I am pleased to share a recent publication in MIS Quarterly Executive:

“Turning Tacit Knowledge into Dynamic Strategies Through Collective Inferencing”
👉 Open access: https://aisel.aisnet.org/misqe/vol24/iss4/7/

The article introduces Collective Inferencing (CI) — a method designed to transform tacit, experience-based knowledge into actionable, adaptive decision strategies in complex digital platform ecosystems. Rather than offering yet another data-driven analytics tool, the approach represents a distinctly cybernetic perspective on organizational design and strategy formation.

At its core, Collective Inferencing addresses a fundamental question: How can collective knowledge in distributed, complex social systems be structured in a way that enables adaptive, viable decision-making?

The proposed method follows a multi-stage analytical process that systematically elicits, reconstructs, and models qualitative statements, experiential knowledge, and implicit inferences. Generative AI is used selectively to support semantic reconstruction and pattern recognition, while model control and interpretation remain firmly in human hands. The result is a dynamic decision model that explicitly integrates feedback loops, path dependencies, and multiple, potentially conflicting perspectives.

From a cybernetic standpoint, the approach offers a concrete pathway for maintaining organizational viability in dynamic and uncertain environments—often described as adaptive landscapes. In particular, the method:

• promotes structural coupling between diverse perspectives through the modular representation of decision domains,
• makes decision complexity explicit and traceable,
• supports recursive learning and governance processes through the simulation of alternative strategies in collective decision contexts.

The article illustrates the method through a case study of the cplace platform ecosystem, developed by collaboration Factory GmbH in Munich. The case demonstrates how heterogeneous experiential knowledge from across the ecosystem can be translated into concrete strategic options for platform governance, innovation, and ecosystem development.

More broadly, the article contributes to ongoing discussions on collective intelligence, digital platforms, and AI-supported decision-making, showing how cybernetic thinking can inform the design of modern organizational and governance models — not by replacing human judgment, but by augmenting collective sensemaking and inferential capacity.

I welcome discussion and exchange on the method, its applications, and its further development in the spirit of cybernetic and systems thinking.