What is AI product design?

Like other kinds of AI business applications, AI product design blends traditional design and product development principles with a deep understanding of how AI systems work. For example, in the user-experience (UX) design phase of product development, AI tools and machine learning (ML) algorithms are essential. They are critical in helping designers build digital products that mimic the human brain’s ability to continuously learn.

AI first appeared in product design in the 1990s, when it was used in computer-aided design (CAD) systems that created real-world simulations for users to test products. In the 2000s and 2010s, as AI applications increased, machine learning algorithms were deployed to improve aspects of product design like data analysis, predictive maintenance and real-time monitoring of manufacturing systems.

Most recently, AI has been leveraged in generative design, an iterative design process where AI systems create multiple design options for engineers to review based on predefined parameters. Generative design helps product managers and designers optimize their design ideas faster. It also helps identify solutions to design problems that are potentially more efficient and less costly than what they could conceive of alone.

AI product design integrates AI into every phase of product development, from ideation and prototyping of a new product to user-interaction design (UI design) and even market testing.

AI tools help streamline workflows, automate tasks and speed product development at every stage. AI-powered systems are faster and less error-prone than humans at conducting user research. Furthermore, well-known AI design tools like Figma and ChatGPT help designers brainstorm and try out products in real-time.

While AI might never replace product designers, it can help them iterate faster and create more high-quality outputs with fewer resources. Here’s a closer look at how AI is used during every stage of product design.

During the research phase, product marketers use AI-driven analysis (AI analysis) to sift through large datasets of user behavior gathered from social media platforms like Facebook, X and LinkedIn for insights.

Machine learning algorithms are crucial at this stage in helping identify trends and possible opportunities for product development. AI tools are also used extensively to explore design concepts and even to suggest ideas for new products.

Brainstorming, also referred to as ideation, is the stage of product development where product managers and creative directors gather in person or remotely to generate ideas for new products.

Generative AI tools, which are AI models that can create text, images and video content, are used extensively during this stage. AI-powered text and image generators create AI-generated mockups and wireframes for applications and digital experiences that humans can review, streamlining workflows and helping designers stay focused on creative tasks.  

Prototyping, a key component of product design, uses AI tools to create interactive prototypes that simulate how users might interact with a new product.

Using AI design tools, designers in the prototyping phase can streamline the process of testing a digital product on various devices. For example, Figma’s AI integrations allow creative directors to experiment with how a layout is going to function on various devices, a process known as responsive design.

During the iteration phase, AI product design tools help designers and product managers iterate faster than in the past and apply AI capabilities to solve complex problems. AI-enhanced iteration streamlines design work, removing hours and even days from development workflows.

One example is in A/B testing, a process where two different versions of a digital experience are tested on users. In A/B testing, AI automation tools test two different UI designs and analyze their performance in various situations, enabling product managers to assess which design yielded a better user experience.

In the deployment phase, product managers and software engineers use AI-powered tools to build consistent user experiences across global markets and to automate the scaling of resources (auto-scaling).

AI models are increasingly being used during this phase to track user engagement with digital products in real time and recommend changes to code and the development of new features. AI tools shorten the feedback loop, making product managers and developers more responsive to fast-moving markets.

Integrating AI tools into product design helps both startups and large enterprises automate repetitive tasks, generate and refine concepts faster and gain real-time insights into customer behavior. Here’s a look at the top AI product design benefits for businesses of all sizes.  

AI product design is transforming how businesses of all sizes find market opportunities and develop products to fill them. Here’s a look at the top use cases for AI product design.

• User experience (UX) design: AI-enhanced UX design incorporates AI-powered tools into layout generation, text alignment, A/B testing and more. AI models can convert hand-drawn sketches into digital wireframes and animate a static mockup into a fully clickable prototype in a few minutes.

• Content creation: Gen AI has supercharged how designers create a wide range of content, such as images, text and video. According to a recent report, 34 million AI images are being created every day. Furthermore, over 15 billion images have been created since 2022, when the first widely available gen AI platform was released.¹ The latest AI models let designers use natural commands to generate high-quality, campaign-ready assets with a fraction of the resources they would have needed before.

• Resource optimization at startups: Startups don’t have the same design resources as larger enterprises, but AI product design is helping them create work that meets the same standards. AI-powered solutions help small teams create scalable design systems by using templates, automation and real-time collaboration tools so they can compete with established brands.

• Automotive design: AI product design is helping designers create faster, more fuel-efficient cars and trucks by using a type of machine learning called supervised learning. In supervised learning, a machine learning algorithm learns from data that humans have already labeled. This approach allows designers to build vehicles based on hyper-specific customer feedback.

• E-commerce design: AI product design for e-commerce leverages AI tools to create more personalized digital shopping experiences for customers based on user data. AI models conduct user research faster and more efficiently than humans. Highly adaptive interfaces that leverage AI can recommend products based on browsing history, geographical location, age and more.

As AI technology continues to grow and businesses experiment with new ways to leverage it, product design remains a rich area for applications. AI models being deployed in product design are some of the most advanced, taking on complex tasks and pushing the boundaries of what AI systems can do. Here are three trends in AI product design that are continuing to drive innovation. 

Rather than trying to create AI systems that can match human creativity, AI in product design is trending toward systems that make design work more efficient and lower barriers to innovation.

Agentic AI, AI systems that reason, use tools and adapt like humans, is making important strides in this area, while gen AI appears to be lagging. According to a recent report, nearly 80% of boardrooms use gen AI, but aren’t seeing the impact they anticipated.²

Agentic AI could have better results than gen AI with certain applications because of its increased level of sophistication in automating complex tasks toward achieving a goal.

Personalization based on customer data is already an area that AI has dramatically improved, but expect newer, more sophisticated AI product design tools to push what’s possible even further.

Advanced AI algorithms can now analyze in-app behavior and respond to it in real time. This process dynamically shifts what content is served to a customer while they browse, shop or play an online game.

Modern digital interfaces evolve instantly based on changes in a user’s current context, such as location, device type or even perceived emotional state.

Digital-twin technology consists of virtual models that allow product managers to simulate how products perform under certain conditions. The use of this technology provides insights into user behavior faster than other approaches have done in the past. Today, digital twins allow designers to test product vulnerabilities and evaluate new features in a controlled environment before going to market.

Digital twins in AI product design help speed insights into product performance, reliability and effectiveness that give users a distinct advantage over their competitors, reducing costs and helping shorten development timelines.