Journal of Information Technology Case and Application Research

The Post AI Enterprise

The rapid evolution of artificial intelligence signals a transition from systems that augment human work to those that increasingly participate in and shape knowledge creation itself. This special issue invites research on the emerging post-AI enterprise, where intelligence is deeply embedded across organizational and societal infrastructure and AI systems act not only as tools, but as autonomous or semi-autonomous contributors to discovery, decision-making, and coordination. Of particular interest is the rise of the AI scientist, AI-enabled systems capable of generating hypotheses, designing experiments, and producing scientific and managerial insights, raising fundamental questions about epistemology, authorship, accountability, bias, explainability, and trust. We seek contributions that examine how AI is transforming core domains such as R&D, logistics, finance, healthcare, agriculture, law, manufacturing, education, and cybersecurity, as well as the governance, design, and consequences of these intelligent systems. We especially welcome studies that examine how organizations assess the quality, fairness, transparency, and consequences of AI-enabled decisions, particularly in settings where algorithmic recommendations interact with human judgment, institutional constraints, and organizational incentives. Beyond mere technological descriptions, we also welcome contributions that explore the managerial, organizational, and strategic dimensions underpinning the successful implementation of AI. By focusing on the convergence of AI, infrastructure, and knowledge production, this special issue aims to advance a deeper understanding of how organizations and societies will operate in an era where intelligence is not only augmented, but increasingly distributed, autonomous, and generative.

Research conducted using all research methodologies are welcome including but not limited to case studies, experiments, surveys, simulations, etc.

Suggested Topics for the Special Issue

We invite original research, case studies, theory-building, applied and empirical research on (but not limited to) the following topics:

• The AI Scientist and Knowledge Production
  • AI systems as generators of hypotheses, theories, and experiments
  • Human–AI co-creation in scientific discovery and innovation
  • Evaluation, validation, and reproducibility of AI-generated knowledge
  • Trust, explainability, and legitimacy of AI-generated insights
  • Bias, error propagation, and epistemic risks in AI-driven research
• Post-AI Workplace and Organizational Design
  • Redesign of jobs, roles, and skills in AI-native organizations
  • Hybrid human–AI teams and new forms of collaboration
  • Managerial decision-making with autonomous AI agents
  • Human reliance, overreliance, and calibration of trust in AI-supported decisions
  • Organizational structure transformation for AI-driven agility and decentralization
  • Cultural change, trust building, and governance in human-AI collaboration systems
  • Skill ecosystems, reskilling pathways, and talent management in the post-AI era
  • Remote, hybrid, and distributed work models empowered by AI collaboration tools
• Intelligent Infrastructure and Core Industries
  • AI-enabled transformation of logistics, supply chains, and transportation
  • Financial systems, fintech, and algorithmic markets
  • AI in healthcare, agriculture, education, law, manufacturing and sustainability systems
  • Cybersecurity, cyber-physical systems, and critical infrastructure protection
• Governance, Policy, and Ethics
  • Accountability frameworks for autonomous and generative AI systems
  • Regulatory approaches to AI in high-stakes domains
  • Data governance, privacy, and ownership in AI-driven ecosystems
  • International coordination, cross‑border governance, and global AI standards
  • Liability, compliance, and audit mechanisms for AI‑powered operations
  • Algorithmic auditing and impact assessment in organizational settings
  • Explainability, transparency, and contestability of AI-enabled decisions
• AI Systems Design and Architecture
  • Design principles for autonomous, adaptive, and agentic systems
  • Human-centered design in AI-intensive environments
  • Integration of AI into large-scale enterprise and societal systems
  • Scalability, reliability, and performance engineering for distributed AI systems
  • AI system safety, robustness, explainability (XAI), and alignment design
  • Monitoring and mitigating bias, model drift, and error propagation in deployed AI systems
• Economic and Societal Impacts
  • Productivity, value creation, and new business models in the post-AI era
  • Labor market implications and workforce transitions
  • Inequality, access, and global implications of AI-driven knowledge systems
  • One person company’s (OPC) evolution, operational paradigm and societal economic impact

• Digital Human Rights in the Age of AI
  • Conceptual foundations and evolving definitions of Digital Human Rights (DHR) in AI-enabled societies
  • Frameworks for identifying, protecting, and operationalizing DHR in organizational and platform contexts
  • Tensions between innovation, surveillance, and individual rights in AI-driven environments
  • Privacy, autonomy, identity, and data ownership in the post-AI enterprise
  • Algorithmic decision-making and its implications for fairness, dignity, and inclusion
  • Governance mechanisms for safeguarding DHR across jurisdictions and institutional contexts
  • Organizational responsibilities and accountability for upholding DHR in AI system design and deployment
  • Cross-cultural and global perspectives on DHR in the context of uneven technological adoption
  • Auditing, monitoring, and enforcement mechanisms for DHR compliance in AI systems
  • The role of policy, regulation, and international cooperation in advancing DHR frameworks
• Methodological Innovations
  • New research methods for studying AI agents and AI-generated outputs
  • Simulation, digital twins, and experimental platforms for AI research
  • Benchmarking and evaluation frameworks for AI-enabled systems
  • Evaluation frameworks for fairness, explainability, robustness, and decision quality in AI-enabled systems
  • Multi-modal data collection and mixed-methods designs for human-AI interaction studies
  • Computational social science approaches to analyze organizational and societal impacts of AI

References

Caton, S., & Haas, C. (2024). Fairness in Machine Learning: A Survey. ACM Computing Surveys, 56(7). https://doi.org/10.1145/3616865.

Confino, P. (2024). Could AI create a one-person Unicorn? Sam Altman thinks so–and Silicon Valley sees the technology ‘waiting for us’. Fortune Media IP Limited. https://fortune. com/2024/02/04/sam-altman-one-person-unicorn-silicon-valley-founder-myth/. Zugegriffen am, 30, 2024.

Engström, A., Pittino, D., Mohlin, A., Johansson, A., & Edh Mirzaei, N. (2024). Artificial intelligence and work transformations: integrating sensemaking and workplace learning perspectives. Information Technology & People, 37(7), 2441-2461.

Khazanchi, D., & Saxena, M. (2026). Navigating digital human rights in the age of AI: Challenges, theoretical perspectives, and research implications. Journal of Information Technology Case and Application Research, 1-14.

Lu, C., Lu, C., Lange, R.T. et al. Towards end-to-end automation of AI research. Nature 651, 914–919 (2026). https://doi.org/10.1038/s41586-026-10265-5

Raji, I. D., Smart, A., White, R. N., Mitchell, M., Gebru, T., Hutchinson, B., ... & Barnes, P. (2020). Closing the AI accountability gap: Defining an end-to-end framework for internal algorithmic auditing. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (pp. 33-44).

Wilson, H. J., & Daugherty, P. R. (2018). Collaborative intelligence: Humans and AI are joining forces. Harvard business review, 96(4), 114-123.