AI UX maturity: The five levels of AI UX transformation
December 2024 | Framework
By Marianne van Ooij
Explore five levels of AI UX maturity and what it takes to design for adaptive, evolving AI systems.
Artificial intelligence has fundamentally changed how digital products behave, while many organizations continue to rely on traditional UX methods for these evolving experiences. As we've explored in the Four Shifts framework, AI requires transformation across multiple dimensions: how we think, how we research, how we design, and how we collaborate.
But where does your organization stand on this transformation journey? And where should you focus your improvement efforts?
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This article introduces the AI UX Maturity Model—a structured framework for assessing your organization's capability to design and deliver effective AI-powered experiences. By understanding your current maturity level across key dimensions, you can identify gaps, prioritize improvements, and create a roadmap for transformation.
Beyond traditional UX maturity
Many organizations have used UX maturity models to evaluate their design practice. However, these traditional models do not address the unique challenges of AI:
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From predictable to probabilistic: AI systems produce varying outputs that can't be specified in advance
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From static to adaptive: AI experiences evolve over time based on user interaction
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From disciplinary to interdisciplinary: AI UX requires unprecedented collaboration between previously separate domains
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From functional to ethical: AI introduces new considerations around transparency, fairness, and user agency
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Organization readiness: Leadership alignment, resource allocation, and governance structures
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Sustaining and evolving: Continuous monitoring, model governance, and experience refinement
The AI UX Maturity Model addresses these considerations by evaluating readiness across dimensions that reflect the unique demands of artificial intelligence.
The five maturity levels
Organizations implementing AI reveal a clear progression in how UX teams adapt to these new challenges:
AI UX maturity: Five levels of organizational evolution
Mapping the journey from experimentation to embedded intelligence
Level 1: Traditional - Fitting AI into conventional ways of working
Characteristic approach:
Applying conventional UX methods to AI without adaptation
Organizations at this level treat AI as just another feature rather than a fundamentally different paradigm. There's minimal recognition of how AI changes the UX equation.
Signs you're at level 1:
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Design specs assume deterministic outputs
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Research focuses on point-in-time validation
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Teams work in silos with sequential handoffs
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Ethical considerations aren't part of the design process
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Success is measured by feature delivery, not user trust
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Leadership views AI as a technical implementation rather than organizational transformation
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No mechanisms exist to monitor AI experiences over time
Example level 1
A financial services company wants to implement an AI-powered investment recommendation feature. The design team creates fixed wireframes for three predetermined recommendation types, with no accommodation for the system's confidence levels or explanation of how recommendations were generated. When the AI produces unexpected recommendations or users question its logic, there is no built-in way to address these scenarios, resulting in low trust and adoption.
Level 2: Developing - Recognizing the differences
Characteristic approach:
Recognizing AI differences with initial adaptations
Organizations acknowledge that AI requires different approaches but make primarily surface-level changes to existing processes.
Signs you're at level 2:
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Some UX practitioners have basic AI literacy
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Research occasionally addresses trust or transparency
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Designs include simple error states for AI failures
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UX and ML teams collaborate on specific questions
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Ethics considerations arise reactively when issues emerge
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Leadership provides limited resources for AI UX adaptation
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Basic monitoring captures obvious issues but lacks comprehensive oversight
Example level 2
A healthcare app team developing an AI symptom checker adds basic confidence indicators ("high confidence" or "low confidence") to their recommendations and creates fallback states for when the system couldn't make a determination. They conduct limited testing to gauge user trust but don't track how trust evolves over time. The UX team consults with data scientists on UI needs but works largely independently on the interface design.
Level 3: Emerging practice - Structured adaptation
Characteristic approach:
Systematically adapting for AI's unique characteristics
Organizations at this level treat AI as just another feature rather than a fundamentally different paradigm. There's minimal recognition of how AI changes the UX equation.
Signs you're at level 3:
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Teams understand AI capabilities and limitations
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Research methods evaluate trust and adaptation
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Design systems include confidence visualization patterns
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Regular cross-functional collaboration occurs
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Ethical considerations are proactively addressed
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Leadership supports cross-functional teams with shared accountability
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Monitoring systems track both technical performance and user experience
Example level 3
A retail company's product search team develops a consistent system for communicating AI confidence in search results, with a visual language that helps users understand when results are exact matches versus AI-powered suggestions. They regularly bring together UX, product, and ML engineers to review performance data and user feedback, using shared metrics that balance technical accuracy with user satisfaction. Their research includes periodic check-ins with users to track how search behavior adapts over time.
Level 4: Integrated - Holistic AI UX practice
Characteristic approach:
Comprehensive, systematic approaches to AI UX
Organizations at this level have mastered current best practices in AI UX with mature frameworks and methodologies. They excel within established paradigms, applying systematic approaches across all four shifts.
Signs you're at level 4:
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Advanced AI literacy across all UX roles
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Longitudinal research tracks trust, adaptation, and bias
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Design frameworks address uncertainty and feedback loops
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Cross-functional teams operate with shared metrics
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Ethics practices are embedded throughout the product life cycle
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Leadership creates organizational structures that support AI UX transformation
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Comprehensive monitoring enables continuous improvement based on real-world usage
Example level 4
A productivity software company creates a co-design process for its AI writing assistant where UX researchers, designers, and ML engineers work as a unified team with shared success metrics. Their research combines quantitative tracking of feature usage with qualitative longitudinal studies that follows users over months to understand how trust and usage patterns evolve. Their design system includes sophisticated patterns for uncertainty visualization, explanation, user feedback collection, and model improvement. They conduct regular bias audits and maintain a public-facing transparency report about system capabilities and limitations.
Level 5: Transformative - Pioneering new standards
Characteristic approach:
Leading practices that reshape how AI and UX evolve together
Organizations at this level don't just implement best practices—they create them. While Level 4 organizations excel at implementing current best practices, Level 5 organizations are creating tomorrow's best practices—pioneering approaches that will eventually become standard across the industry.
Signs you're at level 5:
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UX influences AI strategy at the organizational level
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Novel research methods capture emergent behaviors
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Design systems continuously evolve with AI capabilities
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Seamless integration across disciplines with shared ownership
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Ethical principles actively shape system behavior and business decisions
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Leadership positions AI UX as a competitive advantage and strategic priority
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Predictive monitoring anticipates issues before they affect users
Example level 5
A leading technology platform completely reimagines their recommendation systems around human-AI partnership rather than automated suggestions. Their approach integrates continuous user feedback directly into model training, with interfaces that evolve based on individual preferences and usage patterns.
Cross-functional teams with backgrounds spanning design, engineering, ethics, and data science share ownership of outcomes. They've pioneered novel research methodologies combining multiple data streams with contextual inquiry to identify emergent behaviors. Their ethical framework not only governs implementation but actively shapes which capabilities they develop, sometimes declining technically feasible features that don't align with their human-centered AI principles.
How the four shifts evolve across maturity levels
The Four Shifts framework and AI UX Maturity Model work together to provide a complete picture of transformation. As organizations progress through maturity levels, their approach to each shift becomes more sophisticated:
How the four shifts evolve across maturity levels
Advancing your organization's maturity
Most organizations today operate between Levels 1 and 3, with relatively few reaching Level 4 maturity and only a handful of industry leaders achieving Level 5. Advancing through these levels requires a coordinated approach that addresses all dimensions simultaneously.
Progress often depends on both bottom-up transformation within UX teams and top-down support from leadership. The most successful organizations create reinforcing cycles where improved UX practices demonstrate value, which in turn increases organizational support for further advancement.
Rather than attempting to leap multiple levels at once, focus on concrete steps that move your organization toward the next maturity level across each dimension. Small, consistent improvements in how teams think about AI, research user adaptation, design for uncertainty, and collaborate across disciplines will compound over time.
The maturity model provides a roadmap, but the journey is unique to each organization. By understanding where you stand today and where you aim to go, you can create a transformation path that builds on your strengths while systematically addressing gaps in your AI UX practice.
What's next?
To evaluate your team or organization's current capabilities, review these 20 workflow changes that power transformation, or try our AI UX Maturity Assessment. This tool examines your organization across the Four Shifts, delivering a detailed report with prioritized next steps for advancing through each maturity level.
By understanding your current position and setting a clear direction for growth, you can transform how your organization approaches AI UX—creating experiences that are more adaptive, trustworthy, and valuable for your users.
ABOUT THE AUTHOR(S)
Marianne van Ooij is the founder of AI UX Navigator
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