AI Experience Design: Redefining Interaction in the Intelligent Era
The Evolving Role of AI in User Experience
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The AI era requires us to redefine every single aspect of our modern world. About a two months ago, I wrote about “how AI changes the way we interact with data” exploring how AI copilots might change knowledge workflows end2end. This piece triggered a range of inspiring conversations within our DDVC community. One such conversation was with my friend Pietro Casella, MD at EQT and one of the masterminds behind their Motherbrain platform.
We bounced ideas on the future of human-machine interactions and how AI requires a new paradigm of experience design. Today, I’m excited to have Pietro share an incredibly thoughtful guest post on this topic.
Thank you so much for putting your unique insights and visionary ideas into writing for us below!
AI and Our Need for New Interaction Models
When ChatGPT was released, I remember feeling mesmerized by the blinking cursor, the embodiment of one of the most remarkable inventions humans have ever made. For a moment, I suspended disbelief, wondering what was unfolding on the other side. I attempted to anticipate its responses, treating my subsequent questions like a strategic chess game, seeking its limitations.
Yet, there was an elegance in this pulsating square. Like a pause in a conversation, it represented the AI pondering its response. This simple choice was as effective and theatric as Kubrick's use of a red lamp to depict HAL's physical existence.
As the landscape evolved, I reflected on the new shapes for the AI that software creators chose to intuitively convey its power. This involved the search for metaphors, mental models, and representations, as well as concealing its flaws and embracing the exploratory nature of it all. Creators making choices about their desired version of the future, and investors reassessing their beliefs.
As with every software advancement, the prevailing interaction models are yet to be defined. However, it's crucial to understand the impact of these choices, their significance, viability and evolution. It's equally important to observe the evolving scaffolds and foundations around them, to comprehend the path of least resistance - the path that technology will likely follow.
Crafting the AI Experience, beyond User interfaces
Designing the user interaction experience with AI in your application is similar to User Experience design, but with an additional cognitive dimension. Interacting with AI is often like negotiating with a human; you express ideas and anticipate responses.
Your internal dialogue processes this interaction and plans, while your emotional response reacts and guides you. However, it can also be like interacting with a sophisticated machine, mechanical and predictable but doing things previously only expected from humans. You press a button, and it generates a paragraph or performs a task for you.
Designing in this spectrum, the AI interaction experience involves a series of choices. It starts with an innovative concept that encapsulates all your experiences and preferences, or the decision to apply a metaphor you've observed to see if it "fits the bill". Early AI applications focused on conversational UX, an effective metaphor which now seems basic as new approaches are emerging.
I've noticed that while some choices are logical, others are like trying to fit a round peg into a square hole. They lack the appropriate AI-problem fit. Even though they might still be intriguing and exciting, crafting the AI-experience will require more.
A common issue with AI-experience design arises from the gap between the application designer and the user. The designer comprehends the AI's mechanics, while the user might be encountering these features for the first time. The necessary affordances haven't been created yet.
Consequently, users, unaware of the backstory, perceive AI as a new device, influenced only minimally by its stated purpose. They strive to comprehend it and learn how to use it effectively, which requires adjusting their thinking to master its operation.
The classic user experience essentially involves interacting with visual artifacts that encourage engagement. Through this interaction, users try to create a mental model, often drawing comparisons to their previous experiences. The goal is to complete tasks efficiently; the faster the task is completed, the better the user experience.
Mental models represent a user's understanding of how the software functions, its degrees of freedom, and its responses to inputs. When new technology is introduced, it often feels like a complex, puzzle box waiting to be explored. There's a tension between the developer's desire to explore innovative features and the user's need to understand how it works.
What are then examples of emerging mental models in AI applications? which problems do they solve and what do they imply for the future? I will dive into that in the following sections.
Space for Thoughts: Enhancing AI Responsiveness
AI models have latency, meaning the time it takes to perform a task is perceived as slow for most usecases. While great leaps are being made in performance for example with faster machines, faster models or other techniques, there will be plenty of application opportunities to use more AI. Cost will also be a consideration.
So how do we manage wait time in a better way? A few clever UX models are emerging such as the “type” effect in conversational ux as a way to manage expectations or the (is typing…) present in most real time applications.
Another intriguing solution to the waiting time issue is to reveal the inner workings of artificial intelligence (AI). I particularly appreciate the reverse blurring effect introduced by diffusion models, which are now found in some text applications.
Emerging methods for time management may incorporate more use of animation, anthropomorphisms like "thinking utterances" during a conversation (e.g., "Hmm, let me think" statements), or traditional asynchronous design, such as notifications.
A less common method of managing time occurs behind the scenes with optimistic prompting. As a user types a prompt, some applications attempt to predict the sentence and compute a response in advance, creating the perception of an instantaneous answer. This approach is akin to creating an inner voice for the AI, which is a highly relevant solution.
💡 How will this new hybrid UX (synchronous/ asynchronous) evolve? How can it be choreographed with background thinking activities? Will we have a concept of AI initiating interactions with humans? Which technologies better enable this mixture of background and foreground work?
To Reveal or Conceal: the Visibility of AI
In most auto-completion interfaces you interact with an AI, but a rather anonymous one. You perceive it more as a feature of the editor than an intelligent model. This feels quite natural but some designers have chosen to make the AI more explicitly visible, creating an impression of an intelligent agent. In this scenario, it's not the software but rather the bot that is perceived as intelligent. So, the question to consider is whether you want the AI to be visible or not.
Browse.ai introduced their scraping agent as a floating assistant. This design reminds me of the Clippy Microsoft assistant from the early 2000s, which seems like a fitting choice for a webpage scraping helper. The use of this nostalgic UX approach can be quite entertaining.
However, one must consider the extreme case where the implementation of AI becomes forced and artificial. It's akin to the movie "Airplane!" where the autopilot is depicted as an inflatable pilot, rather than a function of the plane itself. A smoother user experience will integrate the AI seamlessly, making it a substantive part of the software, rather than an artificial add-on.
💡 Innovation in user experience design is still in its early stages, particularly when it comes to effectively portraying AI functionality and navigating its limitations. We can expect more creative solutions to emerge, like "presence" indicators, typography, implicit AI, generative UX and more. Designing for latency will enable apps to have more space for thoughts.
Seamless Integration: the Dilemma of How Much to Reveal
The decision of whether to make AI visible is significant. While some developers feel pressured to showcase the sophistication of AI, over time, AI should become more subtle and serve as an unobtrusive building block of the product.
It might make sense to highlight the AI underpinnings if you want to evoke an additional level of awareness, especially when users need to verify the output or when you want to emphasize the "magical" nature of the functionality. However, in many instances, AI is irrelevant to the product's function.
For instance, in Notion, users are made aware of AI's presence; you "Ask AI" to perform tasks for you. This makes sense because it is common for a writer to ask a friend for an opinion about a draft version of an article.
In contrast, Linear's application of AI presents you with Duplicates without disclosing that an AI method is operating behind the scenes. In search use cases, what matters most is obtaining relevant results, regardless of the method used. The careful use of copy - “Possible” duplicates - introduces the right level of awareness to the user.
One example I appreciate is the 'Instant Open' feature in the Arc browser. 'Instant Open' demonstrates a behavior known as "agentic." This term describes an intelligent, multi-step process that completes tasks on your behalf. It's important to note that this contrasts with "Ask ChatGPT," where you are explicitly informed about the behavior's inner workings. I admire the attention to detail in their choice of copy, subtly setting users' expectations about what is happening.
💡Mentioning AI is like exposing the engine and only relevant when it fits a purpose. Consider if its relevance extends beyond marketing, but focus as much as possible in making AI part of the flow, like any other piece of the stack
Predicting Thoughts with Auto Completion
Other UX implementations include the typeahead or gray suggestion text, which is accepted by a <tab> command. This option is subtle, yet efficient, and creates a sense of the AI intuitively understanding your intentions, which feels truly magical specially when coding. Its effectively one of the most seamless implementations of AI that exists.
While this device isn't new, there are less common variations of this pattern. One variant offers the user multiple options, while another introduces a more active version with a prompt/response function or explicit actions like summarization buttons. other systems show a pop up with the suggestion allowing you to refine it, sidetracking into a conversational rabbit hole. Striking the correct balance of simplicity and functionality is crucial in this context.
💡 What are other ways to represent the inner thinking of AI? Thinking baloons? 💭 🤔 expressions? indicators? What is the equivalent to auto-completion for more complex workflows, is it descriptive actions? As apps do more behind the scenes, further affordances for parallel thinking will be needed.
Embedding AI Deeply in User Tasks
One of the first embodiments of AI was the copilot, a device where users interact with an artificial agent to discuss a task. While initially used for "side conversations", this approach is progressively being applied directly to tasks. It's particularly useful when the task is essentially the conversation itself, such as in a support use case.
However, in many instances, I consider using a co-pilot a simplistic approach to integrating AI in the product. In most cases it disrupts the flow and hinders the AI agent's understanding of the context of your question.