Industry executives and experts share their predictions for 2025. Read them in this 17th annual VMblog.com series exclusive.
The end of the year naturally makes us look to the future,
and 2025 looks poised to be an exciting one for technological developments in 3D
engineering.
The most important technological developments
and trends to keep an eye on for the next year and far beyond include improved visualization
streaming desktop technology, the integration of AI assistants into engineering
software, the power of single and multi-agent systems, and the transformative
effect these tools will have on engineering and development organizations. We
also look to highlight the potential and future of generative design.
Improvements in Cloud Technology Support for Legacy
Applications
While cloud technology dominates many
conversations for both application development and usage, many engineering
sectors still rely on desktop applications. These established engineering tools
have been created and used over the course of years, even decades. Completely
rewriting these applications to be fully cloud-based would be hugely expensive
and time-consuming, taking thousands of man-hours (including requiring
expertise that may no longer be with the company). On top of this, their
clients often rely on these products for everyday operations, so interruption
of service and maintenance is unacceptable. These factors create strong demand
for a bridge between the convenience of cloud technology and existing
engineering applications.
Currently, many vendors offer
cloud-based simulation for desktop through remote desktop, but this method has
significant drawbacks. The user experience is often poor, with a laggy
interface poorly optimized for a web browser and worse visualization quality.
Cloud-based simulation also requires expensive GPU-equipped servers. Finally,
there are the security concerns that come with remote access to a powerful
machine full of potentially sensitive data.
In the next year, developers will have
a new option. Tech Soft 3D is
developing a visualization streamer that aims to provide desktop applications
with a true web-based graphics interface without requiring a full software
rework. The approach minimizes the amount of working data streamed and keeps
the simulation results on-premises. The solution can also eliminate the need
for GPU-equipped servers, drastically reducing costs. Tech Soft 3D is working
directly with their major partners, including juggernauts in simulation
software, to ensure the toolkit supports the needs of the market. The
technology is being built on top of decades of experience working with CAE data
with the CEETRON product line.
As a
Software Development Toolkit (SDK) provider, Tech Soft 3D would license this
technology to its partners, who would customize and integrate this component
technology directly in their own application. Organizations would build out
their own web interface with their legacy tool, along with how the rendering
window works with their software. In the next year and beyond, this technology
can offer a better way for desktop applications to continue to leverage their
invaluable tools while better using cloud-based technology.
AI-Powered Assistants Will Be Intergrated into Engineering Applications
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.
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 responses 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 could create and leverage a multi-agent system to help
them answer the question, “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. Tech Soft 3D strives to
constantly stay a leader in these industries, ready to support our partners in
exciting, fast-paced times of innovation. A constant eye on the future is
essential to these goals.
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.
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