As organizations deploy artificial intelligence systems in decision-making roles, they face a fundamental challenge: the knowledge required for these decisions is scattered across software systems, tacit expertise, and manual documents traditionally designed for human consumption. A new position paper on arXiv, titled "Do we have the knowledge we need? Rethinking human-AI decision-making in corporations," addresses this challenge by proposing a framework that maps task attributes and knowledge availability to recommended agency allocations and control mechanisms.
The researchers — Anne S R Marx, Ricardo M Avelino, Torbjørn Netland, and Mennatallah El-Assady — argue that organizational knowledge evolves over time and must be stored and maintained in a way that remains accessible to both humans and future AI systems. The core question they pose is twofold: how should organizations manage knowledge so it stays useful for humans and AI, and how should agency be distributed between humans and AI across tasks with varying risks and uncertainty levels?
The Knowledge Fragmentation Problem
Corporate knowledge is fragmented across diverse sources: databases, emails, standard operating procedures, and the unrecorded expertise of experienced employees. According to the paper, these sources have traditionally been designed for human consumption — but as AI systems are granted more decision-making authority, they need structured access to the same knowledge. The authors describe how organizational knowledge evolves over time, implying that static knowledge bases are insufficient. Instead, knowledge management must be dynamic and designed for both human and machine readability.
A Framework for Agency Allocation
The framework proposed in the paper maps task attributes and knowledge availability to recommended agency allocations and control mechanisms. The authors illustrate its applicability using two distinct manufacturing tasks: a routine operation (visual quality inspection) and a one-off strategic decision (factory location). These examples show how the same framework can guide decisions for tasks with very different characteristics.
The following table summarizes the key differences between the two tasks and their implications for human-AI agency:
| Task Type | Example | Uncertainty | Knowledge Availability | Recommended Agency |
|---|---|---|---|---|
| Routine operation | Visual quality inspection | Low | High (well-documented standards, historical data) | High AI agency, with human oversight for exceptions |
| One-off strategic decision | Factory location | High | Low (unique factors, tacit knowledge, incomplete data) | High human agency, AI as decision support |
For the routine quality inspection task, the framework would likely allocate more agency to AI, since the knowledge is well-codified and uncertainty is low. For the strategic factory location decision, which involves unique variables and high uncertainty, the framework recommends retaining human control with AI serving as a support tool.
Implications for Supply Chain and Logistics Technology Leaders
Although the paper's examples come from manufacturing, the framework is directly applicable to supply chain and logistics decisions. Consider a warehouse sorting operation — a routine, high-volume task with clear standards — versus a strategic network design decision that must account for multiple variables and future uncertainties. The same logic applies: for routine tasks, AI can take on greater decision-making responsibility, while strategic choices require human judgment augmented by AI insights.
The paper does not prescribe specific technologies, but its framework provides a structured approach for technology leaders evaluating where to deploy AI automation. It emphasizes that knowledge management is not a one-time project but an ongoing process that must evolve as organizational knowledge changes. For supply chain technology managers investing in AI for demand forecasting, route optimization, or customs documentation, the lesson is clear: before granting AI decision-making authority, ensure the underlying knowledge is captured, structured, and accessible to both humans and machines.
The authors conclude with opportunities for future research, including how to operationalize the framework across different industries and organizational contexts. For now, the framework offers a conceptual tool for corporations rethinking the division of labor between humans and AI.
