The transition from physical, three-dimensional products to digital products like websites and apps occurred rapidly. Virtually overnight, product designers found themselves scrambling to adapt old methods \u2014 easel pads, markers, videotape, clay models, sticky notes \u2014 to meet the new demands of the digital age.<\/p>\n
As a result, some companies struggled to produce either physical or digital products quickly enough to meet customers\u2019 needs. Those companies found themselves at a competitive disadvantage compared to companies such as Apple, which quickly adapted digital technology and new ways of thinking about design to move more efficiently through researching, ideating and delivering new products.<\/p>\n
Eventually, new technologies became available that allowed more companies to streamline the design processes with digital tools, like 3D model creation platforms and collaboration tools like Mural and Lucid Spark. However, overhead from the old days remained.<\/p>\n
For example, tasks still had to be coordinated and managed. Design standards still had to be enforced. Product designers still had to communicate and collaborate. More technologies emerged to meet these administrative needs, but they often didn’t integrate with other products. Meanwhile, demand for digital products grew exponentially, and the market rewarded companies that could deliver them faster than ever \u2013 and punished those that could not.<\/strong><\/p>\n The DesignOps approach<\/a> is a way of rethinking digital design tools and processes better so that higher-quality products can go to market faster. More of a mindset than a role or a discipline, DesignOps streamlines and optimizes design workflows, from research and ideation to delivery.<\/p>\n Adapting AI for product design with tools like ChatGPT represent the next stage of DesignOps. By combining AI and DesignOps, you can deliver better products \u2014 faster \u2014 to your customers and clients.<\/strong><\/p>\n No matter how advanced digital tools become, designing modern products will always include a few time-honored \u2014 and time-consuming \u2014 phases. However, much of this work now takes less time, thanks to AI and other modern technologies<\/a>.<\/p>\n The most important part of the research phase is developing a vision for your new product. A lot of manual work and careful thought goes into defining that vision, to say nothing of building leadership consensus and alignment.<\/p>\n You will first need to confirm the need for the product by interviewing potential customers. Of course, you will also need to collect demographic and psychographic data<\/a> on those customers to identify further which customers are best to market to and identify consumer behaviors and patterns within each customer segment.<\/p>\n In addition, you’ll need to research the market to see if similar products exist, and if they do, how could your company make them better?<\/strong> Competitor analysis also comes into play. If similar products exist, how central are they to the competing company? Can you identify their long-range goals for the product? Have they announced plans to branch out into other digital products?<\/p>\n While AI is limited in managing the logistics of conducting in-person interviews, it can help analyze the data you collect from your subjects in many ways. For example, you can upload your data and ask the LLM to explain how you can best use and present your statistics, brainstorm which tests to apply to your data or test various hypotheses.<\/p>\n AI can also help with competitor analysis. Summarizing company press releases can help you track how a product has developed over time and evaluate the company’s goals and prospective markets for a product. On a broader level, LLMs’ ability to scan and summarize publicly available information like annual reports or other financial filings can help identify market and industry trends.<\/p>\n As in the research phase, the ideation phase includes manual processes that AI tools can make less manual \u2013 and thereby faster and easier.<\/p>\n Much of the work in designing a new digital product is mapping user journeys and user flows. Your research into prospective customers, analysis of scientific research, and data about how people generally interact with digital tools will be helpful.<\/p>\n Both user journeys and user flows portray how a customer completes a goal from that customer’s perspective.<\/strong> User journeys offer a higher-level view. For example, if a customer’s goal is to buy a new insurance policy, they might start by reviewing similar policies from different companies, comparing prices, and looking at company websites.<\/p>\n Once they’ve settled on a policy, they have multiple ways to purchase it, from phone calls or visiting a representative’s office to using an app or the company’s website. Customers’ steps in each process represent their journey, which designers typically capture in a journey map. Another type of map, the empathy map, might be used to capture what the customer says, thinks, feels, and does on the way to reaching their goal.<\/p>\n User flows, in contrast, capture the specific steps the customer takes within each path toward their goal. In the digital world, that means tracking each click and decision point within an app or website. Other data, like the time they spend at each decision point, can be captured, too. Product designers might also monitor customers’ words and facial expressions as they navigate the experience.<\/strong><\/p>\n With this data, designers can build prototypes for prospective users to test. In an iterative process, they then use testing results to change the prototypes and submit them for more testing. Again, without applying DesignOps principles and AI tools, prototyping can be lengthy, even for digital products.<\/p>\n AI eliminates the challenge of the “blank page.” It can quickly create templates and low-fidelity reference artifacts to help teams get started. Team members can then iterate and collaborate on them more efficiently, significantly speeding the ideation process.<\/p>\n In addition, similar to how AI can help with competitor analysis in the research phase, it can also help gather intelligence on similar products already in the market. Unlike a simple Google search that identifies similar products, AI goes deeper to analyze<\/a> and compare product specs, features, customer feedback and even make projections about where future products may be headed.<\/strong><\/p>\n The product designer’s last step is preparing the project for developers in the delivery phase. Just as architects must provide detailed, accurate specs when they deliver their designs to a builder, a digital product designer must give the developers precise, accurate specs about every aspect of their products: how users will interact with it, what the interface should look, how animations should flow, and more.<\/p>\n Reaching this level of specificity requires clear documentation of the designer’s process. Designers also must have systems for version control so that developers know they are consistently referencing the final documentation, which underscores the need for excellent communication at all parts of the delivery phase.<\/strong><\/p>\n Finally, a final round of quality assurance (QA) testing before hand-off helps the developers build the product exactly as intended, and another round of user testing ensures that it performs exactly as users need it to. Again, the developers will need documentation from all of the product designers’ rounds of testing and their outputs, such as the prototypes.<\/p>\n Writing test scripts, user flows, and user stories is as necessary \u2014 and time-consuming \u2014 during the delivery phase as it is in the research phase. AI speeds this process by generating these documents that are critical to performing QA and user testing. In addition, tweaking these documents is much easier with each iteration because AI “knows” to change all the downstream steps affected by a change made further upstream. Otherwise, developers might miss downstream steps until they emerge during testing.<\/p>\nHow Using AI for Product Design Helps Employees and Customers<\/h2>\n
Phase 1: Research<\/h3>\n
The AI for Product Design Advantage<\/h4>\n
Phase 2: Ideation<\/h3>\n
The AI for Product Design Advantage<\/h4>\n
Phase 3: Delivery<\/h3>\n
The AI for Product Design Advantage<\/h4>\n