With the introduction of artificial intelligence (AI), we’re able to continually meet users’ needs by creating more immersive experiences faster than we’ve been able to before.
Digital product development has played a profound role in attracting and improving customer experiences. However, customers’ needs and desires are constantly evolving.
In this article, you’ll learn how AI enables designers to provide improved consumer experiences regarding:
- Personalization
- Engagement
- Content recommendations, and
- Automation
Benefits of generative AI in UX and product design
Generative AI shows the potential to completely transform the design processes by reducing workload and enhancing overall efficiency – and less time working on redundant activities means more time for designers to focus on creating more impactful products.
We can see AI support UX and product design through:
- Task management and design automation
- Speeding up the content development process
- Enhanced customer personalization
- Ease in analyzing and applying customer data
- Increased engagement and customer satisfaction
01: Task management and design automation
Automation is where AI can have the biggest impact on UX design. Even now, AI can eliminate the need for repetitive design processes by:
Alleviating human limitations
AI excels at performing repetitive tasks, detecting inaccuracies in design processes, and managing large volumes of information, which can be overwhelming for humans (and freeing time up for people to focus on more creative and strategic work).
Initiating processes
AI can kick-start the design process during sketching and ideation phases, helping to reduce brainstorming time. For instance, it can quickly generate concepts, provide direction options, or create templates instead of starting from scratch.
Analyzing projects
AI conducts thorough analyses of projects to identify potential issues (for example, synthesizing data from user interviews to generate key user insights), streamlining the evaluation process and offering an effective review of a product’s strengths and weaknesses.
Offering insights and recommendations
AI generates detailed reports about a product and user experiences, offering insights into user preferences and the product’s effectiveness. It also provides valuable suggestions, empowering designers to make more informed decisions.
Figma, for example, is already employing AI within its app and add-ons to help support designers in producing concepts. Some of those add-on features include:
- Using generative AI to “mock up a website that helps you discover nearby restaurants and make reservations for them.”
- Build and explore multiple prototypes at once
- Improve brainstorming sessions by being able to generate design mockups and write code in real-time, and scrap whatever isn’t needed.
In product development, developing proofs-of-concept (POCs) and working prototypes are crucial in gaining buy-in and continuing to move forward with the build. AI is able to help designers and developers create POCs much more quickly.
Machine learning (ML), a subset of AI, offers another avenue for automation. Developers train ML models to generate code or design elements based on predefined instructions. They can also automate A/B testing to identify customer challenges, irrelevant features, and the most useful functions for users, streamlining the product development process and fostering more effective product design.
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02: Content development
Content developers can use AI to support developing writing prompts for broad and niche audiences, using customer insights to ensure relevance, and finding source material and inspiration.
AI, particularly through the application of Natural Language Processing (NLP), has brought more insight to the content development process. NLP allows AI to understand users’ keyword search intent, providing valuable insights for content developers to generate the most relevant content to satisfy user needs.
These targeted recommendations facilitate consumer experiences and generate impressive results, which could include the following:
Increased sales
Personalized content recommendations mean consumers find products they want, leading to an increase in sales.
Customer retention
Tailored recommendations increase customer satisfaction. They are encouraged to have repeated experiences with your brand because you simplify their activities through prompt and relevant information.
Customer engagement
Offering relevant content encourages interaction from users. By providing the content they need, users can read, share and offer feedback. Targeted product recommendations also encourage users to download an app, use a new feature and click the “buy now” button.
03: Personalization
Personalization was possible before the introduction of generative AI models, but it was basic and not always sophisticated or predictive.
However, with the expanding capabilities of AI, designers can go much further, offering a personalized experience to each user based on their specific preferences, tastes and interests. This can be a game-changer for those in UX and CX, as 76% of customers become frustrated when companies come up short on personalization.
AI algorithms, particularly those using machine learning and deep learning techniques, can analyze users’ behaviors, preferences, browsing history and interactions with different elements of a website or application, and provide them with highly tailored experiences.
We already see personalization in some of the products we use every day, like Amazon Echo or Spotify (who use adopt data-centric AI models to offer enhanced personalization), but with Generative AI, we may begin to see personalization in virtually all of our digital experiences.
04: User insights
In supporting personalization, generative AI shows a strong ability to help with user research. UX design is successful when backed by in-depth user insights gathered from qualitative and quantitative research, but that’s not always feasible when designing digital products.
One of generative AI’s strong suits is synthesizing and summarizing text to get to the crux of information and using it as a resource to analyze user data to help increase the design and development process. Since there are still a number of instances where AI shows bias in analysis, it still requires human involvement, but it can speed up the early design process.
05: Customer service
According to a Salesforce study, 66% of users expect companies to understand their needs and expectations, but that same percentage of people feel they are treated more like numbers – even with customer service still being performed mainly by humans.
Engagement is among the leading factors driving customer conversion, satisfaction and retention. AI technologies, especially those using natural language processing (NLP) and sentiment analysis, enable developers to amplify engagement levels through quick responses and highly relevant recommendations.
AI-powered chatbots have ushered in a new era of customer experience, streamlining how users interact with websites and applications. Instead of sifting through website pages or waiting for customer support, chatbots can swiftly assist users, answering their queries, troubleshooting issues, facilitating transactions like bill payments, and even engaging users in everyday conversations. This real-time interaction not only simplifies users’ tasks but also enhances their engagement and satisfaction.
