In what ways do you see AI driving the transformation of the design process for digital products?
The integration of AI is amplifying capabilities during the conventional development of digital products. The design process usually starts with an understanding of the people, the business, and the environment, and then moves on to problem definition, idea generation, prototyping, and testing. Then, once you show a new solution to the world, you receive feedback, and sustain an iterative process of further testing and constant learning.
AI's impact spans every stage of this process. It gathers and analyzes a lot of data to understand the problem, while learning from a business’ historical data and existing research. If prompted the right way, Generative AI can provide diverse problem framings and unique perspectives for idea generation. This helps teams create infinite prototypes and designs, which is really valuable at this stage, as it enables them to go very broad. Then those ideas are swiftly tested and refined with AI-powered coding assistants, so businesses can launch multiple options, judge which ones are the best, and continuously adapt based on feedback.
We are already using AI to drive efficiency and quality while scaling. And it doesn't only help us in the development process – it helps customers too, as they can obtain a broad range of options and the product validation process becomes much easier.
What future possibilities do you envision for AI and the shopping experience?
There are many developments right now, with a special focus on stock optimization and UX improvements. Tailored interactions need to be a priority, and AI can be very helpful when it comes to integrating them into the discovery experience. AI-powered solutions can deliver relevant and personalized content in real-time, making it easier for users to find products that match their interests.
We will see shopping assistance evolve thanks to search engines powered by generative AI and the use of easily interpretable data. Users will enjoy a "size finder" feature that will suggest “OK, based on this percentage, this T-shirt should fit you”.
Automated customer service might also become a reality, even including voice interactions based on machine learning. It's just a matter of time before these advancements become an integral part of the shopping journey.
So, can we expect in the foreseeable future to visit a brand’s website and get tailored recommendations for the right outfit based, for example, on specific event characteristics, user preferences, and seasonal weather?
Absolutely! It's just a matter of time and investment. We are currently experiencing a paradigm shift. ML holds a huge potential for optimizing data analysis, especially aimed at providing personalized recommendations and better targeted PDPS (product detail pages). These advancements, combined with the integration of more intuitive AI-powered interaction models, are genuinely revolutionizing user interaction patterns. Brands in the fashion realm are already adopting new shopping assistants powered by ChatGPT, offering their clients the chance to talk or chat with tech instead of using the search field.
I think every user-facing company should be experimenting with Conversational AI. Unlike the old chatbot hype and artificial interactions from a few years ago, this technology allows us to address context and emulate human behavior through its pre-trained model, ensuring more natural conversations.
Speaking of personalization, how can AI help to analyze user behavior and get relevant insights to offer customized content?
When it comes to understanding real user preferences, primary research remains crucial. But AI can be a game-changer by training generative models with customer data, simulating human behavior. Imagine being able to ask AI “Would a 35-year-old male lawyer living in Madrid like this T-shirt?” or “Would he like this new website feature?”– and getting accurate answers!
The accuracy of AI suggestions, often exceeding 90%, has impressed even the best data scientists in the world. So, it's theoretically possible, but we need to be mindful of data privacy and consider implementing opt-in options to respect users' choices. Striking a balance between innovation and safeguarding user privacy is crucial in this AI-driven world.
Now that you are mentioning data privacy, handling data is one of the crucial challenges for companies when implementing AI-driven solutions. What key aspects should they consider?
There are several key aspects that brands should bear in mind, with bias in AI decision-making models at the top of the list. These models can learn from biased data sets, which can lead to inaccurate and discriminatory outcomes. The existence of attack vectors targeting data sets themselves is an important security topic, as it means that an attacker might try to manipulate the training data sets to affect the results of a trained model.
Protection and verification of data sets are very important and must be handled carefully. To address these concerns, brands need to be proactive, regularly auditing AI outputs and making sure the AI's behavior aligns with the brand's values through relevant training data. Protecting sensitive data with proper encryption and access restrictions, as well as using secure data transfer protocols, is crucial to avoid any data leaks and, furthermore, to enable the secure handling of sensitive information. For instance, having a set of technical and organizational measures in place is essential for working with personally identifiable information. Measures must be adequate for data processing, which is particularly sensitive for AI systems, especially during the training and improvement of models.
Another thing to keep an eye on is monitoring AI decisions across different user groups, which helps guarantee that the system treats everyone fairly and impartially. Providing human oversight for critical decisions is essential at this stage.
How do you balance the use of AI with human expertise?
I believe AI and human expertise can harmoniously coexist. You need human creativity skills to combine things and find new ways to build successful digital products, and AI can help you forge those new paths. AI can provide this initial dot creation, but you need to prompt it so that it connects the dots. And if you want to solve a more complex problem, you need the human factor. In a business context, AI serves as a copilot, supporting and updating tasks, particularly in repetitive areas.
However, tasks that require the human factor must be carefully handled, as verifying the accuracy and feasibility of AI-generated results remains vital. Understanding, validating, and confirming the results of AI-generated solutions is essential to ensure optimal outcomes. By blending AI into our decision-making processes while also valuing the wealth of human knowledge and judgment, we get the best of both worlds.
Looking ahead, how will commerce and retail business models transform as AI uses continue to expand?
For e-commerce players, AI holds the promise of making unstructured data more accessible, so they can leverage it to optimize the user experience. The power of AI lies in its ability to help teams personalize the shopping journey through infinite testing capabilities, allowing for tailored suggestions and optimizing conversions for individual user types.
Beyond delivering the best possible experience and expanding business sales, my hope is that AI will lead to a broader transformation in business models, with a strong focus on process optimization, sustainability, personalization, and making interactions easier and more accessible. From improving returns logistics to finding more efficient and eco-friendly routes, the future of commerce and retail is set to be driven by AI's capacity to deliver personalized experiences and foster sustainability across the industry.
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