‍

‍

Simple principles that help designers build clearer, safer, and more realistic AI experiences.

‍

‍

Designing for AI is no longer about drawing screens — it’s about shaping behavior. And before a designer puts the first line into a flowchart, there are a few truths that make the entire process clearer, calmer, and much more realistic.

‍

Today, AI systems act less like traditional tools and more like teammates with their own logic, limits, and ways of learning. Understanding these traits early makes the difference between an AI product that feels helpful and one that feels confusing or unpredictable.

‍

‍

‍

‍

‍

AI is not magic — it’s a material with rules

When designers work with glass, wood, or metal, we respect their limitations. AI is the same. It has its own strengths, weaknesses, and “grain,” and ignoring this leads to unrealistic expectations.

‍

Some systems respond perfectly when the problem is clear. Others struggle the moment the situation becomes open, messy, or emotional. This gap is not a failure — it’s the nature of the material we’re shaping.

‍

As designers, our job is to understand what the system can reliably handle, and build the experience around that reality.

‍

Accuracy always has a price

One of the most misunderstood parts of AI is improvement cost. Getting a system from “good” to “very good” demands large amounts of data, energy, and computing power. Getting it from “very good” to “almost perfect” can be extremely expensive.

‍

For designers, this shifts the conversation from “Let’s make this smarter” to:

• How accurate does it truly need to be?
• In which situations is a small error acceptable?
• When should a human step in?

The best AI products are not the ones that try to be flawless — they’re the ones that know where precision matters and where simplicity wins.

‍

There is no universal AI model

Every AI system is strong in one area and weaker in another. A model built for classification won’t behave the same as a model built for reasoning. A chatbot that’s great at support will not automatically be great at medical advice, legal clarity, or emotional insight.

‍

This means a designer shouldn’t aim for “one system that does everything.”
Instead, the goal is to define a clear, healthy boundary:

• What the system will do
• What it will not do
• What requires confirmation
• What requires transparency
• What requires a human

‍

Clear boundaries create trust. Blurry boundaries create frustration.

‍

Three types of AI systems designers usually work with

In practice, most AI agents fall into a few broad categories. Understanding them helps designers choose the right interactions and expectations.

‍

1. Structured-context agents

These systems work with predictable data and predictable users — like internal dashboards, classification tools, or knowledge retrieval features.
They’re stable, explainable, and easy to test.

‍

Design work here feels familiar:
clean tasks, clear answers, and consistent behavior.

‍

2. Open-behavior agents

Here, the system works with real people, real questions, and real unpredictability. Users may phrase the same request in a hundred different ways.

‍

Design priorities shift to:

• managing ambiguity
• calibrating trust
• showing confidence levels
• offering clarifying questions
• recovering gracefully from mistakes

‍

This is where thoughtful UX makes or breaks the experience.

‍

3. Adaptive, learning agents

These systems learn from each user over time.
No two people will experience the same behavior.

‍

Designers must balance:

• personalization
• privacy
• transparency
• safety
• long-term user relationships

‍

At this level, we are not only designing screens — we’re designing evolving partnerships between humans and machines.

‍

From information to action to autonomy

AI agents grow in capability across three stages. Understanding this evolution helps designers set the right boundaries at every step.

‍

1. Information-level systems

They observe, summarize, analyze, and present.
They help people understand — not act.

‍

2. Action-level systems

They take steps on behalf of the user, either reactively (responding to something) or proactively (predicting something).
This requires clarity, reversibility, and explicit confirmation.

‍

3. Autonomous systems

These systems handle multi-step plans, make decisions, and adapt strategies.
Design work here is not about visual layouts — it’s about governance, oversight, and human control.

‍

The challenge becomes:
How do we protect human agency while letting AI operate independently?

‍

Designers are becoming conversation partners, not decorators

Today, designers sit at the same table as engineers, researchers, and decision-makers — shaping how AI behaves, how much freedom it has, and how much responsibility it carries.

‍

And yes, as a designer myself who has led panel discussions and worked across different teams, I’ve seen one thing again and again: the most successful AI projects began long before any interface was drawn. They began with honest conversations about limits, risks, expectations, and the real needs of the people who will rely on the system.

‍

When designers step confidently into those conversations, the results are safer, clearer, and far more meaningful.

‍