Despite widespread experimentation with generative artificial intelligence (AI) and substantial financial investments in enterprise AI initiatives, many organizations continue to struggle to convert AI adoption into clearly measurable organizational performance improvements. Although AI is often expected to increase productivity, reduce costs, improve quality, and strengthen decision-making, many initiatives remain limited to isolated pilots or proofs of concept rather than producing broader, sustained organizational gains.

This gap between implementation and impact highlights a central challenge for both research and practice: understanding how AI adoption translates into measurable organizational performance. Existing work suggests that the problem often lies not in AI capability alone, but in the organizational and strategic conditions surrounding adoption – including workflow integration, data quality, governance arrangements, and adoption strategies. These barriers can prevent AI from becoming embedded in routine business processes and from generating demonstrable returns, leaving many firms stuck in “pilot purgatory.”

This minitrack focuses specifically on the organizational, technological, and strategic mechanisms that connect AI adoption to measurable performance outcomes. We seek research that goes beyond technical implementation to examine the pathways, organizational complements, and boundary conditions under which AI improves outcomes such as productivity, profitability, innovation, decision quality, process efficiency, and competitive advantage. The minitrack welcomes both conceptual and empirical work that advances understanding of AI not only as a technological innovation, but also as an organizational and strategic driver of performance.

Topics of Interest

We welcome theoretical, empirical, and applied research on topics including, but not limited to:

• Mechanisms linking AI adoption to organizational performance, such as changes in task allocation, decision quality, employee productivity, or process efficiency
• Human-AI interaction, examining how trust, skill complementarity, and adoption behavior shape performance impacts
• The impact of organizational context (e.g., leadership support, digital maturity, data quality, organizational culture) on the relationship between AI use and performance outcomes
• The role of governance and control mechanisms in influencing whether and how AI initiatives scale beyond pilot stages and generate measurable returns
• Boundary conditions of AI value creation, including task characteristics, industry context, regulatory environments, and organizational scale
• Contingency perspectives on AI impact, analyzing when and under what conditions AI improves – or fails to improve – productivity or quality
• Cross-level mechanisms (individual, team, organizational) explaining how micro-level AI use translates into macro-level organizational outcomes

Illustrative Focus Areas by Level

Individual Level:

• Individual skills and AI adoption
• Managerial decision-making and human–AI interaction
• AI leadership capabilities
• User diversity in effective AI use

Organizational Level:

• Organizational AI capabilities and strategic decision-making
• Data readiness and AI capability development
• AI strategy and governance mechanisms
• AI adoption, implementation, and maturity
• Organizational transformation and change management
• Agentic AI and organizational change

Interorganizational & Industry Level:

• AI ecosystems and platform-based value creation
• Cross-industry and cross-organizational comparisons
• National AI policies and regulatory frameworks
• Institutional coordination and governance regimes

Important Dates:

• June 15, 2026 – Paper submission deadline (11:59 pm HST)
• August 17, 2026 – Notification of acceptance/rejection
• Submission details: hicss.hawaii.edu/authors/

Minitrack Co-Chairs:

• Isabell Welpe, Technical University of Munich, welpe@tum.de
• Bo Sophia Xiao, University of Hawai'i at Manoa, boxiao@hawaii.edu
• Christoph Breidbach, University of Queensland, c.breidbach@business.uq.edu.au
• Marek Kowalkiewicz, Queensland University of Technology, marek.kowalkiewicz@qut.edu.au

Full CfP and Fast-Track journal information:
https://aistrategyconference.com/hicss