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如今，AI技术正处于从技术创新到赋能产业实践与组织变革的关键阶段。当企业增长面临创新瓶颈，专业决策遭遇效率挑战时，AI 依托卓越的算法建模与认知协同能力，渗透进生产运营、技能培训与专业诊断等场景。但在不同规模企业、职业群体与应用场景中，AI 的赋能效应与适配难题的差异显著。要如何科学评估其价值、系统化解构风险呢？相关学术探索与实践验证正成为驱动人机协同进化的核心议题。
本专题精选三篇论文分别来自《Journal of Financial Economics》《Journal of Marketing》《Information Systems Research 》，从不同角度深入探讨AI在组织场景中的应用机制与差异化影响的议题。
1.Artificial intelligence, firm growth, and product innovation（人工智能、企业成长与产品创新）
期刊：《Journal of Financial Economics》
作者：Babina Tania; Fedyk Anastassia; He Alex; Hodson James
日期：2023年11月24日
英文摘要：
We study the use and economic impact of AI technologies. We propose a new measure of firm-level AI investments using employee resumes. Our measure reveals a stark increase in AI investments across sectors. AI-investing firms experience higher growth in sales, employment, and market valuations. This growth comes primarily through increased product innovation. Our results are robust to instrumenting AI investments using firms' exposure to universities' supply of AI graduates. AI-powered growth concentrates among larger firms and is associated with higher industry concentration. Our results highlight that new technologies like AI can contribute to growth and superstar firms through product innovation.
中文摘要：
我们研究了人工智能技术的使用和经济影响。我们提出了一种新的利用员工简历度量企业层面人工智能投资的方法。我们的衡量显示,各部门的人工智能投资大幅增加。人工智能投资企业在销售、就业和市场估值方面都有更高的增长。这种增长主要是通过增加产品创新来实现的。我们的结果对使用企业对大学人工智能毕业生供应的敞口来衡量人工智能投资是稳健的。人工智能驱动的增长集中在较大的企业之间,并与较高的行业集中度相关。我们的研究结果表明,人工智能等新技术可以通过产品创新为成长型企业和超级明星企业做出贡献。
Methodology and descriptive evidence：
We use the Cognism resume data to construct a human-capital-based measure of firm-level AI investments. To do this, we first leverage job postings data to learn the most AI-related skills directly from the data; we then focus on the most empirically-AI-related skills and identify them in the resume data. Finally, we aggregate the worker-level data to the firm level by calculating the share of each firm's employees who are AI-skilled.
2.Artificial Intelligence Coaches for SalesAgents: Caveats and Solutions（销售代理的人工智能教练：注意事项与解决方案）
期刊：《Journal of Marketing》
作者：Xueming Luo , Marco Shaojun Qin , Zheng Fang, and Zhe Qu
日期：2021年3月
英文摘要：
Firms are exploiting artificial intelligence (AI) coaches to provide training to sales agents and improve their job skills. The authorspresent several caveats associated with such practices based on a series of randomized field experiments. Experiment 1 showsthat the incremental benefit of the AI coach over human managers is heterogeneous across agents in an inverted-U shape:whereas middle-ranked agents improve their performance by the largest amount, both bottom- and top-ranked agents showlimited incremental gains. This pattern is driven by a learning-based mechanism in which bottom-ranked agents encounter themost severe information overload problem with the AI versus human coach, while top-ranked agents hold the strongest aversionto the AI relative to a human coach. To alleviate the challenge faced by bottom-ranked agents, Experiment 2 redesigns the AIcoach by restricting the training feedback level and shows a significant improvement in agent performance. Experiment 3 revealsthat the AI–human coach assemblage outperforms either the AI or human coach alone. This assemblage can harness the hard dataskills of the AI coach and soft interpersonal skills of human managers, solving both problems faced by bottom- and top-rankedagents. These findings offer novel insights into AI coaches for researchers and managers alike.
中文摘要：
企业正在利用人工智能（AI）教练为销售代理提供培训并提升他们的工作技能。作者基于一系列随机实地实验，提出了与这种做法相关的几点注意事项。实验 1 表明，相对于人类经理，人工智能教练带来的增量收益在不同代理之间呈现倒 U 形的异质性：中等排名的代理绩效提升幅度最大，而排名靠后和靠前的代理增量收益有限。这种模式是由一种基于学习的机制驱动的，即排名靠后的代理在使用人工智能教练而非人类教练时，面临最严重的信息过载问题，而排名靠前的代理相对于人类教练，对人工智能的抵触情绪最强。为缓解排名靠后代理面临的挑战，实验 2 通过限制培训反馈水平重新设计了人工智能教练，并显示代理绩效有显著提升。实验 3 揭示，人工智能与人类教练的组合优于单独使用人工智能教练或人类教练。这种组合可以利用人工智能教练的硬数据技能和人类经理的软人际交往技能，解决排名靠后和靠前代理面临的问题。这些发现为研究人员和管理者提供了关于人工智能教练的新见解。
运用模型：
3. Augmenting Medical Diagnosis Decisions? An Investigation into Physicians' Decision-Making Process with Artificial Intelligence（增强医疗诊断决策？对医生借助人工智能进行决策过程的调查）
期刊：《Information Systems Research》
作者： Joshua Gawlitza，Jussupow， Kai Spohrer ，Armin Heinzl
日期：2021年
英文摘要：
Systems based on artificial intelligence (AI) increasingly support physicians in diagnostic decisions. Compared with rule-based systems, however, these systems are less transparent and their errors less predictable. Much research currently aims to improve AI technologies and debates their societal implications. Surprisingly little effort is spent on understanding the cognitive challenges of decision augmentation with AI-based systems although these systems make it more difficult for decision makers to evaluate the correctness of system advice and to decide whether to reject or accept it. As little is known about the cognitive mechanisms that underlie such evaluations, we take an inductive approach to understand how AI advice influences physicians’ decision-making process. We conducted experiments with a total of 68 novice and 12 experienced physicians who diagnosed patient cases with an AI-based system that provided both correct and incorrect advice. Based on qualitative data from think-aloud protocols, interviews, and questionnaires, we elicit five decision-making patterns and develop a process model of medical diagnosis decision augmentation with AI advice. We show that physicians use second-order cognitive processes, namely metacognitions, to monitor and control their reasoning while assessing AI advice. These metacognitions determine whether physicians are able to reap the full benefits of AI or not. Specifically, wrong diagnostic decisions often result from shortcomings in utilizing metacognitions related to decision makers’ own reasoning (self-monitoring) and metacognitions related to the AI-based system (system monitoring). As a result, physicians fall for decisions based on beliefs rather than actual data or engage in unsuitably superficial information search. Our findings provide a first perspective on the metacognitive mechanisms that decision makers use to evaluate system advice. Overall, our study sheds light on an overlooked facet of decision augmentation with AI, namely, the crucial role of human actors in compensating for technological errors.
中文摘要：
基于人工智能（AI）的系统正日益为医生的诊断决策提供支持。然而，与基于规则的系统相比，这类系统透明度更低，其错误也更难以预测。当前大量研究致力于改进AI技术并探讨其社会影响。但令人惊讶的是，尽管AI系统使决策者更难评估系统建议的正确性，也更难决定是否采纳或拒绝这些建议，却很少有人关注基于AI的决策辅助所带来的认知挑战。由于人们对这类评估背后的认知机制知之甚少，我们采用归纳法来探究AI建议如何影响医生的决策过程。我们开展了实验，邀请68名新手医生和12名资深医生，使用一个会提供正确及错误建议的AI系统对病例进行诊断。基于有声思维记录、访谈和问卷的定性数据，我们归纳出五种决策模式，并构建了一个结合AI建议的医疗诊断决策辅助过程模型。研究表明，医生在评估AI建议时会运用二阶认知过程（即元认知）来监控和控制自身推理。这些元认知能力决定了医生能否充分发挥AI的优势。具体而言，错误的诊断决策往往源于两方面元认知运用的不足：一是与决策者自身推理相关的（自我监控）元认知，二是与AI系统相关的（系统监控）元认知。这会导致医生依赖主观信念而非实际数据做决策，或进行不恰当的浅层信息搜索。本研究首次揭示了决策者评估系统建议时的元认知机制，为理解AI决策辅助提供了新视角。总体而言，我们的研究强调了AI决策辅助中常被忽视的一点——人类在弥补技术错误中扮演的关键角色。
结语
第一篇文章以企业微观视角量化 AI 投资的经济传导机制，填补了 “技术赋能与创新增长因果链路” 的实证空白，为产业升级路径提供了量化的参考依据。
第二篇文章通过实地实验揭示 AI 教练在人机协作中的异质性规律与适配方案，为销售管理实践提供可操作的效能优化框架。
第三篇文章从认知科学维度解构 AI 辅助决策的元认知机制，揭露技术黑箱对专业判断的认知挑战与调节路径，为医疗 AI 系统的人机协同设计提供理论基石。
AI 绝非单一的效率工具，而是融合了技术赋能、行为适配与认知重构的复杂系统。企业增长的创新密码、销售场景的协作模型、医疗决策的认知调节，这三重实践场域印证着：当 AI 深度介入生产关系重塑时，我们既要在微观层面优化人机协作的技能匹配度，又要在中观层面构建技术应用的场景适配体系，更需在宏观层面夯实认知治理的理论根基。对学界而言，这种跨领域的研究图景不仅展现了 AI 影响的多维性，更昭示着未来学术探索需融合经济学的量化洞察、行为科学的机制解析与认知科学的理论建构 —— 当 AI 投资数据能勾勒增长曲线、行为实验能解码协作悖论、认知模型能揭示决策偏差时，破解技术谜题的钥匙从来不在单一学科的框架内，而在跨范式对话的交汇处。
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