By Amberly Riegler (opens in new tab)
Photo credit: iStock
Human beings are creatures of habit*. Our routine behaviors are repeated regularly, often without our conscious awareness. When designing for AI experiences, such as voice or assistive features, we are competing with long-standing habits that people have formed to complete tasks. For teams working in this space, changing these habits means connecting a user’s problem to their solution with enough frequency that it shifts their default way of working. There are multiple models to guide building habit-forming products, such as the habit loop outlined by Nir Ayal in Hooked (opens in new tab).
Even once you understand these models, how to apply this knowledge to AI design decisions isn’t always straightforward. Regardless of which model you’re using, these are the core things you want to think about when it comes to AI:
Implications of habit change
The assumption that every model makes is that the feature or product you’re building is worthy of a habit change. However, we think it’s worthwhile to assess the field before you jump in:
- Creating habits that rely on AI are only for the things we:
- know (from users themselves) will deliver significant value,
- won’t expose users to harm when changing the behavior,
- and are things engaged with frequently enough to drive the change.
- Ask questions like:
- Is automaticity what you’re looking for in terms of customer engagement or just higher engagement?
- Are you hoping to be the default way to complete a job or are you trying to give alternative ways of doing without replacing the old behavior?
- Does the user job you are targeting benefit enough from AI that it warrants an unconscious shift in behavior?
- Do the potential harms of introducing AI outweigh these benefit for our customers?
What’s the immediate payoff for changing behavior?
Changing a habit can be extremely hard (opens in new tab), and people will not change solely because of a new feature. It requires two things, motivation and knowledge on how to act on that. Motivation to receive a reward is the driver of habit change because it makes the effort of doing something in a new way feel worthwhile. Understanding the motivation behind a particular task can guide teams on how to deliver reward in a way that brings real value and drives engagement with features. Targeting people’s motivations by helping them get to what they need, help them feel on top of things, or connecting them with others are immediate rewards that delivers value, strengthens habit, and helps build trust (opens in new tab) in your AI feature.
How do we draw people in at the right place and the right time?
Once the motivation for reward is understood, we can think about giving people the knowledge and support to act upon motivation through external triggers. External triggers bring users to an experience and provide informational cues for what to do next (e.g. pop-ups, tips, smart actions, etc.). To be effective, triggers should be geared toward your target user, appear in context of what they are trying to do, and make clear how to do the new or better thing they are already motivated to do (the reward).
What’s the easiest way people can get to that reward?
To deliver and drive the value of the reward, we need to make accessing it as easy as possible. The physical steps people complete should be simple and less cumbersome when compared to the old way of completing a task. Considering the amount of time, physical effort, level of focus, and disruption to routine can help to design the simplest action possible and ease the friction that comes with habit change. For example, a voice interaction should reduce time and physical effort while also removing the mental effort of formal commanding language. In so doing, it can compete with the existing habit of typing (in scenarios where voice is acceptable to use).
Technology has not only introduced numerous opportunities for people to change habits, but has also accelerated the pace at which we’re exposed to new ways of doing things. This poses challenges for those of us seeking to develop features and products that will prove their use to customers, and in turn become new habits. I hope this framework will help you to objectively evaluate whether the feature you’re working on is a good candidate for building habit and how taking a user centered approach to habit can help your company’s products reach their goals.
At Microsoft, we have a centralized approach to responsible AI, led by Microsoft’s AI, Ethics, and Effects in Engineering and Research (AETHER) Committee along with our Office of Responsible AI (ORA). Together, AETHER and ORA work closely to ensure we build responsible AI into our products and services. You can learn more about our principles at our Approach to AI webpage (opens in new tab) and find resources to help you develop AI responsibly in our Responsible AI Resource Center (opens in new tab).
* The American Journal of Psychology (1903) defines a “habit, from the standpoint of psychology, [as] a more or less fixed way of thinking, willing, or feeling acquired through previous repetition of a mental experience.” Habitual behavior often goes unnoticed in persons exhibiting it, because a person does not need to engage in self-analysis when undertaking routine tasks.
What do you think? Does this map to your own experience working with AI or to what you’ve heard from users? Will this help you build better AI features? Tweet us your thoughts @MicrosoftRI (opens in new tab) or follow us on Facebook (opens in new tab) and join the conversation.
Amberly Riegler is a Design Researcher at Microsoft. She has a demonstrated history of working in AI, voice technology, intelligent search, and assistive experiences; and is skilled in human centered approach to strategy and qualitative research. Amberly has a strong history of academic research paired with a Master’s degree focused in Human Computer Interaction from University of Washington.