制造企業(yè)人工智能準(zhǔn)備的驅(qū)動因素組態(tài)研究

中圖分類號：F425；TP18 文獻(xiàn)標(biāo)識碼：A 文章編號：1007-5097（2025）08-0118-11

Configuration of Driving Factors for Artificial Intelligence Readiness in Manufacturing Enterprises

LIN， ， ， （School of Economics and Management，Dalian University of Technology，Dalian116O24，China）

Abstract：Artificialintellgencereadinessiscrucialforenhancing thelikelihoodofsuccessfulapplicationofartificial intelligenceandunleashing itscommercial value.Thearticleis basedontheAMCtheoretical framework anduses the fuzzysetqualitative comparative analysis method to explore the multiple concurrnt factorsand causal complex mechanisms of artificial intellgence readiness inmanufacturing enterprisesofdiferentscales.Research reveals that a single factor such asperception，motivation，andabilitycannotconstituteanecessrycondition fordriving artificial intellgence readiness insmalland medium-sized orlarge-sized manufacturing enterprises.Thedriving mode for high artificial intellgence readiness in small and medium-sized manufacturing enterprisesis\"demand driven andagile response\"，whilelarge-sized manufacturing enterpriseshavetwo types ofdriving modes，namely\"policy leverageand competition driven\"and \"knowledge transformationandlean breakthrough\".Furtherresearch indicates thatthere isan asymmetriccausalrelationship betweenthe driving pathsof non-high artificial inteligence readinessand thoseof high artificial intelligence readiness in small，mediumand large-sized manufacturing enterprises.

KeyWords： manufacturing enterprises;enterprisescale;artificial intelligencereadiness;AMC framework;fuzzysetqualitative comparative analysis

一、引言

當(dāng)前，人工智能（ArtificialIntelligence，下文簡稱AI）處于技術(shù)躍遷的重要窗口期，AI技術(shù)在多個行業(yè)領(lǐng)域的跨界融合應(yīng)用，有助于不斷塑造發(fā)展新動能、新優(yōu)勢，加快形成推進(jìn)高質(zhì)量發(fā)展的新質(zhì)生產(chǎn)力。近年來，國家陸續(xù)出臺多項政策，旨在鼓勵形成科技創(chuàng)新和產(chǎn)業(yè)應(yīng)用相互促進(jìn)的良好發(fā)展局面。對于制造業(yè)而言，AI已成為開啟智能制造時代的重要技術(shù)驅(qū)動力，通過智能化手段打通傳統(tǒng)工業(yè)生產(chǎn)的全鏈條要素，可以更好地推動制造業(yè)企業(yè)的數(shù)字化、網(wǎng)絡(luò)化和智能化轉(zhuǎn)型，AI與制造業(yè)深度融合已成為制造業(yè)實現(xiàn)轉(zhuǎn)型升級的必由之路。

然而，蘭德智庫的一項研究顯示，超過 80% 的AI項目最終未能成功部署，其失敗率是傳統(tǒng)企業(yè)信息技術(shù)項目失敗率的兩倍之多\"。與其他易于部署的數(shù)字技術(shù)不同，AI在實施過程中面臨技術(shù)（如技術(shù)能力有限）和非技術(shù)（如缺乏領(lǐng)導(dǎo)支持）等挑戰(zhàn)[1]。例如，決定AI運行算法的黑箱特性給應(yīng)用AI技術(shù)的組織帶來了一定障礙和隱患。另外，也有學(xué)者關(guān)注到，即便成功將AI應(yīng)用于企業(yè)，也可能會產(chǎn)生一系列負(fù)面效應(yīng)，例如，增加數(shù)字基建、道德情感、數(shù)據(jù)安全等方面的成本[2]，這阻礙企業(yè)進(jìn)一步擴(kuò)大AI規(guī)模以獲得該技術(shù)帶來的巨大利益。鑒于AI技術(shù)的復(fù)雜性和知識壁壘以及其潛在的負(fù)面影響（如倫理風(fēng)險、監(jiān)管風(fēng)險等），已有學(xué)者指出在應(yīng)用AI之前進(jìn)行前期準(zhǔn)備和部署至關(guān)重要[3]，這對提升成功應(yīng)用AI的可能性、控制風(fēng)險、釋放商業(yè)價值均發(fā)揮關(guān)鍵作用[4]。……