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Transforming Infrastructure Project Delivery with Data Analytics and AI



The recently published report on Data Analytics and AI in Government Project Delivery provides a framework to strengthen project delivery and capability-building through advances in data science and AI.  It focuses on developing the use of project data analytics and AI to help deliver projects, putting the UK at the forefront of this emerging discipline. Our work supporting public infrastructure projects emphasises the opportunity to improve outcomes and taxpayer value with data-driven insights and automation. We witness firsthand the challenges faced by industry to using data effectively and advise on how to overcome them. In this article we reflect on the core themes of the report; the importance of high-quality data, an innovation culture within a data-literate workforce, and partnerships for better project outcomes.

  1.  Better data and availability: Driving value from data and AI requires good quality data. Across infrastructure projects, we have seen how the variety and format of data across multiple systems and contractors can restrict the insight. To improve both availability and quality, projects must consider future data needs as part of inception.
  2. Experimenting together: The government pilot schemes proposed in the report will identify the best opportunities quickly but must come with a shift in culture to ensure success. We have advised projects on how best to focus on new opportunities, including providing a list of high impact and common use cases via The Generative AI Dossier. As technology continues to develop, project teams will need to rapidly explore and understand its potential to enable effective outcomes. 
  3. Data skills and capability at scale: The industry needs to embark on a programme of skills development to build capability and create capacity. To overcome the skills shortage, the industry must look to maximise productivity by adding non-traditional digital and data roles. Traditional project roles e.g. Risk Managers will also need to increase their data-literacy as this role evolves.
  4. Data partnerships: Average productivity levels in the construction industry have remained consistently below the UK average [1]. To overcome this both public and private sectors must work together. The sector must be willing to exchange knowledge and build partnerships to ensure relevant skills are developed and best practice is shared.
  5. Evidence-based decision making: The identification of underperforming projects and empowerment of early intervention leads to increased project outcomes. In a recent conversation with the founder of nPlan, Dev Amratia, we discussed the use of nPlan’s machine learning to accurately forecast project outcomes. The use of these technologies on infrastructure projects can improve the accuracy of forecasting assessments, risk management, and real-time monitoring.

 Every infrastructure project is unique and there is not a universal approach to using data analytics and AI. To drive better project outcomes, the infrastructure industry must lean into this framework and share their expertise to deliver long-term value for public benefit. For more perspectives on generative AI in infrastructure and real estate, check out our blogs on bridging the gap,  transforming real estateovercoming implementation challenges and embracing a human centric approach and listen to the Futureproofed Insight on this publication.

References

[1] Office for National Statistics, "Productivity in the construction industry, UK," ONS, 2021.