AI-Powered Assistants Will Be Integrated into Engineering Applications

Whether you want to call them assistants, co-pilots, 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.

AI Agents Will Revolutionize Learning, Customer Service, Support, and More

Whether in your personal or professional lives, we’ve all had a terrible experience with automated customer service. Calling your internet provider, talking to the bank, or getting support on complex engineering software, the frustration is the same: an impersonal robotic system did nothing to solve your problem and simply got in the way of you finding a resource that could actually help you.

The issue here is clear: Most support bots can't properly understand natural language or context, generate unique responses, or generally tailor its response in any way beyond a limited number of responses. AI agents are different, and poised to revolutionize support, onboarding, and much more.

AI agents are autonomous programs that take on tasks from users and complete them independently. By LLMs, they create and execute action plans based on user needs, often interacting directly with an organization’s tools and data. This ability to understand context and work autonomously is a core part of what makes them so invaluable.

In the year ahead, AI agents are poised to become one of the most transformative developments across a wide range of industries, with significant potential to dramatically enhance the experiences of engineers and developers.

The Power of Simple AI Agents

At their most basic, simple AI agents can handle straightforward, repetitive tasks such as answering FAQs, guiding users through standard onboarding procedures, or assisting with account setup. In addition to reducing the burden on your organization, they improve the quality of service by offering immediate assistance to users across different time zones, reducing wait times, and increasing customer engagement.

Transforming Support and Onboarding

Support and onboarding are prime areas where AI agents can deliver immediate value, and implementation is relatively straightforward. Engineers rely on complex tools, and frequently have unique, context-specific questions. AI agents can understand these contexts, interpret user inquiries, and access relevant information from documentation, knowledge bases, and help desk systems.

Elevating Service with Multi-Agent Systems

Multi-agent systems will take this a step further by handling more complex tasks and offering dynamic, personalized assistance. Specialized agents will collaborate to resolve intricate issues, ensuring that even the most complex customer needs are met efficiently. For example, one agent might diagnose a technical problem, another could retrieve relevant documentation, and a third might initiate a follow-up procedure. This coordinated effort enhances problem-solving capabilities and improves the overall customer experience.

By assigning different agents to address various aspects of a user's needs, multi-agent systems will provide highly personalized support that adapts in real-time to individual preferences and requirements.

Multi-Agent Systems Across the Organization

Multi-agent systems are highly scalable and flexible. They can easily adjust to meet increasing demand or adapt to new challenges by adding or modifying agents. This flexibility allows organizations to maintain high levels of support without overhauling their infrastructure. Each agent in the system learns from interactions and shares insights with others, enhancing overall performance. This continuous learning leads to ongoing improvements in service quality and operational efficiency in a way that can be tailored for a wide array of tasks.

Outside of support, a sales team leverage could create and leverage a multi-agent system to help them answer the request, “Find me all the small to medium-sized manufacturing shops in Germany who are automotive suppliers.” The agents could pull information from across the web from a wide range of sources: general search, show/event information, specialized publications and magazines, databases, LinkedIn, etc. The information would be validated, with duplicates removed and information checked. Information could potentially be cross-referenced with your own CRM information.

This would be accomplished by not one but a series of specialized agents working together. A web search agent, an event screener, a validation tool, a CAD expert. This is a task that would take a human several hours, and they could never realistically search as thoroughly as possible with these tools.

Bottom Line

AI agents, whether simple or part of multi-agent systems, are set to revolutionize learning, customer service, support, and much more. Embracing these technologies is crucial for organizations aiming to enhance user experiences and stay competitive in the evolving digital landscape.

By integrating AI agents into your support and onboarding processes, you can not only meet but exceed customer expectations, positioning your organization at the forefront of innovation. Over the next few years, we expect to see huge investment in developing AI agent systems from organizations across a wide array of industries.

Generative Design will Get More Specialized

Generative design is an area with exciting potential that is often misunderstood. Often misconstrued as inherently AI-based, this innovative design process allows engineers to rapidly iterate and optimize designs.

In simple terms, generative design involves outlining a series of clearly defined criteria, or constraints, and using software tools to create outputs based on these parameters. This type of design process is iterative, with both the output and criteria being refined until the end product is satisfactory. The nature of this process allows engineers to make and evaluate far more designs than is possible with traditional design methods.

We expect to see generative design applications for more specific, customized purposes. Generative design is well-suited to optimizing simple components. We expect to see applications emerge that address more complicated parts and assemblies.

The Future of Engineering and Development

The role of AI in engineering, development, and technology as a whole is going to be transformative in ways we can’t possibly anticipate fully. 2025 is going to be an exciting year, in which AI will transform many of our day-to-day activities including how we interact with engineering software. We look forward to seeing the developments in the next year and far beyond.

This article was written by Eric Vinchon, VP of Product Strategy, Tech Soft 3D (Bend, OR). It was edited for clarity and length. For more information, visit here  .

---