AI in CAD: How is Artificial Intelligence transforming Product Design and Development?

Artificial intelligence is no longer a topic reserved for the IT industry. It is increasingly finding its place in engineering environments, as CAD software vendors continue to introduce new AI-powered capabilities. At Sii, we see the development of these technologies from multiple perspectives, working with both engineering teams and clients delivering product development, industrial engineering, and automation projects. This makes it worth taking a closer look at where the market stands today and what the next few years may bring.

Just a few years ago, these were largely experimental features. Today, AI-driven tools are available across leading design platforms, including SOLIDWORKS, Autodesk Fusion, PTC Creo, and Siemens NX.

As the technology gains momentum, so do the questions surrounding it. Is AI truly changing the way products are designed? What solutions are already available on the market? Should design engineers be concerned about their future? And most importantly, how can organizations use AI to improve the efficiency of engineering and design processes?

From assistant to co-designer

The evolution of AI in CAD systems can be divided into three main categories.

The first is Assistive AI, which helps engineers with everyday design tasks. This is currently the most widely adopted form of artificial intelligence in CAD environments. Features such as automatic mate recognition, sketch repair, smarter operation selection, and intelligent workflow recommendations help reduce the number of repetitive tasks engineers perform every day. Examples of these capabilities can already be found in the latest solutions developed by SOLIDWORKS and Siemens.

The next stage is Predictive AI, which goes beyond supporting users and begins anticipating their needs. These systems can identify similar components, suggest design operations, detect patterns within models, and automatically create selected design elements. In practice, this reduces the time spent on routine tasks while improving consistency across projects.

The most exciting developments, however, are taking place in the field of Generative AI. Rather than simply analyzing data, these solutions can create entirely new design concepts. This is where capabilities such as generating sketches, 3D models, technical documentation, and optimized geometries based on engineering requirements come into play.

What AI capabilities do CAD systems offer today?

Only a few years ago, AI features in CAD software were considered more of a technological curiosity than a practical engineering tool. Today, virtually every major CAD vendor is investing heavily in AI capabilities. While platforms differ in their approach, they all pursue the same goal: improving engineering productivity, reducing repetitive work, and accelerating product development.

Autodesk: From Generative Design to Intelligent Assistance

Autodesk has long been one of the pioneers in applying AI to engineering design. One of the best examples is Autodesk Fusion, where artificial intelligence supports both product development and manufacturing preparation.

Its best-known capability is Generative Design, which automatically creates multiple design alternatives based on engineering requirements, load conditions, and manufacturing constraints.

In recent years, Autodesk has also expanded Autodesk Assistant, a natural language interface designed to support users during day-to-day work. In addition to answering questions, the assistant can perform selected CAD operations, assist with documentation creation, and help accelerate routine engineering tasks.

Siemens: AI as a Digital Co-Designer

Siemens develops artificial intelligence primarily within its NX and Designcenter environments. The company focuses on combining AI with design, simulation, and manufacturing processes as part of its digital twin strategy.

A key element of this approach is Design Copilot NX, a natural language assistant that supports engineers during product design, technical knowledge retrieval, and problem-solving activities.

At the same time, Siemens continues to develop predictive and generative capabilities, including intelligent operation recommendations, design optimization, automated documentation generation, and AI-powered CAM workflows. Increasing emphasis is also being placed on integrating design with simulation, flow analysis, and manufacturability assessments from the earliest stages of product development.

Dassault Systèmes: Intelligent automation in SOLIDWORKS and CATIA

Dassault Systèmes is advancing AI technologies across its two flagship product families: SOLIDWORKS and CATIA. SOLIDWORKS users already benefit from features such as command prediction, intelligent component recognition, automated documentation generation, and assembly process enhancements. The primary objective of these capabilities is to streamline engineers’ daily work.

The development of CATIA within the 3DEXPERIENCE platform is equally noteworthy. Dassault uses AI to predict user actions, automatically create design relationships, support modeling activities, and enable advanced Generative Design workflows. Features such as Command Intelligence and Function-Driven Generative Designer help automate conceptual design and optimize geometry according to technical requirements.

PTC: AI powered by engineering knowledge

PTC develops artificial intelligence primarily within the Creo environment. Recent versions of the platform include Creo AI Assistant, which functions as an expert available directly within the design workspace.

Engineers can quickly access guidance on best practices, design workflows, and common engineering challenges without having to search through documentation or consult other users.

Another key element of PTC’s strategy is the continued development of Generative Design and the integration of AI with simulation and 3D model analysis. The company consistently expands capabilities that support design optimization, identify potential engineering issues, and help organizations make better use of the knowledge already available within their teams.

Emerging players in AI-Powered CAD

Alongside the largest software vendors, a growing ecosystem of startups and specialized AI platforms is emerging across the engineering sector.

Solutions such as MecAgent, Leo AI, and AdamCAD focus on automating design workflows, surfacing engineering knowledge, and enabling natural-language interaction with CAD systems. While their capabilities remain more specialized than those offered by established vendors, they clearly illustrate the direction in which the industry is heading.

Generative Design: the most advanced use of AI in CAD

If there is one area that best demonstrates the potential of artificial intelligence in engineering design, it is Generative Design.

In a traditional workflow, engineers create multiple design options, assess their performance, and select the most suitable solution. With Generative Design, the process looks very different. Engineers define operating conditions, loads, materials, and manufacturing constraints, while the software generates dozens or even hundreds of potential design alternatives.

This approach often uncovers solutions that would be difficult to identify using traditional design methods. As a result, engineers can significantly reduce component weight while maintaining the required strength and performance characteristics. This is precisely why the technology has gained traction in industries such as aerospace, automotive, and advanced product engineering.

However, even the most sophisticated algorithms cannot replace engineering judgment. An AI-generated model may look impressive, but it still needs to be evaluated in terms of manufacturability, production cost, assembly requirements, and long-term serviceability.

Why AI will not replace Design Engineers

Many discussions about AI eventually lead to the same question: will artificial intelligence replace engineers?

From a practical engineering perspective, the answer remains the same. AI is a tool that assists specialists rather than replacing them.

AI can analyze patterns and generate recommendations, but it lacks engineering judgment. It has no understanding of manufacturing realities, cannot take responsibility for design decisions, and has no practical experience gained from building, testing, and improving products in real-world conditions.

Its effectiveness also depends heavily on data quality. Poor or incomplete datasets can lead to inaccurate recommendations, while AI hallucinations remain a challenge even for the most advanced models.

Another important consideration is intellectual property. CAD models are often among a company’s most valuable assets, and using them securely within AI-powered environments requires the right governance, processes, and security measures.

What’s next for Engineers in the age of AI?

Everything suggests that the role of engineers will continue to evolve over the coming years. Instead of spending a significant portion of their time on repetitive tasks, design engineers will increasingly focus on defining problems, making critical decisions, and evaluating solutions proposed by AI.

The idea of interacting with CAD software through natural language is also quickly becoming a reality. Rather than building models step by step, users may simply describe their requirements while the system proposes geometries, runs analyses, and generates documentation automatically.

In the years ahead, we can also expect tighter integration between CAD environments, simulation tools, and manufacturing processes, helping organizations shorten product development cycles even further.

AI success depends on implementation, not technology alone

Today’s market offers a growing number of AI-powered engineering tools. However, selecting software alone is not enough to guarantee success. The real value comes from aligning technology with engineering processes, integrating it into existing workflows, and preparing teams to take advantage of new ways of working.

At Sii, we support clients across product development, mechanical engineering, automation, and the digital transformation of engineering processes. Through projects delivered across multiple industries, we help organizations assess not only the capabilities of AI tools but, more importantly, the business value they can create.