I recently participated in an on-stage discussion about the state of autonomous mobility at the Ride AI Summit in Los Angeles. During the event, I engaged in several conversations with other participants about the robotaxi customer experience. The essence of these discussions reaffirmed what I’ve long maintained—customer experience, rather than just vehicle technology, will determine the winners in the next phase of mobility.
The process of insight generation is changing due to generative AI. While the foundations of insight generation I presented ten years ago remain relevant, the methods, tools, and implications have expanded dramatically. Generative AI is reshaping how insights are derived, validated, and applied across industries.
AI, generative or otherwise, holds immense promise for enterprises looking to improve efficiency, enhance decision-making, and unlock new business opportunities. Yet, despite the enthusiasm, many companies struggle to transition from pilot projects to large-scale AI deployments. The path to effective AI adoption is not as straightforward as acquiring technology or hiring data scientists. Enterprises must navigate challenges from defining the right problems to preparing their data infrastructure, fostering a culture that embraces AI, establishing governance frameworks, and understanding the true costs of scaling AI solutions.
AI’s allure is undeniable, and businesses invest heavily in its promise. Companies today invest significant amounts into generative AI initiatives. This pursuit is fueled by AI’s potential to improve productivity, cut costs, and unlock new opportunities that may create new revenue streams. However, many companies find the path to successful AI challenging because of fundamental mistakes in their approach.
Just before the holidays, I was asked to keynote an event sponsored by the German American Chamber of Commerce. This article is an updated version of that presentation. It is even more relevant today following CES 2025 and the news streaming out of Europe. The automotive industry, especially the European automotive industry, faces even greater challenges. These challenges are not from technology startups but from more formidable forces. China has become an international competitor, and its market is no longer an opportunity for incumbents. Vehicle sales, including sales of battery electric vehicles, are slowing, leading many companies to miss their financial targets and reconsider previously announced investments relating to electric vehicles. The regulatory environment is becoming more restrictive but also less reliable in terms of long-term goals and guidance for the industry. At the same time, Software-Defined Vehicles and AI require large capital investments at a time when the industry is cutting costs and continues to show an inability to deploy capital in the areas that will matter in the future. Labor is reacting to the automakers’ actions and introducing new work-life balance demands.