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AI UX Navigator: A strategic framework for AI-driven design

AI transformation | UX practice

AI is reshaping how design—and organizations—work. But most teams aren’t structurally ready for it. I created AI UX Navigator to fill that gap: a practical toolset that helps UX leaders and cross-functional teams guide responsible, organization-wide AI integration.It’s part repository, part operating system—a collection of frameworks, assessments, and articles built to advance AI maturity across research, design, product, and strategy.

I led the creation of AI UX Navigator from the ground up—defining the model, building out frameworks and methods, and piloting them with real teams. The goal was to translate AI ambition into organizational action.

CLIENT

Self-initiated | Community platform

01. Situation

AI was entering every conversation, but most UX teams were stuck at the margins—either reacting to technical change or siloed from model development. There were no shared playbooks for how to:

  • Align UX with evolving AI product strategy

  • Adapt research to dynamic, generative systems

  • Design with explainability, feedback loops, and trust in mind

  • Collaborate across disciplines with different speeds and mindsets – particularly between UX, ML, and executive stakeholders

02. Task

Develop a framework that would help UX leaders:

  • Assess organizational AI readiness

  • Define strategic UX roles in AI delivery

  • Create cross-functional alignment tools

  • Provide repeatable models and language for responsible implementation

03. Action

  • Developed the Four Shifts of AI UX framework to describe how UX must evolve alongside AI

  • Created foundational tools: AI UX Maturity Model, Org Readiness Assessment, and Case Studies

  • Wrote and published core guidance, including thought leadership articles and workshop materials

  • Piloted frameworks with multiple organizations, capturing feedback and refining content – with participation from UX leads, data scientists, and strategy stakeholders

  • Built the AI UX Navigator microsite as a scalable hub for ongoing use

04. Result

  • Navigator frameworks were adopted by product and research leads in enterprise and startup teams

  • Used to shape org-wide AI initiatives, cross-functional workshops, and GenAI capability planning

  • Published work cited in strategic design and AI thought leadership spaces

  • Created a clear reference point for UX teams navigating AI transition

  • Enabled stronger collaboration between UX, data science, and leadership

Illustrating the process

Artifacts from the development and piloting of AI UX Navigator—capturing how the frameworks evolved through real-world application and cross-disciplinary use.

These case studies are part of an open ecosystem: teams contribute back their learnings, helping others navigate similar challenges.

Four Shifts of AI UX

A strategic model for transforming UX in the age of AI

Four Shifts of AI UX _ AI UX Navigator .png

Outlines four core shifts—Strategy, Research, Design, and Collaboration—that organizations must navigate. Each shift includes new mindsets, roles, and structures.

AI UX maturity model

Assessing readiness for intelligent systems

maturity

Assessing readiness for intelligent systems

Readiness assessment toolkit

From insight to action

org readiness

The guest experience taxonomy was developed in collaboration with internal teams. Aligned to key journey stages—from arrival to sleep to breakfast—it allowed for consistent classification of qualitative input.

Strategic implementation case studies

From theory to transformation

practice & impact

A growing library of real projects—booking insights, research automation, and beyond—that show how Navigator methods work in practice.

Key takeaway

AI UX Navigator emerged from the gap between vision and execution. It reflects what I’ve learned from helping organizations move from vague ambition to applied intelligence—through frameworks that are not only strategic, but operationally real.
And critically, it enables shared language across teams that often struggle to align: designers, engineers, and business leaders.

Navigator methods & frameworks used

 

Original Frameworks

  • Four Shifts of AI UX

  • AI UX Maturity Model & Assessment

  • Organizational Readiness Assessment

  • 20 AI UX Transformational Workflow changes

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Supporting Models (Referenced or adapted)

  • McKinsey’s Influence Model

  • McKinsey’s 5 Pillars of Change

  • Google’s PAIR Framework

  • Microsoft’s HAX Toolkit

These frameworks helped us turn scattered thinking into something operational. For the first time, teams had a shared frame for what AI meant in practice.

— UX Manager, Cross-functional strategy team

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