How Will AI Impact the Design Thinking Framework?

Design thinking is a foundational framework for creating great, transformational products. Our expert explores how we can expect increasingly sophisticated AI tools to impact it.

Written by Garry Paxinos
Published on Jul. 25, 2023
How Will AI Impact the Design Thinking Framework?
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We all know that a deep understanding of our target users’ behaviors and motivations is imperative for building successful apps. Design thinking is a process that provides a structured approach to gathering valuable user insights, which we can then transform into innovative and effective solutions.

In the current business landscape, brands are increasingly recognizing the significance of customer experience beyond mere product and service offerings. According to a McKinsey study, organizations that follow design thinking practices regularly see a 33 percent increase in revenue and 56 percent higher returns to shareholders than those that don’t.

AI And Design Thinking

The design thinking framework increases the odds of creating useful products by connecting a team with real users and guiding their work to solve those users’ problems. Increasingly sophisticated AI tools can streamline and improve the framework by allowing teams to gain new insights and to automate repetitive tasks, freeing them to focus on strategic and creative work.

Businesses are also starting to realize that AI can augment the design thinking framework. According to Adobe’s recent survey, almost two-thirds of UX designers are already using AI tools to automate repetitive tasks and enhance their productivity. Furthermore, PwC did a study that shows AIs overall projected contribution to the global economy by 2030 is a whopping $15.7 trillion. 

Organizations should constantly embrace new ideas and methods, and incorporating AI tools into design thinking principles can unlock new possibilities. So, let’s take a deeper look at why design thinking is vital in the app development process and how AI might influence its future.

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The Importance of Design Thinking 

Design thinking lowers the chance of developing something that no one wants to use. If you correctly implement the design thinking process, you will greatly increase your chance of building a successful final product. Each stage of the process shows you the best way forward with development.

Its structure makes design thinking highly effective for addressing ambiguous and novel problems. It relies on a few key pillars, including brainstorming sessions, prototyping, and testing, which you repeat until achieving an optimal solution. The iterative nature of design thinking fosters a continuous process of experimentation, allowing for ongoing refinement and improvement. This iterative approach helps avoid costly mistakes and ensures that the final app meets user needs effectively.

Your team might be bored with developing products based on old methods, and that’s why it might be a good move to give them more freedom by embracing design thinking. It forces people to think outside of the box and encourages creative solutions. 

A good example of a company using design thinking comes from the fashion house Burberry. They faced the perception that the brand had become outdated and out of touch with the latest trends. They realized the potential of appealing to newer users and thoroughly studied their sentiments and behavior. Burberry then invested in its social media strategy, resulting in increased appeal and stronger connections with the target audience.


How AI Augments Design Thinking

Artificial intelligence tools can enhance the design thinking framework in several novel and surprising ways.


Empathy and definition

For design thinking to be successful, companies need to connect with the users who will interact with the app. By genuinely comprehending their true needs, thoughts, and emotions, and figuring out how your product can enhance their lives, you can create a more impactful solution. 

AI technologies like natural language processing (NLP) and machine learning analyze user feedback in UX and UI design, identifying common themes and sentiments to understand user needs. AI tools such as Google Analytics, Hotjar, UXCam, and IBM Watson streamline data analysis and enhance the user research process.

For example, with one of these AI tools, a company could use heatmaps to identify hidden features that users might struggle to access, prompting improvements for better accessibility. User session recordings could reveal cart abandonment issues due to a confusing form field, leading to a simplified checkout process. A company might even use surveys to directly gather user feedback to address pain points and preferences to enhance the overall customer experience and boost conversions.


Ideate and prototype

In this phase, you brainstorm with your team to generate different solutions for the identified problems. You should focus on encouraging the team to share as many ideas as possible and step out of their comfort zones. Unleashing creativity is the key to generating the best ideas.

Specific AI tools can expedite and streamline the design process, enhancing efficiency. Popular tools for design optimization include Google Optimize or Optimize. These are useful for A/B testing because they facilitate conducting experiments. Another handy tool is Crazy Egg, which generates heat maps or scroll maps to provide valuable insights into user interactions. These tools empower designers to make data-driven decisions and continuously improve the design to meet user needs effectively.

AI tools also enable teams to generate layouts based on user requirements and design guidelines, facilitating the exploration of different design options and providing inspiration. 

An Incoming Feedback” tool, such as in HotJar, enables teams to collect and analyze real-time user feedback on a website through customizable feedback widgets. This data-driven approach empowers the teams to efficiently generate website layouts that align with user preferences and needs, resulting in a more user-centric and effective design.

Additionally, specific tools are available for simplifying color selection tasks by generating color palettes, further streamlining the design workflow. In order to strike a balance, however, designers should not solely rely on AI. They should actively explore and develop their own unique design perspectives alongside these solutions. 


The big test

Testing is the final stage of the process. Here, you present the product to the end users. If they embrace it, you can proceed with development. If they’re not satisfied, however, you need to take a step back, perhaps even returning to the initial stage. 

You can use AI tools to conduct A/B tests, enabling designers to compare the performance of different design variations and determine which elements are most effective in achieving user goals. Furthermore, designers can optimize the design for accessibility, tailoring the product to the needs of users of varying abilities. 

For example, AB Tastys AI algorithms automatically identify visitor segments that are the most likely to convert, which allows designers to target specific user groups with tailored design variations for A/B testing.

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AI Can Unleash New Power for Design Thinking

As AI continues to advance, we can expect its profound impact on design thinking to continue. We might even reach a point where designers can create generative user interface concepts (i.e., design approaches using AI to automatically generate or evolve user interface elements) with specific user inputs and preferences. This leaves room for designers to just concentrate on strategic and creative aspects. Ideally, embracing AI’s potential in design thinking will lead to more harmonious collaboration between AI and human designers, reshaping traditional approaches and creating new applications for the future.

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