Navigating AI’s Impact on Transport Skills: Insights from the International Transport Forum Roundtable on ‘Impacts of artificial intelligence on skills required in the transport sector’

The CCAM-ERAS project was pleased to participate in the ITF-OECD Roundtable in Paris (Feb 6-7, 2025) on "(Work)less is more? Impacts of artificial intelligence on skills required in the transport sector." Representing Panteia as part of CCAM-ERAS, Martin Clarke and Charlotte Byrne contributed to discussions on the evolving skill requirements in the transport sector. We extend our gratitude to the International Transport Forum for organizing this timely and important event.

Key Outcomes & Findings:

AI is Transforming Transport Jobs

As AI becomes increasingly integrated into transport systems, its impact on employment is complex. While automation presents opportunities for efficiency gains, it also redefines traditional roles. High-skilled positions in data analysis, AI system management, and predictive maintenance are seeing growing demand. However, lower-skilled jobs remain at significant risk of automation, particularly in operational roles such as driving and logistics coordination. A crucial concern is ensuring that AI implementation does not exacerbate inequalities in the labour market. While some jobs will be augmented by AI, others may disappear entirely. This transition requires strategic workforce planning and reskilling initiatives to prevent displacement.

Growing Skills Shortages

A significant shortfall exists in AI-related expertise, including machine learning, data analytics, and system integration. The transport sector also lacks sufficient skills in sustainability-related practices, particularly regarding environmentally responsible AI applications. One of the biggest barriers to AI adoption is the workforce’s digital readiness. Many transport professionals lack the necessary digital literacy to work effectively alongside AI-driven systems. Addressing this challenge requires investment in training programs that focus on both technical and soft skills, such as critical thinking and adaptability.

The Need for Education & Upskilling

Traditional education and training systems struggle to keep pace with AI advancements. Universities, vocational institutions, and training providers must rapidly adapt their curricula to reflect the evolving demands of the transport labour market. Lifelong learning is crucial to ensuring that workers can continuously upskill and reskill as AI technologies develop. There is also a pressing need to integrate AI-related competencies into early education to prepare future generations for careers in AI-enabled transport.

Attracting Talent to Transport

The transport sector faces a talent acquisition challenge, particularly among younger generations who perceive it as less attractive compared to technology-driven industries. This issue is compounded by concerns over job security due to automation.

To attract new talent, companies and public sector stakeholders must emphasize diversity, inclusivity, and career development opportunities. Offering competitive salaries, mentorship programs, and clear pathways for career progression can make the sector more appealing. Promoting sustainability initiatives and the role of transport in achieving global climate goals can also attract socially-conscious professionals.

The Role of Policy Intervention

Public authorities play a critical role in shaping AI preparedness through regulatory measures, workforce transition policies, and collaborative training programs. Governments must anticipate the socio-economic consequences of AI adoption and implement policies that support workers in transition.

Key policy measures include:

• Creating incentives for employers to invest in AI training programs
• Enhancing cooperation between industry and education providers to ensure training aligns with industry needs
• Strengthening labour rights and protections to prevent exploitation in AI-driven workplaces
• Developing frameworks to regulate AI in transport while ensuring human oversight remains central to safety-critical tasks
• AI Implementation is Still in Early Stages

Despite AI’s potential to enhance transport efficiency, its widespread implementation remains in the early stages. Many firms anticipate long-term productivity gains, but they face significant barriers such as high costs, regulatory uncertainties, and the need for extensive workforce adaptation.

Current applications of AI in transport include:

• Predictive maintenance – AI is helping to improve reliability and reduce downtime in public transport and freight logistics.
• Traffic management – AI-powered algorithms optimise traffic flow and reduce congestion in urban environments.
• Accident prevention and risk management – AI is being used in sensor-based monitoring systems to enhance road safety.
• Logistics and supply chain optimization – AI-driven predictive analytics improve route planning and reduce emissions.

However, challenges persist. Many transport organisations still rely on outdated systems that are not easily integrated with AI. Furthermore, concerns about data security, accountability, and ethical AI use must be addressed before widespread deployment.

Looking Ahead

The ITF-OECD Roundtable underscored that AI’s impact on transport jobs is not merely a technical challenge—it is a societal one. Ensuring that AI supports both transport efficiency and workers requires a multi-stakeholder approach involving governments, industry leaders, education providers, and trade unions.

Strategic workforce planning, education reform, and governance frameworks will be essential in navigating this transformation. The CCAM-ERAS project remains committed to advancing research and policy discussions on AI’s role in transport and ensuring that the transition to AI-enabled mobility is inclusive and equitable.

Once again, we extend our thanks to the International Transport Forum for convening this critical discussion. We look forward to further collaborations and continued efforts to shape the future of AI in transport.