The four shifts of AI UX: A framework for leading UX change in the age of AI
October 2024 | Framework
By Marianne van Ooij
A practical roadmap for UX leaders navigating the transition to designing smarter, adaptive, and human-centered AI experiences.
AI has fundamentally reshaped digital experiences, requiring UX teams to evolve their practices. This article introduces the Four Shifts of AI UX Transformation—a comprehensive framework to guide UX leaders through the transition from traditional to AI-powered design.
AI isn't merely a new technology—it's fundamentally reshaping how users interact with digital products. As organizations move from experimentation toward wider implementation, UX teams are discovering how AI disrupts established design methods, workflows, and mindsets.
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AI introduces more than new features—it shifts user expectations. As AI systems increasingly personalize, predict, and adapt in real time, UX teams must evolve in tandem, adapting their tools, thinking, and ways of working to meet the demands of intelligent systems.
Success requires more than adding AI features to existing products. It demands rethinking how we work—evolving our research methods, design processes, and cross-functional collaboration. The Four Shifts framework offers a comprehensive lens for this transformation, helping UX leaders navigate the path toward effective AI experiences.
The organizational foundation
Before diving into the specific shifts, it's important to recognize the organizational foundation required for effective AI UX transformation:
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Mindset change: Organizations must move from seeing AI as a feature to understanding it as a transformative force that requires embracing uncertainty and experimentation
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Executive sponsorship: Securing leadership support and developing governance frameworks
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Infrastructure alignment: Ensuring data infrastructure, technical capabilities, and enterprise AI strategy are coordinated
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Ethical frameworks: Establishing governance structures that guide responsible implementation
Without this foundation, individual UX initiatives may struggle to gain traction. UX leaders should identify organizational barriers early and work with executive stakeholders to create conditions for success.
We've seen it pays to be ambitious from the outset—pursuing end-to-end solutions to transform entire domains, rather than taking a piecemeal, use-case-by-use-case approach.
— Alex Singla Global co-leader, QuantumBlack by McKinsey
Introducing the Four Shifts framework
The Four Shifts of AI UX transformation provide a comprehensive lens for guiding UX teams through this transition. These are not isolated best practices—they represent coordinated changes across mindset, research, design, and collaboration.
They span the internal (how teams think and work) and the external (how products behave and evolve), forming an integrated roadmap for navigating change. The framework is intended not just to help UX teams adapt but to lead in shaping AI-powered experiences that are ethical, useful, and human-centered.
The Four Shifts of AI UX: A framework for strategic transformation
Supported by an Organizational Foundation and sustained through Continuous Transformation
Shift 1: Strategic shift — leadership transformation for AI UX
The challenge
Traditional UX leadership approaches were developed for predictable product development cycles with well-defined problems and solutions. AI introduces new levels of complexity, uncertainty, and continuous evolution. This shift requires UX leaders to develop new strategic capabilities that embrace ambiguity while providing clear direction.
Key transformations
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Mindset: Move from deterministic thinking to embracing complexity, emergence, and developing skills for ambiguity management
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Team practices: Introduce AI literacy across UX teams, implement scenario planning, create innovation and experimentation cycles that accommodate AI's probabilistic nature
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Leadership levers: Define a comprehensive AI UX vision, set strategies for talent development, establish new metrics, manage change fatigue and resistance
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Organizational interfaces: Influence broader AI and product strategy, advocate for shared ethics structures, develop stakeholder communication strategies
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Ethics & trust: Define trust principles for AI experiences and champion responsible UX metrics
How to start
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Conduct an AI UX readiness assessment for your team and organization
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Create a strategic roadmap that balances experimentation with delivery
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Establish AI literacy programs that build fundamental understanding across UX functions
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Develop scenarios that help teams understand both opportunities and risks of AI implementations
Real World example
Microsoft exemplifies an organization with a coherent AI UX strategy. Their approach integrates AI across their product suite with consistent design principles that acknowledge AI's unique characteristics. Their leadership has established clear ethical guidelines, invested in cross-functional AI literacy, and created governance structures that balance innovation with responsible implementation.
Shift 2: Research shift — from static to adaptive research
The challenge:
Traditional UX research methods—interviews, usability testing, A/B testing, surveys—were designed for static interfaces. AI-driven products continuously learn and adapt, requiring research approaches that track user sentiment, trust, and adaptation over time.
Key transformations
Mindset: Move from point-in-time validation to longitudinal understanding of evolving user mental models
Team practices: Track trust, confidence, and adaptation metrics; implement user impact measurement frameworks
Leadership levers: Invest in research infrastructure; establish trust metrics; support longer research timelines
Organizational interfaces: Partner with data science to integrate UX research into the AI lifecycle
Ethics & trust: Incorporate fairness and harm analysis into research plans
How to start
Identify one product area where user behavior changes over time and introduce lightweight longitudinal tracking
Add "user trust" as a core research question in your next usability test
Set up mechanisms to capture both explicit feedback (what users say) and implicit behavior (what users do)
Partner with data science teams to establish shared data protocols
Real-world example
YouTube initially optimized recommendations using watch-time and click-through rates, which led to sensationalist content. Their UX and product teams shifted to a longitudinal evaluation framework that prioritized satisfaction, adding user surveys, bounce rate tracking, and perceived quality metrics—helping realign recommendations with user trust and well-being.
Shift 3: Design shift — from rigid to adaptive UX
The challenge
Traditional design assumes fixed paths and predictable flows. AI-driven experiences produce different outputs for different users at different times. Designers must move from prescribing static interfaces to creating flexible, adaptive systems that support understanding, control, and trust.
Key transformations
Mindset: Evolve from fixed flows to adaptive systems with appropriate variability
Team practices: Integrate confidence scores, fallback paths, and transparency mechanisms
Leadership levers: Create interaction patterns for AI, establish ethical defaults and guardrails
Organizational interfaces: Work with engineering to align system behavior with UX principles
Ethics & trust: Embed transparency, fallback experiences, and safety-by-design principles
How to start
Map where uncertainty exists in your AI product and add micro-interactions to guide users
Prototype two versions of a UI: one opaque, one transparent, and test how each affects user trust
Document evolving design patterns and create knowledge management systems for AI UX
Establish guardrails that define what AI can and cannot change in interfaces
Real-world example
Khan Academy's Khanmigo, an AI tutor built on GPT-4, guides students through questions instead of providing direct answers—mimicking the Socratic method. For learners under 18, all interactions are visible to parents and teachers, ensuring transparency and safety. This design fosters active learning while aligning with educational trust models.
Shift 4: Collaboration shift — from silos to cross-functional teams
The challenge
AI implementation depends on collaboration between UX, data science, and engineering. When teams operate in functional silos, that creates disconnects between technical capabilities and user needs. UX teams must work across disciplines to co-design systems that are both technically feasible and user-centered.
Key transformations
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Mindset: Move from isolated UX ownership to shared AI outcomes across discipline
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Team practices: Adopt integrated teams; co-prototype with real model feedback; align on shared metrics
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Leadership levers: Establish shared vocabularies and collaboration rituals; reward cross-functional work
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Organizational interfaces: Co-own KPIs; advocate for structures like ML+UX co-leadership
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Ethics & trust: Share responsibility for bias oversight and performance monitoring
How to start
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Map Invite data scientists to co-review UX flows—ask what data would help improve or personalize the experience
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Hold workshops to align on language: What does "accuracy," "trust," or "confidence" mean across teams?
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Establish shared project management and documentation methods
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Create feedback loops between technical implementation and UX design
Real-world example
Airbnb restructured its personalization efforts by embedding UX researchers into cross-functional teams with data scientists and content strategists. Together, they redefined their ranking algorithm to optimize for "match quality," moving beyond conversion toward more human-centered measures like trip intent and group size. Airbnb now "remembers" past searches and viewed listings, directing returning users to relevant categories and reducing friction in the site visitor's journey.
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
