Boss asked how engineering plans to use AI… again?
Here's your action plan.
ChatGPT isn’t cutting it anymore. There are better, more effective engineering use cases for AI. And now you have a blueprint for how to go from ChatGPT to AI-driven hardware engineering
GET StartedThe case for AI-driven design reviews
Your most important product decisions are made every day in design reviews. Yet your team still struggles with the mundane, admin tasks that plague these reviews. Getting the right people in the right file at the right time is either inconsistent or never even happens. So, every review starts from scratch. And you fail to apply the knowledge from past reviews to future ones.
Voila! A perfect use case for engineering AI: tracking design review issues and applying them to future reviews.
Your 4-step AI implementation plan
The data you have now is a great start. But building the infrastructure to continually gather and feed high-quality data to AI is key. Here’s what that should look like for design reviews:
Enable the right experts to provide quality design feedback.
Enable engineers and suppliers to view and interrogate 2D drawings or 3D CAD models in their browser: asynchronously and without a CAD or PLM license.
Track qualitative feedback as a byproduct of reviewing the design.
The right infrastructure should capture and track all design review feedback automatically. The context between CAD + qualitative feedback is key for training the AI models.
Gather other design review feedback sources.
AI works well with the raw, unstructured data that lives in decks, spreadsheets and emails.
Use AI to simplify design review workflows.
Now that you have the right data, pull up a model, click the ReviewAI icon and ReviewAI will generate lessons learned, complete basic checks, and generate comments right on the model.
Realistically, you can start using ReviewAI within a few weeks with the data you have today. Then, use CoLab at the same time to continually enable high-quality inputs.
Engineering teams who apply AI to future design reviews benefit from many short-term business gains. With CoLab + ReviewAI, engineering teams benefit from:
As your team continues to use and apply AI, you’ll also be feeding ReviewAI high-quality data from your team’s qualitative feedback. This feedback loop means you’ll benefit from exponential business results. Using a combination of AI case studies and proprietary CoLab data, we predict teams who use AI will:
5 reasons you think you’re not ready for AI (and why you should start anyway)
AI is not ready to do my job.
AI will eventually replace me.
We don’t have good data to train AI.
My team isn’t ready. We have too much on our plates as it is.
I can’t put my company data into this! It’ll be used to train other models.
If you’re planning to “start soon”, you’re already too late
This is the most challenging task for large companies. Building an AI plan without the bureaucracy and red tape that surrounds enterprise decision-making. But, to stay ahead, this is what must happen. AI application is an exponential differentiator. Starting now and starting fast means reaping massive benefits over time. The only thing engineers lack today is the plan and the buy-in. Here’s the plan. Now, go get the buy-in.
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"Being a web-based tool, there is no software distribution nor installation and very little training. It is VERY intuitive with easy-to-understand icons. We typically 'train' during one of our event kickoff sessions."
"But one of the best things about CoLab?All the time it saves by cutting out needless slideshow prep and manual admin work...CoLab helps make the onboarding process a breeze"
"In six months, I think the key success point was that we did a cost reduction redesign of our product enclosure. We realized we'd landed a 50% cost reduction on that, and our overall design cycle was half the time of prior design cycles. So something that used to take a full year, we got it done in six months."
"CoLab digitizes all of that. It creates that design environment on a single online platform, so it simplifies that product design journey - makes it much more interactive, collaborative and quicker turnaround times."
"We figured out that CoLab would basically resolve our issues and get us off the paperwork and the manual process, to something more digital. Not only would this satisfy our immediate needs, but also it would open up new opportunities for the organization."
"When it comes to sharing data and being able to transfer accurate files between the three different parties, it's really important for us to be able to make sure we're all working on the same piece of CAD, so that there's no ambiguity when it comes to what we're discussing."
AI-driven hardware engineering is 3 years away. Learn how to get started today.
Every engineering team is different. Your team’s product development challenges might look different than another engineering team. But, the common thread is this: the best engineering teams want to solve the root cause process problem forcing them to delay product launches. So, let’s talk about it.
With a product launch consultation, you’ll talk with a CoLab product expert about:
- Your team’s product development challenges
- Where the gaps in your process live
- Whether a DES can address those gaps
- How your team can start launching products faster
Fill out the form to schedule a product launch consultation.