EA: Based on your work at Bentley Systems and your broader experience with environmental modeling and applied technologies, what do you see as the main barriers to fully embracing digital innovation in sustainable infrastructure planning and delivery? What's holding organizations back?

RF: I believe that many consulting and engineering firms have already adopted digital innovation in their planning and delivery of sustainable infrastructure. These organizations recognize the benefits of the "double transition"—the integration of digital and sustainability goals—and increasingly embed this approach into their business models.

However, a significant number of firms remain digitally immature in their provision of sustainability services for infrastructure. Based on my experience, several factors contribute to this:

• Limited pressure from asset owners and operators. In many cases, clients don't explicitly demand digital sustainability capabilities, which weakens the business case for innovation.
• Poor integration between sustainability and digital teams. There's often a disconnect between Chief Sustainability Officers and Chief Digital or Information Officers. Ideally, these roles should become obsolete as digital and sustainability concerns become embedded across all operations. However, today, I still see siloed teams that fail to share knowledge or coordinate their efforts. As a result, advanced tools like BIM or digital twins may be used in projects, but are not leveraged to achieve sustainability goals, such as reducing embodied carbon.
• Lack of awareness of the return on investment (the answer to the following question addresses the ROI in more detail). Many organizations underestimate the competitive advantage of aligning digitalization with sustainability. Software providers and innovators need to do more to educate the market—through clearer storytelling and evidence—about the tangible value of this integration.
• Weak adoption of sustainability standards and regulations. While sustainable infrastructure can be delivered without digital tools, the process becomes much more efficient—and outcomes more measurable—when supported by data-driven methods. For example, achieving certifications like ISI's Envision rating is far more resource-intensive without integrated digital workflows. Countries with stronger regulatory frameworks and companies that adopt sustainability standards tend to lead in the double transition.

Overall, accelerating the double transition requires stronger client demand, cross-functional collaboration, clearer articulation of value, and more robust policy frameworks.                                                                                                                       

EA: How can digital tools—including AI, geospatial analysis, and digital twins—be most effectively used across the infrastructure lifecycle to plan, adapt, and implement climate-resilient systems? Are there particular use cases or success stories that stand out?

RF: Digital technologies are playing a transformative role in making infrastructure more climate-resilient across the entire lifecycle—from planning to decommissioning. Here are several key areas where they are making a significant impact:

• Risk management. Predicting, monitoring, and managing climate-related risks has become far more effective with recent advances in AI, IoT, and cloud computing. While physical models have long been used in risk assessments, the integration of real-time data and predictive analytics has enabled early warning systems and decision intelligence that are faster and more responsive. For instance, flood forecasting systems powered by AI and sensor networks are now being used to deliver real-time alerts to vulnerable communities and infrastructure managers. Similar approaches can be applied in critical infrastructure assets, such as dams (a success story in US can be found here)
• Climate transparency. Digital platforms are improving how we measure, monitor, and report climate metrics. By making this information accessible to all stakeholders, digital tools help identify emissions hotspots, performance gaps, and opportunities for action. Carbon analysis tools, for example, are increasingly integrated into design platforms, enabling more informed choices early in project planning. Through the integration of the Embodied Carbon in Construction Calculator (EC3) with Bentley's iTwin platform, engineering firms can now quantify embodied carbon directly from their infrastructure models at the design stage. This empowers project teams to compare low-carbon material options and demonstrate their climate performance to clients and regulators in a transparent and auditable manner.
• Data circularity. Just as we promote circularity in materials, we must apply the same thinking to data. Studies suggest that 60–90% of organizational data is never used—what we call dark data. This unused data not only represents lost value but also contributes to digital waste. Emerging AI capabilities and digital twin platforms help unlock the value of this data across the infrastructure lifecycle. A digital twin created for an offshore wind farm during design can also be used decades later in decommissioning—provided the data remains accessible and interoperable. Open platforms such as Bentley's iTwin platform make this continuity possible.
• Collaboration and interoperability. Cloud-based tools now enable multiple stakeholders, from engineers to city officials, to view and collaborate on project data in real-time, from anywhere. This enhances transparency, reduces delays, and fosters multi-disciplinary coordination. However, interoperability is key—an open ecosystem for digital twins avoids vendor lock-in (allowing the asset owner to move all its data and digital twin from one vendor to the other) and ensures more efficient collaboration across diverse tools and disciplines. A great example is HS2 in the UK, where Bentley's open digital twin technology has helped unify data across multiple contractors and stakeholders, providing a real-time collaborative environment to assess climate risk, construction impact, and mitigation measures transparently.
• Improved communication of climate risk. One of the most critical enablers of resilience is the ability to communicate risk clearly. Thanks to advances in geospatial analysis and visualization, we can now present complex scenarios in intuitive, photo-realistic 3D maps. Imagine showing a city mayor a high-resolution flood simulation that visually highlights vulnerable roads and assets. These tools—such as those enabled by Cesium (a 3D geospatial platform for visualization) —are revolutionizing how we engage stakeholders and support actionable decisions. Check here a great example of a 3D visualization tool for the EU-funded Destination Earth Project, which integrates our Cesium technology.

In short, digital technologies are not just tools for efficiency—they're becoming foundational for resilience. The key is to ensure open, integrated platforms that unlock the full value of data across time, stakeholders, and systems.

 

EA: How do you see AI contributing to both increased productivity and a sharper focus on innovation in infrastructure development? What are your tips for ensuring data capture and scenario modeling are aligned with the needs of diverse end-users?

RF: AI is already enabling infrastructure professionals to do more with less—and to do it better. In a sector facing persistent skills and talent shortages, this is especially critical. From accelerating sustainable and resilient design to reducing resource consumption (energy, water, and waste), minimizing operational delays, and enhancing asset inspections, maintenance, and early warning systems — AI is delivering real, measurable value today.

Looking ahead, AI will play an even greater role in helping us make complex, data-driven decisions — particularly in optimizing trade-offs between carbon, cost, time, resilience, and safety. These are decisions that, until now, have been too complex or time-consuming to resolve effectively.

As an environmental engineer with a background in numerical modeling, I'm especially aware of the "garbage in, garbage out" principle. No matter how advanced the model, its output is only as reliable as the quality of the data it receives. To ensure realistic scenario modeling and usable insights, I recommend three key steps:

• Start with a clear data strategy. Before implementing AI, organizations need to ask: What are we trying to achieve with the data? Who are the key stakeholders? What is materially relevant — and what isn't? What data already exists? And what spatial and temporal resolution is sufficient to drive meaningful outcomes?
• Prioritize quick wins. Often, small steps lead to significant results. For example, in a water supply network system (see a success story here), deploying a basic sensor network and an AI-powered monitoring system can help optimize pumps to reduce energy use, detect anomalies indicating potential leaks or bursts, and boost both response time and water conservation. Solutions like these — combining AI and digital twins — can generate immediate returns and lay the groundwork for broader innovation without waiting for large-scale disruption.
• Build the infrastructure right — and the right infrastructure. AI helps improve project delivery by boosting accuracy, efficiency, and coordination. But its value also lies upstream: in better planning and design. By modeling future scenarios, AI helps ensure that infrastructure investments align with long-term community needs. Tools like 3D geospatial systems enhance transparency, improve stakeholder engagement, and support more informed, inclusive decision-making.

In short, I think AI is a fantastic technology upgrade — but it goes beyond that; it's also a strategic enabler. It helps us bridge today's capability gaps while paving the way for smarter, more sustainable infrastructure for the future.

 

EA: In your view, how are hyperscalers and the rapid expansion of data centers transforming the digital infrastructure landscape? What implications does this have for climate-resilient infrastructure?

RF: Hyperscalers and the global growth of data centers are redefining what's possible in digital infrastructure. Their computing power, storage capacity, and global reach are enabling new solutions that were unimaginable just a few years ago.

A great example is Bentley's partnership between Blyncsy (a Bentley brand) with Google, combining Google Street View with advanced AI — specifically computer vision — to detect safety issues on roads, highways, and urban streets. These insights can be generated routinely or immediately after extreme weather events or emergencies — such as the wildfire in Pacific Palisades — helping agencies accelerate the evaluation of damages, and then respond faster and more effectively.

The scale and capabilities of hyperscalers also enable the development of high-fidelity digital twins that integrate large amounts of data from both the natural and built environments — above and below ground. These models are becoming increasingly essential for simulating climate risks, planning adaptation strategies, and enhancing decision-making under uncertainty.

To sum up, hyperscalers are accelerating our ability to build more climate-resilient infrastructure — by making it easier to capture, process, and act on data at unprecedented scale and speed. They're powering innovation and enabling more proactive and intelligent infrastructure planning.