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Design Stage

The Future of UX: AI-Enhanced Workflows and Beyond

September 17, 2025 11:05 AM
Power Talk
🇬🇧 English

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Talk in brief

In this WaysConf 2025 presentation, Andre Fangueiro, Head of Design at Tietoevry and founder of Human Lab, explores how artificial intelligence is reshaping the user experience (UX) design workflow. Highlighting a stark industry gap where 80% of companies believe they deliver great value but only 8% of customers agree, Fangueiro attributes this failure to slow innovation cycles and low designer productivity, which averages just 31% in traditional setups. To bridge this gap, he outlines a transition from traditional UXers to AI-empowered designers (who use tools like Brea, Figma Make, and Vercel's v0 as a "GPS" to automate tasks) and ultimately to AI-native designers. The latter leverage the "Stingray model" to position AI at the center of development, acting as managers of self-driving design systems that generate and test hundreds of concepts rapidly. By adopting these workflows, design teams can dramatically increase their productivity, run more iterations, and deliver higher-quality products.

Key takeaways

  1. 01

    The Value Delivery Gap

    A Bain & Company report reveals that while 80% of organizations believe they deliver great value to their customers, only 8% of customers actually agree. Designers must take responsibility for this gap by addressing slow innovation cycles and poor customer connection.

    Watch from 2:41
  2. 02

    Low Productivity of Traditional UXers

    Internal tracking shows traditional designers spend only 31% of their weekly time on productive UX work and customer interviews, with the rest consumed by coordination and meetings. This lack of capacity directly contributes to the failure to meet user needs.

    Watch from 6:18
  3. 03

    AI as a Design GPS

    Layering AI tools onto the traditional Double Diamond workflow acts like a GPS, where the human designer remains in control but automates repetitive tasks. This approach enables faster prototyping and more iterations without losing human decision-making.

    Watch from 8:33
  4. 04

    Centralizing Insights with Brea

    Using AI-powered insight repositories like Brea prevents user research from being lost in individual designer files. It centralizes video snippets and interview transcripts, allowing the entire team to search and build upon existing customer knowledge.

    Watch from 9:29
  5. 05

    The Stingray Model Shift

    The Stingray model disrupts traditional design by placing AI at the center of the development process rather than the human. Designers shift to managing AI agents that generate thousands of concepts rapidly, which are then validated through real-world data.

    Watch from 16:48
Read edited transcript highlights

These concise notes were edited from automatic captions and checked against the talk structure. They are not a verbatim transcript.

Bridging the Corporate Value Disconnect

In his analysis of product success, Andre Fangueiro references a striking statistic from Bain & Company showing that 80% of major organizations believe they are delivering exceptional value, yet only 8% of their customers agree. Rather than blaming business stakeholders, Fangueiro argues that UX designers must take ownership of this massive gap. He identifies three primary culprits behind this failure: slow innovation cycles that miss market windows, a lack of early and continuous customer connection in R&D, and a general absence of clear customer success metrics.

Watch from 2:41

The Inefficiency of Traditional Workflows

To understand why design teams struggle to deliver value, Fangueiro tracked how his own UX team spent their weekly working hours. The results showed that traditional designers are only 31% productive, spending a mere 9% of their time talking to customers and 22% on actual UX prototyping and flow development. The remaining two-thirds of their week is consumed by administrative tasks, design system alignment, and endless cross-disciplinary coordination meetings. This structural inefficiency severely limits their capacity to iterate and refine products.

Watch from 5:13

Unlocking Research with Centralized AI Insights

One of the biggest challenges in design teams is the loss of user research, which often ends up buried in individual designer files and forgotten. To solve this, Fangueiro's team utilizes Brea, an AI-powered insights tool. By uploading user test recordings and transcripts, the AI automatically extracts relevant insights and highlights specific video snippets. This allows any designer on the team to search the entire repository of past interviews, preventing duplicate research and allowing the team to ask sharper, more mature questions.

Watch from 9:29

Accelerating Prototypes from Figma to Code

Fangueiro demonstrates how tools like Figma Make and Vercel's v0 are shortening the gap between design and front-end development. By using text prompts or importing Figma files directly into these platforms, designers can generate functional, interactive prototypes in a matter of hours rather than days. While these AI-generated outputs are not final products, they serve as highly effective tools for early-stage customer feedback and rapid iteration, allowing teams to test multiple ideas before committing heavy development resources.

Watch from 12:03

The Stingray Model: Placing AI at the Center

At his experimental consultancy, Human Lab, Fangueiro is testing the Stingray model, a methodology developed by the Board of Innovation that seeks to replace the traditional Double Diamond. Instead of keeping the human at the center of the design process, this model centers the AI, turning human designers into managers of automated systems. Operating like a self-driving car, this workflow allows designers to set the destination while the AI generates thousands of concepts and automatically tests them in real-world environments to see what resonates.

Watch from 16:19

Evaluating Concepts with Synthetic Personas

To demonstrate the power of AI-native workflows, Fangueiro showcases a custom application where he uploads a design brief and generates synthetic customer personas with distinct backgrounds and personalities. By querying these AI agents, he can receive immediate, diverse feedback and ratings on different design concepts. This synthetic testing is not meant to replace real humans, but rather to help designers rapidly filter out weak ideas and refine the best concepts before pushing them to public platforms like LinkedIn for final validation.

Watch from 20:10
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