24 Marzo 2017
Alcuni dei migliori professionisti del mondo della progettazione digitale si sono confrontati sulle implicazioni professionali della disciplina e sul suo impatto tra gli addetti ai lavori sia a livello organizzativo che progettuale.
Ospiti Elena Sara Cagnoni e Gigliana Orlandi (Whirlpool), Simone Di Somma (Innaas), Francesca A. Lisi (Università degli Studi di Bari + AI*IA), Guido Vetere (Cervelli nella vasca).
A moderare l’incontro Paolo Ciuccarelli (DensityDesign + Politecnico di Milano).
Organizzato dal Corso di Alta Formazione in User Experience Design.
UX Talk: UX, Data & Artificial Intelligence
As technology continues to advance at an unprecedented rate, the role of artificial intelligence (AI) in user experience (UX) design has become increasingly important. In this UX talk, we will explore the intersection of UX, data, and artificial intelligence, and discuss how these technologies can be leveraged to create more personalized and engaging user experiences.
AI has the potential to transform the way we design and develop digital products by leveraging data to understand user behavior and preferences. By analyzing large volumes of data, AI can identify patterns and trends that can be used to inform UX decisions, such as the layout of a website or the design of a mobile app. Additionally, AI can be used to automate repetitive tasks, freeing up designers and developers to focus on more high-level creative work.
One of the key challenges in leveraging AI for UX design is ensuring that the data being used is accurate and representative of the target audience. Data normalization, or the process of organizing data to eliminate redundancy and improve efficiency, is crucial in this regard. By normalizing the data, we can ensure that the AI algorithms are working with clean and relevant information, leading to more accurate and actionable insights.
Another area where AI can have a significant impact on UX design is in the generation of synthetic data. Synthetic data is artificially generated data that imitates real data, and it can be used to train AI models and test design concepts without the need for large volumes of real user data. This can be particularly useful in the early stages of product development when real data may be limited or unavailable.
In addition to data normalization and synthetic data generation, AI has the potential to revolutionize content generation. From personalized recommendations to dynamic content creation, AI can be used to deliver highly tailored experiences to users based on their individual preferences and behaviors. This can lead to greater engagement and satisfaction, ultimately driving user retention and loyalty.
Several technologies and platforms have emerged as key players in the intersection of AI and UX design. For example, Google’s Dialogflow is a powerful tool for creating conversational interfaces, allowing users to interact with digital products through natural language. Meanwhile, Flutter, Google’s UI toolkit for building natively compiled applications for mobile, web, and desktop from a single codebase, offers a range of features that can be enhanced through AI integration.
One of the challenges of implementing AI in UX design is the need for robust infrastructure to support the deployment and management of AI models. Platforms such as Firebase provide a scalable and reliable infrastructure for building and deploying AI-powered applications, enabling designers and developers to focus on creating compelling user experiences without having to worry about the underlying technical complexities.
OpenAI, a leading AI research laboratory, is at the forefront of developing AI models that have the potential to significantly impact UX design. Their stable diffusion (SD) approach to training AI models has the potential to deliver more consistent and reliable results, leading to more accurate and effective design recommendations.
Finally, large language models (LLMs) have emerged as a powerful tool for generating natural language content. These models, such as GPT-3 from OpenAI, have the potential to revolutionize content creation by generating highly convincing and coherent text based on a few input prompts. By leveraging LLMs, designers can create more dynamic and engaging user experiences, such as personalized messaging and chatbots.
Business Use Case: AI in eCommerce
One of the most compelling business use cases for AI is in the field of eCommerce. From personalized product recommendations to dynamic pricing strategies, AI has the potential to transform the way online retailers engage with their customers and drive sales.
One key application of AI in eCommerce is the use of customer data to personalize the shopping experience. By leveraging AI algorithms to analyze customer behavior and preferences, online retailers can deliver highly tailored product recommendations and promotions, leading to greater customer satisfaction and increased sales.
Furthermore, AI can be used to optimize pricing strategies based on real-time market data and customer demand. By dynamically adjusting prices based on various factors, such as competitor pricing, inventory levels, and customer behavior, online retailers can maximize their profitability while offering competitive pricing to customers.
In addition to personalized recommendations and dynamic pricing, AI can also be used to enhance the overall shopping experience through conversational interfaces. By integrating AI-powered chatbots into the online shopping experience, retailers can provide customers with instant support and guidance, ultimately leading to higher conversion rates and customer satisfaction.
Overall, the use of AI in eCommerce has the potential to deliver significant value to both businesses and customers by creating more personalized, engaging, and efficient shopping experiences. As AI technology continues to evolve, the possibilities for leveraging AI in eCommerce are virtually limitless, and businesses that embrace this technology early on will be well-positioned to gain a competitive advantage in the market.
Tagged: , POLI.design , Yoroom , Milano , Professionisti , Progettazione , Digitale , User , Experience , Design , Designer , UXD , USer Experience , Interface , UX , Whirlpool , Innaas , Università , UX Talk , Talk , Incontro , Studenti , DensityDesign , Politecnico , Politecnico Milano , Paolo Ciuccarelli , Data , Artificial Intelligence , AI