Although generative AI (gen AI) is in its infancy, the technology is already leaving an indelible mark on how physical products and packaging are conceived, innovated, and designed.

From product packaging to car components and retail displays, gen AI enables industrial designers to explore more ideas and product experiences, including previously unimagined ones, and develop initial design concepts significantly faster than with traditional methods.

Additionally, with the ability to visualize concepts in high fidelity much earlier in the design process, companies can elicit more precise feedback from consumers as they work to fine-tune every element of the user experience (see images below). In product research and design alone, McKinsey estimates gen AI could unlock $60 billion in productivity.1

While gen AI tools can bring about extraordinary outputs, they cannot replace human expertise. Just as the industry saw with the arrival of computer-aided design (CAD) and later advancements such as 3-D printing and augmented and virtual reality, while the methods for designing physical products may change, design experts are needed to ensure the meaningful use of the technology and delivery of business value.

In the case of industrial design, experts conducting consumer research often unearth important insights that inspire pivotal design choices. Their skill in identifying the best concepts from the dozens of AI-generated images—assessing outputs with an eye for aesthetics and manufacturability and manipulating images based on user research and their design sense—is crucial in providing a final design that will resonate with users.

Although these tools are relatively new, our teams continue to see their significant impact on productivity. When they are used properly throughout the product development life cycle, we sometimes see a reduction upward of 70 percent in product development cycle times, providing teams with the opportunity to spend more time conducting consumer testing, refining designs, vetting suppliers, and optimizing designs for manufacturability and sustainability. These tools and processes are ultimately a vehicle for growth and innovation, enabling faster development of far better products.

But while R&D and product development leaders can use the technology today to propel innovation, they will need to understand and prepare for the technology’s limitations. In this article, we share ways gen AI can unlock creativity and productivity across the product development life cycle, examine crucial considerations for business leaders trying to create business value, and suggest steps for getting started based on our design work and the use of gen AI tools in our creative process.

When industrial designers create concepts or redesign packaging, consumer durables, experiences, spaces, or just about anything else, their creative processes generally go through a few essential phases: market and user research, concept development, and concept testing and refinement. Gen AI technology can provide tremendous value at each stage, enabling designers to iterate faster, connect the dots in new ways, and catalyze divergent thinking to create products that transform users’ everyday experiences (exhibit).

Almost all good physical product design starts with market research. What features or qualities are most important to potential consumers? How are consumer preferences and tastes evolving and how are our competitors responding? What gaps exist for creating a new category of offerings?

Using gen AI tools trained on large language models—such as ChatGPT, Bard, and others—designers can gather, synthesize, and make sense of existing market and consumer data far more expediently than previously possible. Moreover, because the tools draw insights from many more diverse data sources than humans alone could analyze, they can reveal untapped market opportunities and overlooked consumer needs or expectations. That enables industrial designers to build a much richer baseline of knowledge for stakeholder discussions and consumer interviews. One consumer packaged goods company augmented its market and user research with new insights from gen AI tools about consumer sentiment and how it might use its brand equity to expand into high-growth markets. With this knowledge, the design team broadened the scope of its ethnographic interviews, gaining feedback on important design elements that informed its subsequent work to develop and refine new concepts.

As industrial designers and engineers create new product designs or iterate on the next generation of an existing product or engineering component, text-to-image gen AI tools provide a powerful medium for inspiration and innovation.

The technology’s ability to generate novel, lifelike images based on expert prompts can inspire bolder exploration and bring forward distinctive and potentially first-of-their-kind ideas. These visualizations, data, and other outputs that emerge as designers input rough sketches, ethnographic research insights, and features based on consumer sentiment into a gen AI tool can be a great starting point, drastically accelerating the concept development phase. That said, human intervention by an expert designer is still needed to validate, test, and refine outputs to make them meaningful, manufacturable, and impactful, as the images generated typically can’t be used in their initial state (for instance, some may not align with the company’s vision, others may not reflect the designer’s prompt in any meaningful way, and others still may be completely unmanufacturable).

As with previous technological evolutions, such as the emergence of CAD and 3-D printing, gen AI frees design experts from mundane and time-consuming tasks when preparing concept images, mood boards, and storyboards. By inputting iterative prompts about target performance goals and new specifications, for example, industrial designers can arrive at the “best answer” faster than if they tested different theories individually and then conducted highly manual due diligence (see images below).

Industrial designers at an automotive OEM needed just two hours with the help of gen AI to create the initial design concepts for 25 variations of a next-gen car dashboard with a touch screen interface, charging surfaces, instrument panel, and other components. These concepts were then further refined by the design team using an image-editing software to provide stakeholders with a clearer picture of where the industry was going and how to evolve component interfaces, form factor, color, material, finish, and more for the latest models of electric vehicles (see images below). Without gen AI, creating images with similar detail and quality would have taken at least a week with many more iterations. This process empowered designers to bring a product experience to life in a far more tangible manner and in a fraction of the time.

Given that gen AI outputs currently require significant manipulation, the creation of these images typically happens in the studio. But as the technology develops and its outputs become more refined, industrial designers and engineers are increasingly sitting in meetings with business leaders and conducting consumer research sessions while using gen AI tools to create inspirational images in real time based on live feedback.

With the ability to elevate a conceptual napkin sketch or rough design idea to an immersive visual, industrial designers can also bring new concepts and experiences to life. This can enable more meaningful discussions with business leaders and consumers as they seek feedback on potential opportunity areas, concepts, and future visions.

Executives at a preeminent museum, for instance, could better visualize opportunities to increase accessibility of museum exhibits when industrial designers edited and combined AI-generated images with supplementary visual content (sketches, graphics, and so on) to create storyboards that illustrated novel formats, products, services, and experiences (see image below).

Following the testing of initial concepts with stakeholders, designers can then use the technology to refine product style, apply finishing touches, and map future concepts to inform product road maps—sometimes in hours instead of weeks—before moving to the subsequent phases of design detailing, refinement, engineering concepts, and design for manufacturing.

Leaders seeking to further use the technology in product simulation and testing should watch the gen AI space closely. The technology is rapidly evolving, and as it does, we anticipate even more capabilities will become available to simplify the handoff between design and engineering and dramatically accelerate engineering processes. We’re already seeing the market launch of gen AI software solutions that enable industrial designers and engineers to rapidly turn product concepts into CAD models. That allows them to model products far faster and begin the engineering process more expediently. While the tools are still nascent, we can imagine in the not-too-distant future that these tools will drastically improve and accelerate design-to-engineering handovers.

We also expect to see new tools capable of rapidly analyzing designs for manufacturability and serviceability—for example, to confirm whether a product can be manufactured using a facility’s existing injection molding tools. From an engineering perspective, gen AI is already revolutionizing the way experts approach long-established simulation engineering problems, such as how to optimize the structural performance of products. One gen AI tool for finite element analysis and topological optimization—cornerstone techniques for understanding how a part performs under different conditions and how to produce lightweight yet strong structures—can generate hundreds of improved-design options for parts based on identified criteria, such as forces, pressures, and environmental conditions. In the future, we can expect an even more comprehensive range of capabilities from such tools, including the abilities to transform rough sketches into detailed engineering part models, facilitate material selection and optimization, and identify ways to enhance manufacturability, optimize components for assembly, and reduce costs.

Without a doubt, gen AI outputs can be impressive. However, producing meaningful outputs and turning them into a desirable, user-centric, manufacturable product that matches user preferences, pain points, and expectations doesn’t happen by just pressing a button. To achieve business value, industrial design and engineering expertise are crucial in the following areas:

Adding gen AI to the physical product design tool kit can accelerate and advance product design innovation, but only if teams can effectively use the technology. Based on our work and experience using the tools, we recommend R&D and product leaders consider the following actions to begin building their gen AI capabilities:

Gen AI has begun to reshape physical product design, enabling industrial designers to be more productive, creative, and strategic in building products that solve user needs. While the technology’s outputs can be dazzling, its ability to create business value becomes apparent only when combined with the skilled hands and discerning eyes of design experts. As adoption gains speed and as more designers and engineers integrate this technology into their workflows, we could see some genuinely revolutionary design and engineering solutions blossom. This will potentially lead to an entirely new aesthetic era with ingenious form factors, greater efficiency in material usage and manufacturability, and improved product life spans—benefiting both the companies that create these products and the people who use them.