4 principles for designing automation with great user experience
By: Lars Berntzen Arholm
Tags: Automation, Innovation, User Experience, Technology, Design

4 principles for designing automation with great user experience

Improve user experience by adhering to the principles of transparency, predictability, adaptability and level of automation.

Reference: Scott Adams (2018). “Dilbert”. Available at Dilbert.com

While automation may improve our lives, that highly depends on how we implement it. Unfortunately, there has yet to be a focus on how to design automation which improves user experience. However, major industries such as the aviation industry started successfully automating functions back in the 1980s. This gives us 40 odd years of research to draw on.

Last year, I wrote my master thesis of psychology about automation. More precisely, I wrote about how to design Shore Control Center’s. These are centers which will be used for monitoring and/or controlling autonomous ships.

Based on the extensive reading I did while writing my thesis, I have synthesized the four most important principles for designing great automation.

1. Transparency — guide the user’s mental model

It is not a coincidence that transparency is the first item on this list. Above all else, transparency is key for successful automation.

Transparency refers to understanding how a system works. For instance, imagine if a toaster was see-through. That we could see the heaters glow up, toasting the bread on both sides. Then perhaps it would be so transparent that Hobbes would understand where the bread goes.

Reference: Bill Watterson (1988). “Calvin and Hobbes” Available at https://pbs.twimg.com/media/DbqbSqDUwAAaBVW.jpg

Making a system understandable is nothing new in UX. Products that are simple to understand affords better mental models for our users. However, it becomes a bigger challenge when it comes to automation.

When a user looks at a website, they usually have previous experience from visiting other websites. That makes the website easier to understand, because they roughly know what to expect. We call this external consistency.

However, automation is not as widespread yet. As an example, think of an automated home-device, such as Google Home or Alexa. When you ask the device a question, how does it actually work?

For instance, Google Home seems to work by looking up information on the internet. However, it does not seem able to integrate information in a meaningful way, the same way a normal google search does.

For example, if I do a computer google search for “Viking FK”, my beloved local football team, google gives me news, fixtures, table standings. Google Home, on the other hand, will read me the first paragraph of their Wikipedia article, telling me about when the club was formed.

If you understand how the system works, you can phrase your question accordingly. This makes it more likely that the device will be able to help you. If not, you might go through several frustrating, failed attempts.

With this particular issue, human-centric design also comes into play, as a user should not have to adapt to technology.

For now, though, designers must be very clever. An automatic system may not even have a screen, yet it must still contain enough signifiers to create transparency.

Otherwise, users do not know what to expect from their automated system. Which brings us onto our next topic.

2. Predictability — Understandable and predictable output

Related to transparency, we must also ensure that users can predict what the automatic system will do. That is quite difficult at times, though, and this is a principle even the biggest companies struggle to implement.

Let’s use Spotify’s “Discover Weekly” as an example.

“Discover Weekly” is a playlist of music, automatically created by Spotify, customised to the specific user. The playlist contains music the user might like, but have not listened to yet. It does this by comparing users with overlapping tastes in music, and suggests songs which the other user has not listened to yet. It is a brilliant idea!

However, in my personal “Discover Weekly”, I am mostly given a strange mix of old Scandinavian folk and country music. Strangely enough, that is not really my kind of music. This renders “Discover Weekly” a bit useless for me.

Printscreen from my “Discover Weekly” in Spotify.

Why Spotify suggests this music, I am unsure of. I have a theory based on my own mental model (which might be wrong); I am guessing that Spotify emphasises matching music to my native language (Norwegian) above my preferred genres.

If that is the case, the issue could have been resolved by letting me set rough filters, like prioritizing genres over language. Instead, the only option Spotify provides me for adapting the algorithm is to manually go through the list, disliking each song/artist individually. That is repetitive work better done by a machine.

In this case, I can make a fair guess about how the system works, without being able to resolve the issue. Therefore, a transparent system is not enough, it must also be predictable. In addition, it must allow for users to make adaptations.

3. Adaptability — Let your user make changes

Let’s say you want to send a text message to your buddy Jim about leaving work early to catch the game. Your phone replies “okay, sending message to Tim”, your boss. Oh no, is it about to send the message? Quick, how do I override it?

Automation can be a great feature, but it can also be a massive annoyance. Most of all, it is affected by something called brittleness. Automation rarely fails, it does what it is programmed to do. However, if something is wrong with the input information (Jim vs Tim, for instance), it will perform the wrong action.

Keep in mind that your users are quite smart people. You do not automate functions because they are unable to do them, but rather so they do not have to do them.

Automation should make your customers relax. However, if they are unsure if their automated product will act correctly, their life is not made any easier. Instead, they might be anxious about using the product, and end up doing the task manually instead.

Therefore, be mindful of what and how you automate. Is there a function which is hard to automate perfectly? Let the user solve it manually, or use automation to give them some options. The name of the game is “level of automation”.

4. Level of Automation — Choosing the right one

Often when we speak of automation, we treat it as a unified concept. However, automation should be treated as a range, going from low-level automation all the way up to full automation.

The taxonomy of automation was published by Raja Parasuraman (2000). He argued that by selecting the best fit, we can maximize the benefits and minimize the disadvantages of automation. He divides automation into four functions:

Information acquisition: Sensing and registering input data. Information analysis: Allows for prediction of data, and the integration of information into a single parameter. Decisions and action selection: Creates a hypothesis of the problem, presents options for how to solve it and highlights the best option. Action implementation: Automation selects the action without human intervention and does the action.

That might seem a bit abstract. Let us take a SatNav/car GPS as an example:

Information acquisition: Your SatNav register the number of cars on the road. Information analysis: Your SatNav takes a few pieces of data (cars on the road, time of day, weather conditions) and calculates your estimated time of arrival. Decisions and action selection: You enter a destination. The SatNav calculates different possible routes and presents you with some alternatives, highlighting the route it thinks is best. Action implementation: You enter the destination, and the SatNav gives you a route.

Next time you design automation, take a moment to consider if it actually needs to be fully automated (“Action implementation”)? Would it perhaps suffice to simply provide the users with choices (“Decisions and action selection”)?

I will admit, it looks quite cool when someone is presenting a fully automated system at a technology conference. However, that does not necessarily equal good user experience if the product makes bad choices for users.

By making the right choice at the level of automation, you avoid your users feeling like they are “fighting against the system”. As an example, have a look at this Norwegian commercial.

Rema 1000 (2018). “Smarthus”. Available at https://www.youtube.com/watch?v=sgJLpuprQp8

The commercial was created by a grocery store chain, which slogan is “the simple option is often the best option”. That is a good message to keep in the back of your head as you design automation.