Using AI to personalize UX without killing the human touch

In user experience (UX) design, AI has gone well beyond simply generated content or product recommendations like Netflix or Spotify does. Armed with swaths of user data, AI can adapt whole user flows on the fly.

That said, AI-driven personalization doesn’t make the need for the human touch obsolete. Here’s how we at Fivecube, a digital design agency, leverage AI to design hyper-personalized user journeys – without entering the uncanny valley territory.

What Do We Mean by AI?

Artificial intelligence (AI) is an umbrella term that has lately become a synonym for generative AI tools like ChatGPT and Midjourney. However, it’s a whole branch of computer science that encompasses technologies beyond generative AI, such as:

Machine learning: Using statistics to identify patterns in data (data analytics) and predict future trends (predictive analytics)
Natural language processing: Recognizing natural language and generating natural language responses

Below, we’ll be talking about AI in all of its diversity, including ML, predictive analytics, and NLP.

5 Ways AI Can Drive Personalized UX

AI can power personalized user experience in the form of adaptive interfaces, predictive content delivery, and more.

Real-Time UX Adaptation

AI-powered recommendations are already a reality in solutions like content platforms and online stores. Combined with frontend technologies that support dynamic interfaces (e.g., React Native), AI can also power layout and navigation changes and personalized marketing messages based on individual user behavior.

Predictive Content Delivery

Websites that rely on user-generated content leverage AI analytics to adapt the displayed content to the user’s interests and preferences, as derived from their past interactions with content. Pinterest is a prime example here: it uses ML analytics to create a personalized content feed, which increased engagement across the platform.

User Behavior Predictions

ML analytics can not only analyze past and real-time user behavior but also predict their actions and needs in the short and long run. This allows for identifying the next best action for moving the customer through the sales funnel or displaying the features or content the user is most likely to be interested in.

New Types of Interfaces and Interactions

NLP and generative AI power a new generation of chatbots and virtual assistants that can respond to any questions. At the same time, they can take into account users’ previous interactions with the platform or their information to adapt responses to the user’s context.

Dynamically Generated Content

With advances in NLP and generative AI models, applications can now automatically generate marketing messages and other communications in natural language. ML analytics, in turn, can help tailor them to specific user needs and preferences.

6 Limitations of AI-Driven UX Personalization

When we provide UX design services, we keep in mind that AI use in UX personalization has its limitations, such as:

Garbage in, garbage out. If you don’t collect well-rounded, representative datasets and ensure data quality and integrity, the AI output may end up inaccurate and unreliable.
Noisy data. Just like there can be too much information, there can also be too much data. Having irrelevant data in the training datasets can lead ML algorithms to find patterns in weird places.
Correlations instead of causations. While causal AI is another frontier in current research, most AI analytics tools identify correlations. So, your user analytics is unlikely to show you the “why” behind user behavior trends with 100% certainty.
Risk of the uncanny valley feel. It is possible to overdo AI personalization. Users might get creeped out by recommendations that are too specific, which can make UX feel intrusive.
Privacy and ethical concerns. Users increasingly care about their personal data privacy and ethical AI use, and it has to be taken into account during UX design. Certain regulations, like GDPR, also impose privacy requirements.
Potentially biased results. Predictive analytics, ML algorithms, and generative AI tools may produce biased output. Stereotype-fueled content recommendations risk offending users.

When we provide UX design services, we keep in mind that AI use in UX personalization has its limitations, such as:

Garbage in, garbage out. If you don’t collect well-rounded, representative datasets and ensure data quality and integrity, the AI output may end up inaccurate and unreliable.
Noisy data. Just like there can be too much information, there can also be too much data. Having irrelevant data in the training datasets can lead ML algorithms to find patterns in weird places.
Correlations instead of causations. While causal AI is another frontier in current research, most AI analytics tools identify correlations. So, your user analytics is unlikely to show you the “why” behind user behavior trends with 100% certainty.
Risk of the uncanny valley feel. It is possible to overdo AI personalization. Users might get creeped out by recommendations that are too specific, which can make UX feel intrusive.
Privacy and ethical concerns. Users increasingly care about their personal data privacy and ethical AI use, and it has to be taken into account during UX design. Certain regulations, like GDPR, also impose privacy requirements.
Potentially biased results. Predictive analytics, ML algorithms, and generative AI tools may produce biased output. Stereotype-fueled content recommendations risk offending users.

When we provide UX design services, we keep in mind that AI use in UX personalization has its limitations, such as:

Garbage in, garbage out. If you don’t collect well-rounded, representative datasets and ensure data quality and integrity, the AI output may end up inaccurate and unreliable.
Noisy data. Just like there can be too much information, there can also be too much data. Having irrelevant data in the training datasets can lead ML algorithms to find patterns in weird places.
Correlations instead of causations. While causal AI is another frontier in current research, most AI analytics tools identify correlations. So, your user analytics is unlikely to show you the “why” behind user behavior trends with 100% certainty.
Risk of the uncanny valley feel. It is possible to overdo AI personalization. Users might get creeped out by recommendations that are too specific, which can make UX feel intrusive.
Privacy and ethical concerns. Users increasingly care about their personal data privacy and ethical AI use, and it has to be taken into account during UX design. Certain regulations, like GDPR, also impose privacy requirements.
Potentially biased results. Predictive analytics, ML algorithms, and generative AI tools may produce biased output. Stereotype-fueled content recommendations risk offending users.

How to Preserve Human Touch in the Age of AI

In our experience, UX design can benefit from AI, but it still requires human empathy to work. You should also give some thought to the ethical considerations of using AI in personalization.

Balance Data Insights with Qualitative Research

AI analytics and heatmaps can tell you a lot, especially when you conduct A/B testing. But, once again, data won’t be able to tell you why users behave a certain way. Understanding it requires empathy, which in turn requires talking to actual people.

That’s why, when we provide UX research services, we conduct user interviews to collect qualitative data. This enables us to unravel key pain points and their root causes, validate or debunk our assumptions, and design truly engaging, thoughtful interaction flows in iterations.

Give Enough Thought to Ethical AI Use

Using AI ethically is a topic worthy of a whole series of blog posts, but in a nutshell, it means:

Protecting data privacy and security
Being transparent about AI and data use
Implementing fallback paths for errors
Encouraging feedback loops to let users correct mistakes

Ethical AI use also requires identifying and mitigating bias risks. To that end, review training datasets to ensure they account for diverse user bases and cases. Provide users with override options in case they encounter biased AI output, too.

Final Thoughts

AI has one crucial advantage over UX designers: it can analyze large amounts of individual user data in real time and personalize user experiences accordingly. That said, crafting engaging, outstanding UX also requires the human touch in the form of qualitative research and empathy-driven decisions.

Don’t know how to leverage AI-driven UX personalization for your solution or have concerns about the effectiveness of your current AI features? Our UX audit services can help you identify sources of friction in the existing user flows and the highest-ROI AI personalization use cases.

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