What Does Good Look Like? 🧐
Because You Can’t Improve What You Can’t Define!
Let’s start right at the core of our problem.
Teams say they care about the user experience — they want it to be “good,” “intuitive,” “simple,” “delightful.”
Okay. But here’s my question to them every time:
“What does good actually look like?”
That’s not a throwaway line. It’s the litmus test for whether you’re doing design that can be evaluated, improved, and aligned, or whether you’re designing by opinion.
UX Goals: The Experience We Want Users to Have
Let’s start with UX goals.
UX goals describe the kind of experience your product should create. Not just what it does, but how people experience it.
They’re not about interfaces. They’re about outcomes in the mind, body, or emotion of the user.
Let’s make this real.
If you’re building an online health insurance platform, a UX goal might be:
“Users should feel confident that they’ve selected the right plan.”
That’s not a feature. That’s a psychological state. It’s subjective — but that doesn’t mean it’s vague.
Let’s try another one. If you’re designing a budgeting app for first-time users:
“Users should not feel overwhelmed when setting up their first budget.”
These goals drive everything else.
Where AI Can Help and Where It Can’t
Now you might be thinking: Can AI help me write UX goals?
Yes. And no.
🤝🏻 Where AI helps:
You can paste in messy stakeholder input, and an LLM can help you reframe it as a clear UX goal.
For example:
Input: “We want it to be user-friendly and modern”
Output from the LLM: “Users should be able to complete the onboarding process without assistance and feel confident using the app independently.”
It can also help you reword qualitative research findings into actionable goals.
You drop in ten user quotes, and it might generate candidate UX goals that reflect shared needs or pain points.
😱 Where AI fails:
It doesn’t know your users.
It can’t feel their frustration.
It can’t tell whether that goal is strategic for your business or important to your users.
AI can help you surface language, but not make judgments.
So: use it to brainstorm, summarize, draft. But you set the intent.
UX Metrics: How We Know the Goal Is Happening
As a follow-up, let’s talk about metrics.
UX metrics are how we detect whether a goal is being met.
If the goal is “users feel confident choosing a health plan,” how would you know?
You might look at:
A post-task confidence rating
Number of times users change their selection
Whether they use a help guide or support chat
Time spent on comparison pages
Another example:
If your goal is “users don’t feel overwhelmed,” your metrics might include:
Task abandonment rate
Time spent on setup
Comments expressing confusion
This is where triangulation matters. You don’t pick just one metric. You combine several metrics, both quantitative and qualitative, to obtain a reliable signal.
How AI Can Assist with Metrics
Now: where does AI help here?
🤝🏻 AI can:
Suggest common metrics used for a specific goal type.
For example, if your goal involves user confidence, it might recommend the Single Ease Question (SEQ) or a Likert-scale confidence rating.Help structure surveys and questionnaires. You can prompt it:
“Write a post-task question that measures confidence choosing between two health insurance plans.”
And it can output usable items, sometimes with rationale.
Offer instrumentation ideas like using error logs, clickstream data, or facial sentiment coding.
😱 But here’s what it can’t do:
It doesn’t know which metrics matter most to your team.
It can’t design your research for you, as it doesn’t know your constraints, timelines, or users.
And it may hallucinate; for example, it might “invent” a UX metric or cite a fake paper that doesn’t exist. You must vet every suggestion and be accountable for all of the outcomes generated at all times.
So again: AI can accelerate your thinking. It’s your sketchpad, not your strategist.
UX Targets: Defining What Success Looks Like
Now let’s get to the hard truth: metrics are meaningless without targets.
A target is the threshold at which we say:
“Yes, this experience is working as intended.”
Let’s go back to that health insurance example.
Metric: post-task confidence rating (scale of 1 to 7)
Target: at least 80% of users should rate their confidence as 6 or higher
That gives you a stake in the ground. Now your team can ask:
Are we meeting that threshold?
If not, where’s the breakdown?
What design or content change might help?
Or for the budgeting app:
Metric: first-session completion rate
Target: 90% of new users should complete setup without needing help or exiting
Targets also allow you to manage scope. You’re not chasing perfection; you’re aiming for sufficient usability, confidence, and trust.
Where AI Can and Can’t Help with Targets
🤝🏻 AI can help you:
Suggest target ranges, based on industry averages, especially if you prompt it well.
Compare multiple examples side by side.
Phrase targets clearly and in stakeholder-ready language.
😱 But it cannot:
Know what’s realistic for your specific user base.
Predict feasibility within your constraints.
Account for accessibility needs, cultural context, or technical debt.
Worst case? You’ll end up with “aspirational fiction” that nobody on the team believes in.
As we always say, AI is great at helping you write a sentence.
But it cannot tell you if it’s truly the right sentence to write.
So… What Does “Good” Look Like to You?
Let’s bring it back to where we started:
“What does good look like?”
If your team can’t answer that, clearly and measurably, then you’re not designing; you’re guessing.
Let me be blunt:
You cannot improve what you refuse to define.
And if you don’t define it, someone else will — a stakeholder with a louder voice, or worse, a metric that doesn’t reflect the user experience at all.
That’s why this framework (goals, metrics, and targets) is not “nice to have.”
It’s the foundation of serious UX strategy.
What We’ve Learned
Let’s recap in practical terms:
UX Goals define the experience we want users to have: confidence, clarity, ease, and success.
UX Metrics help us detect those experiences through observable signals such as task success, time, sentiment, and error rate.
UX Targets tell us what “good enough” looks like; the thresholds we can commit to and track.
Together, they let us align design with user reality and communicate with product, engineering, leadership, and AI systems in a common language.
The Role of AI
They give you a second brain for reframing fuzzy ideas into crisp UX statements.
They surface metrics and instruments you might overlook.
They help you prototype thinking: fast, repeatable drafts, but not final answers.
Here’s what they don’t do:
They don’t understand your users.
They don’t know your market.
They don’t own your risk.
So, the best take-home message of this post would be to use AI not to replace research, strategy, or ethics, but to amplify your clarity, your planning, and your iteration.