The gap between users' expectations and AI system realities manifests in several key dimensions that designers must address. Identifying these specific mismatches allows for targeted interventions in interface design:

• Intelligence scope. Users often expect a comprehensive understanding across domains when systems actually excel only in narrow tasks. A language model might write impressive essays but fail at basic arithmetic, confounding users who expect uniform competence.
• Factual accuracy expectations. Users expecting perfect factual accuracy from generative AI encounter hallucinations that damage trust. This mismatch is particularly problematic in information-seeking contexts.
• Temporal understanding. AI systems often lack the ability to track conversational context over time, forgetting previous interactions that users assume are remembered. This leads to frustrating repetition or contradictory responses.
• Agency assumptions. Users frequently attribute more decision-making authority to AI than organizations intend. They might believe algorithmic recommendations represent final decisions rather than suggestions for human review, or conversely, might assume human oversight exists when automated systems operate independently.