Does anything actually need to change?
AI is predicted to change everything. To disrupt every industry, change every job and even transform the way we go about our daily lives. Is the hype and scale real?
When it comes to engineering, and specifically mechanically complex product design – yes, it is.
To design products – from cars and semiconductors to medical devices and power tools – engineering teams currently spend thousands of hours reviewing 3D models and production drawings each year. And, according to a survey of 250 engineering leaders, 23% of engineering time is currently spent on non-value-added work.
There are a number of reasons for this:
- Engineers are currently using up to five different methods to document feedback – spreadsheets, email, free CAD viewers, notepads or sticky notes, PowerPoint slides and CAD screenshots are all commonly used as part of a product design review introducing data risk.
- There’s a lot of repeat mistakes – while products often feature the same parts and suppliers, learnings from similar product reviews are not being applied, meaning the same errors or delays pop up across global programs – even when organizations have the technical know-how to prevent them.
- Engineering design teams are bigger, more specialized and more geographically dispersed. Bringing these teams together to conduct design reviews in person is increasingly impractical. But outdated product review tools are not set up for asynchronous, remote collaboration.
As engineering teams grapple with macro challenges including tariffs, export controls, global competition, and skills gaps, they cannot afford to lose accuracy or efficiency.
The good news is, AI can help solve all these issues.
The current trends:
Large language models (LLMs) like ChatGPT – which had 800 million weekly active users as of May 2025 – are quickly growing in popularity. Further, the cost to deliver AI solutions is falling fast. Comparing AI to other transformative technologies: the price of electric power took over 70 years to fall below 1% of its original price. For computer memory, that time horizon was over 10 years; but for AI compute power, it took less than 2 years.
And AI has already started to affect employment and jobs. As you would expect, the most affected roles are in IT, with AI IT positions growing by 448% in the US between 2018 and 2025, while non-AI IT jobs increased by 9%. In the words of Jensen Huang, “you’re not going to lose your job to AI, but you’re going to lose your job to somebody who uses AI.”
Industries including automotive, mining and agriculture are all seeing the impact of AI with self-driven miles increasing exponentially, AI native mining teams outperforming incumbents by 10 to 1, and the growth of automation for full agricultural processes like weeding.
2024 was the inflection point for AI. In 2025, we’re already on the steep part of the curve.
Hardware design in an AI world:
In hardware product design, AI can already complete certain design checks on 2D drawings and 3D models. In fact, global manufacturers like Techtronic Industries (TTI), the $14B parent company of RYOBI, and wind turbine blade manufacturer TPI Composites are already using this technology in beta.
Both companies are testing AI’s capabilities within product design, with the aim to catch design issues earlier and ensure every review meets a consistent standard.
In the future, applying AI tools within the product design review process will lead to connected, optimized and automated workflows. For example:
- Co-pilots can help engineers get better quality information
- Auto review capabilities can run complete checks against industry and company standards,
- Agents can execute multi-step workflows and processes autonomously
- And generative AI can create 3D CAD and 2D drawings
And AI’s value doesn’t stop there. Using the right AI tools for product design has a number of macro benefits
AI and tackling macro challenges
The speed of product design has come into sharp focus for engineering companies this year. In the automotive industry in particular, AI is helping car manufacturers to double design speeds by enabling suppliers to design in parallel. This is crucial for US and European car manufacturers to keep pace with Chinese competitors.
When it comes to supply chains, AI is assisting manufacturers across industries to vet new suppliers quickly and accurately as tariffs bite into profit margins and some organizations look to reshore.
And introducing AI at the design review stage has impacts right through to the end of product life. By applying AI to surface lessons learned from past design reviews, engineering teams can avoid costly, repeat mistakes. This can create significant savings by reducing scrap rates and warranty claims. In 2024, the US car and cycles industry alone paid over $12 billion in warranty claims.
Future AI bets:
According to Matthew Kropp, Managing Director of Boston Consulting Group, “in five years, your competitors won’t be other companies adapting AI to fit their needs—they’ll be companies built with an AI-first approach from the ground up.”
For engineering leaders to make the most of AI they need to do five things:
- Create an AI first culture and strategy
- Seek out quick wins to build trust
- Start small with data clean up (don’t worry about getting all data in one place just for the sake of it)
- Invest in feedback and knowledge
- Bet on a capable and trusted software tool
To conclude with another comment from Huang, “don’t be that person who ignores this technology.” Fast forward a few years, the engineering companies that don’t leverage AI to automate administrative tasks, prevent errors and reduce costs in product design, won’t stand a chance.
