation complexity.
The variable that determines success
Across all use cases, a common factor emerges in successful deployments: data maturity. Transportation organizations with fragmented, non-standardized or low-quality data systematically achieve inferior results compared to those with a solid data foundation, regardless of the sophistication of the AI model chosen. Investing in data quality and integration before -- or in parallel with -- GenAI projects is not a bureaucratic prerequisite: it is the condition that determines whether a pilot becomes operational or remains an experiment.
For CIOs in the transportation sector, the recommended prioritization points to predictive maintenance, demand forecasting and training automation as starting points with the best risk/value profile. Structured assessment of feasibility, scalability and risk exposure remains the compass for deciding which use cases deserve investment and in what sequence.
