KI-Impact-Scoring als Entscheidungsinstrument: Eine Priorisierungsmatrix zur Bewertung beliebiger KI- Anwendungen entwickelt am Use Case User Experience in Onlineshops

Abstract

Artificial intelligence applications increasingly promise efficiency gains, personalization, automation and improved user experiences. For medium-sized companies, however, the challenge is less the existence of potential use cases than the ability to prioritize them under resource constraints, heterogeneous data maturity and regulatory uncertainty. Building on a bachelor thesis on AI-supported user experience in online shops, this discussion paper reframes the developed prioritization matrix as a generic AI Impact Scoring Tool that can be applied to virtually any AI initiative.

The proposed tool combines a value axis, a feasibility axis and a scenario-based sensitivity analysis. It draws on user-experience theory, technology acceptance, stimulus-organism-response reasoning, multi-criteria utility analysis and design-oriented model development. The e-commerce use case serves as a blueprint: AI-generated product imagery, personalized product recommendations and AI-based interaction systems are assessed to illustrate how different AI applications can be positioned as quick wins, strategic investments, experimental options or deprioritized initiatives.

The central contribution lies in making AI investment decisions more transparent, comparable and adaptable. Rather than providing a one-time ranking, the model offers a reusable governance and decision framework for management teams that need to evaluate changing AI opportunities over time.