MISQE Special Issue on AI in the Enterprise | Full Paper Deadline: March 1st, 2026

Starts: Feb 23, 2026 09:00 (BST)
Ends: Apr 1, 2026 17:00 (BST)

Associated with Communication, Digital Technology, and Organization (CTO)

Dear colleagues,

We cordially invite you to contribute to the MIS Quarterly Executive Special Issue on "The Future of Artificial Intelligence in the Enterprise" (download the Call for Papers as pdf here).

The full paper submission deadline is approaching – March 1st, 2026

Artificial Intelligence (AI) has moved from experimental to essential, with technologies like generative and agentic AI transforming the business landscape. However, while leading firms showcase AI’s potential, many organizations still struggle to integrate it in ways that drive meaningful transformation and sustainable value.

This MISQE Special Issue seeks rigorous, practice-based research that explores how enterprises manage, scale, and realize value from AI. We invite submissions offering actionable insights and frameworks to help leaders navigate challenges related to governance, risk, skills, integration, and regulation.

We welcome work from academics, practitioners, and collaborative teams blending research and real-world experience. Authors are encouraged to submit abstracts to our workshop for early feedback before full paper submission. Suggested topics are provided, but we also welcome other relevant contributions focused on enterprise AI adoption.

Topics include (but are not limited to):

Strategic value and economics of AI (e.g., transformative AI applications, measuring ROI/TCO, investment prioritization frameworks)
AI governance and ethics (e.g., regulatory compliance, responsible AI practices, building trust in algorithmic decision-making)
Enterprise risk management for AI (security implications, bias mitigation, business continuity considerations)
Organizational transformation and workforce impact (e.g., evolving roles, required skills, cultural shifts, change management, trade-offs)
Enterprise architecture implications of AI (e.g., platform building and integration)
Scaling AI from pilots to enterprise-wide deployments (e.g., MLOps practices, infrastructure considerations, technical debt management)
AI partner (ecosystem) management (e.g., vendor selection, make vs. buy decisions, collaborative/federated innovation models)
Industry-specific AI applications (detailed case studies showcasing transformative implementations across sectors)
Data strategy and management for AI (e.g., governance frameworks, quality assurance, accessibility vs. security trade-offs)
The evolving role of IT leadership in AI adoption (CIO/CDO/CAIO relationships, structural changes, new decision models)
Intellectual property considerations in AI development (retaining vs. commercializing IP, protecting AI-generated assets)
Measuring and communicating AI impact (KPIs for AI initiatives, executive reporting frameworks, value narratives)
Balancing AI innovation with operational stability (managing technological disruption, integration with legacy systems)