Paper Type
Complete
Abstract
Artificial Intelligence (AI) usage increases in companies and organizations and is crucial for competitiveness. This leads to an increase in AI projects, posing new challenges for classic IT project portfolio management (ITPPM). Prior research focuses on AI in portfolio management rather than on portfolio management of AI initiatives, leaving gaps in governing exploration-exploitation trade-offs, AI specific risks, and model lifecycles. Design Science Research oriented, we conduct a literature review, expert interviews, and a single case study to determine and validate eleven design requirements (DRs), five design principles (DPs), and AI-specific design specifications (DSs) and map them onto the value-driven ITPPM process model. The adapted artifact embeds portfolio level transparency, data first intake gates, lifecycle stewardship with short and staged decision cycles, multidimensional success metrics, and a two speed path separating exploration from scaling. It integrates uncertainty, compliance, and model evolution into portfolio governance and supports coordinated and scalable AI investment.
Paper Number
1315
Recommended Citation
Kost, Leonard; Webster, Samantha; Schulte, Fenja; Garz, Maurice; and Breitner, Michael H., "Managing the AI Shift: Adapting IT Project Portfolio Management for AI‑Driven Projects" (2026). AMCIS 2026 Proceedings. 6.
https://aisel.aisnet.org/amcis2026/conftheme/conftheme/6
Managing the AI Shift: Adapting IT Project Portfolio Management for AI‑Driven Projects
Artificial Intelligence (AI) usage increases in companies and organizations and is crucial for competitiveness. This leads to an increase in AI projects, posing new challenges for classic IT project portfolio management (ITPPM). Prior research focuses on AI in portfolio management rather than on portfolio management of AI initiatives, leaving gaps in governing exploration-exploitation trade-offs, AI specific risks, and model lifecycles. Design Science Research oriented, we conduct a literature review, expert interviews, and a single case study to determine and validate eleven design requirements (DRs), five design principles (DPs), and AI-specific design specifications (DSs) and map them onto the value-driven ITPPM process model. The adapted artifact embeds portfolio level transparency, data first intake gates, lifecycle stewardship with short and staged decision cycles, multidimensional success metrics, and a two speed path separating exploration from scaling. It integrates uncertainty, compliance, and model evolution into portfolio governance and supports coordinated and scalable AI investment.
When commenting on articles, please be friendly, welcoming, respectful and abide by the AIS eLibrary Discussion Thread Code of Conduct posted here.
Comments
NEXTTRANS