Abstract: The rapid integration of Artificial Intelligence (AI) into enterprise systems has created a critical skills gap that traditional business analytics curricula are not adequately prepared to address. This paper responds to this challenge by presenting the design and initial evaluation of a novel course, AI-Augmented Business Analytics. Grounded in an integrated theoretical framework that incorporates experiential learning, situated learning, and human capital theories, the course leverages the ERPsim simulation on SAP S/4HANA as a dynamic data-generation platform. Within this environment, students manage a virtual company and employ generative AI tools as cognitive partners to analyze operational data through a structured “Data-to-Decisions” workflow. The paper provides a comprehensive description of the course design, including learning objectives aligned with industry frameworks, a multi-faceted assessment strategy, and a detailed 15-week instructional blueprint. Evaluation through student feedback, expert review, and performance metrics offers promising evidence that the course can enhance technical proficiency, AI-assisted collaboration, and strategic decision-making capabilities. Overall, this study offers a detailed, replicable model for information systems educators seeking to bridge the gap between academic preparation and the evolving demands of the AI-augmented workplace.

Keywords: Higher education, Business analytics, Curriculum design & development, Experiential learning & education, Job skills

Download This Article: JISE2026v37n4pp566-624.pdf

Recommended Citation: Towhidi, G., Tabar, S., & Prince, B. (2026). From Data to Decisions: Designing an AI-Augmented Business Analytics Course for IS Job-Market Readiness. Journal of Information Systems Education, 37(4), 566-624. https://doi.org/10.62273/WYJH1772