pAeISSN: 2723 - 6609 e - ISSN : 2745-5254 Vol.
No.
4 April 2026 http://jist.
The Role of Artificial Intelligence in Enhancing Organizational Procurement Productivity: A Systematic Literature Review Oskar Ezra Alan Muin*.
Lanny W.
Pandjaitan.
Lukas Universitas Katolik Indonesia Atma Jaya.
Indonesia Email: oskezra@gmail.
com*, mm.
lanny@atmajaya.
id, lukas@atmajaya.
ABSTRACT
Digital transformation is driving significant changes in various organizational functions, including in the procurement process.
Traditional procurement processes still often rely on manual activities and limited data analysis that can hinder organizational productivity.
This study aims to analyze the role of Artificial Intelligence (AI) in increasing procurement productivity through the use of data analysis and procurement process automation.
The research method used is a literature study by examining various studies related to the implementation of AI in Procurement and supply chain management published in the period 2020Ae The results of the study show that AI technology such as machine learning, predictive analytics, and intelligent automation is able to improve the efficiency of the procurement process through predicting needs, automatic supplier evaluation, and data-based decision-making.
AI implementation also contributes to reducing human error, increasing transparency, and accelerating the procurement cycle.
Thus, the integration of AI in the Procurement system can increase organizational productivity and support digital transformation in supply chain management.
Keywords: artificial intelligence.
Procurement productivity.
Digital Procurement.
Decision Support
System
INTRODUCTION
The development of information technology and digitalization has driven significant transformations in various industrial sectors, including in supply chain management and functions Procurement organization.
Digital transformation enables organizations to optimize business processes through the use of data-driven technologies, process automation, and smarter and adaptive decision-making systems (Chatterjee et al.
Culot et al.
, 2.
In the context of modern organizations, the Procurement It no longer only acts as an administrative activity in the procurement of goods and services, but has evolved into a strategic function that contributes to operational efficiency, cost control, and increased organizational competitive advantage.
Procurement has a crucial role in ensuring the availability of goods and services in a timely manner, with efficient costs and quality that suits operational needs.
However, in practice, many organizations still face various challenges in management Procurement.
The procurement process is often still manual, involves limited data analysis, and relies on individual experience in decision-making.
These conditions have the potential to cause process inefficiencies, increased risk of human error, and delays in the procurement cycle (Liu, 2026.
Logoar, 2.
Along with the development of digital technology.
Artificial Intelligence (AI) is starting to be used to improve the effectiveness and efficiency of the process Procurement.
AI enables organizations to process and analyze large amounts of data quickly and accurately, supporting more data-driven decision-making (Data-driven decision makin.
In the context of supply chain and Procurement.
AI technology can be applied in various activities such as Demand Forecasting, supplier performance evaluation (Supplier Performance Analysi.
, risk detection in the supply chain, as well as automation of administrative processes in procurement (Guida et al.
, 2023.
Ofodile, 2.
A number of studies show that the application of AI technology has a significant impact on improving supply chain performance and Procurement.
Utilization Predictive Analytics Enable organizations to more accurately predict procurement needs based on demand patterns and historical data.
In addition, technology Machine Learning It can also be used to automatically evaluate suppliers taking into account various performance indicators, such as product quality, price, and timeliness of delivery (Ghasemi et al.
, 2025.
Tuo et al.
, 2.
The implementation of AI also contributes to increasing process transparency Procurement through a real-time data-based monitoring system, as well as improving the quality of decision-making through more comprehensive data analysis (Badrinarayanan, 2.
Although various studies have discussed the application of Artificial Intelligence In the context of supply chain management, most studies still focus on aspects of logistics, distribution, and inventory management.
Research that specifically examines the role of Artificial Intelligence in increasing the productivity of the Procurement The organization is still relatively limited (Culot et al.
, 2024.
Teixeira & Ferreira, 2.
In fact.
Procurement is one of the strategic functions that has a direct impact on operational cost efficiency and overall organizational performance.
Based on the research gap, this research aims to analyze the role of the application of Artificial Intelligence in increasing organizational procurement productivity through the use of data analysis, procurement process automation, and technology-based decisionmaking systems.
This research is expected to make a conceptual contribution in enriching the literature on the application of AI in the Procurement function, as well as providing practical implications for organizations in optimizing digital transformation in the procurement process.
METHOD
This research used a Systematic Literature Review (SLR) to analyze the role of Artificial Intelligence (AI) in increasing productivity within procurement organizations.
The SLR approach was chosen because it provides a comprehensive and structured synthesis of previous studies, enabling the identification of trends, patterns, and research gaps related to the application of AI in procurement and supply chain management.
This study applied a qualitative, descriptive-analytical method through a systematic literature review to examine developments in the use of AI in procurement processes and to analyze its contribution to improving organizational efficiency and productivity.
To ensure a comprehensive understanding, relevant literature was identified and selected from various academic sources.
More than 50 scientific articles were collected, with over 30 core studies forming the basis of the analysis.
The selected literature included empirical and conceptual studies, providing a representative overview of recent developments in AI applications in procurement and supply chain management.
As part of the initial analysis stage, key representative studies were summarized, as presented in Table 1.
Table 1.
Literature Review
Author
Year
Application in Procurement Procurement Decision-Making Sustainable Supply Chain
Technology Supply Chain
Operations Procurement Process
Improves efficiency and cost reduction Improves efficiency and sustainability Enhances decisionmaking and Improves forecasting and risk management Enhances resilience and operational Improves decisionmaking and firm
Enhances agility and predictive capability Enhances efficiency and strategic decisions Digital 2026 Transformation & Public
Procurement Improves transparency and governance Drouzi & Rajaa
2026 AI in Green SCM
Sustainable Procurement Katragadda
2026 AI & ML
Supply Chain
Resilience Husnain
Coglianese 2023 AI Governance Ayinaddis 2025 AI Adoption
Badrinarayanan 2024 AI in Procurement Balkan & Akyuz
2025 AI & ML
Chen et al.
Over Al-Huzaili et al.
AI Adoption Model AI & Predictive Analytics Key Findings AI & Machine Learning Supply Chain Integration Culot et al.
Logoar 2025 AI Applications Supply Chain & Procurement Tuo et al.
2024 AI Capabilities Supply Chain Performance Chatterjee et al.
Ofodile 2023 AI Optimization Guida et al.
2023 AI in Procurement Danieli Big Data Analytics Decision-Making / AI
& SCM
Big Data & Deep Learning Decision Support Systems Procurement Systems Organizational Decision Procurement Transformation Procurement Decision Support Organizational Resilience Digital Transformation Improves sustainability and resource optimization Enhances risk mitigation and Improves data-driven decision-making Highlights transparency and algorithmic risks Highlights adoption maturity challenges Improves efficiency and automation Enhances decisionsupport capabilities Improves adaptability and performance Technology Application in Procurement Ghasemi et al.
2025 AI Applications Sustainable Supply Chain
Jubair
2025 AI Integration Mhaskey
2024 AI & ERP
Naife et al.
2025 AI Risk Analytics Nguyen & Wang 2025 AI Optimization Nweje & Taiwo 2025 Predictive AI Pereira et al.
2026 AI & Industry 4.
Richey et al.
2023 AI in Logistics Squirt Predictive Analytics Supply Chain Management Inventory & Forecasting Samuels 2025 AI Review Supply Chain Smyth et al.
Teixeira & Ferreira 2025 AI in SCM Zhang 2025 AI Applications Author Year Supply Chain Systems Procurement Systems Supply Chain Risk Supply Chain Operations Demand Forecasting Digital Supply Chain AI & Prescriptive Analytics Supply Chain Resilience Supply Chain Performance Global Supply Chain Key Findings Enhances sustainability and Improves coordination and performance Improves integration and automation Enhances risk assessment capability Improves operational Improves forecasting Improves sustainability and Enhances integration and efficiency Improves demand prediction accuracy Identifies trends and research gaps Enhances resilience and decision-making Improves operational Improves global supply chain efficiency (Source: Data Processing, 2.
Based on Table 1, various studies show that Artificial Intelligence technology has a significant role in improving supply chain performance and procurement processes.
study by Chatterjee et al.
shows that the use of big data analytics in the supply chain is able to improve operational efficiency through improving the quality of decisionmaking, forecasting accuracy, and optimizing data-driven business processes.
Furthermore.
Culot et al.
emphasized that the implementation of AI contributes to improving the quality of decision-making and overall supply chain performance.
Logoar .
added that the application of AI can improve forecasting accuracy as well as risk mitigation capabilities in the supply chain.
The findings are reinforced by Ghasemi et al.
, who show that the application of AI in the context of sustainable supply chains not only improves operational efficiency, but also contributes to improving the sustainability of supply chain systems.
In addition.
Tuo et al.
show that AI capabilities have an important role in increasing supply chain resilience, which has a direct impact on improving organizational operational performance.
In the context of supply chain operations.
Ofodile .
shows that the use of AI can increase the agility and predictive ability of organizations in responding to demand dynamics.
Meanwhile.
Richey et al.
emphasized that the integration of AI in logistics systems is able to improve efficiency and coordination between processes in the supply chain.
From perspective Procurement.
Guida et al.
show that the application of AI plays a role in improving the efficiency of the procurement process as well as supporting strategic decision-making.
Research also shows that AI adoption in Procurement Able to improve the quality of decision-making through more accurate data analysis (Liu, 2.
In addition, other studies emphasize that the use of AI-based decision support systems can improve speed and accuracy in decision-making Procurement(Brown et al.
, 2.
Furthermore.
Husnain's research shows that integration Big Data.
AI, and Deep Learning decision-making system is able to increase the effectiveness of data analysis by real-time (Husnain, 2.
This is in line with research by Ferreira et al.
which shows that the application of AI has a positive impact on improving performance Supply Chain through optimization of operational processes.
From the perspective of digital transformation.
Danieli emphasized that the application of AI in Procurement can improve transparency and governance of the procurement process (Danieli, 2.
However.
Coglianese reminded that the use of AI also poses challenges related to algorithm transparency and potential bias in decisionmaking (Coglianese, 2.
In the context of sustainability, it shows that AI contributes to improving the efficiency of resource use as well as supporting Green Supply Chain Management (Drouzi & Rajaa, 2.
Meanwhile.
Katragadda emphasized that the application of AIbased Machine Learning can improve the organization's ability to manage risk and uncertainty through improved Resilience (Subba Rao Katragada, 2.
In terms of technology adoption.
Andersson .
shows that the level of maturity of organizations in adopting AI is an important factor in determining the success of the implementation of this technology (Andersson & Rosenqvist, 2.
This is reinforced by Jubair, who emphasizes that the integration of AI in the system Supply Chain can improve coordination and overall operational performance (Jubair, 2.
In addition, the concept Procurement 4.
0 AI-based ones that are able to drive the automation of the procurement process and improve operational efficiency (Althabatah et al.
, 2.
Meanwhile.
Samuel through a systematic literature review identified the main trends of the application of AI in Supply Chain and Procurement, and highlighting research gaps that still need to be further studied (Samuels, 2.
Overall, the literature synthesis shows that the application of Artificial Intelligence not only contributes to increasing operational efficiency, but also strengthens the organization's decision-making capabilities, improving supply chain resilience, and supporting digital transformation in the Procurement function.
However, the success of AI implementation is highly dependent on the readiness of organizations to manage data, technology, and human resources in an integrated manner.
To improve the transparency and replication of the study, the SLR process in this study adopts systematic stages that include identification, screening, eligibility, and inclusion of relevant literature.
This approach ensures that the literature selection process is carried out objectively and in a structured manner, resulting in a valid and academically accountable synthesis.
This research was carried out through several main stages as follows:
Identify topics and formulation of research questions related to Artificial Intelligence in Procurement and supply chain management.
Collection of scientific literature from various reputable academic databases.
Literature selection based on inclusion and exclusion criteria.
Analysis and synthesis of the literature to identify research patterns, trends, and gaps.
Sources and Data Collection This research was carried out through several main stages as follows:
Identify topics and formulation of research questions related to Artificial Intelligence in Procurement and supply chain management.
Collection of scientific literature from various reputable academic databases.
Literature selection based on inclusion and exclusion criteria.
Analysis and synthesis of the literature to identify research patterns, trends, and gaps.
Literature Selection Criteria In the literature selection process, this study uses several inclusion and exclusion criteria to ensure the quality of the sources used.
Inclusion criteria include:
A Scientific articles that discuss the application of Artificial Intelligence in the supply chain or Procurement.
A Articles published in scientific journals or proceedings of international A Publications published in the 2020Ae2026 time frame.
A Articles that discuss the impact of AI on operational efficiency or organizational decision-making.
Exclusion criteria include:
A Articles that do not directly discuss Artificial Intelligence in Procurement or supply chain A Articles that are not available in full-text A Non-academic publications such as blogs, non-scientific reports, or sources that do not go through a peer-review process Through the selection process, this study identified a number of relevant literature to be analyzed in this study.
Data Analysis Techniques Data analysis was carried out using a thematic analysis approach to the selected Each article is analyzed to identify several key aspects, namely:
Types of Artificial Intelligence technology used Areas of application of AI in Procurement The impact of AI application on procurement efficiency and productivity Research contribution to the development of technology-based procurement systems.
The results of the analysis are then synthesized to identify the pattern of the application of Artificial Intelligence in Procurement and its implications for increasing organizational productivity.
Research Conceptual Framework As part of the Systematic Literature Review-based research approach, a conceptual framework is needed to describe the flow of research thought and the relationships between the variables analyzed.
This framework is compiled based on the synthesis of literature that has been studied in the previous stage.
Figure 1 below shows the research framework that describes the flow of analysis in this study, starting from a literature review related to Artificial Intelligence in Procurement, followed by the identification of the role of AI in the Procurement process, to its implications for increasing organizational Literatur Review .
AI in Procurement Processes Productivity Improvement Figure 1.
Research Framework (Source: Data Processing, 2.
Based on the research framework, this study emphasizes that the use of Artificial Intelligence in Procurement not only plays a role as an operational tool, but also as a strategic enabler in increasing the efficiency and effectiveness of the procurement Furthermore, based on the results of the literature review that has been conducted, this study proposes a conceptual framework that describes the relationship between the application of Artificial Intelligence and the increase in organizational procurement Within this framework.
Artificial Intelligence capabilities play a key role in enabling organizations to automate the procurement process, improve data analysis capabilities, and support more effective decision-making.
The implementation of this technology is ultimately expected to increase procurement productivity through increasing the efficiency of the procurement process, reducing human error, and accelerating the procurement cycle.
To clarify the relationship between the variables studied, the conceptual model of the study is presented in Figure 2 below.
Artificial Intellegence Capabilities Data Driven Decision Making Procurement Automation Procurement Productivity Figure 2.
Conceptual Model of the relationship between AI and Productivity Procurement Source: Data Processing, 2026 Figure 2 shows that Artificial Intelligence capabilities play a role as the main variable that affects Procurement productivity through two main mechanisms, namely Procurement automation and data-driven decision making.
Both mechanisms function as mediators that bridge the use of AI technology with improving the organization's procurement performance.
RESULTS AND DISCUSSION
The Role of Artificial Intelligence in the Procurement Process Based on the results of the literature synthesis, the application of Artificial Intelligence (AI) shows significant contribution in improving process efficiency and effectiveness Procurement.
AI enables organizations to process large amounts of data quickly and accurately, supporting data-driven decision-making (Data-driven decision Studies show that the integration of AI in Supply Chain able to improve visibility, coordination, and responsiveness to changing demands (Robert Glenn Richey Jr.
Soumyadeb Chowdhury.
Beth Davis-Sramek.
Mihalis Giannakis, 2.
In context Procurement.
AI technology has been used in a variety of key activities, such as Demand Forecasting, supplier evaluation, as well as risk detection in the supply Utilization Machine Learning Allows supplier evaluations to be carried out more objectively and comprehensively by considering various performance indicators simultaneously (Al-Huzaili.
Sami & Mokhtar.
Ahmad & Muhamat, 2025.
Liu, 2026.
Logoar, 2.
In addition.
AI also enables proactive risk identification through historical data analysis and real-time, so that organizations can improve their ability to manage uncertainty in the supply chain (Tuo et al.
, 2024.
Umar et al.
, 2.
AI-Based Procurement Automation Implementation Artificial Intelligence (AI) in Procurement It plays an important role in driving the automation of the procurement process.
This technology allows organizations to reduce reliance on manual processes, thereby improving operational efficiency and minimizing the potential for human error.
Concept Procurement 4.
demonstrate that the integration of AI in the system Procurement can improve the speed and accuracy of the procurement process (Althabatah et al.
, 2.
In addition.
AI also supports automation in various administrative activities, such as document processing, contract analysis, and vendor management.
This is reinforced by research showing that AI-based systems are able to improve process efficiency Procurement significantly through integration with other digital technologies, such as Big Data Analytics and systems Enterprise Resource Planning (ERP) (Johnson et al.
, 2023.
Smyth et al.
, 2.
Data-Driven Decision Making in Procurement One of the main contributions Artificial Intelligence (AI) in Procurement is his ability to support Data-driven decision making.
The utilization of AI enables organizations to produce Insight more accurate through complex and dynamic data Studies show that the use of Big Data Analytics and AI significantly improve the quality of decision-making as well as organizational performance (Chatterjee et al.
Teixeira & Ferreira, 2025.
Zhang, 2.
Furthermore, the integration of AI with Decision Support Systems enable organizations to improve the speed and accuracy of decision-making, especially in uncertain business environment conditions (Brown et al.
, 2020.
Wilson & Burleigh.
In addition.
AI also supports prediction-based decision-making through the use of Predictive Analytics, which allows organizations to anticipate future needs as well as risks(Nweje & Taiwo, 2025.
Rungta, 2.
The Impact of AI on Procurement Productivity Based on the results of the literature analysis, the application of Artificial Intelligence (AI) has a direct impact on productivity increase Procurement organization.
The improvement is reflected in operational efficiency, reduced procurement costs, and cycle acceleration Procurement.
In addition, the quality of decision-making has also improved because it is supported by more comprehensive data analysis (Chatterjee et al.
Chen et al.
, 2.
AI also contributes to improving Supply Chain Resilience and the organization's ability to deal with uncertainty.
Studies show that organizations that adopt AI have better ability to manage risk as well as improve overall operational performance (Naife et al.
Nguyen & Wang, 2025.
Subba Rao Katragada, 2.
In addition, the application of AI also supports increased transparency in the process Procurement, thereby increasing accountability and trust in the procurement system (Coglianese, 2023.
Daniel, 2.
In the operational context, the implementation of AI also encourages increased agility and predictive capabilities of organizations through the utilization of Predictive Analytics as well as digital system integration.
This allows organizations to respond to changing demand and market dynamics more quickly and accurately (Brown et al.
, 2020.
Rungta, 2.
To gain a more comprehensive understanding of the development of research related to the application of Artificial Intelligence in Procurement and supply chain management, a synthesis of various relevant previous studies was carried out.
A summary of the results of the literature synthesis is presented in Table 3.
Table 3.
Literature Synthesis Author & Year Research Focus Method Chatterjee et Big data analytics in supply chain Empirical Culot et al.
AI in supply Systematic Logoar AI applications in supply chain Systematic Key Findings Improves decision-making, forecasting accuracy, and firm performance (Chatterjee et al.
Enhances integration, decision-making, and performance outcomes (Culot et al.
, 2.
Improves forecasting, inventory management, and No Author & Year Research Focus Teixeira et AI in SCM Smyth et.
Liu .
Ghasemi et Tuo et al.
Badrinarayanan .
AI capability & supply chain AI in Procurement Ofodile et AI optimization in supply chain Conceptual Guida et al.
AI in Procurement Literature Danieli .
Digital Procurement Conceptual Drouzi & M.
Rajaa .
AI in green supply chain Literature Katragadda AI-driven resilience in supply chain Conceptual Husnain AI & big data decision systems Conceptual Ivanov .
Agentic AI & digital twins in supply chain Conceptual Coglianese .
AI governance in Procurement Conceptual Richey et al.
AI in logistics & SCM Conceptual Digital supply chain AI adoption in Procurement decision-making AI in sustainable supply chain Method Literature Empirical Quantitative Bibliometric Empirical Technical Key Findings risk mitigation (Logoar.
Enables data-driven optimization and digital transformation (Teixeira & Ferreira, 2.
Improves firm performance and supply chain integration (Smyth et al.
, 2.
Improves efficiency and decision-making quality (Liu.
Enhances sustainability, efficiency, and resilience (Ghasemi et al.
, 2.
Improves resilience and operational performance (Tuo et al.
, 2.
Improves efficiency, automation, and cost reduction (Badrinarayanan, 2.
Enhances agility and predictive capability (Ofodile.
Improves Procurement efficiency and strategic decision-making (Guida et al.
Improves governance, transparency, and compliance (Danieli, 2.
Enhances sustainability and resource optimization (Drouzi & Rajaa, 2.
Improves risk mitigation and adaptability (Subba Rao Katragada, 2.
Improves data-driven decision-making capabilities (Husnain, 2.
Enhances real-time decisionmaking, integration, and adaptive supply chain systems (Ivanov, 2.
Highlights transparency issues and algorithmic bias (Coglianese, 2.
Improves coordination and operational efficiency (Robert Glenn Richey Jr.
Soumyadeb Chowdhury.
Beth Davis- No Author & Year Research Focus Method Althabatah Procurement 4.
Conceptual Andersson AI adoption in Empirical Jubair .
AI integration in supply chain Conceptual Ferreira et al.
AI & supply Empirical Balkan & Akyuz .
AI & ML in Procurement decision support Systematic Pereira et al.
AI in digital supply chain & Systematic (PRISMA) Mhaskey .
AI & ERP integration in Procurement Conceptual Key Findings Sramek.
Mihalis Giannakis.
Enhances automation and digital Procurement Processes (Althabatah et al.
, 2.
Highlights maturity level and adoption challenges (Andersson & Rosenqvist.
Improves coordination and system performance (Jubair.
Improves operational efficiency and performance outcomes(Teixeira & Ferreira.
Enhances decision-support capabilities and identifies benefits & challenges in Procurement (Balkan & Akyuz, 2.
Improves efficiency, sustainability, and integration of supply chain technologies (Pereira et al.
, 2.
Improves system integration, automation, and decisionmaking (Mhaskey, 2.
Source: Data Processing, 2026 Based on Table 3, it can be identified that most of the research emphasizes the role of Artificial Intelligence (AI) in improving supply chain performance and procurement processes through the use of data analysis, process automation, and technology-based decision-making systems.
Technologies such as machine learning, predictive analytics, and decision support systems are the main components in supporting digital transformation in the Procurement function.
These findings are also supported by research by Badrinarayanan .
which shows that the implementation of AI in Procurement contributes to increasing operational efficiency, process automation, and reducing procurement costs through the use of databased technology.
In addition, research by Ofodile .
confirms that the application of AI in the supply chain allows for increased agility and predictive ability of organizations, so that the procurement process can be carried out more proactively and adaptive to market dynamics.
Furthermore, a synthesis of the literature shows that the application of AI provides three main implications for Procurement productivity.
First, increasing operational efficiency through process automation and reducing manual activities.
Second, improving the quality of decision-making through a data-driven decision-making approach.
Third, increasing transparency and risk management capabilities through a real-time data-based monitoring system.
However, some studies have also identified challenges in AI implementation, such as limited data quality, complexity of system integration, and issues related to algorithm governance and transparency.
In addition, the level of maturity of the organization in adopting AI technology is also an important factor that affects the success of the implementation (Ayinaddis, 2025.
Smyth et al.
, 2.
Overall, these findings show that the integration of Artificial Intelligence in Procurement not only impacts improving operational efficiency, but also plays a strategic factor in improving organizational competitiveness in a dynamic business environment.
Challenges and Limitations of AI Implementation Although the application Artificial Intelligence (AI) provides a wide range of benefits, a number of studies show that the implementation of this technology also faces significant challenges.
One of the main challenges is the limited quality and integration of data, which can affect the accuracy and reliability of AI-based analysis results (Culot et al.
, 2024.
Drouzi & Rajaa, 2026.
Robert Glenn Richey Jr.
Soumyadeb Chowdhury.
Beth Davis-Sramek.
Mihalis Giannakis, 2.
In addition, the readiness of digital infrastructure and human resource competence are important factors in the successful implementation of AI in Procurement (Andersson & Rosenqvist, 2026.
Chatterjee et al.
, 2023.
Daniel, 2.
Organizations that do not have adequate digital capabilities tend to experience difficulties in adopting this technology optimally, potentially hindering the digital transformation process.
From a governance perspective, the use of AI also raises issues related to algorithm transparency and potential bias in decision-making (Coglianese, 2023.
Wilson & Burleigh, 2.
This is an important challenge in the application of AI, especially in the context of Procurement which involves strategic decisions and has a far-reaching impact on the organization.
In addition, the complexity of integrating AI technology with existing systems, such as Enterprise Resource Planning (ERP) and other digital platforms, are also obstacles in optimal implementation (Johnson et al.
, 2.
The level of maturity of the organization in adopting technology (Digital Maturit.
and resistance to change also affect the success of AI-based digital transformation (Andersson & Rosenqvist, 2.
CONCLUSION
The literature review indicates that Artificial Intelligence (AI) plays a significant role in enhancing procurement productivity by automating processes, reducing manual work and human error, and enabling faster, more accurate, and data-driven decisionmaking through machine learning and predictive analytics.
AI also improves transparency and strengthens supply chain risk management, while serving as a strategic enabler for resilience, sustainability, and overall organizational competitiveness.
However, its implementation remains constrained by challenges such as poor data quality and integration, limited digital infrastructure, insufficient workforce capabilities, organizational resistance to change, and governance concerns including algorithm transparency, data security, and bias.
Overall, the success of AI adoption in procurement depends not only on technological capabilities but also on organizational readiness and effective governance.
Future research should focus on developing practical frameworks for integrating AI into procurement systems, particularly in addressing data quality issues, enhancing humanAeAI collaboration, and ensuring ethical and transparent AI governance.
REFERENCE