Data and AI Drive Mobility Shift
An article by Jean Christophe Lalanne, TNP Consultants, published in the white paper “The Mobility Revolution”.
Whatever the mode of transportation—from bicycles to airplanes, trains to buses—we have all experienced how digital technology has transformed the way we move. From planning a trip to curating a seamless experience, from executing every detail to evaluating it afterward to improve future performance, digital tools are now embedded in every step of the journey. And now, with the rapid rise of artificial intelligence in its many forms, our perception of mobility is undergoing yet another profound transformation. Companies involved in transportation, directly or indirectly, can no longer avoid rethinking their business models and operational processes.
The New Role of Artificial Intelligence
Climate imperatives, economic pressures, the pursuit of enhanced experiences, safety requirements, and multi-criteria journey optimization—these are all domains poised to benefit massively from the rise of AI. The data is already there: rich, diverse, and abundant. Some is personal, tied to individual travel behavior; some stems from the journey itself; and much of it is continuously enriched by the entire ecosystem—operators, suppliers, service providers, and financiers—all of whom contribute to a growing collective intelligence.
Rethinking The Traveler Experience
In the near future, a driver’s in-vehicle experience may be guided by a sophisticated, voice-activated tablet that offers hundreds of functions, driven by embedded software and systems connected to a central platform. Vehicles will be highly autonomous thanks to AI, while still giving control back to the driver, who will make choices based on preferences, needs, or constraints. We can envision strong integration between the car and connected service platforms accessible via mobile or desktop—allowing users to find the best compromise between speed, cost, sustainability, and comfort. A unified dashboard might consolidate information in real time, keeping travelers updated on both external conditions and internal system changes.
AI-driven digital platforms will also help scale new transport modes—shared mobility services like bikes, scooters, or car sharing, as well as seamless multimodal journeys involving air, rail, and bus travel. These services will be increasingly customized by intelligent systems. In fact, a McKinsey study shows that 46% of users are already open to replacing private cars with one of these new options.
AI-Powered Operational Performance For Mobility Providers
Inside mobility companies, data exploitation will continue at scale, with traditional and generative AI powering every layer of the value chain. AI will enable tailored offerings, cost optimization, margin improvements, and smart allocation of human contact—reserved for moments where it truly adds value to the customer experience. Predictive tools will be used for everything from capacity planning and destination selection to energy use and revenue optimization.
The complex software that powers cars, trains, and aircraft will increasingly be developed and maintained through generative AI frameworks. Teams across marketing, R&D, engineering, operations, and support will all be equipped with intelligent agents to support every phase of their work. Beyond improving productivity, AI will dramatically enhance how well products and services align with evolving customer expectations.
Mobility as a Catalyst for the Usage Revolution
Exploring the future of mobility also means looking closely at logistics, especially in the age of last-mile delivery. Over the past few years, the logistics chain has expanded and modernized rapidly to meet the rising demand for home delivery—no matter the location, no matter the item. This shift is reshaping the entire industry.
Today, logistics requires fully integrated, end-to-end management involving a multitude of actors. That, in turn, demands constant optimization in every dimension. The growing complexity of scenarios and combinations calls for powerful algorithms and unprecedented access to data. The consumer usage revolution is irreversible, and companies must now find ways to meet expectations while balancing profitability, sustainability, security, and real-time availability.
This means that every mobile vector must evolve into a truly intelligent agent—interoperating with systems for production, storage, data, and multidimensional performance measurement. There’s little doubt that many of the solutions developed for passenger transport can be adapted for logistics applications serving consumers.
Real-World Applications and Outlook
Looking ahead to some potential use cases, one can easily imagine the widespread adoption of intelligent, voice-activated conversational interfaces—acting like a personal assistant for every step of the journey. These AI agents would help plan multimodal trips, monitor execution in real time, and adjust dynamically in the face of disruptions, whether it’s a missed connection, technical issue, or delay.
Drawing on user data, the same assistant could recommend the optimal transport solution—technologically and finan
cially—tailoring options based on the best-fit vehicle, energy source, and route. Logistics will also benefit from intelligent orchestration, allowing transport resources to be dynamically selected based on the specifics of each customer delivery.
The Prerequisites for Transformation
This radical shift in mobility usage starts with awareness. New parameters—energy efficiency, environmental impact, multimodal integration, and shared access—are reshaping how tomorrow’s travelers think about movement. Digital technology and artificial intelligence are the key enablers of these transformations. That’s why businesses must treat them not as support tools, but as central pillars in their strategic planning.
Only by doing so can they remain relevant in a future where mobility will be smart, sustainable, and entirely redefined by data and intelligence.
