Anti-Patterns

Common AI UX failures — why they happen, the consequences they cause, and what to do instead.

Chatbox everywhere

Why it fails

Chat has high friction — typing is slow, affordances are absent, discoverability is zero.

Consequence

Low engagement, task abandonment, users preferring manual workarounds.

Instead

Embed AI directly in the native interface surface. Reserve chat for exploratory tasks.

Hidden AI behaviour

Why it fails

AI modifies data, records, or communications without user awareness.

Consequence

Catastrophic trust failure. Potential legal liability. Irreversible data damage.

Instead

Every AI action must be visible, attributed, and reversible. No silent writes.

Irreversible automation

Why it fails

AI takes consequential actions — sending emails, publishing records — with no rollback.

Consequence

Users lose control. Errors cannot be corrected. Trust evaporates permanently.

Instead

Design undo into every AI action. Queue consequential actions before executing.

Fake confidence

Why it fails

All AI outputs rendered with equal visual authority regardless of accuracy or certainty.

Consequence

Users over-rely on wrong outputs. Errors discovered late, often after consequences.

Instead

Signal uncertainty visually. Design distinct treatments for high vs low confidence.

Excessive prompting burden

Why it fails

Users need to write detailed expert prompts for basic tasks. They become prompt engineers, not product users.

Consequence

Adoption fails outside technical early adopters. ROI on AI investment never materialises.

Instead

Start with zero-config. Smart defaults, context inference, and steering controls replace manual prompting.

AI interrupting workflows

Why it fails

Chat bubbles, suggestions, and notifications appear during focused tasks the user did not ask for help with.

Consequence

Distraction, frustration, and AI feature disablement by power users.

Instead

AI assistance should be available on demand. Invoked, never imposed.

Replacing deterministic UI

Why it fails

Using AI for tasks where a dropdown, filter, or form would be faster, more reliable, and more predictable.

Consequence

Slower task completion, inconsistent outcomes, unnecessary AI cost.

Instead

AI augments deterministic UI — it does not replace it. Use AI for ambiguous, open-ended, or generative tasks.

Chat for simple actions

Why it fails

"Ask Aria to change the price" for a task that should be a direct edit field.

Consequence

Higher friction, lower accuracy, user frustration with AI as a gatekeeper.

Instead

Keep direct manipulation for deterministic actions. AI handles synthesis and generation.

Generic AI with no context

Why it fails

AI assistant has no knowledge of what the user is working on, their history, or the product they are in.

Consequence

Low-value, irrelevant suggestions. Users stop engaging with AI features.

Instead

Ground every AI feature in product context: current view, user history, workspace state.

No memory, no learning

Why it fails

AI treats every session as a fresh start — users re-explain preferences, context, and constraints repeatedly.

Consequence

Fatigue, abandonment. Users feel unheard and treat AI as a novelty, not a tool.

Instead

Persist user preferences, history, and explicit corrections across sessions with editable memory.