An Essay Exploring the Epistemic Intersection of Human Intuition and Machine Intelligence
By Xule Lin (ORCID) — SKEMA Business School, Université Côte d'Azur and Kevin Corley (ORCID) — Imperial Business School, Imperial College London
Accepted for publication in Strategic Organization. Full citation and DOI forthcoming. This is the accepted manuscript (AAM); the Version of Record will be available via SAGE.
Where to read this: The AAM is available here and on SSRN at papers.ssrn.com/sol3/papers.cfm?abstract_id=6629679. Until the Version of Record is assigned, either link serves as the canonical pointer; this README will be updated once SAGE assigns DOI, volume, issue, and pages.
Note on affiliations: Author affiliations in the manuscript reflect those at time of publication. At the time of writing, Xule Lin is a Research Associate at Imperial Business School and will join SKEMA Business School as Assistant Professor in August 2026.
As artificial intelligence capabilities expand beyond pattern recognition to theoretical insight generation, interpretive qualitative research confronts a question of epistemic responsibility: how can scholars integrate AI capabilities while remaining accountable for their theoretical interpretations? This essay proposes 'interpretive orchestration' as a framework that transforms researchers from analysts into skilled orchestrators of human-AI collaboration. The framework addresses two challenges that become opportunities. The translation challenge of articulating tacit knowledge (theoretical orientations, contextual understanding, embodied intuition) into forms AI can process deepens researchers' awareness of their own expertise. The judgment challenge of evaluating AI-generated patterns for theoretical significance highlights the accountability our scholarly communities require, particularly through "1.5 order data": patterns invisible to human perception yet requiring human interpretation for recognized theoretical significance. Three strategic models guide this orchestration: Socratic tension surfaces implicit assumptions through deliberate contradiction; Euclidean documentation enables reproducible analysis through systematic context-building; Vitruvian mastery reads across independent analytical passes for synthetic insight. By embracing orchestration, researchers discover that AI can amplify rather than replace human capability. The future of interpretive research lies neither in rejecting AI nor surrendering to automation, but in systematic approaches to human-AI collaboration that preserve the scholarly-accountable judgment our communities require while drawing on AI's capacity to generate theoretical insights across scales humans cannot process alone.
Keywords: qualitative methods, generative AI, human-AI collaboration, epistemic responsibility, interpretive orchestration, tacit knowledge
manuscript.md— Accepted manuscript, full textLICENSE— CC BY-NC-ND 4.0CITATION.cff— Citation metadata (GitHub renders as "Cite this repository")
Full citation will be added once the Version of Record is assigned. In the interim, the author-accepted manuscript is available via SSRN at the link below.
Lin, X., & Corley, K. G. (forthcoming). Interpretive orchestration:
An essay exploring the epistemic intersection of human intuition and
machine intelligence. Strategic Organization. SSRN working paper:
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6629679
[Volume/Issue/Pages and DOI to be updated when the Version of Record
is published.]
For the authoritative version, please cite the Version of Record once available via SAGE.
The accepted manuscript text is shared under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0). The Version of Record is subject to the publisher's separate copyright terms.
For correspondence, reach Xule Lin at x@linxule.com.