Why is Tesla’s market cap significantly higher than Ford if it lost $717M in 2018 Q2 while Ford earned $1.1B? 
The way Henry Ford’s Model T was produced and sold in 1913 is not very different than what major Original Equipment Manufacturers (OEMs) still do today. Auto industry has been very traditional and players did not feel the need to change their business models, instead focused on vehicle development and operational improvements. Companies also did not work with big data before, since sales models through dealers did not give them access to consumer data. Now companies need to find a way to transform to survive a disruption: Mobility is shifting towards shared, autonomous, connected and electric driving, each with tremendous implications on the OEMs. There are many trends behind this shift, but machine learning is the prominent driver of change and offers vast opportunities:
- Shared: A study at University of Michigan claims that car ownership could shrink by 43% due to increased adoption in ride sharing services . As these services know more about customers, they leverage data to offer more affordable and flexible systems. OEMs need to think about other ways than selling cars to sustain and grow revenues (e.g. monetizing data, innovative sales and pricing models)
- Autonomous: According to IHS Markit, 33M+ autonomous vehicles will be sold globally in 2040 . There are already ~50 key players on this space utilizing machine learning, many as partnerships between large tech companies and OEMs .
- Connected: As a platform of IoT devices, cars are communicating with drivers, each other, and the broader mobility ecosystem. OEMs need to be successful in collaborating with many different stakeholders to enable the connected ecosystem and get opportunities to leverage data.
Faced with these challenges and opportunities, 10 key automotive OEMs are expected to spend $345B targeting these issues . Moreover, according to an MIT study, 78% of OEMs already claim to have invested in skills and training for AI . Within this context, as a 115 year-old auto giant, Ford has been going through a significant commercial and cultural transformation to turn these into a competitive advantage. The company has been researching on AI for 20+ years, but initial focus was mostly on product development . In 2015, Ford announced its Smart Mobility business line to innovate in connectivity and autonomous driving with a long term perspective. It has been addressing the above challenges/opportunities since then  :
- Ford established Greenfield Labs in Palo Alto as a business incubator in partnership with IDEO to explore opportunities in mobility with a human-centered-design approach (currently 205 employees).
- Ford, Uber and Lyft announced a commitment to SharedStreets, a data platform designed to better leverage data to improve urban mobility. It also acquired Chariot, a San Francisco-based start-up that runs group shuttles for commuters.
- It acquired a majority stake of Argo AI (autonomous driving) for $1B and bought Velodyne (sensors) and Autonomic (connected car software). Ford is planning to invest $4B in total until 2023. It also partnered with Dominos Pizza to develop a fleet of self-driving pizza delivery vehicles.
Ford appointed the ex-chairman of its Smart Mobility division, Jim Hackett, as CEO in May 2017. Hackett is known as a successful visionary, but market cap trends above imply that so far investor expectations were not met on company’s outlook . Mobility segment is burning cash ($479M loss in 2018 Jan-Sep) with uncertainty of success going forward . Meanwhile traditional competition is moving fast: BMW’s mobility services (e.g. DriveNow) claims to be profitable since 2016 and BMW merged its mobility operations with Daimler this year, everything from car-sharing and ride-hailing to parking services and electric vehicle charging  .
In my view, going forward Ford needs to address following points:
- Build more internal capabilities to leverage early gains through machine learning applications (e.g. cost improvements in supply chain, data monetization, financing, targeted advertising) and clearly communicate the results to the shareholders to manage expectations
- Beyond the significant investments in the connectivity and autonomous driving technology, build more partnerships globally to expand its role in connected car ecosystem, therefore enable collection and utilization of more data to monetize (data integration with dealers can help)
- Build a unique value proposition to attract and retain talent for software engineers and AI experts to compete with giants such as Google and Facebook.
- Develop alternative ways to diversify business using AI applications in long term (e.g. innovation in business portfolio, leveraging predictive power of AI in after-sales)
- Ensure collaboration with government to limit exposure to political risks as much as possible (e.g. tariffs, taxation)
Future is uncertain for car manufacturers as the expanded mobility ecosystem continuously increases competition and creates new trends. How can traditional OEMs like Ford utilize machine learning and data to transform their businesses?
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