5 answers
Ellis
Student
Reno, Nevada
1
Question
Asked
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What are some ways that generative AI will disrupt the engineering industry #Spring25?
Are there ways this may happen that people haven't talked about much? Particularly considering the non-software portions?
#Spring25
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5 answers
Stacey Vespa
Mechanical Engineer
4
Answers
Columbia
Updated
Stacey’s Answer
In terms of taking over engineering jobs that aren’t working on AI itself, it’s a little mix in my opinion. Engineering requires a decent amount of thinking through and problem solving which generative AI can’t really do for now. There are some tedious portions with most groups that can benefit from automation etc. with AI, but overall you still need the human aspect to work through it all and make sense of it.
Dhiren Gadhia
Software Engineering & Architecture
1
Answer
Houston, Texas
Updated
Dhiren’s Answer
Hi Ellis,
Generative AI is set to disrupt the engineering industry in several transformative ways, some examples are below:
Automated Design & Prototyping:
Generative AI can rapidly create and optimize designs for components, structures, and systems by analyzing constraints and objectives. This allows engineers to explore a broader solution space and arrive at innovative, efficient designs much faster than traditional methods.
Accelerated Product Development:
AI-powered tools can automate repetitive tasks such as code generation, simulation setup, and documentation. This speeds up development cycles, reduces human error, and frees engineers to focus on higher-value creative and analytical work.
Enhanced Simulation & Testing:
Generative AI can create synthetic data and virtual environments for testing prototypes, reducing the need for costly and time-consuming physical tests. This is particularly valuable in fields like aerospace, automotive, and civil engineering.
Process Optimization:
AI can analyze vast datasets from engineering processes to identify inefficiencies, predict maintenance needs, and optimize workflows. This leads to cost savings, improved safety, and better resource utilization.
Knowledge Capture & Transfer:
Generative AI can summarize technical documentation, generate training materials, and assist with onboarding new engineers, helping organizations retain and disseminate institutional knowledge.
Customization & Personalization:
AI enables mass customization by generating tailored solutions for unique customer requirements, whether in product design, construction, or manufacturing.
Collaboration & Communication:
AI-powered assistants can facilitate collaboration by generating meeting summaries, design rationales, and project documentation, improving communication across multidisciplinary teams.
Risks and Considerations:
Job Disruption: Some engineering roles may be automated, requiring re-skilling and adaptation.
Ethical and Quality Concerns: Ensuring the reliability and safety of AI-generated designs is critical, especially in regulated industries.
Data and Infrastructure Needs: Effective use of generative AI requires high-quality data and robust computational infrastructure, which may require new investments.
Overall, generative AI will not replace engineers but will augment their capabilities, enabling new levels of innovation, efficiency, and creativity across the engineering industry.
Hope this helps.
Generative AI is set to disrupt the engineering industry in several transformative ways, some examples are below:
Automated Design & Prototyping:
Generative AI can rapidly create and optimize designs for components, structures, and systems by analyzing constraints and objectives. This allows engineers to explore a broader solution space and arrive at innovative, efficient designs much faster than traditional methods.
Accelerated Product Development:
AI-powered tools can automate repetitive tasks such as code generation, simulation setup, and documentation. This speeds up development cycles, reduces human error, and frees engineers to focus on higher-value creative and analytical work.
Enhanced Simulation & Testing:
Generative AI can create synthetic data and virtual environments for testing prototypes, reducing the need for costly and time-consuming physical tests. This is particularly valuable in fields like aerospace, automotive, and civil engineering.
Process Optimization:
AI can analyze vast datasets from engineering processes to identify inefficiencies, predict maintenance needs, and optimize workflows. This leads to cost savings, improved safety, and better resource utilization.
Knowledge Capture & Transfer:
Generative AI can summarize technical documentation, generate training materials, and assist with onboarding new engineers, helping organizations retain and disseminate institutional knowledge.
Customization & Personalization:
AI enables mass customization by generating tailored solutions for unique customer requirements, whether in product design, construction, or manufacturing.
Collaboration & Communication:
AI-powered assistants can facilitate collaboration by generating meeting summaries, design rationales, and project documentation, improving communication across multidisciplinary teams.
Risks and Considerations:
Job Disruption: Some engineering roles may be automated, requiring re-skilling and adaptation.
Ethical and Quality Concerns: Ensuring the reliability and safety of AI-generated designs is critical, especially in regulated industries.
Data and Infrastructure Needs: Effective use of generative AI requires high-quality data and robust computational infrastructure, which may require new investments.
Overall, generative AI will not replace engineers but will augment their capabilities, enabling new levels of innovation, efficiency, and creativity across the engineering industry.
Hope this helps.
Richard A. (Tony) Eckel
49
Answers
Updated
Richard A. (Tony)’s Answer
Ellis,
Engineering is a "broad" category, and the current focus is on the Large Language Models (LLMs).
For AI to be disruptive, it needs to be able to replace brains attached to hands doing work. We have seen this in terms of customer service and documentation because those are particularly communication intensive processes and LLMs are communication intensive technologies.
AI Computational advances have helped in repetitive engineering tasks like layout of integrated circuits where clear metrics of efficiency and design rules can be implemented based on past practices. Finite Element Analysis will benefit as computational modeling is improved to optimize design parameters of mechanical, civil, and aeronautical design problems. Testing, whether software or mechanical, will also benefit from a more rigorous application of standard rules of coverage. Patent work will be streamlined as searches can be automated to assemble sets of documents that apply to the patent prosecution. Documentation, especially surrounding compliance and international translation, will be automated reducing cost and improving market access.
But the Creative and Analytical side of engineering and design will reside within the human brain until the AI technology can "Learn" on its own and implement a moral code (Think about how and why that is important!).
Keep in mind that all LLM is helping us do now is repeat what has happened before; AI cannot advance science, deduce a connection, or justify a decision - only repeat what happened before. If evolution was AI limited, Neandertals might still be dominant!
Tony
Engineering is a "broad" category, and the current focus is on the Large Language Models (LLMs).
For AI to be disruptive, it needs to be able to replace brains attached to hands doing work. We have seen this in terms of customer service and documentation because those are particularly communication intensive processes and LLMs are communication intensive technologies.
AI Computational advances have helped in repetitive engineering tasks like layout of integrated circuits where clear metrics of efficiency and design rules can be implemented based on past practices. Finite Element Analysis will benefit as computational modeling is improved to optimize design parameters of mechanical, civil, and aeronautical design problems. Testing, whether software or mechanical, will also benefit from a more rigorous application of standard rules of coverage. Patent work will be streamlined as searches can be automated to assemble sets of documents that apply to the patent prosecution. Documentation, especially surrounding compliance and international translation, will be automated reducing cost and improving market access.
But the Creative and Analytical side of engineering and design will reside within the human brain until the AI technology can "Learn" on its own and implement a moral code (Think about how and why that is important!).
Keep in mind that all LLM is helping us do now is repeat what has happened before; AI cannot advance science, deduce a connection, or justify a decision - only repeat what happened before. If evolution was AI limited, Neandertals might still be dominant!
Tony
R. Ulises Mendoza
7
Answers
Updated
R. Ulises’s Answer
You are looking for disruptive tools, in my experience these are one of the bests, that I can recommend that I've used or I'm using currentluy:
- Gemini
- AI Studio from Google
- Gpt
- For Coding Copilot but With Claude Sonet 3.7 & 4
- Image Generation: Veo and Flux Kontext
- Audio, Gemini
- Just LLM: Gpt, Llama 4.
- Open Source: Sonet, DeepSeek, Gemma, Llama
- Gemini
- AI Studio from Google
- Gpt
- For Coding Copilot but With Claude Sonet 3.7 & 4
- Image Generation: Veo and Flux Kontext
- Audio, Gemini
- Just LLM: Gpt, Llama 4.
- Open Source: Sonet, DeepSeek, Gemma, Llama
Jen Lee
Managing Partner & General Manager, Retail & Consumer Goods @Microsoft
2
Answers
New York, New York
Updated
Jen’s Answer
Hi Ellis,
Some thoughts about how GenAI will impact engineering:
1. Workflow Automation and Productivity Gains
GenAI can automate repetitive tasks like code generation, documentation, and testing, reducing development time significantly. Engineers can focus more on creative problem-solving and innovation rather than routine work.
2. Design and Simulation
In fields like civil and mechanical engineering, GenAI-powered tools can generate design concepts, run simulations, and create digital twins to test structural integrity and sustainability before physical prototypes are built.
3. New Roles and Skills
By 2027, a large portion of the engineering workforce will need to upskill to work effectively with AI tools. Skills like prompt engineering, data science, and AI model integration will become essential. Expect new roles such as AI Engineer, combining software engineering with machine learning expertise.
4. Industry-Wide Applications
Smart Manufacturing: AI-driven predictive maintenance and process optimization.
Supply Chain Engineering: Real-time analytics and risk forecasting.
Software Development: AI-native workflows where most code is AI-generated.
5. Future Outlook
Generative AI won’t replace engineers—it will augment them. Human creativity, critical thinking, and domain expertise remain irreplaceable. The biggest disruption will be in how engineers work, not whether they’re needed.
Recommended Resources to Prepare for AI in Engineering
Free Courses:
https://www.elementsofai.com/ – Introductory AI concepts.
https://www.coursera.org/learn/ai-for-everyone – Beginner-friendly overview.
https://ocw.mit.edu/ – Advanced fundamentals.
Certifications:
Microsoft AI Fundamentals (AI-900) – Great starting point.
AWS Machine Learning Specialty – For cloud-based AI applications.
Hands-On Platforms:
https://huggingface.co/ – Experiment with GenAI models.
https://platform.openai.com/playground – Practice prompt engineering.
Industry Insights:
https://www.mckinsey.com/
https://spectrum.ieee.org/
You probably also want to consider Agentic workflows—where autonomous AI agents reason, plan, and act with minimal human intervention—are poised to fundamentally reshape engineering processes. Here’s how I think they will affect the industry:
1. From Static Automation to Dynamic Autonomy
Unlike traditional automation (e.g., RPA), which follows rigid rules, agentic workflows adapt to real-time data and unexpected conditions. AI agents can break down complex engineering tasks into iterative steps, refine actions, and coordinate across systems without constant human oversight. This means greater flexibility and resilience in engineering operations.
2. Accelerating Design and Simulation
Agentic AI can interact directly with CAD/CAE models, diagnose missing inputs, and auto-apply best practices for simulations. For example, platforms like SimScale are using agentic workflows to:
Guide novice users through simulation setup.
Automate boundary condition assignments.
Enforce company best practices across distributed teams.
This reduces onboarding friction and cuts model setup time dramatically.
3. Transforming Engineering Workflows
Agentic systems can handle tasks such as:
RTL generation and verification planning in chip design.
Documentation and compliance checks in civil engineering.
Predictive maintenance and anomaly detection in industrial systems.
Synopsys’ AgentEngineer™ framework shows how multi-agent systems will progressively optimize workflows from assistive automation (L1) to highly autonomous orchestration (L5)
4. Productivity Multiplier, Not Job Killer
Agentic workflows create a “Jevons paradox” effect: as engineering tasks become faster and cheaper, demand for projects rises—increasing the need for skilled engineers to orchestrate and validate AI-driven processes. Roles will shift toward AI orchestration, governance, and strategic decision-making rather than manual execution.
5. Key Benefits for Engineering
Speed: Reduce cycle times from weeks to hours for design and verification.
Quality: Improve defect detection and compliance through intelligent agents.
Scalability: Handle complex, multi-step workflows across global teams.
Innovation: Free engineers to focus on creative problem-solving and system-level thinking.
6. Challenges to Address
Reliability: Ensuring deterministic outputs in multi-agent workflows.
Governance: Maintaining traceability, auditability, and compliance.
Skill Gap: Engineers must learn AI orchestration, prompt engineering, and context management.
Some thoughts about how GenAI will impact engineering:
1. Workflow Automation and Productivity Gains
GenAI can automate repetitive tasks like code generation, documentation, and testing, reducing development time significantly. Engineers can focus more on creative problem-solving and innovation rather than routine work.
2. Design and Simulation
In fields like civil and mechanical engineering, GenAI-powered tools can generate design concepts, run simulations, and create digital twins to test structural integrity and sustainability before physical prototypes are built.
3. New Roles and Skills
By 2027, a large portion of the engineering workforce will need to upskill to work effectively with AI tools. Skills like prompt engineering, data science, and AI model integration will become essential. Expect new roles such as AI Engineer, combining software engineering with machine learning expertise.
4. Industry-Wide Applications
Smart Manufacturing: AI-driven predictive maintenance and process optimization.
Supply Chain Engineering: Real-time analytics and risk forecasting.
Software Development: AI-native workflows where most code is AI-generated.
5. Future Outlook
Generative AI won’t replace engineers—it will augment them. Human creativity, critical thinking, and domain expertise remain irreplaceable. The biggest disruption will be in how engineers work, not whether they’re needed.
Recommended Resources to Prepare for AI in Engineering
Free Courses:
https://www.elementsofai.com/ – Introductory AI concepts.
https://www.coursera.org/learn/ai-for-everyone – Beginner-friendly overview.
https://ocw.mit.edu/ – Advanced fundamentals.
Certifications:
Microsoft AI Fundamentals (AI-900) – Great starting point.
AWS Machine Learning Specialty – For cloud-based AI applications.
Hands-On Platforms:
https://huggingface.co/ – Experiment with GenAI models.
https://platform.openai.com/playground – Practice prompt engineering.
Industry Insights:
https://www.mckinsey.com/
https://spectrum.ieee.org/
You probably also want to consider Agentic workflows—where autonomous AI agents reason, plan, and act with minimal human intervention—are poised to fundamentally reshape engineering processes. Here’s how I think they will affect the industry:
1. From Static Automation to Dynamic Autonomy
Unlike traditional automation (e.g., RPA), which follows rigid rules, agentic workflows adapt to real-time data and unexpected conditions. AI agents can break down complex engineering tasks into iterative steps, refine actions, and coordinate across systems without constant human oversight. This means greater flexibility and resilience in engineering operations.
2. Accelerating Design and Simulation
Agentic AI can interact directly with CAD/CAE models, diagnose missing inputs, and auto-apply best practices for simulations. For example, platforms like SimScale are using agentic workflows to:
Guide novice users through simulation setup.
Automate boundary condition assignments.
Enforce company best practices across distributed teams.
This reduces onboarding friction and cuts model setup time dramatically.
3. Transforming Engineering Workflows
Agentic systems can handle tasks such as:
RTL generation and verification planning in chip design.
Documentation and compliance checks in civil engineering.
Predictive maintenance and anomaly detection in industrial systems.
Synopsys’ AgentEngineer™ framework shows how multi-agent systems will progressively optimize workflows from assistive automation (L1) to highly autonomous orchestration (L5)
4. Productivity Multiplier, Not Job Killer
Agentic workflows create a “Jevons paradox” effect: as engineering tasks become faster and cheaper, demand for projects rises—increasing the need for skilled engineers to orchestrate and validate AI-driven processes. Roles will shift toward AI orchestration, governance, and strategic decision-making rather than manual execution.
5. Key Benefits for Engineering
Speed: Reduce cycle times from weeks to hours for design and verification.
Quality: Improve defect detection and compliance through intelligent agents.
Scalability: Handle complex, multi-step workflows across global teams.
Innovation: Free engineers to focus on creative problem-solving and system-level thinking.
6. Challenges to Address
Reliability: Ensuring deterministic outputs in multi-agent workflows.
Governance: Maintaining traceability, auditability, and compliance.
Skill Gap: Engineers must learn AI orchestration, prompt engineering, and context management.