Abstract

Earlier pilot work on Scrum teams using AI-assisted HCI design reported that generative co-design tools compress sprint feedback loops but introduce a hidden cost “AI process debt” that slows later iterations when model opacity is left unmanaged. That study proposed two lightweight practices (a daily model-state check and an explainability-extended Definition of Done) without formalising them. This poster offers a conceptual extension rather than new empirical data. Recent quantitative work on process debt in agile teams by Gustavsson et al. provides the missing empirical anchor, and the rise of agentic AI coding assistants amplifies rather than replaces the opacity problem identified for generative prototyping. We consolidate both safeguards into a single framework, AID-Scrum (AI-Debt-aware Scrum), built from four interlocking artefacts: an AI Process Debt Ledger, a model-state traffic-light protocol for Daily Scrum, an XAI-extended Definition of Done, and a five-level maturity model. We close with five falsifiable propositions to guide empirical validation. The contribution is a research agenda and a set of ready-to-pilot artefacts for Scrum teams moving from generative co-design to agentic co-delivery.

Recommended Citation

Ostrowski, S.(2026). From Generative Co-Design to Agentic Co-Delivery: A Conceptual Framework for Managing AI Process Debt in Scrum. In M. Valenta, B. Mannová, R. Pergl, A. Przybylek, M. Lang, H. Linger, C. Schneider, N. Iivari, & E. Insfran (Eds.), Making ISD Sustainable: Reloaded with AI and Automation (ISD2026 Proceedings). Prague, Czech Republic: Czech Technical University in Prague. ISBN: 978-80-01-07585-2. https://doi.org/10.62036/ISD.2026.25

Paper Type

Poster

DOI

10.62036/ISD.2026.25

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From Generative Co-Design to Agentic Co-Delivery: A Conceptual Framework for Managing AI Process Debt in Scrum

Earlier pilot work on Scrum teams using AI-assisted HCI design reported that generative co-design tools compress sprint feedback loops but introduce a hidden cost “AI process debt” that slows later iterations when model opacity is left unmanaged. That study proposed two lightweight practices (a daily model-state check and an explainability-extended Definition of Done) without formalising them. This poster offers a conceptual extension rather than new empirical data. Recent quantitative work on process debt in agile teams by Gustavsson et al. provides the missing empirical anchor, and the rise of agentic AI coding assistants amplifies rather than replaces the opacity problem identified for generative prototyping. We consolidate both safeguards into a single framework, AID-Scrum (AI-Debt-aware Scrum), built from four interlocking artefacts: an AI Process Debt Ledger, a model-state traffic-light protocol for Daily Scrum, an XAI-extended Definition of Done, and a five-level maturity model. We close with five falsifiable propositions to guide empirical validation. The contribution is a research agenda and a set of ready-to-pilot artefacts for Scrum teams moving from generative co-design to agentic co-delivery.