The Intuition Machine

Intuition is often framed as being at odds with quantitative data or rigorous qualitative research. But whether we like it or not, intuition guides how we approach data analysis, what we choose to focus on in qualitative research, and how we interpret all of it.

“Intuition will tell the thinking mind where to look next.”

– Jonas Salk

In the context of UX research, intuition directs your attention towards the areas of inquiry that are most likely to yield breakthrough insights. “Intuition will tell the thinking mind where to look next,” as Jonas Salk once said.

So I wanted to present a simplified model of how intuition works, particularly in the context of UX research, where intuition is the product of a systematic, iterative process of observing patterns, predicting outcomes, and learning through feedback. This model is based largely on the work of Daniel Kahneman (system 1 and system 2 thinking) and Gary Klein (specifically the recognition-primed decision model), but also incorporates my first-hand experience of forming intuition through interviews and other UX research methods. I call it The Intuition Machine.

The Intuition Machine

The following image is a simplified visualization of the intuition process:

We make an initial observation about people and the world, from which we form sense-making patterns. These patterns allow us to make predictions about reality, and feedback about what was right or wrong reshapes our patterns. The cycle repeats and our intuition is formed when the patterns match our understanding of reality.

Let’s break down each component of the machine:

• Initial observation: Observing people and their contexts by listening to what they say, noticing what they do, and empathizing with their feelings and emotions

• Patterns: Making sense of the initial observation by forming mental models that serve as working theories to explain what’s going on

• Predictions: Generating expected outputs based on known inputs and the patterns and mental models you’ve formed

• Reality: What is actually happening in the world, at least insofar as we perceive it

• Feedback: How the expected outputs compare to what’s actually happening, where discrepancies between predictions and reality reshape your patterns

Intuition operates at multiple levels and across a range of timescales. UX researchers might traverse the intuition machine multiple times during a single interview and again across multiple interviews, as we iteratively build mental models of the phenomenon we’re studying.

More broadly, the intuition machine is something everyone does as they develop products to meet the needs of users. It’s constantly humming along in the background, often subconsciously, and it never really stops. Everything ultimately ladders up to a general intuition about our users and the worlds they live in.

Example within a single UX research interview

Imagine you’re conducting an interview to understand why users of a B2B productivity tool aren’t converting from a free trial to paid subscription. As you learn information about the user (the initial observation), you’ll form a first impression of what type of person this is and what their motivations are (the patterns). You’ll then use the patterns to make a prediction about what their answer to a subsequent interview question is, after which you’ll adjust your understanding of the person based on whether your prediction was right or wrong.

Let’s break it down even further:

1. Initial observation and prediction: Early on the user might mention how they’re constantly pressed for time (the initial observation), leading you to form a rough pattern.

   1. Pattern: This user is in a hurry and doesn’t like time-consuming processes

   2. Prediction: Maybe this user didn’t convert because setup takes too long

   3. Feedback: The user mentions setup but seems more disengaged by the UI clutter, falsifying the prediction

2. A new prediction: You adjust your pattern based on the feedback in the prior loop.

   1. Pattern: The cluttered UI seems to be creating cognitive overload, making it hard for this user to discover valuable features

   2. Prediction: Maybe this user isn’t aware of a key feature they’d find valuable

   3. Feedback: The user highlights confusing terminology, suggesting that the problem isn’t clutter per se, but rather unclear labels that obscure functionality

3. Fine-tuning intuition: You refine your understanding again.

   1. Pattern: Confusion stems from unclear labels paired with a cluttered UI with too many options

   2. Prediction: Maybe the product needs clearer labels and a flatter navigation

   3. Feedback: After showing a quick mockup with clearer labels and a flatter navigation, the user realizes that a feature they mentioned they wish the product included is actually available

4. Intuition: As your prediction now aligns with reality, your intuition settles into place and you conclude that this user needs clear labels and a navigation structure that makes it easy to discover key value-added features.

Example across UX research interviews

We can scale the intuition machine across multiple UX research interviews. Each user represents another trip through the machine, and each time we’re left with an understanding of why each user isn’t converting and clues to what the bigger picture—the thing that explains why so many users are struggling—might be.

Let’s break it down:

1. Initial observation: The first user struggled with unclear labels and feature discovery.

   1. Pattern: The current design makes it hard to discover key features

   2. Prediction: The second user we interview will have a similar challenge

   3. Feedback: Actually, the second user didn’t struggle with navigation but just assumed the product wasn’t applicable to their use case

2. A new prediction: You adjust your understanding of the bigger picture.

   1. Pattern: The design makes it hard to discover new features and even when users find those features it’s unclear what the benefits are

   2. Prediction: The third user will struggle with understanding terminology and how our product is applicable to their problem

   3. Feedback: While the user struggled to articulate how our product solves their problem, they also mentioned that our free trial blocks access to features that seem helpful

3. Fine-tuning intuition: You now have a better understanding of the bigger picture.

   1. Pattern: Our product—from the words we use to the navigation and all the way through to the business model—makes it difficult for users to understand the benefits

   2. Prediction: The fourth user will struggle with understanding the benefits

   3. Feedback: The fourth user did not realize a key piece of functionality existed, and when it was pointed out to them they excitedly asked how they could sign up for the paid plan, confirming the high-level prediction that your product’s benefits are unclear

4. Intuition: Most users who aren’t converting to the paid plan don’t understand the benefits of the product, and we need to make several design changes, including clearer labels, flatter navigation, and a more fully-featured trial product.

Scaling beyond UX research

The intuition machine can be used to understand how everyone—designers, engineers, product managers, and executives—form an understanding of the world. We all observe the world, make predictions, and adjust our understanding based on whether our predictions were right or wrong.

For UX researchers, this means not only sharpening our own intuition but also designing processes that help teams build collective intuition about users. When intuition is grounded in high-quality observations and iterative learning, it becomes a powerful tool for navigating ambiguity, prioritizing problems, and unlocking breakthrough insights.

Updated on 8-Jan-2025 to remove a couple repeat words/phrases.