Scholarlink

<p>Commercial organisations are increasingly reconfiguring sales–procurement work around data, models, and human judgement. In the post-generative AI (GenAI) landscape, flexibility must deliver speed without eroding accountability. This study contributes to flexible management theory by explaining how flexibility is organised when decision authority becomes elastic and partly machine-mediated within everyday organisational work. Drawing on a qualitative, abductive study of 24 semi-structured interviews with business-to-business (B2B) practitioners across fast-moving consumer goods, automotive, manufacturing, health technology, and consulting, this study integrates the dynamic capabilities perspective with the “situation-actor-process” and “learning-action-performance” framework. This study advances hybrid-intelligence flexibility as an organising logic for GenAI-mediated work, defined as the elastic allocation of decision rights and execution across humans and GenAI agents and contingent on task ambiguity and decision stakes. Hybrid-intelligence flexibility is operationalised through three micro-foundations: data asset liquidity, decision-scope elasticity, and interpretive governance. Evidently, liquidity strengthens sensing by accelerating reliable access to commercial signals, elasticity calibrates autonomy at the task level to support timely yet defensible actions, and interpretive governance reduces reversals as workflows reconfigure. The study develops analytically generalisable, middle-range propositions on when autonomy should expand or contract, and identifies boundary conditions shaping heterogeneous flexibility outcomes. It further advances responsible flexibility not as an ex-post constraint but as an antecedent of durable flexibility. Together, these contributions move the literature beyond GenAI use-case inventories towards a mechanism-based explanation of flexible management. For managers, a clear sequence emerges: increase data asset liquidity, codify the decision scope, and institutionalise interpretive governance.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Organising Flexibility in Generative AI-Mediated Work: Evidence from B2B Sales–Procurement

  • 期刊论文

  • 发表日期：

    2026年6月23日

查看全文
  • Mohit Malhan

摘要

Commercial organisations are increasingly reconfiguring sales–procurement work around data, models, and human judgement. In the post-generative AI (GenAI) landscape, flexibility must deliver speed without eroding accountability. This study contributes to flexible management theory by explaining how flexibility is organised when decision authority becomes elastic and partly machine-mediated within everyday organisational work. Drawing on a qualitative, abductive study of 24 semi-structured interviews with business-to-business (B2B) practitioners across fast-moving consumer goods, automotive, manufacturing, health technology, and consulting, this study integrates the dynamic capabilities perspective with the “situation-actor-process” and “learning-action-performance” framework. This study advances hybrid-intelligence flexibility as an organising logic for GenAI-mediated work, defined as the elastic allocation of decision rights and execution across humans and GenAI agents and contingent on task ambiguity and decision stakes. Hybrid-intelligence flexibility is operationalised through three micro-foundations: data asset liquidity, decision-scope elasticity, and interpretive governance. Evidently, liquidity strengthens sensing by accelerating reliable access to commercial signals, elasticity calibrates autonomy at the task level to support timely yet defensible actions, and interpretive governance reduces reversals as workflows reconfigure. The study develops analytically generalisable, middle-range propositions on when autonomy should expand or contract, and identifies boundary conditions shaping heterogeneous flexibility outcomes. It further advances responsible flexibility not as an ex-post constraint but as an antecedent of durable flexibility. Together, these contributions move the literature beyond GenAI use-case inventories towards a mechanism-based explanation of flexible management. For managers, a clear sequence emerges: increase data asset liquidity, codify the decision scope, and institutionalise interpretive governance.