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To move beyond experimentation, organizations must align the use of AI tools with strategic priorities, define success metrics, and foster collaboration across departments. It’ s not just about technology— it’ s about mindset. Embracing AI requires cultural change, cross-functional teamwork, bridging the skills gap, and a strong understanding of where AI fits best.
Developing trustable AI is crucial. Leaders need to have the appropriate assurance and governance to develop effective, unbiased, explainable and safe tools. Alongside this, they need to be thinking how to design and integrate AI tools into the existing systems and workflows to encourage trust and adoption by users so the benefits can be realized, and AI can augment and enrich human capability.
There’ s enormous potential for AI tools to deliver value, improving outcomes in safety, resilience, and effective and efficient operation of transportation networks, but the cyber and physical security elements must be considered carefully and with an eye to the future development of capability when deploying.
Rethinking procurement and data strategy is another key priority for transportation leaders. Traditional procurement processes can’ t keep pace with rapidly evolving AI tools. Forward‐looking organizations are experimenting with new, agile approaches to procurement and data sharing— supported by clear governance, privacy protections, and data ownership.
Underpinning these efforts is a mature data strategy: maturity in managing the emerging datasets required to train AI systems – the communication systems to collect the data, arranging equitable access or shared data systems as explored in our previous report- How digital infrastructure will shape the future of transportation systems, privacy enhancing technologies, provenance tracking, assigning ownership and liability – this all helps enable successful assurance and reduced risk when scaling a deployment.
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