Scaling data-driven innovation in the transport sector
The transport and mobility sector is undergoing profound transformation, spurred by changing needs and attitudes, net zero de-carbonisation policies, Mobility as a Service offerings, connected and automated vehicles, and tighter competition.
Much of this transformation is data-driven – the range and volume of data that exist about users, vehicles, transport options and services offer huge potential to improve mobility, create new business models, and improve the user experience. Given this, it’s no surprise that the sector sees the potential presented by advances in Data Science, AI and machine-learning, and continues to invest heavily in data-driven innovation.
Earlier this year, we attended the techUK Transport AI conference. That event reinforced how bold the sectors vision for AI is, the extent of investment in it, and the potential value that it can bring. Understandably, much of the focus was on exploring the use cases for AI, highlighting the benefits and risks, and on running pilot projects to prove safety and viability.
A common theme that stood out is that, whilst there are lots of promising pilot projects, there isn’t currently a clear and consistent path for taking grassroots innovation in AI and scaling it up to the regional and national levels necessary to realise its full potential.
The fact that there isn’t a defined path for scaling AI innovation in the sector isn’t surprising. Regulatory and governance frameworks for AI are still being shaped and, until there is more certainty about what these will mean in practice, there will be caution about scaling beyond a certain level. Public attitudes towards AI are also mixed and can change quickly and unpredictably in response to developments in other industries. Understanding attitudes to AI will be key to judging the best way to scale innovation in a way that creates trust and acceptance.
Working through uncertainties like this isn’t easy, but there are examples of delivering data-driven digital transformation at national scale that can provide a blueprint. One great example of this is our work with the DfT/Defra Joint Air Quality Unit, who we collaborated with to deliver the award-winning Drive in a Clean Air Zone service. There were three key ingredients that made this work a success: (1) we took a user-centred approach to delivery. This involved carrying out face-to-face research with users from all impacted sectors to understand their needs and attitudes and co-design the service, building trust, confidence and buy-in in the process; (2) we established a leadership and governance partnership with representation from central government, local government and the private sector. This helped to drive collaboration, as well as bringing together the diverse experience and perspectives necessary to make the transformation work for everyone; and, (3) we mapped out a ‘scale-up pathway’ that incrementally took the service from research and development prototype, to working proof-of-concept, to controlled pilot, to live national service. Safety, security and readiness criteria were defined and assessed at every stage to control progression through the pathway.
These same principles and frameworks can be applied to scaling AI-based innovation and prove that scaling can be done at pace, but in a safe and controlled way.
In summary, we think that the transport and mobility sector should start work now to develop a blueprint and pathway for scaling grassroots innovation in AI to regional and national level. The blueprint should be based on exemplars and lessons learned from delivering other digital transformation at scale in the sector and be developed in partnership between the public and private sector. By doing this, the future of transport stands the best chance of realising AI’s full potential at pace, whilst ensuring that it is safe, secure, and viable at the same time.
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