In my book and previous posts I build a broad case for the key role big data and AI play in next-generation mobility, and provide several examples from transportation and logistics. Next-generation mobility is about intelligent, connected vehicles that utilize some form of electrified propulsion, and on-demand shared transport services of people and goods offered through such vehicles. Many of these vehicles will be capable of autonomous movement. Next-generation mobility will help us address some of our biggest challenges, such as pollution and climate change, urbanization and congestion, aging population, and traffic fatalities, while enabling us to maintain economic prosperity by operating highly optimized supply chains that span the globe. It will give rise to new value chains. It is important to understand these value chains, and identify the new monetization opportunities they offer, particularly the opportunities to monetize the various forms of big data given its key role.
While we continue to dream broadly about next-generation mobility, we tend to focus on the impact of autonomous vehicles (that may or may not be electrified), and shared mobility services. Despite the growing number of announcements about technological achievements, investments, partnerships and acquisitions relating to autonomous vehicles, we are still several (5-10) years away from the broad utilization of vehicles with Level 4 or Level 5 driving automation, particularly for passenger transportation. I focus exclusively on these two levels of driving automation because only once we achieve these levels will we be able to take full advantage of the benefits offered by autonomous mobility. However, before being able to assess the broad impact of electrified vehicles with L4/L5 driving automation, what I’ve been calling ACE vehicles, we will be in the position to evaluate the effects of electrified vehicles with lower levels of driving automation (Level 2 or 3)
Most incumbent automotive OEMs envision privately owned autonomous (L4/L5) vehicles that will be used for personal mobility. However, as we will see in the next section, most of the use cases being tested today involve corporate fleets of autonomous vehicles. Of those, the cases involving people transportation are about shared mobility.
Six Use Cases For Next-Generation Mobility Vehicles
I have identified six general use cases where autonomous vehicles can have transformative impact. I have ordered them based on which of the following factors need to be successfully addressed by each use case:
- Create the right technologies at reasonable costs and energy efficiency to enable autonomous vehicles to deal with environments of increasing complexity that can change unpredictably;
- Devise scalable business models;
- Build the appropriate transportation and electrification infrastructures;
- Institute the appropriate regulations;
- Resolve liability issues;
- Address issues relating to cybersecurity and data privacy, and
- Achieve consumer acceptance of autonomous mobility.
The six use cases are in order of increased complexity include:
- Specialized vehicles operating in controlled environments. Corporations have already started deploying specialized autonomous vehicles, some of which may be electrified, in environments where employee injury risk is high and labor shortages exist. Initial examples involve hauling ore in open-pit mines, planting, maintaining crops and harvesting in farms, large warehouses, and transporting containers within ports. Trials, and even deployments, for additional specialized applications will continue in increasingly less controlled environments such as urban garbage collection.
- Trucks used in long-haul freight logistics. Several factors motivate the use of autonomous, and increasingly electrified, long-haul trucks for freight transportation between distribution centers that are outside city limits. These include: digital, global and highly optimized supply chains, shortage of long-haul truck drivers, fuel savings, driver safety, supply chain economics. Utilizing freeways, and maybe even specially designated lanes in freeways, provides for environments that are more stable and consistent and less complex, compared to urban settings. The first significant tests for this case will start in 2018 and involve truck platooning.
- Vehicles used for short-haul package handling. The continued explosive growth of ecommerce is leading to the dramatic increase for package delivery and merchandise returns logistics. Logistics companies are looking for ways to effectively address last-mile on-demand delivery in urban and suburban environments and are exploring numerous different options involving autonomous vehicles most, if not all, of which will be electrified. Tests of such vehicles have started and more are planned for the near future.
- Passenger shuttles. Autonomous, electrified passenger shuttles will come in different forms. Today they are providing transportation in a variety of university campuses, and other controlled environments that have low traffic, and impose low speed restrictions. Additional trials are on the way, including trials in retirement communities. Over time such shuttles will be introduced in increasingly complex urban settings to provide, first-mile/last-mile transportation to private and public mass transit systems, as well as transportation for the elderly and people with disabilities. Though the technology employed by the special-purpose autonomous vehicles is impressive, the business cases and associated models, as well as the necessary regulation have not yet been fully defined. One has to remember the Segway, which also employed an impressive array of technologies to address personal urban mobility but encountered several obstacles, including business model and regulation-related issues, in establishing a viable business case. , and
- Vehicles used for ride-hailing. I have already discussed in other publications the advantages that autonomous vehicles will provide to companies offering urban and medium-haul (up to 150 miles) ride-hailing and ride-sharing services. They include the ability to reduce operating costs, control the overall passenger experience, and reduce urban traffic congestion and pollution. While trials of growing scope will continue, the broad use of autonomous vehicles (electrified or not) for such applications is several years away. These vehicles may not initially be electrified but ultimately they will evolve to be such.
- Vehicles used for private transportation. The growing proliferation of mobility services particularly those that blend on-demand with scheduled mobility (in the form of public and private mass transit) will negatively impact private car ownership but will not eliminate it. Automotive OEMs will definitely offer vehicles with Level 4 and Level 5 driving automation initially at their high-end models, and, as the price of the components that comprise the autonomy platform drops, to lower-price models that target broader segments of the market. Some of these vehicles may be shared.
The following observations are made from the use cases presented above:
- Five use cases (1-5) involve fleets rather than private vehicles. A case can be made on whether autonomous farming equipment will be privately owned or owned by fleet operating companies.
- Five cases (2-6) are about next-generation mobility. Two of the use cases (2-3) are about freight transportation and three (4-6) about passenger transportation. While next-generation mobility will increasingly be adopted, and even as consumers continue to transition from vehicle ownership to vehicle access to address their transportation needs, autonomous vehicles will need to operate side-by-side with human-driven vehicles in streets and highways. This means that the existing value chains will not disappear. Instead they will be supplemented or augmented.
- Monetization will proceed from the “simpler” use cases to the more complex ones. We are already seeing monetization in terms of operating cost reduction in the first use case. Once deployed, truck platooning will enable the reduction of fleet operating cost reduction due to fuel savings. Additional monetization opportunities will emerge as the other use cases are deployed.
- Use cases 2-5 involve fleet-based next-generation mobility and give rise to new value chains, one of which will be discussed in the next section. These value chains will co-exist with the value chains that I presented in my book, and will impact (and potentially disrupt) many industries beyond automotive, including energy, financial services, insurance, telco, utilities, and others.
A New Value Chain For Fleet-Based On-Demand Mobility
In previous posts I have presented in more detail certain aspects of the autonomous vehicle fleet-based on-demand personal mobility value chain that addresses on-demand passenger transportation. This value chain consists of four basic components as shown in Figure 1:
- Designing, testing, and manufacturing the vehicle. In addition to all the steps associated with the design, test, and manufacturing of conventional vehicles, autonomous vehicles include the platform (hardware and software) that enables their Level 4 or Level 5 autonomy. This is what I’ve been calling the Operating Platform. In the case of autonomous electrified vehicles, this platform also encompasses the propulsion system.
- Creating a fleet. This involves ordering a fleet of vehicles that have specific configurations, financing the fleet’s purchase, insuring it, and leasing the vehicles to the fleet operator. The configuration of vehicles may be specific to: a) the next-generation mobility application, e.g., single passenger transportation and on-demand small package delivery, or multi-passenger shuttle transportation, b) the environment where they will be operating, e.g., taking into a account the characteristics, and infrastructure (transportation, electrification, etc.) of a specific city such as New York, c) the regulations of the operating environment, e.g., those of a city, state, or country, d) insurance laws, and e) data privacy and cybersecurity laws. Vehicles for passenger transportation may also be pre-configured to incorporate what I’ve been calling the User Experience Platform (UX Platform). Alternatively, the fleet operator (see below) may install its UX Platform once it takes delivery of vehicles. While in several cases the fleet creator and the fleet operator could be the same corporate entity, we will likely see different corporations, such as today’s car rental companies, but also investment banks, REITs, etc. that create and finance various types of autonomous vehicle fleets used for on-demand shared passenger mobility, and then lease them to fleet operators.
- Operating the fleet. This involves taking reservations, dynamically determining the price for every trip, pre-positioning vehicles in certain locations in order to address anticipated demand and thus minimizing customer wait-time, safely transporting passengers, managing the network of vehicles while they operate to ensure the smooth execution of the reservations, monitoring each vehicle’s health and addressing problems as they occur during each vehicle’s operation, and being responsible for the overall user experience. Waymo will likely be the first pure play autonomous vehicle fleet operator offering on-demand ride-hailing and ride-sharing services. Ride-hailing companies such as Uber and Lyft may evolve their current business models and also become operators of ACE vehicles. Vehicle manufacturers have started establishing Mobility Services units, such as GM’s Maven, that intend to operate ACE vehicle fleets to offer ride-sharing, ride-hailing, and car-sharing services.
- Servicing and maintaining the fleet. This involves parking the vehicles when they are not in use, recharging or refueling them, cleaning and generally maintaining and servicing the vehicles to ensure their maximum availability and the best rider experience, dealing with accidents as they occur, e.g., towing a vehicle, and repairing the vehicles as necessary. Vehicle rental companies are viewed as natural candidates for fulfilling this role. However, despite their fleet servicing and maintenance experience, to become valuable participants of the next-generation mobility value chain such companies will require digital transformations including the replacement of their legacy software systems with new data-driven software platforms.
The Value Added By Big Data and AI
The value of big data and AI is important in every component of the fleet-based next-generation mobility value chain. In particular, big data and AI enable:
- Designing, testing, and manufacturing the vehicle. Data from fleet creators, fleet operators, and fleet servicing companies, along with vehicle and component manufacturing data should be utilized to improve vehicle designs and improve their manufacturing. In some cases it may also be possible for different manufacturers to share production data in an effort to improve their economics. Cloud-based design and manufacturing systems such as the one built by our portfolio company Divergent3D makes such sharing possible. Simulation systems such as those built by our portfolio company Metamoto can utilize data from fleet operators and combine it with synthetic data to test the performance of autonomous vehicle Operating Platforms.
The performance of an Operating Platform improves over time as more data is collected from test and production vehicles. Such improvements to the perception, localization, and planning components of the Operating Platform enable us to move from the “simpler” to the more complex use cases. The same argument can be made about mapping, particularly as we will need to constantly update the high definition maps that are required by autonomous vehicles, or in order to create a new high definition map.
- Creating a fleet. The specification of vehicle configuration provided to vehicle designers and manufacturers by the fleet fleet creators and operators can be driven by each fleet’s operating performance data, on a vehicle by vehicle or aggregate basis. Such data can be used by fleet creators to negotiate the per vehicle price and other related terms with vehicle manufacturers, fleet financing institutions, and insurance companies.
- Operating the fleet. Vehicle design and configuration data, data about every job fulfilled by each vehicle in the fleet, along with each vehicle’s service and maintenance data, and data from the various sensors that are used to instrument cities and transportation infrastructures can be exploited to continuously improve a fleet’s operation, establish demand-based pricing for each trip, minimize wait times and travel times, and increase each vehicle’s operating hours.
The data described above, together with passenger data and routing data from each trip, scheduling, traffic, weather and many other forms of data can be “stitched together” and exploited using AI to configure and personalize each vehicle’s cabin for each type of passenger, individual passenger, or group of passengers through the vehicle’s UX Platform thus enhancing the overall customer experience. Customer experience should extend outside the vehicle as well and, as I mention in the book, should address the consumer’s entire ground transportation needs. The creation of end-to-end personalized ground transportation solutions is a new and potentially differentiated opportunity for companies working on next-generation mobility. Our portfolio company Safegraph is creating and analyzing data from a variety of sources in order to provide such solutions.
- Servicing and maintaining the fleet. Vehicle performance data (range, fuel economy, tire performance, etc.), along with data about the jobs each vehicle is fulfilling (distance of each ride, number and type of passengers, location of each trip’s origin and destination, etc.), accidents and breakdowns, can be exploited in order to understand how to best maintain and service each vehicle in an effort to provide the best customer experience, while maximizing each vehicle’s utilization and controlling the fleet’s operating costs. The more data we collect relating to the servicing and maintenance of such vehicles, the better optimization we will be able to make. Airlines provide a very good example of what is possible by continuously collecting such data.
Figure 2 depicts the necessary data flows across the value chain shown in Figure 1.
The Monetization Opportunities Of Big Data and AI
As an investor to startups developing next-generation mobility solutions, and advisor to large corporations in this area, I’ve been particularly interested on how to monetize the value provided by big data and AI. In the particular value chain we’ve been discussing, monetization will result from creating new revenue streams but also from lowering operating costs across the value chain. With that in mind, I have identified the following opportunities:
- Vehicle manufacturing. The use of data and AI will lower the automakers’ warranty costs and could also lower their marketing budgets to fleet creators, fleet operators, as well as to consumers who will buy such vehicles for their private use. Moreover, automakers can achieve higher margins by utilizing 3D printing-based, small-batch manufacturing systems that use big data and AI to satisfy complex requirements of fleet creators and operators. The Operating Platform will be autonomous vehicle’s most valuable and highest margin component. This is why there are so many companies from incumbent automakers to Tier 1 suppliers and startups are working on the development of such platforms. The Operating Platform will need to constantly be updated, particularly as it becomes embedded in vehicles that will operate in new areas, providing continuous data monetization to automakers, component providers, and cities. Furthermore, an extensible Operating Platform offers monetization opportunities through subscriptions to receive OTA updates, and transactions to acquire new features or information from the automaker, the Platform provider, data providers, e.g., HERE, or a network of partners including fleet operators. For example, Tesla is already using subscription- and transaction-based business models to monetize the introduction of new features to its vehicles, e.g., battery range extenders, many through OTA updates.
- Fleet creation. By using the vehicle use, performance, service, and maintenance data, fleet creators may be able to negotiate lower per vehicle price with vehicle manufacturers, and fleet insurers, thus positively impacting their overall investments. This data will also help fleet creators determine the terms under which they will lease a fleet to an operator in order to maximize their revenue opportunity while reducing their risk.
- Fleet operation. In addition to the per trip charge, big data exploited by AI offers fleet operators several other revenue-generation and cost-reduction opportunities. The key to realizing these opportunities is to a) understand how next-generation mobility changes the notion of Brand Loyalty and b) enable Passenger Commerce.
Brand loyalty has always been important to monetization. It impacts revenue and costs. To date under the car ownership-centric model automakers measure brand loyalty by their ability to protect their market share. They achieve this by retaining a consumer (or household) within their brands, and ideally, over time, transition them from the entry-level to their more expensive brands. For example, in the case of Toyota this may mean moving a household that initially purchases, or leases, a Corolla to later leasing a Camry and eventually a Lexus RX-series SUV. Under these scenarios brand loyalty is tested every 3-10 years depending on how frequently the household changes vehicles. During the intervening years the automotive OEM, at best, interacts infrequently with the household’s members. In fact, the OEM’s dealers, particularly while the vehicle is under the manufacturer’s warranty, interact more frequently with household members, and build a richer relationship with them, particularly with each vehicle’s driver.
The transition from the car ownership-centric personal transportation model to the hybrid model that characterizes next-generation mobility and which combines car ownership with on-demand shared mobility services will change the prevailing notion of brand loyalty. Loyalty will not be measured by the willingness to continue purchasing a particular OEM’s vehicles, but will be measured by the miles traveled using a particular OEM’s vehicles. The emerging transportation model combined with the new way of measuring brand loyalty will mean that the OEM’s value delivery doesn’t stop with the sale of a vehicle, as it does today, but becomes a lifetime journey with the customer. It addresses the consumer’s journey both inside and outside a personally-owned vehicle and the vehicles used by the fleet operator. The new loyalty model will lead to the development of Passenger Commerce. The ongoing challenge for the participants in this new value chain will be what share of the customer’s miles traveled they have at any point in time, which will translate to share of wallet. Data can play a huge role in understanding loyalty, influencing it and monetizing it.
Passenger Commerce is an activity that is offered while providing each personalized transportation solution, and is enabled by understanding each customer’s needs and preferences by exploiting the available data. Passenger commerce could be conducted through the UX Platform while the consumer is being transported via:
Subscriptions to access content, e.g., an annual subscription to Apple Music, or a particular service, e.g., entertainment concierge.
Transaction-based purchases of goods, services, and content. For example, a traffic data provider, e.g., Inrix, can offer congestion data about a particular city on a particular day. An early example of such transaction-based commerce today is offered by Hertz for its Neverlost GPS and travel information platform. Hertz is able to charge extra for the vehicles that are equipped with this platform because of the information it provides to driver and passengers.
Advertising-based access to contents, goods, and services while being transported. For example, rides to certain establishments, e.g., a specific restaurant or casino can be sponsored by the establishment itself.
Redemption of loyalty points. Automakers and fleet operators can reward their customers for their loyalty using a system similar to that used by airlines or hotel chains. These points can then be redeemed in much the same way these and other industries use such programs. For example, for every 5,000 ridesharing miles in a particular automaker’s vehicles, the consumer receives points that are redeemed towards free cellular data to be used in their personal vehicle.
- Fleet servicing and maintenance. By tracking fleet utilization data it is possible to optimize each vehicle’s operation per location (which will undoubtedly be different in New York than in San Francisco, or in Singapore), while controlling the fleet operator’s costs, and improving vehicle economics, fleet management and maintenance economics, lower the fleet insurance economics, and increase the vehicle’s utilization and the corresponding fleet’s performance thus improving the overall economics of the fleet operator.
By understanding the emerging value chain for the envisioned autonomous vehicle use cases, and appreciating the role of big data and AI, we can create opportunities that will lead to high-value business models at a time when the traditional automotive business models continue to face margin compression and declining value, while today’s formulation of the models used in ride-hailing and ride-sharing are not going to fare much better, despite the high valuations commanded today by the companies that offer such mobility services.