Retooling vs. retrofitting: Elevating UX from interface design to strategic partner
March 2025 | Article
By Marianne van Ooij
Elevating UX from interface design to organizational leadership unlocks value across the business by aligning user insight with strategic decision-making.
Many organizations attempt to introduce AI by retrofitting it into existing workflows. A model is trained, new features are scoped, and UX is brought in to design the interface. While this approach may result in functional tools, it often falls short of realizing AI's full potential.
In recent months, several well-known technology companies have announced ambitious generative AI features. These announcements generated excitement, especially when they promised more personalized, conversational, and intelligent experiences. Yet, when it came to delivering on these expectations, the process proved slower and more complex than anticipated.
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This pattern isn't unusual for emerging technologies. But it highlights an important reality: developing AI products requires more than building intelligent models or interfaces. It requires rethinking how organizations work, how teams collaborate, and how solutions evolve over time.
Successful AI implementation requires retooling—not retrofitting. This means moving from static roadmaps and siloed execution to integrated, adaptive, cross-functional collaboration where the entire process—from strategy to design to delivery—evolves to support AI's dynamic nature.
A strategic role for UX
In this retooled organizational model, UX has the opportunity to from being a downstream function to a strategic partner. UX professionals help frame problems, guide ethical and inclusive design choices, and ensure that AI systems support real human needs.
They also help shape the way AI systems communicate with users—building clarity, confidence, and control into the experience. In environments where AI is still unfamiliar or evolving, this kind of support becomes essential.
Moreover, UX teams are uniquely positioned to spot gaps between intention and impact. They can identify when an AI system is behaving in unexpected ways, or when users are struggling to understand its logic. This insight becomes a critical input for continuous improvement.
This expanded role requires UX professionals to develop new capabilities:
Systems thinking
AI introduces complex interactions between user needs, business goals, and technical possibilities. UX professionals need to understand this ecosystem to identify where and how AI can create meaningful value.
Effective UX leaders map these relationships, visualizing how changes in one area might ripple through the entire system. This systems perspective helps organizations prioritize where to apply AI for maximum impact while minimizing unintended consequences.
Continuous adaptation
AI systems learn and evolve over time, requiring design approaches that accommodate this dynamic nature. UX professionals develop structures that support continuous learning—both for the AI and for users interacting with it.
This means moving from point-in-time designs to adaptive systems that evolve with use. It also means creating research approaches that track how user behavior, mental models, and trust levels change as they become more familiar with AI capabilities.
Trust architecture
Trust is the foundation of successful AI adoption. Users must understand what the system can do, how it works, and when to rely on its judgments.
UX creates frameworks that build appropriate trust—not blind acceptance, but calibrated confidence based on system capabilities. This includes designing for transparency, creating effective feedback mechanisms, and establishing confidence indicators that help users interpret AI outputs.
Cross-functional translation
AI development requires unprecedented collaboration between traditionally separate domains: design, data science, engineering, ethics, and business strategy.
UX professionals increasingly serve as translators across these disciplines, helping create shared understanding and aligned goals. They develop visualization tools, shared vocabularies, and collaborative methods that bridge the gap between technical capabilities and human needs.
Leadership's role in enabling success
For UX to succeed in this expanded strategic role, active support from leadership is essential. This support takes several forms:
Investing in cross-functional AI teams
Effective AI implementation requires integrating UX, product, engineering, and data science from the earliest stages of development. Leaders should create team structures that support this integration, with shared goals, accountability, and recognition.
These cross-functional teams need dedicated time and space to develop shared languages and practices. They need permission to challenge traditional workflows and iterate based on real-world learning. Most importantly, they need incentives that reward collaborative outcomes rather than individual or functional achievements.
Encouraging transparency and experimentation
AI development involves uncertainty and learning. Leaders need to create environments where teams can be honest about limitations, share early prototypes, and learn from failures.
This transparency should extend to how AI capabilities are communicated externally. Setting realistic expectations with users and stakeholders builds trust and allows for gradual improvement rather than disappointment when ambitious promises aren't immediately fulfilled.
Aligning incentives and goals
AI success should be defined not just by technical performance or feature completion, but by user outcomes and business impact. Leaders should establish metrics that balance technical excellence with user experience quality.
This balanced approach helps prevent situations where technically impressive AI systems fail to deliver value because they don't accommodate real-world user needs or contexts. It also encourages teams to focus on meaningful problems rather than showcasing technology for its own sake.
Prioritizing infrastructure and tooling
Developing effective AI experiences requires specialized infrastructure that supports design exploration, usability testing with dynamic systems, and monitoring of real-world performance.
Leaders should invest in tools that allow designers to prototype with real AI capabilities, researchers to conduct longitudinal studies of user adaptation, and engineers to build systems that communicate confidence levels and explanations.
AI and sustainable business value
When AI systems are thoughtfully integrated, they can increase efficiency, support better decision-making, and create new forms of value. But these benefits don't happen automatically. They require strategic design, strong cross-functional partnerships, and organizational commitment to human-centered implementation.
UX becomes essential to this value creation process when it operates at a strategic level. By bringing user perspective into AI development from the earliest stages, UX helps organizations focus on problems worth solving rather than technology for its own sake.
This approach helps avoid common pitfalls:
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Technical solutions looking for problems
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Features that impress in demos but fail in real-world use
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AI capabilities that users don't trust or understand
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Implementation efforts that ignore context and workflow
The recent rollout delays and limitations seen across major tech companies aren't signs of failure. They're reminders of how complex this transformation is—and how important it is to build the right foundations.
Organizations that recognize UX as a strategic partner in AI development are more likely to create solutions that deliver lasting value rather than short-term excitement.
Creating the foundations for success
As you consider your organization's approach to AI implementation, consider these key questions:
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Does UX have a seat at the table when AI strategy and capabilities are being defined?
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Are your teams structured to support collaboration between UX, data science, and engineering?
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Do your design and research methods account for AI's probabilistic and adaptive nature?
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Are you measuring success based on user outcomes and business impact, not just technical performance?
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Have you developed appropriate guardrails for responsible AI use?
Organizations that answer "yes" to these questions are building the foundation for sustainable AI success. They're moving beyond the hype to create solutions that genuinely improve user experiences while delivering measurable business value.
By establishing UX as a strategic partner in AI transformation, organizations can navigate the complex journey from technical possibility to human value—creating experiences that adapt, learn, and grow alongside the people they serve.
ABOUT THE AUTHOR(S)
Marianne van Ooij is the founder of AI UX Navigator
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