Designing Behavior Change with AI: How Anticipation Can Transform User Experiences

Why Studying Anticipatory Experiences Is Important

We live in an age of unprecedented information abundance. The digital revolution has flooded our lives with choices, bombarding our senses with a constant stream of data. However, these benefits have come at a cost.

The human brain has not evolved to handle this information overload. We struggle to process it all, leading to a state of cognitive overload—decision fatigue, anxiety, and even negative bias—that is, a tendency to overemphasize negative experiences more than positive ones. Figure 2 shows Heart and Brain, an Awkward Yeti comic that illustrates our tendency toward negative bias.

However, within this challenge lies a remarkable opportunity: design, when it is empowered by the combined forces of AI, ML, and BD, can evolve beyond simply presenting information. It can become a filter rather than a turbine creating more information. Consider the familiar experience of logging in to Netflix. Instead of the anticipated experience of a relaxing evening of entertainment, the user must often spend precious leisure time scrolling through endless lists of content.

A study suggests that Netflix users dedicate an average of 18 minutes per session to simply browsing content rather than watching TV. This reality highlights a fundamental design flaw: the platform prioritizes presenting options over facilitating users’ choice. The abundance of choices results in high levels of cognitive fatigue. The Twitter posts shown in Figure 3 could not better illustrate this situation. Users’ struggle is real.

This is where the concept of anticipation comes in.

Understanding User Anticipation: Implicit Versus Explicit Cues

In UX design, anticipation manifests as both explicit and implicit affordances. Consider Donald Norman’s infamous door-handle example: its design—pull, push, or slide—provides an explicit cue, triggering an anticipatory understanding of how the door operates. Anticipatory design goes beyond this. It involves using technology to foresee the future, facilitating better decision-making, planning strategies, and the setting of future-oriented goals. By leveraging anticipatory technologies, UX designers can create intelligent, anticipatory systems that learn from user interactions and adapt to individual needs and actions—thus, this is, person-centric design.

Consequently, we are moving from a human-centered to a person-centered design approach. Until recently, we took a human-centered approach to fitting people into a service or system. The same solution had to work for a general group of people—what we commonly call a persona—through whose eyes we saw what a service or system must be to achieve success. Now, through BD and AI, we’re shifting services toward being configured to be more responsive to how a person wants to live and interact with the service, according to the capacities, needs, expectations, and environment of the user.

Explicit Cues: Understanding User Intent

Explicit cues are clear signals that users provide about their needs. For example, this could be a search query, a clicked button, or a voice command. Anticipatory design can leverage such explicit cues to suggest relevant actions or information. Imagine a fitness app that can automatically suggest workouts based on the user’s previous activity levels and calendar schedule.

Implicit Cues: Learning from Users’ Behaviors

Beyond explicit cues, an AI can also learn to anticipate the user’s needs and actions from implicit user data, which could include browsing history, location data, sensor data, or even collective usage patterns.

Similar to the way in which design-thinking methods provide a framework for human-centered design, anticipatory design is fueled by explicit and implicit cues that provide the core logic behind anticipatory experiences. Anticipatory design can streamline the user experience in several ways, making interactions faster, easier, and more delightful. Let’s explore some examples.

Simplifying Selections or Decisions

With ML algorithms, it is possible to establish personalized, preselected defaults; recall the user’s preferences; and serve up content that meets them. Figure 4 shows a good example of anticipatory design: the Spotify feature “Your Daily Drive.”

When Spotify suggests a playlist, it is not shooting in the dark, but making an educated guess based on the user’s past listening habits and preferences—thus, anticipating the user’s needs. This eliminates the need for the user to browse through countless options and instead lets the user jump right into a personalized listening experience, saving the user from experiencing the burden of creating his own playlists. Features like this can significantly reduce cognitive overload by providing convenience and relevance.

Editing the Users’ Choices

Curation is essential in choice editing. It can reduce decision fatigue by removing options and streamlining choices for users. The Easysize service, shown in Figure 5, provides an example of choice editing: an AI-powered clothing company uses BD and AI to curate information for users, resulting in improved size recommendations and reduced returns. Their use of contextual data exemplifies the importance of such data for anticipatory experiences. The high percentage of returns due to incorrect sizes represents a significant cost to the fashion industry and the environment. According to the company, nearly 30 to 40% of all online fashion is returned due to the wrong size and fit. This translates into worldwide spending of $32 billion on handling size-related returns annually—about $98 per person in the US. Given that the returned items must move from one place to another, this represents a substantial logistical effort and costs for brands—and even impacts the sustainability of our planet.

Their secret is a lot of contextual data. Most of today’s anticipatory experiences rely heavily on contextual data. The result is service optimization for the most common use cases.

Eliminating Decisions

Anticipatory design lets UX designers create services that surprise and delight users by reducing the number of choices they need to make. However, designers must be cautious about how much decision-making they eliminate. It is hard to achieve the right level of decision elimination. However, a successful example comes from Betterment, as shown in Figure 6.

This AI-assistant advisor utilizes advanced algorithms and AI technology to analyze users’ financial goals, risk tolerances, and investment preferences, then creates and manages personalized investment portfolios on their behalf.

Because it proactively anticipates users’ needs and preferences by continuously monitoring market conditions and portfolio performance, Betterment stands out as an excellent example of an anticipatory experience. Betterment automatically adjusts investment allocations and rebalances portfolios to optimize returns and minimize risk, without requiring active involvement or decision-making from the user.

By leveraging AI to manage investments, Betterment provides users with a seamless, personalized investment experience, helping them achieve their financial goals more efficiently and effectively.

Anticipatory design can be vital in designing highly autonomous agents such as this one. Therefore, UX designers must develop their future-thinking skillset to successfully navigate the design of these complex systems by analyzing system states, anticipating outcomes, and forecasting future events. To create successful interactions between the user, the environment, and autonomous systems, anticipatory design requires a human-centered AI mindset. Thus, anticipatory design brings future-oriented goals to life and helps us imagine possible alternative futures.

The Future of Anticipatory User Experiences

Designing for anticipation means designing for a future that, while not yet here, is already moving forward. Its future-oriented view makes anticipation both appealing and complex and marks a new approach to UX design. Traditionally, UX design has focused on optimizing users’ individual tasks, ensuring that they can efficiently complete interactions such as booking a flight or making a purchase. However, anticipatory design transcends tasks and moves our focus toward designing for outcomes. Instead of asking “How can users efficiently sign up for a gym membership?” designers now ask, “How can users achieve healthier life habits?”

The ability of anticipatory design to proactively anticipate users’ needs and suggest actions that move them closer to their desired outcomes is driving this shift. Therefore, we are now designing for behavior change. This proactive approach fosters users’ deeper engagement with a product and empowers them to achieve their goals more effectively. For a business and their UX designers to step into the role of behavior-change agent and act on behalf of their users, they need to understand how to deliver on the user’s intent.

In AI, intent refers to the underlying goal or purpose behind the user’s action. Thus, anticipatory design doesn’t just consider what the user is doing—clicking a button or viewing content—but what the users is trying to achieve—learning a new skill. By understanding the user’s intent through various cues—whether explicit cues such as search queries or implicit cues such as browsing behavior—UX designers can craft user experiences that proactively address the user’s needs before they even fully materialize. An anticipatory system selects, executes, and monitors actions in response to an explicit intent—a representation of an anticipatory goal.

This intent reflects a desired future state such as saving for retirement, improving one’s health, or mastering a new skill. Building trust and transparency become crucial, requiring both businesses and UX designers to adopt a person-centric approach that prioritizes an individual user’s needs.

Anticipatory design empowers UX designers to forecast or even backcast users’ future desires or intentions, influencing their behaviors and learnings. Ultimately, we want to create solutions that not only fulfill users’ needs, but also nudge them toward positive behavior change and growth.

The Future of Design

The digital revolution has created an overwhelming flood of choices and data that bombard us every day. This is exciting, but also exhausting. Our clear path forward must be to leverage UX research and strategic design, building a powerful union that changes how we’re shaping technology today. I see a fascinating opportunity: anticipatory experiences that are powered by anticipatory design.

Imagine a future in which technology anticipates people’s needs and behaviors, seamlessly streamlines processes, and acts as a helpful partner rather than a demanding child. We’ve seen a similar shift in technology before: the rise of Web-based services in the early 2000s. Today, there is a growing appetite for experiences that reduce mental strain and simplify our lives.

The aim of my research is to map the potential of anticipatory experiences, exploring whether there actually is a growing demand for experiences that reduce cognitive load and make our lives easier. Whether you are a UX designer, product manager, or just a technology enthusiast, this study supports anyone who is interested in creating user-friendly technology that works within our human limitations, not against them.

This article is my way of starting this conversation. I want to raise awareness about the opportunities and pitfalls of anticipatory experiences. I hope to inspire further research and development in this area and that we can collectively gather insights that can help us design and shape a better future. Ultimately, I believe that this is the future of design, and I’m excited to see where it takes us.

What are your thoughts on the potential benefits and challenges of anticipatory experiences? Please share your thoughts in the comments. I would love to hear your point of view.