Revolutionising Infrastructure With AI

Steve Lewis, partner and Head of Infrastructure at EY Hong Kong and Gillian O’Boyle, partner in EY’s UK technology consulting business, outline the five guiding principles and ambitions to create opportunities for greater AI adoption in the infrastructure sector from EY/FIDIC’s new report “How AI can unlock a new future for infrastructure”. The report explores how to unlock the value of AI and truly transform the infrastructure sector.

Artificial Intelligence (AI) offers a transformative pathway for the infrastructure sector, but its adoption is still in its infancy. To drive urgently needed transformation, we must take decisive action to embrace AI on a larger scale. EY’s new report with International Federation of Consulting Engineers (FIDIC) “How AI can unlock a new future for infrastructure” explores how AI can be used to help the sector build assets more efficiently, cost-effectively and sustainably.

The report outlines five guiding principles and ambitions to create opportunities for greater AI adoption in the infrastructure sector. These ambitions aim to facilitate further automation, reduce duplication, strengthen controls, and enable evidence-based decision-making.

1. Determine the Purpose: Proactively Align Infrastructure with Societal Needs

We need to increase certainty that infrastructure will meet its intended purpose before funding commitments are made. This involves taking a proactive industry approach, with incentives and enablers initiated by asset owners and governments.

Actions we can take:

• Prioritise end-user needs and societal benefits in all planning and decision-making processes
• Ensure assets are developed with resilience, responsiveness, and sustainability as key priorities
• Use AI technologies like spatial location intelligence to analyse site conditions and optimise planning

2. Plan for End-to-End Delivery: Integrate AI Throughout the Project Lifecycle

We need to focus on enhancing the quality and reliability of project planning and preparation. This involves developing comprehensive digital strategies that integrate AI technologies throughout the project lifecycle.

Actions we can take:

• Invest more time in thorough up-front planning to ensure projects are deliverable and affordable
• Implement AI-driven solutions from initial planning through operation and maintenance phases
• Enforce data continuity and interoperability across all project stages

3. Confirm the Operating Model: Create Flexible Frameworks for AI Integration

We need to set up projects and programmes with governance and contract arrangements that support the ambitions of wider portfolios. It’s about creating flexibility and addressing data ownership and liability concerns in the AI era.

Actions we can take:

• Continuously reassess and adapt traditional delivery and operating models
• Establish flexible contractual frameworks that encourage innovation while managing risks
• Integrate AI tools to create more agile and responsive operating models

4. Integrate Ways of Working: Build AI Capabilities Across the Workforce

The fourth principle and ambition emphasises the need to ensure the right systems and teams are in place to operate more efficiently. This involves building capacity and capabilities for AI implementation across the workforce.

Actions we can take:

• Invest in training and upskilling programmes to ensure the workforce can work alongside AI technologies
• Foster partnerships between industry stakeholders and educational institutions
• Cultivate a culture of collaboration and continuous learning, emphasising cross-disciplinary skills and AI literacy

5. Operate Responsive Assets: Leverage AI for Optimised Asset Performance

We need to ensure operational considerations are at the forefront of decision-making throughout the asset lifecycle. This involves leveraging AI for optimised asset performance and maintenance.

Actions we can take:

• Implement AI-driven predictive maintenance systems and digital twins
• Use data-driven solutions to analyse asset operational performance against assumptions and targets
• Incorporate feedback loops from operational data into future project planning and design processes

The infrastructure industry stands at a pivotal moment. These five guiding principles and ambitions provide a roadmap for action. But the transformative power of AI goes further than this. It opens up opportunities to collaborate across the different phases of the lifecycle like never before and enables new, innovative business models to evolve. For example:

1. Collaborative Data Ecosystems: This model envisions a network of interconnected platforms where stakeholders can seamlessly share data, insights, and AI models. By breaking down data silos, we can unlock new levels of efficiency and innovation across the entire project lifecycle.
2. Value-Driven Data Strategies: This approach focuses on extracting tangible value from the vast data resources generated by infrastructure projects. By treating data as a valuable asset, organisations can uncover new revenue streams and drive operational improvements.
3. AI-Centric Contracting: This model reimagines traditional contracting approaches to incentivise AI adoption. We can accelerate the industry’s digital transformation by incorporating AI-related criteria into tender evaluations and rewarding AI-driven innovations.

These models set the stage for a new era in infrastructure, but their successful implementation hinges on cultivating a different mindset, developing crucial skills, and leveraging appropriate tools.

By embracing them and leveraging the power of AI, we can address the immense challenges we face and usher in a new era of innovation and efficiency.

Find out more about the opportunities of AI in our new report.