The Future of Design & Engineering with AI: Efficiency, Creativity, OpenAI & 3DEXPERIENCE

In today’s fast-paced world, design and engineering processes are increasingly complex and time-consuming. You’re likely all too familiar with the bottlenecks caused by repetitive design iterations, the overwhelming task of compliance documentation, and the often tedious process of managing change requests. Add in the challenges of BOM interrogation, supplier rationalization, and running countless simulations, and it becomes clear that these tasks can slow down your team’s ability to innovate.

These hurdles take valuable time away from the creative and strategic work that drives true progress in design and engineering. This is where AI can be transformative—by integrating it into a structured process I call the Critical Thread, you can streamline operations and unlock new levels of efficiency and innovation.

1st up, what is the Critical Thread?

The Critical Thread is the backbone of a product lifecycle, connecting every stage from conceptualization through to production and even aftermarket support. It ensures seamless data flow and process management across the entire lifecycle, enabling teams to work in a unified, traceable, and efficient manner. You might call it the Digital Thread, but we call the efficient thread, the Critical Thread. In other words, the Critical Path along the Digital Thread.

Here’s a quick breakdown of the Critical Thread’s key components:

1. Conceptualization & Requirements Gathering: Where product concepts and detailed requirements are defined.
2. Design & Change Management: The stage where product designs are created, managed, and revised.
3. Product Validation & Simulation: Virtual testing and validation of designs before they move into manufacturing.
4. Production & Process Planning: Defining how products will be manufactured, including resource management, materials, and workflows.
5. Quality & Compliance: Ensuring all products meet regulatory standards and tracking certifications.
6. Integration with MES/MOM & ERP: Aligning manufacturing execution systems and enterprise resource planning for real-time data flow.
7. Aftermarket Support: Maintaining product performance post-production with spare parts management and servicing.

By following this thread, teams can connect their work from start to finish, ensuring that designs flow seamlessly from concept through to manufacturing and beyond.

How AI Enhances the Critical Thread

Now, imagine overlaying AI across this Critical Thread, automating many of the repetitive, time-consuming tasks and enabling your team to focus on more creative, high-value work. By integrating AI—specifically OpenAI models—into your Critical Thread, you can enhance your existing tools and those of 3DEXPERIENCE, such as CATIA, ENOVIA, Simulia, and Delmia to dramatically improve your processes.

AI adds value to each step of the Critical Thread by:

• Streamlining Design Iterations: AI can optimize design iterations by analyzing parameters in real-time, cutting down the time spent revising designs.
• Automating Compliance Documentation: AI handles compliance documentation generation, reducing manual effort and improving accuracy.
• Predicting Design Flaws: AI-powered simulations help predict potential issues before physical prototypes are built, reducing costly errors.
• Improving Production Planning: AI enhances manufacturing process simulations, ensuring that digital models transition smoothly to physical production.

By enhancing your Critical Thread with AI, you’re not just improving efficiency but also empowering your team to make smarter, faster decisions, driving innovation and reducing time to market.

The Business Case for AI in Design & Engineering

Let’s break down the business case by highlighting the key benefits of integrating AI into your Critical Thread:

Reduced Time to Market:

AI-driven design optimization accelerates your design cycles by automating iterations and revisions. Within your Critical Thread, AI’s impact on Design and Change Management is evident as it speeds up transitions from the Engineering Bill of Materials (EBOM) to the Manufacturing Bill of Materials (MBOM), getting your designs production-ready faster. This allows you to respond more quickly to market demands, giving you a competitive edge.

Increased Productivity:

Your Critical Thread handles the flow of information across design, PLM, simulation, and manufacturing. By automating routine tasks such as compliance reporting and document generation, AI frees up engineers to focus on more valuable work. This results in higher throughput and more creativity during Conceptualization, Product Validation, and Process Planning stages.

Improved Design Accuracy:

AI-powered simulations allow you to detect potential design flaws early in the process. This is crucial in the Product Validation and Simulation phase of the Critical Thread, where predictive algorithms analyze historical data and simulation results to prevent costly issues downstream. AI reduces errors and rework, ensuring smoother transitions through each stage of production.

Making Financial Sense: Tracking ROIs & KPIs

Here’s how I’d approach tracking the financial benefits of AI within your Critical Thread:

• Time Savings: First, I'd be looking at measuring reductions in design iteration times and compliance documentation generation. These savings, when tracked across the Critical Thread, can be quantified in terms of faster project timelines and quicker time to market.
• Cost Reduction: 2nd, let's look at saving money, with AI’s impact on reducing rework and errors, especially during the Product Validation and Simulation stages - this will lead to measurable cost savings. We’ll track how much you save in material usage, prototyping, and overall project costs.
• Increased Throughput: 3rd, by AI automating tasks in Design and Change Management, Product Structure, and Virtual Manufacturing, your team will be able to handle more projects. We’ll measure the increase in projects completed per month, driving more revenue.
• Compliance Efficiency: and 4th, through AI-generated documentation we'll significantly reduce manual effort, especially in PLM and Quality Management phases. We’ll track how much faster and more accurate your compliance processes become.

How to Build the Business Case for AI Integration: A Step-by-Step Approach

Next, let’s walk through how I’d go about creating the business case for integrating AI into your Critical Thread:

Step 1: Assess the AS-IS State of Your Critical Thread

Evaluate your current design, PLM, simulation, and manufacturing processes (+ some ERP/MOM pieces!), and identify inefficiencies across the Critical Thread:

• CAD and PLM: Identify bottlenecks in design cycles and traceability challenges.
• Simulation and Virtual Manufacturing: Examine how efficiently your digital models integrate into manufacturing.
• System Integration: Assess where silos exist between PLM, MES/MOM, and ERP systems.

This step helps us see where AI can deliver the most value within your existing Critical Thread.

Step 2: Identify Key Areas for AI

With the AS-IS analysis complete, let's then move onto pinpointed where AI can have the greatest impact within your Critical Thread, such as:

• AI-Driven Design Optimization in CAD to streamline iterations.
• Automated Documentation in PLM to reduce compliance risks.
• Predictive Simulation Insights to improve design accuracy and reduce rework.

Step 3: Run Pilot Programs

We’ll start with small-scale pilot programs to test AI in specific Critical Thread workflows, such as compliance reporting or simulation optimization. Track KPIs to validate the impact and ensure AI delivers measurable improvements.

Step 4: Address System Integration

AI can bridge silos across your ERP, MES/MOM, and PLM systems, ensuring that your critical thread remains intact. AI will automate data flow, eliminate manual processes, and strengthen system communication.

Step 5: Track ROI

Finally, we’ll track the impact of AI across KPIs like time savings, cost reductions, throughput improvements, and compliance efficiency. By measuring success at every stage of the Critical Thread, we’ll make a strong case for broader AI adoption.

By integrating AI into your Critical Thread, you can ensure that your processes are not only more efficient but also smarter, more flexible, and ultimately more innovative. AI doesn’t just optimize tasks—it transforms the entire lifecycle, empowering you to accelerate delivery, reduce costs, and enhance product quality.

Practical Applications of AI in Design and Engineering

When it comes to design optimization, you know how crucial it is to find the right balance between creativity, functionality, and manufacturability. However, manually iterating through designs and ensuring they meet all requirements can be a time-consuming and tedious process. This is where AI can make a transformative difference.

By integrating AI into your design workflows, you can automate the generation of optimized designs and receive real-time feedback on adjustments, all while staying aligned with your production constraints and regulatory standards.

Let’s dive into how AI can take your design process to the next level.

AI-Driven Design Optimization

Design optimization is a key area where AI delivers significant value, particularly in reducing manual effort and speeding up iteration cycles. AI transforms the design process by leveraging advanced algorithms that can quickly generate, evaluate, and refine designs based on specific criteria such as material specifications, performance requirements, and cost constraints. Here’s how it works:

Automated Design Generation

In traditional design workflows, your engineers manually adjust parameters, test iterations, and review outcomes—often a time-consuming process. However, with AI, you can completely shift how this process is managed.

• Speed and Efficiency: AI can instantly generate multiple design iterations using inputs like material properties, weight limits, and performance requirements. What once took days or even weeks of manual labor can now be done in a fraction of the time. The AI models simulate various configurations and provide engineers with a selection of optimized designs to choose from, all while adhering to predefined constraints.
• Advanced Problem Solving: For complex products with intricate design challenges—such as multi-part assemblies or components requiring strict tolerances—AI can analyze the full scope of material behavior, structural integrity, and environmental factors. It then uses this information to propose designs that might not have been apparent through traditional methods, thereby opening up new avenues for innovation.
• Integration Across the Critical Thread: Within the Critical Thread, AI-driven design generation seamlessly integrates with your existing CAD tools like CATIA. As part of the Design and Change Management phase, AI ensures that design data flows smoothly into PLM systems for traceability and feeds into Product Validation and Simulation for early-stage testing and refinement.

By automating this generation process, AI allows your team to focus on more strategic work, reducing the number of iterations needed to finalize designs and accelerating the transition from conceptualization to production.

Real-Time Design Adjustments

One of the most powerful applications of AI is its ability to make real-time design adjustments based on predefined constraints, whether they come from manufacturing limitations, cost targets, or regulatory requirements.

• Adaptive Intelligence: With AI integrated into CATIA, your design environment becomes much more responsive. As you input constraints—such as material availability, structural strength, or manufacturability—AI evaluates them against the design in real time. For example, if a material change alters the structural integrity of a component, AI can automatically suggest modifications to ensure the design still meets performance standards.
• Design Standards and Compliance: AI helps engineers adhere to strict industry standards from the outset. By continuously analyzing the design against predefined rules—such as weight limits, stress tolerances, or safety factors—AI minimizes the risk of non-compliance, reducing costly rework later in the product lifecycle. In this way, AI optimizes designs not only for functionality but also for compliance, allowing you to stay ahead of regulatory requirements.
• Instant Feedback for Decision-Making: During the early stages of the Critical Thread—particularly during Design and Change Management—AI can provide immediate feedback on how design choices impact downstream processes, such as manufacturing feasibility or material sourcing. This allows your team to make informed decisions quickly, preventing bottlenecks in later phases of the lifecycle. AI also reduces the back-and-forth typically associated with design adjustments, enabling faster approvals and fewer delays.
• Seamless Integration with Manufacturing: Once real-time adjustments are made, the optimized design is automatically integrated into the downstream phases of the Critical Thread, including simulation, virtual manufacturing, and BOM management. AI ensures that all adjustments align with the EBOM, MBOM, and Bill of Process (BOP), so when the product reaches the manufacturing stage, there’s no need for further manual interventions or redesigns.

There's a good old saying: "you can lead a horse to water, but you can't make him drink". You can put in place the best collaborative Design & Engineering Platform (3DX) but it doesn't mean your enterprise has any more time to collaborate. AI removes much of the early stage and mundane reviews.

By leveraging AI for Automated Design Generation and Real-Time Design Adjustments, you not only save time and reduce manual effort, but you also ensure that your designs are fully optimized from the start.

This significantly shortens the time-to-market, improves design accuracy, and increases overall productivity. These optimizations also feed directly into your Critical Thread, enhancing its performance by ensuring all phases of the product lifecycle are seamlessly aligned and managed.

With AI driving your design process, you can confidently move forward with designs that are both innovative and manufacturable—meeting all necessary standards, timelines, and cost constraints.

Documentation Automation for Compliance and Reporting

In many engineering workflows, producing compliance documentation is a time-intensive, repetitive task. Engineers often have to manually generate reports, ensuring every detail is correct and meets the necessary regulatory standards. This process not only takes valuable time away from higher-priority projects but also opens the door to human error. AI can fundamentally change this by automating compliance documentation and reporting, making your processes more efficient, accurate, and reliable.

Streamlining Reporting

Compliance reporting is a non-negotiable requirement in most industries, but it doesn’t have to be a burden on your team. With AI, you can automate the generation of these reports using standardized templates that adhere to your industry’s regulatory requirements. AI models are capable of pulling relevant data from your design systems, such as PLM, and auto-generating reports that meet compliance standards without needing manual intervention.

• Efficiency Gains: AI drastically reduces the time engineers spend on documentation, turning a task that may have taken hours or days into something handled almost instantly. Whether it’s generating reports for safety standards, environmental regulations, or industry-specific certifications, AI can streamline these tasks, allowing your engineers to focus on more value-added activities.
• Consistency and Accuracy: One of the key advantages of AI-driven documentation is that it ensures consistency across all your reports. By using the same templates and pulling data directly from your design tools, AI minimizes the risk of inconsistencies or errors that can occur with manual reporting. This leads to a higher level of accuracy and fewer compliance issues down the line.

Automatic Updates

Another major pain point in compliance documentation is ensuring that reports stay current as designs evolve. Typically, any time a design changes—even slightly—engineers have to go back and update their reports manually. This not only increases the risk of compliance gaps but also leads to delays in the design cycle. AI addresses this problem head-on.

• Real-Time Synchronization: With AI algorithms in place, your documentation is automatically updated as soon as changes are made to the design. AI continuously monitors design iterations and applies those changes directly to the relevant reports. This ensures that your documentation is always up to date and aligned with the most current design data.
• Minimizing Compliance Gaps: Compliance gaps are a major risk, especially in highly regulated industries. By automating updates, AI minimizes the chance of oversight, ensuring that your documents reflect the latest designs and remain compliant at all times. This can be particularly useful in environments with stringent safety or certification requirements, where even small errors can result in costly delays or penalties.

By integrating AI into your documentation processes, you not only reduce the time and effort spent on compliance reporting but also increase the accuracy and timeliness of your reports. AI ensures that as your designs evolve, your documentation evolves with it—keeping you compliant and ahead of potential risks, all while freeing up your engineers to focus on what truly matters.

AI-Powered Simulations for Engineering Validation

Simulations play a critical role in validating designs before they reach production. However, traditional simulations often require multiple iterations to catch all potential risks and design flaws, which can slow down the development process. This is where AI takes simulations to the next level. By integrating AI into your simulation tools, you can leverage predictive analytics to assess risks early in the design phase, drastically reducing the likelihood of costly failures down the line.

If I were in your shoes, I’d look at AI as not just a tool for improving simulations but as a way to anticipate and prevent issues before they even arise. It’s about getting ahead of potential design flaws and strengthening your product before it hits manufacturing.

Predictive Analytics for Risk Assessment

When it comes to predicting potential risks in design, AI is a game changer. Instead of relying solely on manual simulations, AI can analyze large amounts of historical data to identify patterns that may indicate a future failure or weak point in your design. Here’s how I’d approach it if this were my business:

• Historical Data Insights: AI can tap into the wealth of data from previous projects and simulations to identify risks based on similar conditions. For instance, if a specific material or design configuration caused failures in past projects, AI can flag these risks in new designs, allowing you to address them before they become critical issues. This means that from the very beginning of the design phase, you’re already working with insights that can reduce errors and improve product reliability.
• Early-Stage Risk Detection: One of the most powerful aspects of AI is its ability to detect risks at the very early stages of development. I’d use AI to run predictive simulations that test the limits of a design, evaluating stress points, material durability, and performance under various conditions. By catching these potential failures early, you can avoid costly rework or delays later in the development cycle.
• Continuous Improvement: The more data AI has to work with, the smarter it becomes. As you continue to run simulations and refine your products, AI learns from each project, becoming better at predicting risks. I’d make sure to integrate this AI-driven approach into your engineering validation process to ensure that every iteration is smarter and more resilient than the last.

With AI-powered predictive analytics, you can take a proactive approach to risk assessment. Instead of reacting to failures after they occur, AI allows you to prevent them before they even make it to the prototyping stage. This leads to not only more reliable designs but also faster time-to-market, as you’ll spend less time fixing errors and more time perfecting your product.

Technical Solution: Integrating OpenAI with 3DEXPERIENCE

The integration of OpenAI into the 3DEXPERIENCE platform can significantly streamline design and engineering processes, specifically within conceptualization, requirements gathering, design, and manufacturing planning. This approach automates many of the repetitive tasks involved in these stages and ensures your teams can focus on innovation and high-value work.

Step 1: Enhancing Conceptualization & Requirements Gathering with Azure OpenAI

Natural Language Processing (NLP) for Market and Requirements Analysis:

An OpenAI can analyze unstructured data (e.g., customer feedback, market reports, competitor analysis) to automatically generate and prioritize product requirements. The NLP models extract key insights from CRM systems and other sources, helping your engineers and designers create more accurate product definitions and objectives.

As an example, your designers can input broad product concepts, and OpenAI analyzes relevant data to recommend features or materials that align with customer needs and market trends.

AI-Driven Requirements Prioritization:

OpenAI can rank and prioritize requirements based on potential value, helping teams focus on features that will have the most impact. This is particularly useful in complex design environments like aerospace or automotive, where multiple stakeholders influence requirements.

Your system could flag high-priority requirements by analyzing data from previous projects and market conditions, enabling your engineers to focus on key design criteria early in the process.

Step 2: AI for Design and Change Management (3DEXPERIENCE, CATIA, ENOVIA)

Generative Design in CATIA:

By integrating CATIA Generative Design, design teams can leverage AI-driven generative design to automatically generate multiple design alternatives. This reduces the time spent manually iterating through designs and provides options that optimize for performance, cost, or manufacturability.

Your engineers can specify design parameters such as weight, material, and structural strength, and AI will instantly produce several optimized design alternatives, which can then be further refined.

AI-Powered Change Impact Analysis in ENOVIA:

Using OpenAI, your teams can automate the analysis of design changes to predict downstream impacts across the Critical Thread. OpenAI can evaluate the ripple effects of changes made in the EBOM or CAD models, providing insights into how these changes affect manufacturing processes, material sourcing, or product validation.

As an example, a change in material specifications triggers OpenAI to flag potential issues with suppliers or production constraints, allowing teams to make informed decisions before changes are implemented.

Step 3: Automating Documentation and Compliance Reporting with CoPilot

Automated Design Documentation Generation:

Through your new AI Assistant you can automate the generation of compliance documentation directly within ENOVIA or CATIA. By pulling data from design and requirements specifications, the AI Assistant can generate reports that meet industry regulations, significantly reducing the time spent on manual documentation tasks.

After a design change is made, your AI Assistant automatically updates the relevant compliance documents, ensuring that all regulatory requirements are met without manual intervention.

Real-Time Collaboration for Change Management:

With your AI Assistant, design teams can collaboratively manage change requests and approval workflows in real time. The AI Assistant tracks design revisions and generates real-time reports that document the changes, ensuring traceability and compliance within ENOVIA’s change management system.

Step 4: Product Structure, MBOM, and Bill of Process (BOP)

AI-Driven BOM Optimization:

Integrating OpenAI with the BOM management processes ensures that engineers can automatically generate and optimize the Engineering Bill of Materials (EBOM) and transition it into the Manufacturing Bill of Materials (MBOM). OpenAI can analyze BOM structures to suggest part substitutions, cost optimizations, and supplier rationalizations, ensuring smooth transitions into the manufacturing planning phase.

Your AI suggests cost-effective alternatives for certain components in the BOM, which can then be validated within the broader production workflow to ensure compliance with performance requirements.

Automating the Bill of Process (BOP):

OpenAI can help automate the creation and management of the Bill of Process (BOP) by analyzing design data and generating optimized process plans. This ensures that manufacturing operations align with the design specifications, reducing the risk of errors during production.

After finalizing your MBOM, OpenAI generates a detailed BOP, outlining the necessary steps, materials, and resources required for production, automatically incorporating changes from design revisions.

Step 5: Orchestration and Workflow Automation

Next, your AI-powered workflow manager: This can automate workflows between design and engineering phases, ensuring that data flows seamlessly from conceptualization to production. For instance, once the MBOM is approved, your AI-powered workflow manager can trigger downstream processes like supplier orders or manufacturing simulation, eliminating manual handoffs.

Once your AI-driven design changes are approved, the AI-powered workflow manager initiate material sourcing, scheduling production runs, and updating the project timeline based on real-time data.

Driving Efficiency and Innovation in Design & Engineering

By integrating OpenAI, an AI Assistant, and Cloud services into your 3DEXPERIENCE ecosystem, you create a powerful, AI-enhanced Critical Thread that connects design, engineering, and manufacturing planning in a seamless, automated flow. This not only enhances the speed and accuracy of your design process but also empowers your teams to focus on innovation and high-value tasks, driving your business forward in a competitive landscape.

Andrew Sparrow

The engineering landscape is evolving at a rapid pace, and businesses that fail to adapt risk being left behind.

AI is no longer a futuristic concept—it’s a powerful tool available right now, and the companies that embrace it will gain a competitive edge in efficiency, innovation, and time to market.

I believe that by integrating AI into your processes today, you can eliminate inefficiencies, reduce risks, and unlock new opportunities for growth. The time to act is now—those who hesitate may find themselves struggling to keep up. Don’t let your competition get ahead. Start exploring how AI can transform your engineering operations and position your business for long-term success.

An Outcome-based MVP is a great way to start, off the back of a personalized business-case.

Excited to hear from you