人工智能赋能政府采购：全球六大洲的治理演进与中国启示

随着人工智能(Artificial Intelligence,AI)识别技术的不断发展和政务数字化转型的深入推进,政务AI识别将迎来更加广阔的发展空间。政府采购合同录入和发票识别都属于政府采购管理流程中的合同执行与资金支付阶段,是确保政府采购活动合规、高效进行的重要环节。基于此,提出一种面向政务场景的智能表单自动化处理工具。该工具融合光学字符文本识别、语义模糊匹配与视觉驱动机器人流程自动化技术,通过多模态协同实现字段提取、语义纠错与跨系统自动填报的全链路自动化。结果表明,该工具在合同字段解析、发票信息提取及结果录入等任务中取得了平均99.2%的综合准确率,显著降低了人工录入成本,提高了政务流程的处理效率。研究成果为轻量化AI在基层政务场景中的落地提供了可推广的技术路径。

本论文系统性解析大数据技术在破解供应商动态画像模糊、全生命周期成本失准及隐蔽性违规行为识别滞后等核心难题中的赋能路径，揭示传统管理模式因数据孤岛效应与动态响应迟滞引发的供应链脆弱性困局。研究创新构建基于量子化数据治理的智能决策中枢，通过多源异构数据超融合引擎实现供应商技术适配性、设备运行兼容性及市场波动传导性的协同建模，形成“工艺参数链-风险拓扑网-价值释放场”三位一体的闭环管理体系。深度耦合边缘智能计算与数字孪生验证平台，突破招标文件合规性实时校验、异常投标因果溯源及供应链中断风险预判等关键技术瓶颈，推动采购管理从经验驱动向熵值驱动调控的认知跃迁，为新型电力系统构建兼具敏捷响应能力与抗毁伤韧性的供应链基座提供方法论范式与工程实践锚点。

由于传统评审方式在工作效率、准确性与专业性方面均存在局限,大语言模型可通过自然语言处理、数据分析与智能决策支持等能力,为评审流程提供智能化转型方案。分析了大语言模型在流程自动化、风险识别、决策辅助与专家知识结构化等方面对项目评审带来的创新机制,并通过案例验证了其在提升效率、优化资源配置与防控风险方面的价值;其次指出大语言模型在数据安全、可解释性与伦理治理等方面仍面临挑战,并提出相应对策。研究显示,大语言模型将有力推动政府投资评审体系向智能化、精准化方向发展,为智慧政府的建设提供技术支持。

先进计算技术正驱动非招标采购向智能化系统化转型,其规模化落地的关键在于构建“算力+架构”协同的底层支撑体系。聚焦资质核查、合同解析、专家评审与文件生成四大场景,系统阐述异构计算、分布式推理等架构如何赋能多模态解析、多模型协同与人机交互式生成,显著提升采购效率、准确性与合规性。结合国家政策导向,系统梳理了先进计算赋能下非招标采购智能化的技术演进路径、典型应用成效及核心挑战,并指出未来需在算力友好型模型设计、可验证的生成校验机制及工程化部署策略等方面持续突破,以构建高效、可信、可扩展的智能采购体系。

Federal agencies are increasingly deploying large language models (LLMs) to process public comments submitted during notice-and-comment rulemaking, the primary mechanism through which citizens influence federal regulation. Whether these systems treat all public input equally remains largely untested. Using a counterfactual design, we held comment content constant and varied only the commenter's demographic attribution -- race, gender, and socioeconomic status -- to test whether eight LLMs available for federal use produce differential summaries of identical comments. We processed 182 public comments across 32 identity conditions, generating over 106,000 summaries. Occupation was the only identity signal to produce consistent differential treatment: the same comment attributed to a street vendor, compared to a financial analyst, received a summary that preserved less of the original meaning, used simpler language, and shifted emotional tone. This pattern held across all names, prompts, models, and regulatory contexts tested. Race effects were inconsistent and appeared driven by specific name tokens rather than racial categories; gender effects were absent. Writing quality predicted summarization outcomes through argument substance rather than surface mechanics; experimentally injected spelling and grammar errors had negligible effects. The magnitude of occupation-based differential treatment varied by model provider, meaning that selecting a model implicitly selects a level of fairness -- a dimension that current procurement frameworks such as FedRAMP do not evaluate. These findings suggest that socioeconomic signals warrant attention in AI fairness assessments for government information systems, and that fairness benchmarks could be incorporated into existing federal IT procurement processes.

We present a neurosymbolic approach, i.e., combining symbolic and subsymbolic artificial intelligence, to validating offer documents in regulated public institutions. We employ a language model to extract information and then aggregate with an LTN (Logic Tensor Network) to make an auditable decision. In regulated public institutions, decisions must be made in a manner that is both factually correct and legally verifiable. Our neurosymbolic approach allows existing domain-specific knowledge to be linked to the semantic text understanding of language models. The decisions resulting from our pipeline can be justified by predicate values, rule truth values, and corresponding text passages, which enables rule checking based on a real corpus of offer documents. Our experiments on a real corpus show that the proposed pipeline achieves performance comparable to existing models, while its key advantage lies in its interpretability, modular predicate extraction, and explicit support for XAI (Explainable AI).

Public procurement refers to the process by which public sector institutions, such as governments, municipalities, and publicly funded bodies, acquire goods and services. Swiss law requires the integration of ecological, social, and economic sustainability requirements into tender evaluations in the format of criteria that have to be fulfilled by a bidder. However, translating high-level sustainability regulations into concrete, verifiable, and sector-specific procurement criteria (such as selection criteria, award criteria, and technical specifications) remains a labor-intensive and error-prone manual task, requiring substantial domain expertise in several groups of goods and services and considerable manual effort. This paper presents a configurable, LLM-assisted pipeline that is presented as a software supporting the systematic generation and evaluation of sustainability-oriented procurement criteria catalogs for Switzerland. The system integrates in-context prompting, interchangeable LLM backends, and automated output validation to enable auditable criteria generation across different procurement sectors. As a proof of concept, we instantiate the pipeline using official sustainability guidelines published by the Swiss government and the European Commission, which are ingested as structured reference documents. We evaluate the system through a combination of automated quality checks, including an LLM-based evaluation component, and expert comparison against a manually curated gold standard. Our results demonstrate that the proposed pipeline can substantially reduce manual drafting effort while producing criteria catalogs that are consistent with official guidelines. We further discuss system limitations, failure modes, and design trade-offs observed during deployment, highlighting key considerations for integrating generative AI into public sector software workflows.

Infrastructure construction, often dubbed an"industry of industries,"is closely linked with government spending and public procurement, offering significant opportunities for improved efficiency and productivity through better transparency and information access. By leveraging these opportunities, we can achieve notable gains in productivity, cost savings, and broader economic benefits. Our approach introduces an integrated software ecosystem utilizing Data Mesh and Service Mesh architectures. This system includes the largest training dataset for infrastructure and procurement, encompassing over 100 billion tokens, scientific publications, activities, and risk data, all structured by a systematic AI framework. Supported by a Knowledge Graph linked to domain-specific multi-agent tasks and Q&A capabilities, our platform standardizes and ingests diverse data sources, transforming them into structured knowledge. Leveraging large language models (LLMs) and automation, our system revolutionizes data structuring and knowledge creation, aiding decision-making in early-stage project planning, detailed research, market trend analysis, and qualitative assessments. Its web-scalable architecture delivers domain-curated information, enabling AI agents to facilitate reasoning and manage uncertainties, while preparing for future expansions with specialized agents targeting particular challenges. This integration of AI with domain expertise not only boosts efficiency and decision-making in construction and infrastructure but also establishes a framework for enhancing government efficiency and accelerating the transition of traditional industries to digital workflows. This work is poised to significantly influence AI-driven initiatives in this sector and guide best practices in AI Operations.

In an October 2023 executive order (EO), President Biden issued a detailed but largely aspirational road map for the safe and responsible development and use of artificial intelligence (AI). The challenge for the January 24-25, 2024 workshop was to transform those aspirations regarding one specific but crucial issue -- the ability of individuals to challenge government decisions made about themselves -- into actionable guidance enabling agencies to develop, procure, and use genuinely contestable advanced automated decision-making systems. While the Administration has taken important steps since the October 2023 EO, the insights garnered from our workshop remain highly relevant, as the requirements for contestability of advanced decision-making systems are not yet fully defined or implemented. The workshop brought together technologists, members of government agencies and civil society organizations, litigators, and researchers in an intensive two-day meeting that examined the challenges that users, developers, and agencies faced in enabling contestability in light of advanced automated decision-making systems. To ensure a free and open flow of discussion, the meeting was held under a modified version of the Chatham House rule. Participants were free to use any information or details that they learned, but they may not attribute any remarks made at the meeting by the identity or the affiliation of the speaker. Thus, the workshop summary that follows anonymizes speakers and their affiliation. Where an identification of an agency, company, or organization is made, it is done from a public, identified resource and does not necessarily reflect statements made by participants at the workshop. This document is a report of that workshop, along with recommendations and explanatory material.

Public sector use of AI has been quietly on the rise for the past decade, but only recently have efforts to regulate it entered the cultural zeitgeist. While simple to articulate, promoting ethical and effective roll outs of AI systems in government is a notoriously elusive task. On the one hand there are hard-to-address pitfalls associated with AI-based tools, including concerns about bias towards marginalized communities, safety, and gameability. On the other, there is pressure not to make it too difficult to adopt AI, especially in the public sector which typically has fewer resources than the private sector$\unicode{x2014}$conserving scarce government resources is often the draw of using AI-based tools in the first place. These tensions create a real risk that procedures built to ensure marginalized groups are not hurt by government use of AI will, in practice, be performative and ineffective. To inform the latest wave of regulatory efforts in the United States, we look to jurisdictions with mature regulations around government AI use. We report on lessons learned by officials in Brazil, Singapore and Canada, who have collectively implemented risk categories, disclosure requirements and assessments into the way they procure AI tools. In particular, we investigate two implemented checklists: the Canadian Directive on Automated Decision-Making (CDADM) and the World Economic Forum's AI Procurement in a Box (WEF). We detail three key pitfalls around expertise, risk frameworks and transparency, that can decrease the efficacy of regulations aimed at government AI use and suggest avenues for improvement.

Public entities around the world are increasingly deploying artificial intelligence (AI) and algorithmic decision-making systems to provide public services or to use their enforcement powers. The rationale for the public sector to use these systems is similar to private sector: increase efficiency and speed of transactions and lower the costs. However, public entities are first and foremost established to meet the needs of the members of society and protect the safety, fundamental rights, and wellbeing of those they serve. Currently AI systems are deployed by the public sector at various administrative levels without robust due diligence, monitoring, or transparency. This paper critically maps out the challenges in procurement of AI systems by public entities and the long-term implications necessitating AI-specific procurement guidelines and processes. This dual-prong exploration includes the new complexities and risks introduced by AI systems, and the institutional capabilities impacting the decision-making process. AI-specific public procurement guidelines are urgently needed to protect fundamental rights and due process.

Tenders are powerful means of investment of public funds and represent a strategic development resource. Thus, improving the efficiency of procuring entities and developing …

The advent of Digital Procurement 4.0 marks a transformative shift in government contracting systems, integrating artificial intelligence (AI), data analytics, and automation to enhance transparency, efficiency, and ethical compliance. This study explores the redesign of public procurement frameworks using AI-driven models that ensure not only cost-effectiveness but also adherence to legal, ethical, and performance standards. Traditional procurement systems often grapple with inefficiencies, corruption, lack of accountability, and delayed service delivery. Digital Procurement 4.0 presents an opportunity to counter these limitations through predictive analytics, blockchain-based audit trails, robotic process automation (RPA), and intelligent contract management systems. This paper proposes a comprehensive AI-driven framework that embeds real-time risk detection, compliance verification, vendor performance monitoring, and ethical safeguards throughout the procurement lifecycle. By integrating natural language processing (NLP) for contract analysis, machine learning algorithms for bid evaluation, and automated compliance checkers, governments can ensure fairness, reduce fraud, and promote value-for-money outcomes. Moreover, digital twin technologies enable simulations that forecast procurement outcomes under varying socio-economic scenarios, thus enhancing strategic decision-making. The research draws on recent case studies from digitally advanced governments, demonstrating how AI integration has improved procurement efficiency by up to 45%, reduced fraud incidences by 30%, and enhanced stakeholder trust. Additionally, the study outlines a regulatory and governance blueprint to mitigate algorithmic bias and ensure accountability in AI-led procurement systems. Particular emphasis is placed on ethical algorithm design, data transparency, and participatory oversight mechanisms involving civil society and independent watchdogs. Ultimately, this paper underscores the national importance of adopting Digital Procurement 4.0 in public sector governance. As public expenditure accounts for over 12% of global GDP, optimizing this function through technology has widespread implications for fiscal sustainability, public trust, and socio-economic development. This research offers policy recommendations, implementation strategies, and a roadmap for governments aiming to build ethical, efficient, and AI-enabled contracting ecosystems.

Public administrators seeking to improve their performance through the use of artificial intelligence (AI) avoid thinking about procurement … the importance of procurement several years …

There is increased interest amongst governments and public sector organisations about how to best integrate artificial intelligence into their day-to-day business processes. Yet, a large majority of technical know-how is concentrated outside of the governmental sector, and many governmental organisations are likely to rely on public procurement for their AI systems. Thus, there is a clear need for new insight into the process of AI procurement, challenges that may be encountered, and guidelines on how to potentially overcome such challenges. This chapter aims to make an initial contribution of such insight. Methodologically, this chapter presents a multiple case study of four European countries (Estonia, the Netherlands, Serbia, and the United Kingdom) who have drafted guidelines and recommendations for how to procure AI in the public sector. As a result of this research, it is possible to provide an overview of challenges that may be encountered during the procurement of AI in the public sector and potential solutions for the public sector to overcome such challenges.

This study explores the transformative potential of artificial intelligence (AI) in public sector procurement, emphasizing efficiency, transparency, and regulatory compliance. By examining AI-driven innovations, the research highlights their ability to automate market analysis, optimize the preparation of technical specifications, and improve procurement process management while addressing legal and ethical considerations. A mixed-methods research approach was employed, incorporating quantitative and qualitative surveys, a detailed comparative analysis of international best practices, and an experimental “Centralized Procurement Support and Review Model”. Data collection included expert interviews, legal framework analysis, and practical tests comparing AI-supported and traditional procurement methods. The experimental findings indicate that AI integration in procurement reduces execution time by up to 50%, improves supplier selection accuracy by 25%, and strengthens compliance with procurement regulations and policies. Additionally, results from a targeted survey among procurement officers indicate a high level of readiness to adopt AI solutions, with 92% expressing interest in AI-assisted decision-making. The pilot project demonstrated a 20% reduction in administrative burden and significantly enhanced process transparency. Furthermore, practical tests revealed that AI-driven specification drafting could reduce preparation time from two hours to 30 minutes, significantly streamlining procurement operations. A comparative study of AI-based procurement solutions in the United Kingdom and Estonia suggests that the proposed model is tailored to Latvia's regulatory environment, ensuring alignment with the European Union's digital transformation objectives. Successful implementation of AI solutions in public procurement requires a practical deployment strategy, which includes enhancing employee qualifications, developing advanced data management systems, and effectively integrating regulatory requirements into procurement processes. Furthermore, this study emphasizes the importance of continuous monitoring and improvement of AI systems to ensure that procurement processes remain efficient, fair, and adaptable to evolving market dynamics and legal frameworks.

This study examines the transformative role of digital and artificial intelligence (AI)-enabled public procurement systems in enhancing governance efficiency within smart city environments, with a specific focus on Yerevan, Armenia. As urban administrations increasingly adopt data-driven governance models and digital infrastructures, public procurement remains a critical yet underexplored domain for innovation in transition economies. Despite ongoing e-government reforms in Armenia, procurement systems continue to face challenges related to procedural inefficiencies, limited transparency, and institutional constraints. To address these challenges, the paper develops a Governance Efficiency Framework that integrates digitalization, AI capabilities, and multi-criteria decision-making principles to assess and optimize public procurement processes in urban settings. The framework incorporates key dimensions such as transparency, operational efficiency, accountability, and data integration, enabling a comprehensive evaluation of procurement performance. The empirical application of the framework to the case of Yerevan provides insights into the structural and technological determinants of procurement efficiency in a transition economy context. The findings indicate that while digitalization has contributed to improvements in transparency, significant limitations remain in efficiency and system integration. A scenario-based analysis further suggests that AI-enabled analytics, process automation, and digital procurement platforms have the potential to reduce administrative delays, enhance transparency, and support more strategic and evidence-based decision-making under assumed implementation conditions. By bridging the fields of public procurement, digital governance, and smart city research, this study contributes both theoretically and practically. It offers a structured and adaptable framework for policymakers and urban administrators seeking to modernize procurement systems and strengthen governance efficiency in evolving digital environments.

… for incorporating AI into public procurement (Makarius et al., 2020). Recent advances in AI, especially the development of AAIs, offer a significant opportunity to transform procurement …

Public procurement is an important part of public finances; therefore, its management is challenging for the quality of the citizen’s relationship with the public authorities. Existing electronic public procurement optimization tools are systematically attempting to standardize procedures by improving access to information and transparency in management. Nevertheless, the next day requires the definition of the transition to modern tools and technologies of the fourth industrial revolution. This study attempts to identify common and additional critical success factors from implementing e-procurement in the 3.0 and 4.0 eras. Identifying the key challenges will be the basis for the roadmap plan suitable for maximizing the achievement of new public management in Industry 4.0.

This study examined the issue of corruption in Ukraine within a socio-political context, highlighting that despite the implementation of digital tools like Prozorro and Dozorro, the country continued to face reputational challenges due to persistent corrupt activities. The aim was to explore how digital solutions can serve as an effective tool to combat corruption in public procurement processes and their potential to enhance transparency, accountability, and efficiency. The study employed a mixed-methods approach, integrating qualitative and quantitative techniques to explore the use of digital tools in combating corruption in Ukraine. Data from Prozorro and Dozorro systems from 2020 to 2024 were analysed, along with reports from NABU (National Anti-Corruption Bureau of Ukraine) and SAPO (Specialised Anti-Corruption Prosecutor’s Office), and high-profile journalistic investigations like the Panama Papers and Pandora Papers. Despite advanced digital initiatives, Ukraine continued to face reputational challenges due to ongoing corruption reports. The study found that digital tools, such as blockchain and AI, have potential to enhance transparency and efficiency in public procurement. Challenges during wartime were also highlighted, showing the complexity of combating corruption under such conditions. The research emphasised the importance of international collaboration and public involvement in overseeing government activities. It concluded that a comprehensive strategy combining legal reforms, digital technologies, public oversight, and international cooperation is essential to reduce corruption and foster trust in public institutions. The practical value of this research lies in the development of a multi-faceted anti-corruption approach that integrates these key elements to build a culture of integrity and trust within public institutions

… Many studies about the use of Machine Learning in public procurement have … smart e-sourcing tool to measure the metrics that condition access of enterprises to public procurement. …

The terms governing the provision of supplies, services, or works by an economic operator to a governmental entity are set into a public contract that is signed, following a procurement process. This article explores whether the public administration can utilise smart contracts to incorporate the terms governing the provision of supplies, services, or works. The fundamental elements of a contract are assessed, in order to determine whether a smart contract can be considered as fulfilling these requirements. Following this assessment, the main hurdles to the use of smart contracting are examined and a possible solution proposed. The case for utilising smart contracting within the realm of public procurement is finally advocated.

The adoption of Artificial Intelligence (AI) in local government procurement is transforming how green infrastructure projects are planned, evaluated, and implemented. As cities face increasing environmental challenges, AI-driven procurement offers a data-driven, efficient, and transparent approach to integrating sustainability into infrastructure development. This chapter investigates the potential of AI in optimizing procurement processes for green infrastructure, enhancing decision-making, reducing costs, and ensuring regulatory compliance. Key AI applications include predictive analytics for environmental impact assessments (EIA), automated supplier evaluations, and real-time monitoring of infrastructure projects. AI can streamline procurement by minimizing inefficiencies, improving risk assessment, and promoting accountability in public spending. However, despite its advantages, local governments face challenges such as data privacy concerns, algorithmic bias, digital readiness, and regulatory limitations. Case studies of AI-driven green procurement initiatives were presented to highlight best practices and lessons learned. It also discusses ethical considerations and policy recommendations for fostering AI adoption in local governance. By integrating AI into procurement strategies, local governments can accelerate sustainable urban development while ensuring long-term resilience. This chapter aims to provide policymakers, urban planners, and procurement officers with insights into leveraging AI for more effective and sustainable green infrastructure procurement.

Smart contracts have been well-received by researchers and practitioners for the unique features of automatic execution, transparency, and nontampering in a blockchain environment. However, little is known about the current development status of knowledge and practice regarding the application of smart contracts in various industries, especially from the procurement perspective. Thus, this paper aims to address the gap with a mixed method of bibliometric analysis and systematic literature review. Based on the evaluation of 174 filtered publications, the review has analyzed the current development status of this research area with its distributions in years and journals, cooperation networks between authors, institutions, and countries, keywords cooccurrence network, and classifications of the application of smart contracts. The results show the application of smart contracts has attracted global attention since 2016 with the Ethereum and Hyperledger fabric as the main platforms in various industries, especially in information communication technology (ICT), public management, supply chain, energy, finance, and healthcare. Various functions and benefits of smart contracts, as well as their potential advantages, have been identified and articulated from the procurement perspective. A research framework has also been developed to highlight future procurement needs in business operations across the industries via an integrated procurement approach of smart contracts.

Introduction The traditional procurement system in the construction industry has been plagued by inefficiencies, often serving as a significant obstacle to project delivery. Thus, this study examines the dynamics of adopting smart contracts for project procurement for optimal project success and delivery, with insights and recommendations from the South African Construction Industry. Method The study employed a quantitative research approach utilizing descriptive and inferential statistics of Mean Item Score (MIS) and Exploratory Factor Analysis (EFA) for data analysis, based on a purposive sampling technique. Results The MIS results for the benefit, legal & regulatory constraints, and best practices of smart contracts range between 3.73 - 4.41 values, while the Kaiser-Meyer-Olkin (KMO) values were higher than the recommended 0.6 value for the EFA and Cronbach's Alpha value of 0.969 across the indicators. Discussion The study's findings revealed two categorized benefits of adopting smart contracts: administrative and operational efficiency of project procurement and procurement optimization; two components of legal and regulatory constraints: Transactional and legal encumbrance to smart contract implementation and legal gaps and ambiguity and two best practices: smart contract reliability practices for project procurement and consistent stakeholders’ engagement for smart contract protocol standardization. The study concludes that Smart contracts can transform global project procurement within the construction industry. The study recommends the development of a green paper on smart contract adoption and integrating smart contracts into standard forms of construction contracts.

… E-Procurement is now proving to be the ideal environment for … procurement governance principles very effectively and seamlessly along the procurement cycle despite e-procurement is …

Digital transformation in public procurement is reshaping governance by embedding transparency, efficiency, and accountability into state operations. This scoping review explores the strategic adoption of e-Government tools in procurement systems, guided by Arksey and O’Malley’s framework as refined by Levac et al. It systematically maps 48 peer-reviewed articles and policy documents published between 2000 and 2025. The review identifies key technologies, including eProcurement platforms, blockchain systems, and AI-driven analytics, implemented across procurement procedures such as tendering, contract management, and auditing. Thematic synthesis reveals enabling conditions such as institutional readiness, ICT infrastructure, and policy support, alongside persistent barriers including infrastructural deficits, behavioral resistance, and regulatory fragmentation. While digital systems improve procurement outcomes, the review highlights underexplored issues such as adaptive corruption, digital exclusion, and trust dynamics. It concludes with implications for research and practice, recommending longitudinal studies to assess impact over time, inclusive platform design to mitigate exclusion, and regulatory agility to address evolving governance challenges and ensure sustainable, equitable digital transformation in public procurement.

… of the workings of digital technologies beyond the superficial … digital technologies for procurement governance purposes. … scholarship in digital procurement governance has tended to …

… digital procurement governance by focusing on data governance risks and new types of technology governance … It is submitted that these are the two sources of governance risks most …

… The allure of the potential benefits of deploying digital … governance challenges. This can in turn result in excessive experimentation with digital technologies for procurement governance …

… However, given the limited obligations that result from the EU AI Act for digital procurement governance, this Chapter will not explore the issue in detail. Similarly, minimising the …

This paper adopts the Fuzzy Analytic Hierarchy Process (Fuzzy AHP) to identify electronic governance (e-governance) key attributes that enhance public procurement transparency. To this end, interviews were conducted with three decision-makers having sufficient knowledge and expertise in the field. The results highlight the existence of one dominant factor: process transparency; three secondary factors (digital interoperability, electronic procurement platforms, and sustainable development); four third-order factors (accountability, open data culture, smart contracts, and information technology audits); and three factors (information and communication technologies infrastructure, user-centered perspective, and electronic record management) that experts judged to be less significant in the context of our study. Hence, the contribution of this paper is threefold. Firstly, it provides an overview of the relationship between e-governance and public procurement, an area with a notable gap in the management literature. Secondly, it uses the Fuzzy AHP logic with a simple step-by-step guide, allowing researchers to understand and implement this approach in various fields. Thirdly, it provides a hierarchy of decision-making factors, as well as verification of the model via consistency tests. However, drawing factors exclusively from the literature review suggests that a preliminary field study could have identified other relevant variables, thus offering a lead for more in-depth future research.

Scope-3 emissions from construction supply chains (CSCs) account for the majority of the construction sector’s greenhouse gas (GHG) footprint. However, procurement-driven decarbonization (PDD) remains constrained by persistent data quality (DQ) deficits, including boundary divergence, limited verification, incomplete information, and fragmented interoperability. This PRISMA-guided systematic literature review (SLR) synthesizes 68 studies to examine how governance mechanisms (GMs) and digital technologies (DTs) can be co-designed within procurement workflows to improve the reliability of carbon data. By integrating quantitative matrix-based analysis, qualitative thematic coding, and a governance–technology pairing logic, the review identifies a division of labor across DQ dimensions. Standard-based governance and boundary rules strengthen completeness, consistency, and interpretability. At the same time, DTs enhance accessibility and timeliness and provide targeted improvements in accuracy and logical coherence when embedded within structured schemas. Assurance emerges as the most reliable mechanism for accuracy, information-management standards for timeliness, and early stakeholder involvement for accessibility. These insights translate into procurement-oriented measures, including European Standard (EN)-aligned scope definitions; ISO 14083-aligned logistics accounting; Industry Foundation Classes (IFC)/Level of Information Need (LOIN)-based information requirements; selective assurance; uncertainty-aware disclosure; and integrated digital measurement, reporting, and verification (MRV) systems combining Environmental Product Declaration (EPD) platforms, Artificial Intelligence (AI) validation, and blockchain. Collectively, these measures enable comparable, verifiable data and support scalable decarbonization.

Compliance management for procurement internal auditing has been a major challenge for public sectors due to its tedious period of manual audit history and large-scale paper-based repositories. Many practical issues and potential risks arise during the manual audit process, including a low level of efficiency, accuracy, accountability, high expense and its laborious and time consuming nature. To alleviate these problems, this paper proposes a continuous compliance awareness framework (CoCAF). It is defined as an AI-based automated approach to conduct procurement compliance auditing. CoCAF is used to automatically and timely audit an organisation’s purchases by intelligently understanding compliance policies and extracting the required information from purchasing evidence using text extraction technologies, automatic processing methods and a report rating system. Based on the auditing results, the CoCAF can provide a continuously updated report demonstrating the compliance level of the procurement with statistics and diagrams. The CoCAF is evaluated on a real-life procurement data set, and results show that it can process 500 purchasing pieces of evidence within five minutes and provide 95.6% auditing accuracy, demonstrating its high efficiency, quality and assurance level in procurement internal audit.

ABSTRACT: AI procurement means employing advanced data analytics and automation to enhance and accelerate the procurement process. Procurement teams are streamlining repetitive tasks like opening purchasing requests, selecting suppliers, processing invoices, and managing contracts so they can devote more time to making decisions on strategic decision-making and addressing complex challenges and initiatives. AI applications like spend pattern analysis, predictive analytics, and risk profiling are capable of assisting you in communicating with vendors, achieving savings, and reducing risks,3 through the provision of 3-1 actionable insights. In order to effectively facilitate AI applications for procurement orchestration, some principles are required. Examples of these are building AI-based solutions to detect trends and issues in carrying out impact assessments; process re-engineering for cloud-native platforms to enable you to make quicker decisions; and implementing AI models to drive automated workflows (i.e., discrete and repeatable procurement processing, etc.). Microsoft Azure, for instance, and AI as a Service can also be used to support procurement reporting and analytics within cloud platforms. Ultimately, AI monitoring services or software can assist with compliance monitoring. Virtual assistants and AI chatbots can also assist individuals in learning how to utilize them by simplifying difficult tasks. Finally, machine learning assists you in becoming proficient at procurement orchestration, thereby making it simpler to rate and estimate suppliers. The AI-driven approach converts conventional procurement from a reactive, paper-driven process to an proactive, strategic driver of business value. It makes individuals accomplish more, costs less, and gets along better with suppliers.

This paper presents a Process Automation Framework (PAF) that systematically improves procurement efficiency and transparency from requisition to payment. Integrating workflow orchestration, rules-driven approvals, robotic process automation, process mining, and API-based ERP integration, the framework reduces cycle time, minimizes manual error, and creates end-to-end audit trails. PAF operationalizes standardized data models, supplier master governance, dynamic risk scoring, and automated three-way matching to curb maverick spend and strengthen policy compliance. It embeds privacy-by-design controls, role-based access, and cryptographically verifiable event logs to deter fraud and enable defensible audits. Analytics services expose real-time KPIs for on-time approvals, first-pass match rates, and cost per transaction, while digital supplier portals increase visibility into orders, deliveries, and payments. Interoperability is achieved through reusable integration patterns, canonical payloads, and low-code connectors, enabling incremental adoption without disruptive rip-and-replace. Methodologically, the framework follows a four-stage pathway: discovery, design, deploy, and de-risk. Discovery maps current processes using event logs, conformance checking, and stakeholder interviews to quantify waste and control gaps. Design translates policy into BPMN workflows and micro-service contracts with embedded controls and exception paths. Deploy provisions bot workers, secure connectors, and telemetry to capture ground-truth events. De-risk applies continuous monitoring, anomaly detection, and post-implementation reviews to refine controls and prioritize backlog items. Expected outcomes include twenty to forty percent cycle-time reduction, improved first-pass match rates, higher on-contract spend, and materially lower processing cost per purchase order and invoice. Transparency increases via immutable logs, accessible audit trails, and supplier-facing status dashboards for cost, compliance, and service-level performance. The framework advances sustainability and ethics by integrating sanctions screening and ESG attestation into automated onboarding and renewal. To scale, PAF defines capability maturity metrics, a change-management playbook, citizen-developer guardrails, and value dashboards that link automation benefits to financial and control outcomes. It offers a practical blueprint for procurement leaders seeking measurable efficiency, resilient compliance, and trustworthy transparency, bridging policy intent and operational execution through disciplined automation and evidence-driven oversight.

The integration of data analytics into legal compliance and contract management is transforming traditional processes by automating risk assessments, enhancing regulatory adherence, and optimizing negotiations. This paper reviews state-of-the-art applications of advanced analytics, focusing on technologies such as predictive analytics, machine learning, and natural language processing (NLP). These tools enable organizations to streamline contract drafting, detect compliance risks in real-time, and derive actionable insights to enhance negotiation strategies. The proposed framework leverages predictive analytics to identify potential regulatory and contractual risks before they materialize, reducing legal exposure and operational inefficiencies. Machine learning models are employed to analyze historical data, detect anomalies, and provide evidence-based recommendations for compliance measures. NLP further enhances contract management by automating the review, interpretation, and redlining of legal documents, thus minimizing human error and increasing process efficiency. Key attributes of this framework include scalability and cross-sector applicability, allowing organizations in industries ranging from finance to manufacturing to adapt these technologies to their specific needs. By automating routine tasks and providing advanced analytical capabilities, this approach frees legal teams to focus on strategic decision-making and complex problem-solving. The paper also addresses ethical considerations in adopting these technologies, including concerns about data privacy, algorithmic transparency, and potential biases in automated decision-making. It emphasizes the importance of designing systems that ensure fairness, accountability, and compliance with ethical and legal standards. In conclusion, automating legal compliance and contract management through data analytics offers significant benefits, including enhanced efficiency, reduced risk, and improved negotiation outcomes. By incorporating cutting-edge technologies into legal workflows, organizations can achieve greater operational resilience and adaptability in an increasingly complex regulatory landscape. This research highlights the potential of data-driven solutions to revolutionize legal practices and establishes a foundation for future innovation in this critical domain.

This systematic review examines the adoption of legal technology within the interconnected domains of contract management, data governance, and compliance monitoring, with the aim of exploring how emerging digital innovations are reshaping legal workflows, improving operational efficiency, and strengthening risk management strategies. Drawing on evidence from 72 peer-reviewed journal articles and conference proceedings published between 2015 and 2022, the study integrates legal, technological, and organizational perspectives to provide a comprehensive understanding of current practices and trends. The synthesis identifies three central categories of technological application: (1) Contract Management—covering automation tools, AI-assisted contract review, natural language processing (NLP)–based clause extraction, and blockchain-enabled smart contracts, with an emphasis on their impact on drafting precision, negotiation speed, and full lifecycle management; (2) Data Governance—encompassing secure data storage, metadata management frameworks, privacy-preserving computation, and the integration of blockchain, advanced encryption, and identity management solutions to ensure regulatory compliance and data integrity; and (3) Compliance Monitoring—highlighting the use of AI, machine learning, predictive analytics, and real-time compliance dashboards to detect anomalies, flag violations, generate automated audit trails, and enable proactive policy enforcement. Findings reveal that legal technology adoption not only streamlines routine administrative functions but also facilitates predictive decision-making, enhances transparency, fosters cross-functional collaboration, and mitigates compliance risks. Nevertheless, implementation challenges such as system interoperability, integration costs, evolving data privacy regulations, and the requirement for continuous professional upskilling present persistent obstacles. This review offers a consolidated knowledge base for legal practitioners, policymakers, and researchers, underscoring critical success factors, identifying persistent research gaps, and outlining best practices for leveraging advanced digital tools to develop more agile, transparent, and resilient legal systems.

Problem definition: In this research, we study how buyers’ use of artificial intelligence (AI) affects suppliers’ price quoting strategies. Specifically, we study the impact of automation—that is, the buyer uses a chatbot to automatically inquire about prices instead of asking in person—and the impact of smartness—that is, the buyer signals the use of a smart AI algorithm in selecting the supplier. Academic/practical relevance: In a world advancing toward AI, we explore how AI creates and delivers value in procurement. AI has two unique abilities: automation and smartness, which are associated with physical machines or software that enable us to operate more efficiently and effectively. Methodology: We collaborate with a trading company to run a field experiment on an online platform in which we compare suppliers’ wholesale price quotes across female, male, and chatbot buyer types under AI and no recommendation conditions. Results: We find that, when not equipped with a smart control, there is price discrimination against chatbot buyers who receive a higher wholesale price quote than human buyers. In fact, without smartness, automation alone receives the highest quoted wholesale price. However, signaling the use of a smart recommendation system can effectively reduce suppliers’ price quote for chatbot buyers. We also show that AI delivers the most value when buyers adopt automation and smartness simultaneously in procurement. Managerial implications: Our results imply that automation is not very valuable when implemented without smartness, which in turn suggests that building smartness is necessary before considering high levels of autonomy. Our study unlocks the optimal steps that buyers could adopt to develop AI in procurement processes.