Management of organisations and teams with human and AI employees: A Systems-Theoretic Approach to the Honey Badger Framework

Description of the PaperTitle: Management of Organisations and Teams with Human and AI Employees: A Systems-Theoretic Approach to the Honey Badger FrameworkAuthor: Georgios Fradelos, PhD (Geneva, dated 5 January 2026)This academic paper presents the Honey Badger Management Framework (HBMF), a novel agile management system designed specifically for hybrid workforces combining human professionals and general-purpose AI assistants (e.g., chatbots and agents). Inspired by the honey badger's traits of fearlessness, resilience, and determination, HBMF introduces a structured yet adaptable approach that addresses gaps in traditional (e.g., PMP, PRINCE2) and conventional agile methodologies (e.g., Scrum, SAFe, Kanban).Core Features of HBMF Short, cancellable 7-day sprints (max three per month) for rapid iteration and risk truncation. Defined roles: Manager (strategy/task decomposition), Guru (compliance/knowledge transfer/dashboard oversight), and Specialists (in two competing sub-teams with mandatory knowledge-gap declarations). Mandatory AI integration: General-purpose AI assistants as formal team members for knowledge tasks (accessible to all, including top management; inclusive of free/open-source options). Embedded ESG compliance: Sustainability integrated operationally into sprints, roles, and dashboards (e.g., via AI offloading for energy efficiency and transparency mechanisms). Structured communication and protective mechanisms like intra-team competition grounded in tournament theory. Theoretical and Analytical FoundationsThe framework is grounded in a multi-theoretic synthesis (agency theory, dynamic capabilities, stakeholder theory, behavioral economics, etc.) and analyzed through systems-theoretic lenses: Queueing theory (Kingman's formula) for utilization buffers/slack. Redundancy analogies (e.g., dual-modular for error detection in AI outputs, with caveats on limitations). Real options theory for cancellable sprints. Mathematical concepts (discrete optimization, probability, game theory, information theory, energy modeling) provide qualitative rigor without requiring managerial computations.Structure and Contributions Literature review and comparative table position HBMF as uniquely combining mandatory AI team membership, competing sub-teams, dual governance, knowledge nudges, and operational ESG. Advantages: Risk mitigation, knowledge transfer, scalability, ESG alignment. Challenges: AI costs, competition risks, resource intensity (with mitigations). Appendix A: 35 hypothetical case studies across industries illustrating practical application. Future research: Calls for empirical validation, sector adaptations, and long-term ESG impact studies. The paper positions HBMF as a forward-thinking, resilient paradigm for 2026-era organizations navigating AI disruption and sustainability demands, emphasizing conceptual rigor over pure cultural agile mindsets. It is self-referential to the author's 2024 book (The Honey Badger Guide) and claims first-mover novelty in its specific architectural integration.