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Autonomous and Near-Autonomous Vehicles, Captura de pantalla 2026-02-06…
Autonomous and Near-Autonomous Vehicles
Definition and Criteria
Entered overlooked segments such as commercial fleets, robotaxis, and driver-assistance users rather than replacing private cars immediately.
Early systems had inferior performance, requiring supervision and working best in controlled environments.
Rapid technological progress driven by AI, sensors, and large-scale data collection.
Potential to disrupt the traditional car ownership model by enabling Mobility-as-a-Service (MaaS).
Evidence from the case
Initial users: tech-savvy drivers, logistics fleets, and passengers in pilot programs.
Performance vs incumbents: initially less reliable than human drivers in complex situations but competitive on highways and improving steadily.
Adoption: innovation led by tech companies and startups; established automakers responded through partnerships and investment.
Displacement: not fully achieved yet, but shifting attitudes toward shared mobility suggest early disruption signals.
Veridict
Potentially Disruptive :check:
Business implications
Established players: risk commoditization of vehicles as software captures more value; insurance and professional driving sectors may shrink.
New entrants: strong opportunities in autonomous software, mobility platforms, data ecosystems, and sensors.
Strategic implications: prioritize tech partnerships, invest in automation capabilities, and prepare for a transition from product-based to service-based mobility.
