The Power of AI in Product Design

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Artificial intelligence (AI) technologies are rapidly penetrating and proving their value in nearly every niche imaginable—and product design is no exception. Recently, McKinsey estimated that generative AI could unlock $60 billion in productivity gains in product research and design and reduce product development cycle times by 70 percent or more,  further highlighting the value of AI tools in this space.

To understand how product designers and engineers can leverage AI to such powerful effect, Mouser spoke with Chris Wlezien, McKinsey alumnus and product design and innovation thought leader, to discuss the current state of AI in product design, where AI is providing the most value, how AI is actually enhancing engineers rather than replacing them, and how product design leaders can lead the charge in this effort.
 

Chris Wlezien is a seasoned Product Design & Innovation leader and external advisor to McKinsey’s Design practice. With over 15 years of experience, 30+ patents, and a track record of $500M+ in cost savings, he has led cross-functional teams to create groundbreaking, market-ready products. His engineering expertise, manufacturing acumen, and strategic insight drive transformative innovation across diverse industries, consistently inspiring teams to push creative boundaries.

Mouser Electronics: Tell us about your professional journey to your role with McKinsey and as a product design and innovation thought leader.

Chris Wlezien: For the past 15 years, I have been in design engineering consulting, starting at different innovation companies and focusing mostly on new product innovation, concept generation, and consumer insights. Eventually, I found myself working at McKinsey, first in new product innovation and then in product optimization. Currently, I serve as an external expert advisor to McKinsey and other clients, observing the way different companies do product design and then sharing those insights with other industries. 

How would you describe the current moment in terms of the confluence of AI and product design?

Everyone knows about AI, and most people see the significant value these tools can bring to their work. Many product engineers and designers are experimenting with them, observing how they can enable faster or more effective work, and making them a permanent part of workflows. 

Despite so much anxiety and doomsaying over the past couple of years about the effect AI would have on engineering jobs, I’m actually seeing a different picture. Many engineers using this technology are far from obsolete—they are actually more effective and valuable than ever before.

How would you describe the current anxiety in the product engineering world toward AI?

In general, an assessment, maybe an overreaction, exists that AI is going to replace absolutely everything and do absolutely everything. In my experience, though, that’s not the norm. I really haven’t met many people who are drastically afraid of that, myself included.

But many engineers and organizations do feel a little paralyzed as to how to get started with these tools because of fears of how they could impact their jobs, a lack of understanding of the tools and their capabilities, the quality of the output and how to verify it, or just a general lack of direction.

Having said that, some anxieties are valid. My colleagues and I are worried that some people will use these tools in an inappropriate way. They’ll design the AI from the bottom up, feed it some inputs, and then just run with whatever the AI produces. Instead of striving to create the best possible product, they might go with what AI thinks is the best possible product, even if it’s missing many insights. So, they end up selling terrible products that are difficult to manufacture and don’t meet customer needs.

Although AI has many strengths, in reality, it’s not going to solve every problem. In my opinion it’s best used to accelerate and enhance the work of product designers and engineers, not to take their jobs.

How do AI tools accelerate the work of product engineers? What does the typical interaction between product design engineers and AI look like?

Basically, AI is doing to product engineering what two-dimensional and three-dimensional computer-aided design modeling did to drafting. AI is an assisting tool that enables engineers to go many times faster and work more efficiently. 

AI tools that provide specific solutions to specific needs are working best in the product development space—for example, creating product requirements documents or helping the engineer set up an analysis. With their ability to find and combine existing insights, large language models are great for getting an engineer’s arms around best practices or examples of what others have done to solve the same problem. 

AI tools are also great at unearthing what I call the “unknown unknowns.” Often, the thing that goes wrong when engineers test a product is the one thing they didn’t think about: the unknown unknown. In my opinion, this is AI’s best feature. 

Another great use case is concept generation. Product engineers are using generative AI tools like Midjourney to produce and iterate on concept art rapidly. A task that has traditionally taken engineers or designers weeks to accomplish can now be done in hours. 

That being said, engineers tend to get the most value from this technology when they understand its limitations. AI is designed to gravitate toward the familiar patterns in our collective knowledge to effectively synthesize information. This means that, while AI is unlikely to spark amazing, unexpected, truly innovative breakthroughs, it is amazing, even miraculous, as an engine for understanding, interpreting, and parsing knowledge effectively. It's insanely good at augmenting human creativity by providing starting points, rather than wholly original designs. 

How can product design leaders help engineers see and embrace AI’s potential to enhance the speed and quality of their work?

In promoting these tools, I encourage organizations to be flexible. Essentially, too many AI tools exist and are changing too rapidly for you to make a list of 10 options and then decide which is the best one through technical decision-making.

Instead, leaders should decide that their organization will give its engineers the freedom to explore and try these tools in their own workflows, without undue pressure, and eventually to identify and implement those tools that make their work output better, faster, and cheaper. 

Finally, we as leaders should fully understand the tools ourselves and how we can use them. Some leaders might look at AI as a reason to reduce head count and costs, but I would rather use AI tools to make my engineering team twice as productive and to create amazing products that more people will love and want to buy. 

Chris Wlezien, McKinsey External Advisor

1 Bryce Booth, Jack Donohew, Chris Wlezien, and Winnie Wu, “Generative AI Fuels Creative Physical Product Design but Is No Magic Wand,” McKinsey Digital, March 5, 2024, https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/generative-ai-fuels-creative-physical-product-design-but-is-no-magic-wand.