EXPERIMENTING WITH SERVICE DESIGN AND AI: LESSONS LEARNT FROM AN EDUCATIONAL GENERATIVE AI LAB

The rapid evolution of Generative AI (GenAI) of the last few years – and Agentic AI lately – is reshaping how services are imagined, designed, and delivered. Designers are increasingly required to operate within hybrid human–machine contexts, developing new competencies that merge creativity, data literacy, and technological awareness. In response, the Specializing Master in Service Design by POLI.design – Politecnico di Milano and IBM iX co-developed an experimental Gen-AI Lab aimed at exploring the possibilities and criticalities of adopting AI within the service design process. Conducted in the first half of 2025, the Lab brought together academics, practitioners, and graduate-level service design students to explore how AI assistants can augment design methods, support creative processes, and generate measurable value for service innovation. The Lab followed a structured, hands-on format across four appointments (Enablement, Inception, Research, and Concept & Prototyping), each mapping to specific phases of the service design process. Over 30 AI assistants, aligned to prioritized service design tools, were made available to the student groups. Participants engaged with a range of prompts, experimenting with AI both as an adjunct team member and as a personal assistant. IBM contributed industry-grade methodological scaffolding, including efficiency–efficacy mappings, workflow orchestration patterns, and the identification of high-value AI use cases derived from enterprise service design practice. The Lab provided clear evidence of both opportunities and challenges in integrating GenAI into service design. AI demonstrated strong performance in data processing, rapid ideation, and divergent exploration. It accelerated tasks such as storyboarding and initial concept generation and, in some cases, improved the depth and coherence of personas and early prototypes. Yet several limitations emerged: AI rarely generated critical questions unless prompted; insights risked becoming detached from their sources; misaligned prompts among team members increased unwanted divergence; and homogeneous or “mainstreamed” outputs emerged, especially when contextual data were insufficient. Furthermore, AI-generated images exhibited higher levels of bias and hallucination than text generation. Participants nevertheless reported an expanded creative space, enhanced capacity for reflection, and increased methodological awareness through the ability to “double-check AI with AI”. The educational experience highlights the need for a human-centered, critically informed approach that positions designers as curators, data strategists, and guardians of the creative and ethical vision within human–non-human design ecologies. Findings directly inform the next iteration of the Lab (within the Specializing Master in Service Design 2026–27 edition), with planned advancements including practitioner-based AI agents (e.g., interviewing experts or information architects), deeper exploration of Agentic workflows, and focused experimentation in less mature phases of the design process. Additionally, AI capabilities will be explored not only as design tools but also as building blocks of the designed services. The collaboration demonstrates how academia and industry can jointly shape a more mature AI-augmented service design culture, one in which human creativity and machine intelligence co-evolve toward more meaningful, responsible, and innovative service futures.