2025 & Beyond
AI is not a replacement for UX thinking—it is a force multiplier. We should use AI to accelerate research, explore ideas more broadly, and communicate more clearly, while keeping judgment, empathy, and accountability firmly in human hands. What follows outlines how I integrate AI into a typical UX process, and what that means for the teams and products on which I work.
Mayur Chaudhary
VP, Experience Design, Publicis Sapient
Read his whitepaper
AI can take a lot of friction out of the design process, especially in early discovery, when web, mobile, and tablet experiences require rapid iteration.
Leveraged intentionally, AI helps teams explore ideas faster, align more quickly, and minimize rework. Final product decisions should remain in the hands of people, particularly in regulated spaces like healthcare, where synthesis, tradeoffs, and real-world constraints matter.
AI should be applied as a design accelerator, not as a design decision-maker. Its role is to speed up research, broaden exploration, and improve communication—while judgment, empathy, and accountability remain human-led. This approach supports faster progress without compromising quality, trust, or usability.
AI helps us see patterns faster
AI helps us explore more options
AI helps us communicate more clearly
Designers remain responsible for tradeoffs, quality, and outcomes
Research & Discovery : Synthesizing interviews, feedback, and data to surface patterns and edge cases more efficiently
Strategy & Ideation : Exploring alternative flows and cross-device scenarios to challenge assumptions early
Design Execution : Accelerating low-fidelity iteration, drafting microcopy, and checking consistency across breakpoints
Judgment & Prioritization: Determining what matters most and why
Empathy & Context: Understanding user needs, constraints, and environments, including compliance-driven and healthcare settings
Design Quality: Ensuring clarity, accessibility, and usability across devices
This project team used AI to generate an initial draft of a payment calculator dashboard (blue) based on a highly descriptive prompt. Changes to the draft were completed by updating the text prompt, making iterations quick and easy. Once approved, designers shifted to higher fidelity for next steps (green).
Faster synthesis and clearer conversations
Stronger alignment across discipline
Reduced rework and more intentional design decisions
A measured, scalable approach to AI adoption
Exercising these values ensure that deliverables are grounded, ethical, and aligned with real user needs.
AI outputs are treated as drafts, not final decisions
Insights are validated against real user data
Accessibility, transparency, and trust are foundational requirements
AI supports—rather than replaces—cross-functional collaboration