Artificial intelligence is rapidly changing the way engineers design, simulate, and optimize products. From generative design tools to AI-powered digital twins and natural language interfaces, engineering software is becoming more intelligent and accessible than ever before.

In an episode of our foward-looking Beyond 3D Podcast, Tech Soft 3D's Jonathan Girroir chatted with Digital Engineering 24/7 Senior Editor Kenneth Wong about where AI is already creating value in engineering and what might be next. We cover:

• Generative Design & Natural Language

• AI-Assisted User Interfaces

• Digital Twins

• AI Empowered Simulation

• Future Problems with AI in Engineering

• FAQs about AI in Engineering

Click here to listen to the podcast that informed this article

How AI Is Changing Engineering Software

Generative Design Was the First Step

Mr. Wong discussed his view that the beginning of artificial intelligence's direct impact on the product lifecycle started in generative design a few years ago. Something of the first version of AI involvement in engineering workflows, users could provide their design criteria to software and receive design concepts in return. While useful, this represented only the first wave of AI in engineering.

Ready to learn more about the misconceptions reality of generative design? Check out our piece "Generative Design: Insights, Myths, and More"

Natural Language Is the Next Frontier

More and more, other areas of AI tech are being incorporated into engineering spaces, especially natural language capabilities.

Users have traditionally been limited in how they populate and interact with 3D environments. This process typically consisted of rather tedious manual pulling of objects from a library and assembling them into something resembling the desired outcome. Natural language commands allow users to interact with software in a much more human manner.

Mr. Wong used an example of a warehouse to explain how much of an improvement this is: with a capable natural language AI tool, users could simply instruct the software to fill a warehouse with the objects one would typically expect to find there, rather than manually placing individual items.

These capabilities are likely to appear in more and more of the software engineers use every day.

The next stages will likely involve simply telling our design tools how to edit a component by adding a hole, narrowing a space, or making other geometric changes. The improvements on natural language design tools offer the potential to make workflows faster, more intuative, and far more accessible.

AI Will Change the Engineering User Experience

The integration of AI tools into CAD and simulation software will inevitably change user interfaces and experiences.

We could see software equipped with AI-powered assistants that suggest design starting points based on a user's requirements. Starting with a typical engine block, bracket, bolt, or other common component, engineers could then refine those concepts manually or continue iterating with AI-powered tools.

Exactly what these experiences will look like remains to be seen. Mr. Wong pointed to text-based generation tools like Bing and Canva as examples of how AI has already transformed user experiences in other industries.

These features are not present in mainstream CAD software yet, and it is important to remember how new this technology remains. ChatGPT, Midjourney, and other text-based AI tools only entered mainstream awareness relatively recently. Developing CAD and simulation tools enhanced with natural language capabilities will take time, but the direction of travel is becoming increasingly clear.

Will AI Replace Engineers?

As AI capabilities continue to expand, a common question emerges: what happens to the role of the engineer?

Mr. Wong believes that human engineering expertise is not going to become obsolete. While AI tools may enhance and streamline engineering workflows, the role of the human expert remains as important as ever.

An experienced engineer can often estimate the outcome of a simulation or test before running it, drawing on years of practical knowledge and intuition. Those skills cannot simply be replaced by automation.

It is also important not to discount the role of less tangible elements of design, including aesthetics, sound, usability, and how a product makes users feel. These considerations remain deeply human.

While engineering roles may evolve, the expertise, judgment, and creativity that engineers bring to the design process will continue to be invaluable.

AI-Powered Digital Twins

Digital twins combine geometry with vast quantities of operational data collected from real-world assets. AI can play an important role in helping organizations manage and interpret this information.

As a framework for discussion, imagine the physical and digital twin of a green-energy wind turbine. Pulling from its huge array of sensors, weather information, corrosion data, pressure readings, and other operational metrics are generated continuously. The sheer volume of information often exceeds what humans can reasonably analyze on their own.

Artificial intelligence can process enormous datasets, identify patterns, and highlight potential issues before they become major problems. These capabilities can improve predictive maintenance strategies and help organizations identify areas of potential failure that might otherwise go unnoticed.

Also noteworthy, as disucssions of the limitations of AI gather steam, this is the type of work where AI tends to shine most. AI models tend to thrive with processing large amounts of predictably structured data and drawing insights from it.

AI-Powered Simulation

Traditional simulation methods often involve running physics-based calculations across every region of a component to understand how it behaves under different conditions.

Today, engineers are experimenting with reduced-order models that leverage AI to identify relationships between simulation inputs and outcomes.

By learning from previous simulations, AI models can identify correlations between parameters and results. This makes it possible to generate useful predictions without always requiring a complete physics-based simulation.

The result is faster analysis, shorter development cycles, and the potential to make advanced simulation capabilities accessible to a wider audience.

If natural language interfaces can be successfully integrated into simulation software, the simulation process itself could become significantly easier to learn and use, helping more engineers benefit from advanced analysis tools.

The Challenges Ahead

Interoperability Remains a Major Obstacle

Mr. Wong identified interoperability as one of the most significant issues facing the future of AI-enabled engineering workflows.

He shared the example of BMW and Ericsson. Both organizations are investing in digital twin and metaverse technologies, but they operate within different ecosystems.

Today, it is highly unlikely that a simulated BMW could seamlessly operate within Ericsson's simulation environment designed to test 5G infrastructure. If digital twins are to reach their full potential, organizations will need ways to share and utilize data across platforms and environments.

This challenge mirrors many of the interoperability questions that companies like Tech Soft 3D have been addressing for years across CAD and engineering data formats.

Defining What AI Actually Means

Another challenge involves the language we use to describe these technologies.

Artificial intelligence has become an umbrella term that covers a wide range of technologies, including machine learning, neural networks, natural language processing, deep learning, and generative design.

Jonathan Girroir emphasized the importance of developing a shared understanding of these concepts and using terminology consistently.

Mr. Wong added that industries should be careful not to overuse the term. Just as Product Lifecycle Management (PLM) can mean very different things depending on who is using the phrase, AI risks becoming a catch-all label for anything involving automation or advanced software.

Maintaining clarity around terminology will become increasingly important as AI technologies continue to mature.

What Comes Next?

Both Kenneth Wong and Jonathan Girroir remain optimistic about the future of AI in engineering.

While the technology is still evolving, its influence is already visible across design, simulation, digital twins, and engineering workflows. The most successful organizations will likely be those that view AI not as a replacement for engineering expertise, but as a tool that amplifies it.

As natural language interfaces, AI-assisted simulation, and intelligent digital twins continue to mature, engineers can expect software that is faster, more accessible, and increasingly capable of supporting complex decision-making.

Tech Soft 3D is working to ensure engineering teams have the tools they need to leverage AI technology in modern workflows. HOOPS AI, our newest toolkit, offers the fastest path from CAD to production machine learning. Check out the product itself, or follow the link below to learn more