Design engineers are under more pressure than ever to design faster, design smarter, and shift-left problems before they become expensive downstream. Leaders and managers expect AI to be part of workflows now to increase daily efficiency and reduce costs. But at the same time, supply chain disruptions, material costs, tariffs, workforce shortages, and the constant need to reskill teams are creating real strain that no amount of AI enthusiasm fixes. Manufacturing is feeling it, too. Everyone is trying to get a competitive advantage in this fast-paced, dynamic world, and they’re increasingly using AI to get ahead.
Remember that simulation, generative design, digital twins, and AI-assisted validation tools can catch problems faster and earlier than many humans can, but they still require engineers who understand the manufacturing realities behind them. Image: Adobe Stock
In this final article of our four-part series on design for manufacturability (DFM), we focus on the digital tools driving DFM in Industry 4.0 and beyond. Here are links to the previous articles in case you missed one and would like to catch up:
Let’s start with the topic that’s taken over every conversation in engineering these days — artificial intelligence.
Agentic AI is the next big thing for engineering workflows
AI is a broad topic, but the following graphic with concentric circles helps simplify the main idea. In the largest circle, AI is the overarching term to describe algorithms that help computers think like humans. They synthesize information, solve complex problems, and can make decisions. Machine learning is a subset of AI that uses advanced algorithms so that computers can “learn,” meaning adapt to change and new data, either under human supervision or unsupervised. Deep learning is what it sounds like — more advanced algorithms that use neural networks for even more in-depth data analysis, pattern recognition, and complex problems. And then we have generative AI, which includes ChatGPT and other chatbots that humans can communicate with to potentially gain knowledge and serve as assistants or colleagues to accelerate work tasks. Today’s hot topic is agentic AI, which takes generative to a whole new level.
A breakdown of artificial intelligence, machine learning, deep learning, and generative AI. Image: PopovaZhuhadar, CC BY-SA 4.0 <https://creativecommons.org/licenses/by-sa/4.0>, via Wikimedia Commons
Design World moderated the Future of Engineering Summit on March 25, 2026, where experts gathered to discuss agentic AI in engineering workflows, why agentic AI projects usually fail, and how they can give teams and companies a competitive advantage. Ryan Qi, principal worldwide business development and go-to-market leader for AWS, discussed the difference between generative AI and agentic AI, emphasizing that humans need to be above the loop, rather than in the loop, for agentic AI to be worthwhile.
He shared how generative AI, such as ChatGPT and similar algorithms, can generate content. Humans ask it a question or give it a prompt, and it synthesizes a lot of information to provide an answer or output. Humans need to be highly involved in the conversation to guide and coach generative AI, prevent or correct hallucinations, and ensure outputs make sense. With agentic AI, the point is for humans to be less involved in the details and provide oversight while one or more agentic AI systems do all the heavy lifting. The idea is for teams to scale with multi-agent systems capable of autonomous decision-making that goes on in the background.
To view all sessions from the summit, visit https://www.future-of-engineering-summit.com/recap/spring-2026, and gain further insight from these articles:
Augmenting workflows with AI-enabled digital tools
As many highly experienced workers retire, a huge knowledge gap remains with fewer people to turn to. Generative AI comes into play here to help capture, retain, and build upon that knowledge so that anyone in the company who needs the knowledge has access to it. However, manufacturing is also augmenting its workforce with automation to offset declining employment, which changes the skill sets and knowledge required.
The following are graphs from Deloitte’s 2026 Manufacturing Industry Outlook, showing manufacturing employment decreasing, the cost of employment increasing, and the cost of materials and components increasing. The survey showed that more manufacturers are implementing smart manufacturing, automation, robotics, and digital tools, especially AI — all of which require upskilled workers and add more knowledge that needs to be transferred or communicated to design engineers.
Graphs showing the trends in manufacturing employment, employment cost, and material and component costs. Image: Deloitte 2026 Manufacturing Industry Outlook
On the design side, AI-enabled digital tools also come in as augmentation and to help accelerate product development while reducing downstream risks and costs. Here is a list of the types of digital tools available:
- CAD-integrated DFM checkers: Software that runs inside or alongside the CAD environment and evaluates geometry against manufacturing rules in real time — wall thickness, draft angles, undercuts, tolerance stacks, weld accessibility. These are the original “shift-left” DFM tools, historically rules-based and increasingly AI-enabled.
- AI-assisted design validation: The next generation of the above. Rather than relying on a fixed rule set, these tools learn from production data and prior designs to flag issues that a static rule set probably wouldn’t catch. They also increasingly propose fixes rather than just flagging problems.
- Generative design: AI-driven generation of geometry from a problem specification, including load case, material, and manufacturing process. This produces topology-optimized parts that are inherently shaped for the constraints of how they’ll be made. As discussed in part 3 of this article series, this is important in additive manufacturing, as well as CNC, injection molding, casting, and extrusion.
- Digital twins: Virtual replicas of a part, a process, a production line, or an entire facility. Specifically in DFM, the high-value use cases are virtual commissioning and process tuning.
- Integrated simulation environments: FEA, CFD, mold-flow analysis, and tolerance analysis running inside or tightly coupled to the CAD software. These catch structural, thermal, and flow problems before prototyping.
- Cost-estimation and “should-cost” tools: Software that estimates manufacturing cost from a 3D model, including material, labor, tooling, and process. These are increasingly AI-driven and close the loop between design choices and economic consequences in real time, which is especially helpful in today’s dynamic economy.
- Additive-manufacturing-specific DFM: Tools focused on 3D printing that provide printability checks, support structure optimization, build orientation analysis, and thermal distortion prediction. This is a specialized branch with its own rule sets.
- PCB and electronics DFM: A whole parallel ecosystem for printed circuit board design to evaluate solder pad spacing, drill alignment, thermal relief, and panelization. This is conceptually similar to mechanical DFM but with different rules and tools.
- Instant quoting and design-for-supply tools: Browser-based platforms, especially when working with contract manufacturers, where a designer uploads a file and gets immediate manufacturability feedback plus a quote. These have democratized DFM for small teams and prototype work.
- Agentic AI for manufacturing workflows: The newest category, and the one Deloitte is flagging hardest for 2026. These are AI agents that can reason across multiple systems, such as CAD, ERP, supplier databases, and MES, and take autonomous action. In the DFM context, agents can flag a manufacturability issue, propose alternative suppliers, quantify cost impact, and queue up the change order for human approval.
Agentic AI in practice
As a real, live, working example of agentic AI, during the Future of Engineering Summit, Marc-Florian Uth, senior applications engineer and strategic partnerships lead at Synera, gave a presentation on agentic AI’s ability to help design engineers run manufacturing and cost analyses to streamline the communication that multiple teams would need to have to come to conclusions. The following is a clip of Uth’s presentation showing the demo.
In Uth’s example, we saw a tool that looks very similar to a generative AI tool, except it requires little human prompting or interaction. It’s using multiple datasets to synthesize, and the human is still above the loop — the human worker can still make the ultimate decision.
In other news, our sibling publication Engineering.com recently reported that Siemens and Xometry partnered up on an on-demand manufacturing marketplace that will soon be integrated into Siemens Designcenter. This is an example of a real-time quoting and DFM feedback tool that will be embedded into a product design platform. They also have a website with the same information, but embedding it into a platform can pool more information in one space and speed things up further.
Our colleagues at Engineering.com also shared a case study on how Acme Space used three AI agents to generate new designs, analyze them, and make sure they’re manufacturable. They claim to have found a solution to AI hallucinations by using a multi-agent system, where successive stages of AIs check each other’s work, and then human engineers complete the final design. The company says that the system and process have dramatically shortened development timelines with a fraction of the engineers of a traditional aerospace company.
In another aerospace example, the DLR Institute of Structures and Design in Germany developed a fully digital process for designing, manufacturing, and assembling aircraft cabin components to enable faster, more flexible production. They call it DiCADeMa, which stands for Digital Cabin Architectures and Design for Manufacturing, and leverages software and robotics to determine the mounting positions of cabin compartments. Below is a video from their YouTube channel, which contains more details on their program and process.
DFM refresher for engineers wrap-up
In this four-part series, we covered a very brief history of DFM, its core tenets, the mindset shift needed, how additive manufacturing changes engineering graduates’ expectations of manufacturing, and AI-enabled digital tools used to augment workforce and knowledge gaps and expedite product development cycles.
In summary, consider taking away these four main points:
- Design for manufacturability is a mindset and a methodology, not a software license or a last-minute checklist. The tools are getting remarkable, but they don’t replace the discipline, and they certainly don’t replace the mindset and cultural shifts that engineers and teams need.
- Engineers need manufacturing knowledge. This is an ongoing challenge, and the teams getting the most value from digital tools are those in which designers have a decent sense of what will happen to their design on the shop floor and how it will perform given the realities of the shop floor and potential variations in production.
- Digital tools can augment people and knowledge, but they can do more. AI can certainly help assist with workforce shortages and capture knowledge, but teams that can scale multi-agent AI systems and create reliable digital twins gain a huge competitive advantage. But it’s not just about buying software; it’s about pairing good tools with engineers who know what to do with the output.
- Humans need to stay above the loop. Not in the loop — above it, monitoring and being accountable for the final decisions. Engineering judgment is still the most valuable thing on the team.
And if it helps, here are four corresponding questions worth bringing back to your team:
- Do we have the right mindset? Is DFM treated as a discipline and a guiding principle, or is it something we hope happens automatically when the software tool flags something?
- Do we have the right methods? Is there a defined workflow, or is everyone doing it differently? Does the company or team have a clear methodology?
- Do we have the right knowledge? And what happens to it as our most experienced engineers retire?
- What digital tools actually make sense for us? Not for the company in a case study, but for our products, our volumes, our suppliers, and our team?
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This content is also available in an on-demand webinar. Register here: Design for Manufacturability in 2026: How Digital Tools Improve Scalability and Success
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Filed Under: AI Engineering Collective, NEWS • PROFILES • EDITORIALS, ASSEMBLY, DIGITAL TRANSFORMATION (DX), ENGINEERING SOFTWARE, MANUFACTURING
