Last month, Ford Motor Company announced that it had rehired over 300 of its veteran engineers after Artificial Intelligence (AI) failed to match their skills and experience in manufacturing tasks. Coupled with the fear of losing out on Wall Street and the blind acceptance of AI’s superior capabilities over humans, led them to a widespread adoption of AI systems, while “mistakenly” assuming that it would produce high-quality products. Like any new shiny technology, AI also wowed the Ford senior executives. They learned the hard way that AI as a replacement technology is not the answer. The question remains: in what form of AI will we realise the AI opportunity in the transport sector?
Britain’s transport sector is undergoing its most significant structural transformation in a generation with AI and automation are reshaping every aspect. This transformation is not theoretical, it is already underway. AI-powered route optimisation, autonomous mobile robots in warehouses, AI-based management systems, and predictive maintenance platforms are live across the sector. Furthermore, paved by the UK’s Automated Vehicles Act 2024, and the government’s confirmation of commercial robotaxi pilots later this year have spearheaded this transition.
The challenge is not the existence of the AI opportunity. It is its accessibility. The UK has the legislative architecture and strategic ambition to get this right. The Modern Industrial Strategy, the DfT’s Transport AI Action Plan, and the establishment of Skills England together represent a policy environment that is better equipped than at any point in recent history to guide a just transition. All we now need to focus is on execution!
The Purpose of AI
Like any new technology, understanding the purpose for its introduction and adoption remains key. AI in transport is not a technology looking for a problem. It is a direct response to some of the most persistent challenges the sector faces: congestion, carbon emissions, safety risk, workforce shortages, and the growing demand for faster, more reliable movement of people and goods. The UK government’s Transport AI Action Plan, published in June 2025, sets out the strategic purpose bluntly: “delivering cheaper, cleaner, safer journeys for all”. These are not competing ambitions.
At its core, AI enables transport systems to process vast volumes of data, from sensors, satellites, vehicles, and infrastructure and turn it into decisions that are faster, more accurate, and more consistent than human judgement alone can achieve. In practice, this means predicting where a train component will fail before it does, optimising a delivery route in real time as traffic changes, detecting a hazard on a port quayside before a worker encounters it, managing the flow of passengers through a network with minimal delay and making our roads safer with the introduction of autonomous vehicles.
Ultimately, the purpose of AI in transport is to make a system that moves the entire economy work better, for the businesses that depend on it, the workers who operate it, and the communities it serves.
But amidst the media frenzy of the job loss narrative, we miss the real narrative of the purpose of AI of improving the quality of jobs through AI usage, realised by better pay and quality of life.
Realising the AI Opportunity: Human-AI Cooperation
For realising any transformation, especially with megatrends like AI, we need an enabling infrastructure (policies, funding etc.) and a workforce ready for the future. If there is anything we can learn from the experience from Ford, it is that the future is not about using AI as a replacement for human roles, but “AI as a co-worker assisting human roles”.
In transport, where safety is non-negotiable, the most effective systems are those where AI handles complexity and humans handle consequence. Human-AI cooperation is not a transitional arrangement, but a necessity for ensuring a safe and robust system.
AI can process data at scale and speed no human can match. But it cannot replicate the contextual judgement, ethical reasoning, and adaptability and most importantly creativity that experienced transport workers bring. A remote fleet supervisor knows when a telematics alert signals genuine risk, a rail engineer understands what a predictive maintenance flag means in the context of local track conditions, a seafarer reads a situation that falls outside any algorithm’s training data.
However, building a robust Human-AI cooperation in the workforce requires a systemic shift in the mindset of both corporations as well as individuals. It is clear the jobs exist and the workers who can fill them also exist. What is missing is the connective tissue of a structured reskilling pathway that turns operational experience into AI usage competency.
The workers most immediately affected by AI and automation include HGV and taxi drivers, track maintenance engineers, port operatives, all of whom possess deep operational expertise that is genuinely valuable in AI “augmented” roles. But translating that experience into new employment as well as better AI co-workers, requires structured, funded, and credible reskilling pathways.
The onus lies on both on individuals and organisations for this. At an individual level, current or future labour force doesn’t need to build the skills to develop an AI model but should focus on becoming proficient in using AI tools to make their work more efficient in terms of both quality and speed of delivery. This would become (and has already become) a key differentiator in today’s job market. At an organisational level, corporations need to create a long-term plan (with short term wins for answering their shareholders) on reskilling their labour force. Additionally, the Modern Industrial Strategy’s £1.2 billion annual skills commitment and the work of Skills England must be directed with this in mind.
The Next Steps
AI and automation adoption in the UK transport is a moment of genuine opportunity for the workers at its centre. The opportunity is that AI, deployed deliberately, responsibly and governed well, reduces the physical toll of transport work, improves job security and predictability, and opens credible pathways to higher-skilled, better-paid roles.
Realising the AI opportunity depends on treating reskilling not as a welfare measure for workers left behind by technology, but as a strategic investment in the experienced, safety-conscious, operationally expert workforce that Britain’s AI-powered transport system will depend on.
Ultimately, it is the people who power the nation and the economy.
