With the development of various tools, the need for automation in the BIM industry is felt more strongly than ever. It is no longer possible to imagine a large project or an engineering company progressing without automation, as today automation has become an inseparable part of the BIM process.
At the same time, with the rapid growth of artificial intelligence tools and LLMs, a fundamental question has occupied the minds of many Revit users:
Which is better—Dynamo or AI tools?
Before LLMs entered the field in their current form, there were only two ways to achieve automation in Revit:
1. Developing dedicated plugins and software using the API and languages such as C#.
2. Using Dynamo to automate repetitive and time-consuming tasks.
The first method required deep programming knowledge—a skill many civil, architectural, or MEP engineers had neither the interest nor the time to learn.
The advent of Dynamo changed everything. Initially introduced as a standalone plugin, it quickly won over users and soon became an inseparable part of Revit.
The great advantage of Dynamo was that automation no longer required programming knowledge. It was enough to connect nodes with wires to create a functional script.
This shift allowed any user—regardless of their specialization—to automate lengthy and time-consuming tasks with ease. The overwhelming enthusiasm of users eventually forced Autodesk to integrate Dynamo directly into Revit.
Of course, Dynamo had its own limitations. If the required node did not exist, Python (Inside Dynamo) or plugin development had to come into play again. This challenge led developers over time to release a wide range of packages that significantly expanded Dynamo’s capabilities.
The Rise of a New Player: Artificial Intelligence
In recent years, AI tools have entered the scene. This time, the need for coding knowledge has almost reached zero. The user only needs to describe their need in the form of a simple chat, and AI will take care of the rest.
And here the main question arises again:
Is Dynamo still our first choice, or should we move toward AI tools?
This post is written to answer exactly this question. We will practically compare the applications of Dynamo and AI tools in Revit, explore their advantages and limitations, and examine in which areas each performs better.
Main Applications of Dynamo in Revit
Tasks automated with Dynamo can generally be divided into two categories: data management tasks (non-modeling tasks) in Revit and modeling tasks.
Data Management Tasks:
These tasks do not involve creating or modifying geometry. Examples include changing parameter values, managing the naming of elements and views, creating new views, and similar actions. Due to their simplicity and ease of implementation, these are among the most widely used and important tasks in Dynamo.Modeling Tasks:
These involve creating or modifying model elements, such as modeling walls, structural elements like columns, or MEP elements such as pipes. Because of the inherent complexity of modeling through code, these tasks are more difficult to implement and are less common compared to data management tasks.
Examples of Data Management Tasks
Creating new sheets
Renaming views and sheets based on project standards
Filling parameter values under specific conditions
Creating shared parameters
Creating schedules
Creating view templates and applying them to views
Creating levels and grids
Exporting data and information to Excel
Updating parameters and information based on an Excel file
Examples of Modeling Tasks
Modeling project walls using coordinates
Modeling beams and columns with the help of grids
Modeling ducts and pipes
Updating elements and changing their positions
Creating openings in walls and floors
In practice, modeling tasks are usually limited to specific types of projects or structures where the geometry follows a repetitive and predictable pattern—for example, warehouses or modular designs. In such cases, Dynamo can be efficient because the same rules apply across the entire model. But for most real-world projects, modeling requires too many variables and on-the-spot decisions, which makes full automation impractical.
By contrast, data management tasks are highly practical since they follow a predictable structure, require fewer inputs, and are easier for users to execute.
Practical Advantages and Limitations of Dynamo
Dynamo’s biggest strength is its simple, visual interface: by connecting nodes with wires, you can simulate operations without needing to master a programming language. That said, a basic grasp of programming ideas (logic, algorithms) plus knowing what each node does and its inputs/outputs helps a lot—you can reach solid results with some focused time.
It is also important to note that Dynamo offers an extensive set of node libraries. These libraries allow users to quickly adjust input parameters and even create custom nodes, making repetitive editing tasks faster and more flexible. This feature ensures that Dynamo can scale beyond simple graphs by leveraging community-built or custom-developed nodes.
Example — Get all walls in the project
Dynamo node | equivalent to this C# (Revit API) Code |
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Advantages
Native integration with Revit: Dynamo is fully integrated; you can run scripts directly with Dynamo Player. There’s no compiling, packaging, or external runner needed.
By contrast, if you write the same logic as code, you often need to compile a DLL or use intermediate tools—adding time and making debugging more cumbersome.
Limitations
Scaling & maintainability of large graphs: The same simplicity can turn into a constraint when graphs get long and complex. Many nodes make the canvas hard to navigate, finding the right node becomes tedious, and adding a new feature mid-graph can be time-consuming. The bigger the graph, the harder future extension and maintenance become.
In practice, this is where many users start moving to code—first Python inside Dynamo, then DesignScript, and eventually external plugins (compiled add-ins)—because classes, functions, and modular structure make development and maintenance cleaner.No guaranteed linear execution: Dynamo does not truly execute nodes strictly in sequence for complex graphs; parts may run in parallel. Although documentation suggests execution can follow connection order, in real, complex scripts that’s often not reliable. Users typically resort to “waiting” nodes to enforce the desired order.
At the same time, for more complex tasks—especially those requiring advanced data handling or interface manipulation—C# development still holds significant advantages. Compiled add-ins often provide better performance, structured debugging, and long-term maintainability compared to large Dynamo graphs.
Short Summary
For small, quick tasks, Dynamo is excellent: connect a few nodes and you’re done. For large, complex scripts, it’s often not the most efficient path—both because big graphs are hard to maintain and because enforcing strict execution order can be tricky.
A Review of AI Tools in Revit
Another powerful option has recently emerged: AI-powered tools. Among these, chatbots optimized with LLMs for Revit stand out. For this article, we will use BIMLOGIQ Copilot as a representative example.
Unlike writing Dynamo scripts or coding plugins, this approach requires no technical expertise. Users can achieve their goals in Revit simply by chatting with the AI. This eliminates the need to understand Revit’s internal structure, saving significant time and energy that can instead be invested in project modeling.
Direct Comparison Between Dynamo and AI
Speed of Development and Learning: Chatbots require no training—just a clear request. Dynamo, however, requires learning a new software environment and consistent practice.
Flexibility and User Control: Dynamo offers full control over nodes and flow, but is limited by available nodes and API access. AI chatbots like BIMLOGIQ Copilot offer flexibility, allowing saved commands and future edits, though results can sometimes require refinement due to LLM variability.
Required Technical Skills: Chatbots need no technical skills beyond expressing a request clearly. Dynamo requires at least basic scripting concepts and some knowledge of the Revit API for deeper understanding.
Experience Needed for Writing Good Prompts: Although chatbots require no coding knowledge, users should be aware that crafting effective prompts can take some experience. To achieve precise results, it often helps to phrase requests clearly and iteratively refine them. Over time, users typically become better at writing prompts that yield the exact outcome they want.
Cost of AI vs Free Dynamo: Another practical factor is cost. Dynamo is completely free and comes integrated with Revit, while most AI-powered tools require paid subscriptions. However, the investment in AI tools is often justified, as the productivity gains and time savings can far outweigh the associated costs.
Automation and Creativity: Dynamo outputs are predictable since scripts are explicitly defined. AI chatbots, on the other hand, may introduce creative solutions, requiring users to review and refine results.
Risks of AI Outputs: One crucial aspect to consider is risk. With Dynamo, users have complete control over the logic and outcome of their scripts. AI tools, however, may produce outputs that are not fully predictable. This means that while AI can deliver results quickly, users must carefully review and validate the outputs before applying them in a production environment.
Comparing a Few Scenarios
The following time estimates assume that the user is already experienced: prompts for AI chatbots are written clearly and efficiently, and Dynamo scripts are created by users familiar with the environment. For beginners, actual times may be longer depending on skill level.
Example 1: Filter walls by parameters (Function = Exterior, Fire Rating = 2 HR).
Dynamo: ~10 minutes, very easy.
AI Chatbot (here BIMLOGIQ Copilot): < 1 minute.
Example 2: Create and populate sheets from an Excel file.
Dynamo: ~30 minutes, requires strict Excel formatting.
AI Chatbot(BIMLOGIQ Copilot): < 1 minute, adapts to Excel format automatically.
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Example 3: Create openings in floors at all column locations.
Dynamo: >1 hour, medium complexity
AI Chatbot (BIMLOGIQ Copilot): < 1 minute.
Key Insight:
Modifying Dynamo scripts requires almost the same effort as writing them from scratch, while AI chatbots can handle changes instantly thanks to preserved context in conversations.
The Future of Automation in Revit
Growth of AI Tools: LLMs will continue to improve, enabling automation of even complex modeling and data tasks.
Coexistence of Dynamo and AI: Rather than replacing Dynamo, AI will complement it, creating a balanced strategy.
Standardization and Data Security: As AI adoption grows, protocols for securing and standardizing project data will be essential.
Personalization and Learning: Future tools will learn user/team patterns, providing smart suggestions and becoming partners in creativity and decision-making.
Summary and Practical Recommendations
Automation in Revit is no longer optional—it is essential. Both Dynamo and AI tools have unique strengths:
Best suited for Dynamo:
Teams needing precise, stable control.
Projects focused on standardization and repeatability.
Users who want to deepen their knowledge of Revit API.
Best suited for AI:
Users prioritizing speed and simplicity.
Projects requiring high flexibility and fast solutions.
Teams preferring to focus on modeling and design decisions.
Recommended Strategy:
Combine both tools. Let Dynamo serve as the backbone of stable automation, while AI accelerates workflows and adds creativity.
Final Conclusion
The future of automation in Revit is clearly moving toward artificial intelligence. While Dynamo has proven itself as a reliable and powerful tool for structured automation, AI chatbots bring something fundamentally new to the table: speed, simplicity, and creativity.
Dynamo will always have its place. For teams that need stable, repeatable scripts and precise control, it remains a backbone of BIM automation. It provides predictability and a visual interface that many users value, especially in highly standardized projects.
But AI chatbots—such as BIMLOGIQ Copilot—are rapidly reshaping how we think about Revit automation. They eliminate the steep learning curve, freeing users from the need to master scripting concepts, API structures, or node management. With just a natural language request, even complex tasks can be executed in seconds. This is not just a time-saver—it lowers the barrier of entry, enabling every Revit user, regardless of technical background, to harness automation.
Another advantage of AI is adaptability. Where Dynamo scripts often need time-consuming rewrites when requirements change, AI chatbots can adjust instantly. By continuing the conversation, users refine results in real time, with the chatbot maintaining full context. This level of interactive flexibility is something Dynamo alone cannot provide.
Beyond speed and flexibility, AI chatbots also bring creative problem-solving. While Dynamo follows the logic predefined by the user, AI can suggest alternative methods, optimize workflows, and even uncover solutions that users may not have considered. In this sense, AI acts not just as a tool, but as a collaborative partner in the design and modeling process.
Looking ahead, the most effective strategy is not to choose between Dynamo and AI, but to use them together. Dynamo ensures structured, reliable automation, while AI accelerates workflows, sparks creativity, and empowers non-technical users. Yet, if we look at the long-term trajectory, the balance is shifting: AI chatbots will drive the next generation of BIM automation.
In short:
Dynamo is the foundation, valuable for precision and stability.
AI is the accelerator, making automation faster, more flexible, and more accessible.
The future of automation in Revit belongs to AI—but Dynamo will continue to complement it. The real opportunity lies in our ability to use both wisely, blending the predictability of Dynamo with the agility of AI to maximize project efficiency, creativity, and productivity.
