Design

Name: Helen Zhao, Research Student

Problem Statement

The emergence of generative AI has introduced transformative possibilities for service interactions, enabling dynamic conversational agents, context-aware recommendations, and predictive support systems.

These technologies are reshaping customer expectations, creating demand for more intuitive, personalized, and anticipatory service experiences.

However, current implementations often fall short of this potential due to fundamental mismatches between AI capabilities and service design practices. As such, this research aims to establish design principles and adaptive practices for creating service experiences with generative AI. 
Outcomes

This research will develop a GAI/AI service design methodology rooted in design thinking processes.

The expected outcomes are as follows:

•    core methodologies for structuring, prototyping, and refining AI-powered service experiences
•    best practices for embedding AI across research, ideation, and validation phases
•    models for aligning cross-functional workflows in AI service development
 

Name: Matthäus Wilga, Research Collaborator

Conversational AI agents are rapidly proliferating into physical spaces, creating significant potential to transform service experience through novel interactive touchpoints, end-to-end omnichannel tracing, and real-time support at the moment of need.

Physical service environments, however, are highly social and prone to situational distractions that need to be anticipated and responded to, yet it remains under researched what it takes to integrate AI-driven, adaptive guidance into such interactive, dynamic settings.

This research examines how conversational AI agents can be designed as a credible, helpful “sparring partner” in physical spaces. The work focuses on high-impact contexts such as retail, where agents can expand the scope and availability of advice, increase conversion and basket size, and reduce churn, and professional teams and workplaces, where they can amplify unheard voices, facilitate interdisciplinary thinking, and stimulate discussions.

Outcomes

Conversational AI agents succeed only when they align with how people actually behave. The aim of this research is therefore to develop evidence-based principles and adaptive practices for deploying conversational AI agents in ways that are socially acceptable, operationally viable, and measurably beneficial:

• Core principles for interaction design and the integration of conversational AI agents into social, physical service environments.
• A structured approach for their conception, introduction, and iterative refinement across discovery, piloting, and scaling phases.
• Best practices and use-case playbooks for deploying AI agents in physical service contexts (such as retail, professional teams and workplaces), including guidance on shared use and evaluation metrics.