Cadalyst

Ask a self-taught Fusion user how they learned the software, and the answer is often some combination of "YouTube and trial-and-error." This can work, but only up to a point. Video tutorials are good at showing which button to click, but they're less reliable at explaining why — and when you’re using parametric modeling, that gap can catch up with you. ASCENT’s updated Autodesk Fusion: Introduction to Parametric Modeling. Image source ASCENT. Image source ASCENT.

Image source:  陈佳乐 /stock.adobe.com. Model Context Protocol — MCP — is an open standard for connecting AI systems to external software tools and data sources.

Image source: Kunwer Studio/stock.adobe.com. As AI moves from isolated task assistance to connected, agentic workflows, AEC firms are rethinking the workstation’s role in design, analysis, and project delivery. This article explores how local AI infrastructure, Lenovo workstations, and NVIDIA technologies can help firms run AI-enabled workflows securely and efficiently within their own environments.

The growing complexity and scale of infrastructure projects has created a pressing need for faster, more precise engineering workflows. With the AEC industry facing mounting workloads and workforce shortages, many organizations are implementing artificial intelligence (AI) to help tackle complex projects on tight schedules.

Architecture, engineering, and construction (AEC) professionals rely on a variety of digital tools throughout project lifecycles. From initial concept design sketches through detailed design, construction, and operational models, project participants work with numerous discipline-specific tools and generate massive amounts of data to complete projects of all types — ranging from government infrastructure projects to commercial properties and buildings.

With infrastructure data residing in a variety of locations and formats, project participants often spend significant time searching for information rather than acting on it. Data sources might include design models, photographs, IoT sensor data, record drawings, and various other documents that span across project lifecycles. Image source Saulo Studio/stock.adobe.com.

Water system managers and engineering teams face increasing pressure from rising demands, climate pressures, aging infrastructure, changing regulatory requirements, budget constraints, and growing expectations for system resilience. Image source: Phoophinvo/stock.adobe.com. What You'll Learn How AI is transforming water infrastructure workflows, from flood modeling to asset management. Where AI-enhanced Autodesk tools — including InfoDrainage, InfoWorks ICM, and Info360 — deliver measurable value.

In this episode of Cadalyst SmartCasts, we explore how GPU-accelerated simulation is transforming engineering workflows, bringing powerful analysis directly onto the workstation. Instead of waiting hours or days for results from CPU-based systems or shared servers, engineers can now run high-fidelity simulations locally — enabling faster iteration, deeper insight, and better design decisions earlier in the process.

As point clouds reach billions of data points and image sets grow into terabytes, the bottleneck isn’t capture — it’s processing. In this Cadalyst SmartCast, we explore how AI-driven reality capture is transforming massive datasets into usable, decision-ready models — and why local GPU performance is now mission-critical.

The infrastructure industry continues to look for ways to encourage and inspire young people to enter the profession. Understanding infrastructure begins in classrooms, labs, and studios — where future engineers first learn how to think about systems, constraints, and the long-term consequences of design decisions. At Bentley Systems’ 2025 Year in Infrastructure Conference, that idea took center stage with the presentation of the company’s inaugural Educator of the Year Award.