To say that AI is finding its way into all manner of digital products is a misnomer.

Setting aside the efficiency advantage that AI creates for design and engineering, its downstream user experience (UX) benefits include improved usability, deeper personalization, and proactive help.

Already, we’re seeing the benefits of AI-driven features across all of the industries we support.

But what specific AI-driven capabilities should product designers prioritize? Especially if their goal is to deliver more user-friendly digital products?

6 ways to use AI for better UX

AI flips the script in digital product design, allowing for far more dynamism than traditional static design. AI-driven features help:

• Personalize experiences based on user interaction data
• Learn and adapt to user behavior over time 
• Recognize intent to anticipate and respond to user needs
• Make proactive recommendations that save time, money, and effort

We already have many real-world examples: conversational study companions; GenAI-powered coding tools; advanced text analysis for faster, more accurate compliance assessments.

All of them speak to the underlying AI integrations that make them possible.

1. Personalize user experiences

As talented as today’s designers are, few have the resources to analyze data the way that AI can. Not in a scalable way, at least. Nowadays, forward-thinking design teams use integrated AI to augment and enhance design decisions, algorithmically, based on vast amounts of user data. 

You can use AI to analyze clickstream data, for example, to make truly data-driven optimizations to different navigation paths. Similarly, you can employ AI-powered predictive analytics that make suggestions based on: 

• Patterns in user behavior
• What users will need in the future
• Customer feedback
• Market trends
• Usability data

Recommendation Systems

If you’re watching something on Netflix, there’s a high likelihood that a recommendation system helped you make that decision. Recommendation systems use machine learning algorithms to suggest content, products, or actions based on user behavior and preferences.

But take a moment to think outside the context of entertainment. What if, instead of your next binge watch, an AI recommender system proactively recommended your next health check-up? Or the training and certificate programs best-suited to fill your skills gaps as an employee?

Across all of these examples, the fundamental rule is the same: 

Create personalized value for the end user at every turn.

2. Lean on predictive analytics

Good designers already keep the pulse on user needs and pain points; AI helps them do it more comprehensively and accurately. If nothing else, AI-powered behavioral insights help to confirm the design team’s hypotheses; in many cases, they help reveal weaknesses, inefficiencies, and vulnerabilities across the user journey.

One of the principal advantages of AI-driven behavioral analysis is scalable real-time customer-journey analysis. Even as product lines and user bases expand, you can rely on the same speed and accuracy of insight. In other words, the design teams can do a lot more with the same headcount.

Proactive support

Proactive support is yet another use case for AI-driven behavioral analysis. If you own a car, wouldn’t it be nice to know of potential issues ahead of time? AI makes this level of personalized, proactive support possible. In fact, proactive issue identification and resolution is one of the reasons Gartner predicts that agentic AI will resolve 80% of common customer service queries by 2029—autonomously.

3. AI-powered chatbots and virtual assistants

AI-powered chatbots and virtual assistants have come a long way from clunky, scripted responses. Today, they do much more than answer basic questions—they help people get things done faster and with less frustration. Whether it’s troubleshooting a tech issue, guiding a new user through a product, or handling routine tasks so teams can focus on bigger challenges, AI-driven assistants are making digital interactions smoother and more intuitive.

Smarter, more helpful conversations

What makes AI chatbots truly useful now isn’t just that they’re available 24/7—it’s that they can actually understand users. With machine learning and natural language processing, they don’t repeat pre-written answers, but learn from past interactions, pick up on context, and tailor responses in real time.

• They remember past conversations, so users don’t have to repeat themselves.
• They suggest solutions before a user even asks, based on behavior and history.
• They know when to escalate an issue to a human, ensuring people get the right help at the right time.
• They integrate across platforms, so whether someone reaches out via chat, voice, or email, the experience stays seamless.

This way AI chatbots are shifting from a simple help tool to a real-time problem solver.

4. Enhance accessibility and interactivity

It’s no accident that Alexa can understand and respond to what a person mumbles from across the room. At its best, voice UI is designed to allow for natural language interactions. The same goes for a gesture UI, which can treat natural body or hand movements as system inputs.

Beyond ease of use, voice and gesture controls can improve accessibility, ergonomics, and even safety. BMW Gesture Control lets drivers take calls, change music volume, and change the rearview camera angle, without interrupting every driver’s first priority: the road.

5. Sentiment analysis and feedback

Sentiment analysis is alive and well, especially in customer support. AI-enabled phone systems can help support teams tailor their response based on urgency, irritation, and other signals. Yet the world of designing for emotional insights goes well beyond support.

Through facial recognition technology and voice analysis, AI-powered systems can tailor experiences based on perceived emotion, in real time. You can imagine how powerful this capability is in employee training, for example, a system that can detect boredom or frustration, then adjust the learning path accordingly. 

The same goes for patient care, mental health monitoring, and many other use cases. 

Continuous improvement

Feedback generates a lot of valuable data, often on a day-to-day basis. Use this feedback to continuously improve AI models, wherever they’re deployed—on your teams, along the customer journey, or within the product itself. 

6. Real-time user context

When you know what users are doing, in real time, you can send timely signals that boost engagement. Instead of forcing people to search for the next step, AI can surface what they need—right when they need it.  These might be context-specific notifications, purchase options, or subscription offers. 

For product and design teams, real-time context is especially valuable when working with IoT-connected devices and behavior-driven AI (IoB). A connected thermostat, for example, doesn’t just respond to manual inputs—it learns from usage patterns, external conditions, and real-time activity in the home. AI can bring this level of intelligence to any digital product, helping it adapt to users’ needs as they evolve. Whether guiding first-time users through a setup process or predicting when someone might need support, AI-driven context awareness can make interactions feel seamless and personalized.

Name of the game: your people

Ultimately, integrating AI into digital product design comes down to people: your users; and your team. Yes, human-centered design can be considerably enhanced by AI, but not without a deep understanding of what those humans actually need. We find that the best examples of integrated AI, from connected car apps to digital health products, begin with this important premise in mind. That AI can bring us closer to that understanding is just icing on the cake.

The same goes for the teams tasked with designing and implementing these AI-driven features. Many organizations find that they need to reimagine core processes, shore up their data and analytics, and overcome skills shortages to successfully integrate AI. This puts their people—design and engineering—in the best position to execute.

At Transcenda, we’re well positioned to help organizations integrate AI. AI and data science are core parts of our product design and engineering services. Our team develops AI models and analytics solutions that support the development of smarter, more user-friendly digital products. 

Ready to elevate your digital products with AI? Contact Transcenda today to start your journey toward smarter, more intuitive user experiences.