As we move into 2025, we ask five leading AEC software developers to share their observations and projections for BIM 2.0
AI: Our Generation’s Paradigm Shift
Tom Kurke, VP, Ecosystems & Venture, Bentley Systems
Today’s infrastructure projects are becoming more complex. Demand for better, more resilient infrastructure is increasing in the face of rapid urbanisation, climate change, the energy transition, and more. The sheer scale of data created from design to construction to operations makes infrastructure a prime area for AI disruption. AI is not just a trend, but a transformative force that will shape the AEC industry and the built environment, paving the way for smarter, more efficient project delivery and asset performance.
Of course, AI isn’t new to infrastructure sectors. We recognise its potential to process vast amounts of data to provide insights that were previously unattainable. Because more than 95% of the infrastructure that will be in use by 2030 already exists today, owner-operators need to ensure existing infrastructure is resilient, efficient, and capable of meeting current and future demands. AI-driven asset analytics generate insights into the condition of existing infrastructure assets, while eliminating costly, manual activities. AI allows operators to predict when maintenance is needed before failures occur. AI agents analyse digital twins of infrastructure assets—bridges, roads, dams, or water networks—to identify issues and recommend preventive action, avoiding costly breakdowns or safety hazards.
But when we take a step back, AI also has huge potential in the design phase of the infrastructure lifecycle. In design, AI can automate repetitive tasks—such as documentation and annotation—so that engineers can focus on higher-value activities.
For example, through a copilot, professionals can quickly create, revise, and interact with requirements documentation and 3D site models through natural language to automatically make real-time design changes with precision and ease. Or, with a design agent, they can evaluate thousands of layout options and suggest alternative designs in real-time, helping them make better design decisions sooner, saving time and money. We have calculated that users can accelerate drawing production by up to ten times, and improve drawing accuracy using AI-powered annotation, labelling, and sheeting that automatically places labels and dimensions according to organisational standards that are optimised for legibility and aesthetics.
AI’s true power will be measured by its ability to improve outcomes—more sustainable designs, faster and safer builds, and more reliable infrastructure systems. As we look to the future, the possibilities seem endless. But to begin to understand what’s possible for tomorrow, we need to be able to harness data – the foundation of AI.
For the effectiveness of AI to take shape, we need to leverage the power of open data ecosystems. Open ecosystems break down barriers and facilitate seamless data exchange across platforms, systems, disciplines, organisations, and people. They ensure secure information flow and collaboration are unimpeded, without vendor lock-in, and preserve context and meaning—ultimately enabling more effective AI-driven analysis and decision-making over the infrastructure’s lifecycle.
This digital thread allows users to connect and align data from various sources—from the engineering model to the subsurface and from enterprise information to operational data, such as IoT sensors and more—to provide the full context needed for smarter decision-making.
Still, that is not enough. To truly unlock the value of AI, the digital twin must be augmented by 3D geospatial capabilities and intelligence. A 3D geospatial view is the most intuitive way for owner-operators and engineering services providers to search for and query information about infrastructure networks and assets. After all, infrastructure is of geospatial scale.
A 3D geospatial view changes the vantage point of an infrastructure digital twin from the engineering model to planet Earth—geolocating the engineering model, and all the necessary data about the surrounding built and natural environment. It enables a comprehensive digital twin of both the built and natural environment, with astonishing user experiences and scale, from millimetre-accurate details of individual assets to vast information about widespread infrastructure networks
By adding AI, to a data-rich, digital twin of the built and natural environment, we can create better and more resilient infrastructure. AI-driven automation, detection, and optimisation can take organisational performance and data-driven decision-making to new levels throughout the lifecycle of a project or asset. Generative AI can help significantly boost productivity and accuracy, while machine learning algorithms can identify inefficiencies, forecast maintenance, and suggest design modifications before physical construction commences.
This powerful combination will unlock unprecedented efficiency, sustainability, and resilience, transforming how we design, build, and maintain the world around us. With open data ecosystems fostering limitless innovation and AI continuously powering and automating 3D-contextualised digital twins, we are entering an era of smarter infrastructure.
Read more opinions
The Future of BIM: Harnessing the Power of Data
Amy Bunszel, executive VP of AEC Solutions, Autodesk
Embracing AI and Boosting Sustainability Across Project Lifecycles
Daniel Csillag, CEO, Graphisoft
Unlocking the Future of BIM with Interoperability
Mark Schwartz, SVP, Trimble
Design transformed: 2025 predictions from Vectorworks
Dr. Biplab Sarkar, CEO, Vectorworks
