Whether you want to call them assistants, copilots, or companions, integrated AI-based tools are already starting to be leveraged in consumer applications. Soon, we expect to see these tools make the jump into engineering applications. When they do, they promise to revolutionize how engineers work by streamlining processes, empowering creativity, and lowering barriers to entry.
AI assistants can be found in Outlook, PowerPoint, VS Studio, Photoshop, Grammarly, Apple Intelligence and many more. These assistants leverage large language models (LLMs) to provide functionality to their users. Engineering-based assistants will likely use the same foundation.
AI assistants in engineering software will both simplify and streamline engineers’ ability to control their tools. By leveraging natural language models, engineers will be able to interact with applications directly through voice and text commands.
Similar to the enhanced search options in Microsoft products, an AI-powered search tool would be able to help engineers learn contextually, find the functionality they’re looking for, and increase productivity.
Engineers will especially appreciate the ability of AI assistants to automate time-consuming, repetitive tasks in CAD and other design, analysis and manufacturing software. The ability to generate scripts and automate commands without coding reduces the time and expertise needed for engineers to leverage these time-saving capabilities.
Overall, the integration of AI assistants with engineering software will drastically improve efficiency and ease of use for their users. In addition, training them with your organization’s unique data and workflows will allow them to provide utility unique to your circumstances.
Integration of AI Assistants with Your Systems
One of the biggest limitations to LLM-based tools currently is how we as users are forced to interact with them. Anyone who has used a tool like ChatGPT in their personal or professional life knows you spend a significant amount of time copying and pasting information into the prompt to guide the prompt and the tool’s ability to help you. This could be text, code, data, or information grabbed from a Google search.
We are seeing LLMs integrate directly with organizational information to cut out this time-consuming and error-prone stage. In the next year, we will see more tools that have direct access to data, email and scheduling, technical documentation, API info, support requests, and more.
For engineers and developers, these integrations will let them more effectively utilize AI tools while reducing repetitive copy-pasting and prompt iteration. They will be able to generate code and ensure it compiles without constant user modification. In support cases, they can reproduce reported issues. Other parts of work will be more convenient, with assistants generating customized reports, design reviews, analysis, and meeting prep with all the relevant info you need.
Bottom Line
The underlying technology of these tools is still largely in its infancy, and in the next year, we can expect to see even more improvements in how they are used and what they are capable of. These assistants will transform how engineers and their organizations work, allowing them to focus on their areas of expertise and reduce wasted time. As they get access to more detailed, user-specific information, their utility will become similarly tailored to the needs of the organizations leveraging them.
