Schemes for the Developing Enhancing Skills

Skills foresight

This phase of the research project provides a forward-looking perspective on how Connected, Cooperative and Automated Mobility (CCAM) will reshape skills demand—focusing not on predicting job numbers, but on how work itself is evolving across the entire mobility system.

A central finding is the emergence of a structural shift from traditional, manual and mechanical tasks toward hybrid technical–digital roles. Across sectors, including manufacturing, logistics, maintenance, and passenger transport, work is becoming increasingly software-driven, data-enabled, and system-oriented. As a result, demand is rising for capabilities in software engineering, data analytics, cybersecurity, artificial intelligence, and systems integration, while purely mechanical and routine activities steadily decline.

The analysis also highlights that policy and governance are key drivers of skills change, often shaping demand as strongly as technological advancement. Regulatory requirements linked to safety, data protection, cybersecurity, and liability are accelerating the need for new competencies—particularly in areas such as compliance, system validation, and operational oversight—and are creating new points of pressure across both industry and public institutions.

Importantly, the transition is characterised less by job loss and more by task reconfiguration and role transformation. Existing occupations are evolving rather than disappearing: for example, drivers and technicians increasingly shift toward supervisory, diagnostic, and digitally supported roles, while entirely new positions—such as remote operators and system integrators—are emerging.

However, the findings underline significant challenges. Skills gaps are already evident and are expected to widen, driven by the mismatch between rapid technological change and slower adaptation in education and training systems. Shortages in digital and cybersecurity expertise, combined with capacity constraints in SMEs and public authorities, risk slowing CCAM deployment if not addressed.

Overall, this phase highlights that the successful transition to CCAM depends on building a workforce with cross-disciplinary, adaptable skillsets, supported by continuous upskilling, more flexible learning approaches, and stronger alignment between industry needs, education systems, and policy frameworks.

Read the full analysis here CCAM_ERAs_D4.5 Skills foresight report v0.2

Use Case Impact Analysis of skills

This work continued to translates the transition to CCAM into a set of concrete, real-world use cases, examining how impacts differ depending on operational context, deployment environment, and service model. This approach highlights that CCAM is not a single uniform shift, but a diverse landscape of applications, each with its own trajectory, benefits, and challenges.

The analysis focuses on a portfolio of use cases across both freight and passenger mobility, including:

Automated operations in controlled logistics environments (e.g. ports, terminals, warehouses)
Hub-to-hub and long-distance freight transport
Urban and last-mile delivery services
Shared automated mobility services (e.g. on-demand shuttles, robotaxis)
Fixed-route automated public transport (e.g. buses and shuttle loops)
Integrated mobility platforms and service ecosystems (e.g. Mobility-as-a-Service models)

A key finding is that deployment potential varies significantly across these use cases. Applications in controlled or semi-controlled environments—particularly in logistics and fixed-route operations—are more mature and scalable in the near term. These settings allow automation to deliver clear efficiency gains, cost optimisation, and operational predictability. In contrast, use cases operating in complex, mixed-traffic urban environments face slower progress due to greater technical uncertainty, regulatory constraints, and public acceptance challenges.

Across all use cases, the transition is best understood as a reconfiguration of tasks and operational models rather than wholesale replacement. Routine, repetitive activities are increasingly automated, while human roles evolve toward system supervision, remote operation, exception management, and service delivery functions. This results in a hybrid operating model where humans and automated systems are tightly integrated, particularly visible in logistics coordination and shared mobility services.

At the same time, the analysis shows that value creation shifts alongside these operational changes. Data becomes central to how services are delivered and optimised, enabling more integrated, responsive, and platform-based models. New service concepts—such as on-demand mobility and digitally coordinated logistics—emerge, supported by continuous data exchange and system interoperability.

However, several cross-cutting challenges remain consistent across use cases. These include uncertain business models in early deployment phases, fragmented regulatory environments, and varying levels of readiness across stakeholders and regions. These factors influence both the speed of adoption and the ability to scale solutions effectively.

Overall, this phase highlights that CCAM will evolve through a portfolio of use cases progressing at different speeds, requiring targeted, context-specific strategies to unlock their full operational, economic, and societal potential.

Read the full report here D5.1 Report analysing the impact of CCAM on each use case