Building a Smarter Industry: The Role of AI in Knowledge Management
- Enrique Galicia
- Mar 27
- 5 min read
The AEC industry has always been built on knowledge—hard-earned expertise, evolving best practices, and insights gained from real-world projects. Yet, despite this wealth of information, many firms struggle to capture, organize, and apply knowledge in a way that drives better decision-making.
The problem isn’t a lack of data—it’s that finding, validating, and making sense of information takes too much effort. Searching for a key document, verifying if a design detail follows the latest code, or trying to remember what went wrong in a past project often leads to frustration, wasted time, and preventable errors.
This is where AI steps in—not as a replacement for expertise, but as a tool to bridge knowledge gaps, improve access to critical information, and make workflows more intelligent. The real question isn’t whether AI will change how we work—it’s how we can use AI to work smarter.
Through the development of custom-enhanced applications, the ability to connect to multiple Large Language Models (LLMs), and the creation of specialized nodes and structures that support AI-driven automation, new workflows are emerging that don’t just process information but enhance knowledge retention, optimize design workflows, and improve deployment strategies.
The Challenge: More Data, Less Clarity
Every project generates an overwhelming amount of information—BIM models, specifications, emails, RFIs, and meeting notes. Each file contains valuable insights, but without structured access, that knowledge remains buried.
Take a common scenario: a contractor is looking for the latest version of a construction drawing labeled “Issued for Construction.” But was it truly final, or just named that way? Manually verifying its accuracy means checking revisions, cross-referencing emails, and hoping that the document control process was followed correctly.
Now, imagine an AI-driven system that understands the context of project documentation. Instead of just retrieving the file, AI could verify its revision history, cross-check related approvals, and flag potential discrepancies. Rather than spending hours manually validating information, teams get instant, reliable answers.
Through customized knowledge management tools, it’s possible to connect multiple LLMs, Dynamo Nodes, and structured AI workflows to ensure that not only is information stored, but that it is retrieved in a way that fits into the workflow dynamically.
For example, in ongoing developments, nodes have been crafted to retain project history, ensuring that previous design decisions, lessons learned, and deployment structures are not only accessible but automatically suggested when similar issues arise in future projects.
Another challenge is knowledge retention. Senior professionals carry decades of industry expertise, but when they leave, much of that knowledge disappears. AI doesn’t just store information—it helps structure and retrieve insights in a way that makes them accessible to future teams. Instead of relying on memory or manual searches, firms can start learning from past projects in a systematic, scalable way.
AI as a Tool for Smarter Knowledge Management
AI isn’t about replacing human decision-making—it’s about enhancing it. Instead of making final calls, AI helps professionals analyze patterns, surface key insights, and structure information in ways that would be nearly impossible manually.
Through custom-enhanced applications, AI workflows can be integrated across multiple decision-making processes, leveraging the ability to compare different models, refine results based on project-specific requirements, and provide enhanced insights that improve execution.
For example, in recent developments, AI-powered applications have been used to:
Connect multiple LLMs to interpret BIM project data and provide automated insights on design efficiency, compliance, and coordination conflicts.
Automate model structuring in Dynamo—allowing AI to enhance decision-making when defining MEP layouts, routing optimizations, and space planning constraints.
Create structured AI-powered nodes that interact with real-time project data to retain key project knowledge and apply iterative learning for future applications.
Think about how BIM coordination works today. A project manager reviewing a Revit model might have to manually check for clashes, analyze MEP layouts, and compare different routing options. With AI, that same workflow becomes smarter and faster—AI can analyze past projects, suggest clearance adjustments based on industry standards, and even propose alternative design configurations.
Similarly, AI can assist with automated compliance checks, flagging potential code violations before submission. Instead of manually reviewing regulations, AI-powered systems can pre-screen designs, ensuring they align with industry requirements. This doesn’t eliminate the need for human oversight—it simply reduces the amount of manual effort required to maintain compliance.
However, for AI to be truly effective, it needs to be more than just a static tool—it must be tested, iterated, and refined based on real project workflows. Access to rapid prototyping and AI-driven development environments allows teams to explore what works, refine models, and adapt AI to real-world needs.
Thriving in a Constantly Changing Industry
AEC has never been known for rapid technological adoption, but AI is shifting the landscape faster than any previous innovation. The firms that will thrive in this new era won’t be the ones that resist change—they’ll be the ones that experiment, adapt, and refine their AI strategies continuously.
Instead of debating whether AI is ready, the better question is: How can we make AI work for us today?
Instead of fearing automation, we should focus on integrating AI into workflows that improve efficiency and reduce repetitive tasks.
Instead of seeing AI as an external tool, we should test and refine its applications based on real-world needs.
Instead of waiting for AI to be fully developed, we should engage with AI in ways that align with industry best practices.
The professionals who embrace AI—not as a replacement for their expertise, but as an enhancement to their decision-making process—will be the ones who set the standard for the next generation of AEC innovation.
Final Thoughts: AI as a Partner in Industry Transformation
The real potential of AI isn’t in automation alone—it’s in unlocking smarter ways to work. AI helps us retrieve knowledge faster, structure data more effectively, and improve collaboration across teams.
The future of AEC isn’t about removing human expertise from the process—it’s about giving professionals better tools to amplify their impact. AI can:
Capture and structure industry expertise, ensuring lessons learned are retained and shared.
Improve decision-making by rapidly validating information, reducing the risks of errors and miscommunications.
Enhance workflow efficiency by eliminating bottlenecks, allowing teams to focus on innovation instead of repetitive manual tasks.
Enable rapid testing and prototyping, helping firms refine AI-driven solutions based on real project needs.
Through custom-developed AI-enhanced applications, we’ve seen how connecting multiple LLMs, advanced Dynamo nodes, and structured AI-assisted development workflows can optimize decision-making, improve knowledge retention, and provide teams with the tools to prototype AI-driven solutions faster.
The real question isn’t whether AI will change the industry. It already is. The challenge now is to actively shape its role so that it works in ways that benefit us the most.
The smarter industry of tomorrow won’t be built by waiting for AI to mature. It will be built by those who engage with AI today, refine it, and make it an essential part of how they work.
Next Steps: Where Do We Go From Here?
The real question isn’t whether AI will change the industry, it already is. The challenge now is to actively shape its role so that it works in ways that benefit us the most. AI isn’t just about automation; it’s about unlocking smarter ways to work, helping firms retrieve knowledge faster, structure data more effectively, and improve collaboration across teams.
This transformation was a key discussion at Tech Rize LA, where we joined the panel Empowering Growth: Scaling Knowledge in an Ever-Changing Landscape. Alongside industry leaders Nicole Buhles, Chun Liu, Anne Carpenter, Robert Yuen, and Enrique Galicia. The discussion made one thing clear: firms that treat AI as a knowledge engine will lead the future of AECO.
The smarter industry of tomorrow won’t be built by waiting for AI to mature. It will be built by those who engage with AI today, refine it, and make it an essential part of how they work. If you’re ready to bridge the gap between AI potential and real-world implementation, let’s connect.
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