Published January 5, 2026 | Version v1
Preprint Open

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

Authors/Creators

  • 1. Independent Researcher

Description

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.

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