11/29/2024

Exploring the disconnect between AI product expectations and reality, and how to bridge it through AI-native design

Written by: Jonathan Haas

The Promise and the Disconnect

We’ve all experienced it: that moment when an AI product fails to meet our expectations in ways both subtle and dramatic. Maybe it’s the chatbot that confidently provides wrong information, the image generator that misses crucial details, or the coding assistant that produces plausible-looking but fundamentally broken solutions. These moments of disconnect aren’t just frustrating—they reveal a deeper truth about the gap between how we imagine AI should work and how it actually does.

The Expectation Inheritance

Our expectations for AI interactions don’t emerge from a vacuum. They’re inherited from:

1. Science fiction and popular media
2. Traditional software experiences
3. Human-to-human interactions
4. Marketing promises and tech hype

This inheritance creates a mental model where AI systems should be:

• Consistently competent
• Contextually aware
• Naturally conversational
• Reliably truthful
• Seamlessly integrated

Reality, however, tells a different story.

The Three Valleys of Disappointment

The gap between expectation and reality manifests in three distinct ways:

1. The Competency Valley

AI systems often exhibit what seems like advanced capability in one moment, only to make elementary mistakes the next. This inconsistency is particularly jarring because it differs from human learning patterns—we expect expertise to be uniform and stable.

2. The Context Valley

While humans naturally carry context through conversations and tasks, AI systems often struggle with:

• Maintaining coherent dialogue history
• Understanding implicit references
• Carrying information across sessions
• Adapting to user preferences over time

3. The Integration Valley

Current AI products often feel bolted onto existing software paradigms rather than naturally integrated into workflows. This creates friction points where:

• AI capabilities feel disconnected from other features
• Interactions require constant context-switching
• Results need manual verification and integration
• The system can’t learn from user corrections

Better Models: A Partial Solution

Advancing model capabilities will naturally close some of these gaps. We can expect improvements in:

1. Reliability: Reduced hallucination and more consistent performance
2. Context Understanding: Better grasp of nuanced instructions and situational awareness
3. Knowledge Integration: More accurate and up-to-date information processing
4. Output Quality: Higher fidelity and more precise results

But better models alone won’t bridge the fundamental experience gap.

The Need for AI-Native Design

To truly align AI products with user expectations, we need to fundamentally rethink how we design these experiences. This means:

1. Embracing Uncertainty

Instead of trying to hide AI’s probabilistic nature:

• Make uncertainty visible and manageable
• Provide confidence levels with outputs
• Offer multiple solution paths
• Build in verification mechanisms

2. Designing for Collaboration

Rather than positioning AI as either servant or oracle:

• Create interfaces that support joint problem-solving
• Enable easy correction and refinement
• Build feedback loops that improve over time
• Support hybrid workflows that combine AI and human capabilities

3. Rethinking Interaction Patterns

Moving beyond command-response patterns to:

• Continuous ambient assistance
• Proactive but non-intrusive suggestions
• Natural multimodal interactions
• Persistent learning relationships

Building Better Bridges

To close the experience gap, product teams need to:

1. Set Better Expectations

• Be explicit about capabilities and limitations
• Show rather than tell what the system can do
• Provide clear recovery paths for failures
• Build trust through transparency

2. Create New Interaction Models

• Design for AI’s strengths rather than human metaphors
• Develop new patterns for uncertainty and probability
• Build interfaces that grow with the user
• Enable graceful degradation when limits are reached

3. Enable Learning Loops

• Capture and utilize interaction history effectively
• Build in mechanisms for continuous improvement
• Create shared context over time
• Allow for personalization without privacy compromise

The Path Forward

The future of AI products lies not in perfectly mimicking human capabilities, but in creating new types of experiences that:

• Acknowledge and work with AI’s fundamental nature
• Create value through human-AI collaboration
• Build trust through honest capability representation
• Evolve alongside both user needs and AI advancement

A New Kind of Interface

The goal isn’t to make AI disappear into the background of our existing tools, but to create new kinds of interfaces that:

• Make AI’s capabilities and limitations clear
• Support fluid collaboration between human and machine intelligence
• Enable new ways of thinking and working
• Grow more valuable through sustained use

The future of AI isn’t about closing the gap between expectation and reality—it’s about creating new expectations that align with AI’s true potential for augmenting human capability.