Last week I attended Ford’s Capital Markets Day. Ford held the CMD to report on the progress of its three…
AI has three roles in new mobility. It is an enabler, a differentiator, or a monetizer. The recent explosion of interest in generative AI begs the question: could generative AI contribute to new mobility and if so, in which of the three roles? This post attempts to answer this question by presenting a few ideas and identifying problems that may inhibit the broad use of generative AI in new mobility.
AI is viewed by many exclusively as a prediction technology. The availability of large, diverse, and information-rich data sets combined with the power of neural networks, and more recently with the addition of Foundation Models and Large Language Models, has been responsible for achieving incredible results even in complex, multi-faceted situations. Every aspect of new mobility has benefited from AI’s prediction power. New mobility will continue to reap even more impressive rewards. Getting there will be accomplished as new mobility’s AI systems play three roles.
During the automotive industry’s current boom phase OEMs are announcing big, multi-year investments in new vehicle platforms that combine electrification with increasing driving automation. Because under new mobility data and loyalty will become central forms of value, OEMs must also consider deploying the loyalty-enhancing data-driven services these platforms enable. The services they introduce and the business models they use to monetize them will determine whether they become like Apple, AT&T or Foxconn in the customer relationships they develop.
This post presents an analysis of the autonomous vehicles innovation lifecycle. It introduces four dimensions for assessing AV innovation performance over time. Finally, it presents six requirements that will need to be addressed before the use of autonomous vehicles can scale for consumer transportation and logistics.
