Jianxun Chen is an Artefact partner specializing in luxury and retail, with 20 years of B2C experience driving end-to-end data & digital transformation.

Artefact China successfully hosted the second edition of its AI for Fashion closed-door event. The session brought together industry leaders to explore how AI can move beyond experimentation toward scalable deployment in fashion and retail, and how a human-centered approach can guide the next stage of intelligent transformation.

While AI has unlocked vast possibilities for the industry, large-scale implementation remains challenging. Overall, only a minority of retailers have successfully operationalized personalization at scale, and many organizations are still constrained by gaps in talent readiness and change management, slowing their transformation journeys.

Across the industry, three core challenges consistently emerge.

  • Consumer preferences are evolving at unprecedented speed, making it increasingly difficult to capture emerging trends in time.
  • As expectations for personalized experiences continue to rise, brands struggle to deliver personalization consistently at scale.
  • Beyond technological deployment, AI adoption requires significant organizational and talent transformation.

True transformation requires rethinking how AI empowers people — consumers, clients, and frontline teams.

1. AI for consumer insights: Turning social voice into strategy

From the consumer & market perspective, AI can move beyond traditional social listening to a “Voice-to-Insight Engine”.

  • Deep Decoding: Using GenAI to extract high-granularity data (occasions, preferences, personas) from unstructured social chatter.
  • Trend Detection: Identifying “acceleration signals” to forecast macro trends 3–6 months before they hit retail, allowing for smarter product and inventory decisions.
  • Actionable Intelligence: Mapping these cultural shifts directly to internal product categories to reduce reliance on “gut feel” and prioritize design efforts.

For a global fashion client, Artefact supported the deployment of AI to track brand evergreen, develop category and product insights, monitor campaign performance, and identify emerging market trends. These insights were used to better inform product development decisions and align internal priorities with evolving consumer signals.

2. AI for clienteling: Personalization at scale

“The goal is to deliver a hyper personalized client experience along the client lifecycle.”

From the client perspective, AI can drive clienteling from simple segmentation to agentic, hyper-personalized experiences.

  • Sales Empowerment: Equipping Sales Associates (SAs) with “Copilots” on platforms like WeCom to manage client lifecycles, recommend products, and generate personalized content.
  • Business Impact: Case studies showed that AI-driven product recommendations can lead to a 3X conversion rate uplift and significantly higher “hit rates” compared to standard top-seller lists.
  • Omnichannel Integration: Seamlessly connecting online (DCom) and offline retail data to create a 360-degree view of the customer.

For a luxury fashion client, Artefact implemented machine learning models to provide personalized product recommendations for each individual client. Building on these recommendations, AI was used to create tailored “total look” suggestions directly within the clienteling application, allowing sales associates to present complete styling options to clients. This enhanced personalization contributed to improvements in both conversion and units per transaction.

3. AI for sales & retail training: The “smart coach”

From the employee perspective, AI can make learning interactive and adaptive.

  • Virtual Role Play: SAs can practice sales scenarios with predefined AI personas and receive instant, objective scoring on their verbal and content performance.
  • Interactive Quiz Center: Mentors can use AI to automatically generate quizzes from training documents, while SAs get personalized learning paths based on their individual KPIs.

For example, for a global sportswear brand, they adopt GenAI to enable 1:1 role play training for all retail employees. GenAI acts like a customer, with a specific persona under a specific scenario, to engage with the retail employee; and GenAI acts like a coach to evaluate the performance of the retail employee in the engagement session. The new AI-powered approach delivers unlimited 1:1 role play sessions, which was not feasible before due to the coach’s bandwidth limitation.

In collaboration with a global sportswear brand, Artefact deployed a GenAI-powered solution to enable 1:1 virtual role-play training for retail employees. The system simulates different customer personas and scenarios, while also providing structured performance feedback after each session. By removing traditional trainer bandwidth constraints, this approach significantly expanded access to individualized practice opportunities.

4. AI is about people: Navigating the transformation

“AI is a co-pilot, not an autopilot. The organizations that adopt AI best are those that combine human intelligence well with AI.”

To drive AI use cases adoption in fashion, we need to drive the change of our organization and talents, emphasizing that technology is a “Co-pilot,” not an “Auto-pilot”.

  • Leadership and Culture: Success requires clear leadership endorsement and a shift toward an “Augmented, Not Replaced” mindset to preserve professional identity.
  • Overcoming Silos: We discussed appointing “Business Translators”—individuals who bridge the gap between data science and the frontline—to ensure AI tools are grounded in daily reality.
  • AI for Change Management: using AI to drive a new format of change management (e.g. vibe coding to show UI/UX faster to business, AI to make video to drive the interest and commitment to change) is becoming more and more convincing.

AI in fashion isn’t just about algorithms; it’s about giving people the tools to understand consumer & market, build deeper relationships with clients, and grow professionally in an AI-driven era.