The mobility sector is undergoing rapid technological changes; organizations must adapt their technology operating models to keep pace. In this Viewpoint, we propose a differentiated, multilayer operating model for planning, operating, and governing technology systems, based on their position on the technology maturity S-curve. Adopting this approach will help companies find the right balance between agility and efficiency for each technology system in their portfolio.


A confluence of technological breakthroughs has disrupted the transport and mobility value chain. Travelers are accessing mobility services in new ways, new classes of vehicles are emerging, and transport networks and traffic flows are increasingly monitored and optimized in real time. This sector has often found itself at the forefront of progress, but most of its advances have always been more relevant to moving atoms than to moving bits. The latest challenge facing mobility players is the current technology revolution’s emphasis on capturing, integrating, and leveraging large flows of data.

This challenge is pressing for public transport operators for two key reasons. First, as government bodies overseeing critical public infrastructure, the guiding mantra must remain “move slowly and make sure that nothing breaks,” which is diametrically opposite to the stance often adopted by digital-native innovators: “move fast and break things.” Second, because such entities usually oversee all key aspects of transportation within a certain geographical area, it is necessary to align and integrate previously differentiated and siloed units, each responsible for its own mode of transport.

For example, the sensors embedded in advanced operational technology (OT) systems are gathering expanding amounts of data on transport infrastructure, vehicles, and passengers. Integrating this data from different OT systems will unlock new insights and synergies, for both optimizing existing operations and for planning new development.

In addition, several key disruptive technologies presently transforming other industries are driving the need for IT/OT integration within the mobility sector (see Figure 1). These include big data analytics, blockchain and distributed ledger technology (DLT), artificial intelligence (AI)/machine learning (ML), and digital twins/metaverse. For example, the widespread use of sensors and Internet of Things devices generates an abundance of data that can be leveraged to optimize transport operations, reduce costs, and improve safety. Moreover, DLT can enhance payment authentication, which helps refine the transparency, security, and efficiency of transport operations.

show modalFigure 1. Technological disruption in mobility sector
Figure 1. Technological disruption in mobility sector

Finally, mobility involves asset-intensive operations, and digital twins — especially those powered by AI and ML — play a crucial role in facilitating sustainable asset management from the outset. Digital twin technologies offer advanced abilities for planning and designing networks while integrating essential modeling tools to simulate the behavior of urban transportation infrastructure. This enables informed decision-making and optimizes operations. In addition, digital twins can contribute to a more effective asset management approach by utilizing real-time data feeds to monitor asset conditions. This allows for identification of potential issues and implementation of predictive maintenance schedules, which ultimately optimizes operations. Consequently, downtime is minimized, and reliability is enhanced, leading to improved performance and cost savings for transportation systems.


Most leading transport operators have adopted an operating model structured by mode of transport, which reflects the traditional way of managing transportation. As such, most OT systems have been developed to manage transport operations for specific modes of transport and are operated by dedicated specialists, independently from OT systems for other transport modes. There has long been a tendency to consider IT separately from OT, with IT mostly seen as a support function implementing standardized, corporate-wide services.

With software continuing to eat the world, there is now a rapid convergence of IT and OT, driven by SDx (software-defined everything) and XaaS (everything as a service) — see Figure 2. This ongoing merger, both between IT and OT, and OT systems for multiple modes of transport, presents a major challenge to transport operators in terms of both organization and culture. Successfully navigating this challenge will be critical to unlocking additional innovation opportunities for transport operators. For example, the ability to analyze integrated data flows is central to smart city technologies, such as dynamic traffic operations, smart parking, and the deployment of autonomous and multimodal public transport services.

show modalFigure 2. Convergence between IT and OT
Figure 2. Convergence between IT and OT

Seeing both the need and opportunity to integrate their technology operations, many public transport operators have begun to centralize all technology operations under a single organizational unit. When successfully implemented, this modification has the additional advantage of improving efficiency, an important goal for all companies, but particularly for publicly funded entities. For example, Transport for London, the local government body responsible for most of the transport network in the UK city, realized significant OPEX efficiencies (estimated at an approximate annual reduction of 50%) thanks to the full centralization of both IT and OT operations into a single organizational unit.

It is important to note that no single technology operating model archetype should be viewed as the best practice target state for technology organizations (see Figure 3). Instead, each archetype has a distinct set of advantages and disadvantages for different technologies in an organization’s portfolio and any trade-off may shift for any given technology as it matures and develops. For example, significant benefits can be gained through full IT and OT centralization, but two important drawbacks must be considered. First, successful centralization can be a daunting task; leaders of operational units may be wary of giving up control over their critical OT systems and could, therefore, often show resistance to the proposed changes. Second, a higher level of centralization typically decreases an organization’s flexibility, overall agility, and ability to innovate. Centralized organizations are a good fit for managing mature technologies toward the end of their lifecycle but are too regimented to succeed in identifying, incubating, and scaling innovative technologies.

show modalFigure 3. Technology operating model archetypes
Figure 3. Technology operating model archetypes

With significant technological disruption expected to continue affecting the transport and mobility sector over the medium term, responding to the convergence of IT and OT by centralizing the technology function would deliver efficiency at the expense of agility and could lead to less-than-optimal results for transport operators.


Large technology functions commonly oversee sizeable portfolios of products and systems, comprised of technologies of varying degrees of maturity. Moreover, given the broader trend in technological development, the maturity of any single technology will evolve from promising but uncertain innovation to effective competitive advantage and eventually to a commonly available solution and ultimate obsolescence. This evolution has been well studied and is commonly known as the “technology maturity S-curve” (see Figure 4).

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  • By Arthur D. Little
  • 27/05/2024
  • mobility sector, technology operating models
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