article review in project management

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Peer-reviewed

Research Article

How do project managers’ competencies impact project success? A systematic literature review

Roles Conceptualization, Supervision, Writing – review & editing

* E-mail: [email protected]

Affiliation ESPAE Graduate School of Management, Escuela Superior Politécnica del Litoral (ESPOL), Guayaquil, Ecuador

Roles Formal analysis, Methodology, Writing – original draft, Writing – review & editing

Roles Investigation, Methodology, Software, Writing – original draft

Roles Conceptualization, Formal analysis, Writing – review & editing

Roles Writing – review & editing

• Paola Ochoa Pacheco, 
• David Coello-Montecel, 
• Michelle Tello, 
• Virginia Lasio, 
• Alfredo Armijos

• Published: December 7, 2023
• https://doi.org/10.1371/journal.pone.0295417
• Peer Review
• Reader Comments

Despite the existence of systematic literature reviews focused on examining the factors contributing to project success, there remains a scarcity of reviews addressing the relationship between the project managers’ competencies and project success. To fill this gap in the literature, this review aimed to evaluate peer-reviewed articles, published between 2010 and 2022, and analyze the impact of project managers’ competencies on project success. The Web of Science, Scopus, ScienceDirect, and ProQuest electronic databases were first consulted in September 2021, with an update in August and October 2022. A total of 232 titles were analyzed. Ten articles met the criteria and were fully reviewed. A content analysis and a citation network were carried out to analyze the included articles. The analysis revealed that the existing literature has primarily explored the influence of competencies from the personal and social dimensions, such as leadership, communication, and emotional intelligence, on project success. Conversely, competencies from other dimensions have received less attention in the literature. In addition, this review contributes to the literature by providing a holistic categorization of competencies associated with project success and examining and organizing project success criteria into three dimensions.

Citation: Ochoa Pacheco P, Coello-Montecel D, Tello M, Lasio V, Armijos A (2023) How do project managers’ competencies impact project success? A systematic literature review. PLoS ONE 18(12): e0295417. https://doi.org/10.1371/journal.pone.0295417

Editor: Jamshid Ali, University of Tabouk: University of Tabuk, SAUDI ARABIA

Received: July 19, 2023; Accepted: November 21, 2023; Published: December 7, 2023

Copyright: © 2023 Ochoa Pacheco et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All data are available either within the manuscript (Tables 1 , 2 , 3 , 4 ) or as supplementary files . Hyperlinks are provided within the manuscript in the reference list.

Funding: The authors received no specific funding for this work.

Competing interests: The authors declare no conflict of interest.

1. Introduction

The profound economic, technological, and social changes experienced in recent years [ 1 , 2 ] have compelled organizations to devise strategies and implement initiatives to adapt to uncertain environments [ 3 ]. Projects allow organizations to face these challenges by leveraging their expertise and capabilities to deliver solutions aligned with business objectives [ 4 ]. Project management (PM) has been acknowledged as a valuable discipline for managers and professionals implementing strategic organizational transformations [ 1 ]. Given the shortage of qualified talent to execute strategic initiatives and drive change [ 5 ], the project managers’ (PMGs) competencies have garnered significant attention from scholars [ 6 – 11 ] and PM institutions [ 12 , 13 ]. Consequently, a substantial body of literature has devoted considerable effort to delineating the competencies that have the potential to enhance projects’ positive outcomes [ 14 – 18 ].

There has been a growing interest in exploring the individual and organizational factors contributing to project success (PS). At the individual level, the PMGs’ leadership style [ 19 ], job satisfaction [ 20 ], trust [ 21 ], job crafting [ 22 ], and work-family conflict [ 23 ], among other factors have been associated with PS. At the organizational level, scholars have highlighted that PS can be influenced by innovative climate [ 24 ], organizational culture [ 25 ], cultural diversity [ 26 ], governance [ 27 ], knowledge sharing and perceived trust and cohesion of the team [ 28 , 29 ], among others.

Despite the existence of systematic literature reviews (SLRs) that summarize the available evidence regarding factors associated with PS [ 30 ], there remains a scarcity of SLRs focusing on PMGs’ competencies [ 31 , 32 ] and their impact on PS. Only a limited number of SLRs [ 33 ] have been dedicated to identifying the competencies essential for achieving PS. However, to the best of our knowledge, an SLR focused on analyzing the relationship between PMGs’ competencies and PS has not been conducted before. To fill this gap in the literature, this SLR analyzes the existing evidence regarding the relationship between PMGs’ competencies and PS. Therefore, the present SLR was designed to address the following research questions: (RQ1) Which PMGs’ competencies are the most examined in the existing literature? (RQ2) Which success criteria are the most considered when measuring PS in the existing literature? (RQ3) Which PMGs’ competencies have a relationship with PS?

This SLR contributes to the literature on the PM discipline in four ways. Firstly, it fills a gap in the existing literature by employing the SLR methodology to comprehensively synthesize the available evidence from published empirical studies concerning the relationship between PMGs’ competencies and PS. Secondly, it employs a thematic analysis and a holistic perspective to categorize the PMGs’ competencies associated with PS. This methodological approach provides a comprehensive framework for understanding the diverse competencies relevant to PS. Thirdly, it offers an insightful analysis of a graphical representation that showcases the primary authors and institutions that have significantly influenced the conceptualization of PMGs’ competencies. Lastly, it examines the criteria utilized for measuring PS in the included articles and organizes them into three dimensions, enhancing the understanding of the multifaceted nature of PS assessment. By addressing these aspects, this SLR contributes to advancing knowledge in PM.

The subsequent sections of this paper are structured as follows. Section 2 presents the conceptualization of PMGs’ competencies and PS. Section 3 outlines the procedure for conducting the SLR, encompassing the search strategy, study selection, data extraction, and analysis. The findings derived from the SLR are presented in Section 4. Lastly, the paper concludes by discussing the implications of the results, highlighting the strengths and limitations of the SLR, and offering final remarks.

2. Competencies and project success

This section provides an overview of the conceptualizations of competencies adopted in the PM literature, and briefly discuss the evolution of the PS dimensions.

2.1. Competencies

Various conceptualizations of competencies have been explored in the existing literature [ 16 , 34 – 38 ]. Within the PM discipline, several studies [ 18 , 39 – 42 ] have aligned with the classical definition proposed by Boyatzis [ 35 ]. According to his framework, competencies encompass the underlying characteristics of an individual, including knowledge, skills, abilities, attitudes, and more, that collectively enable the achievement of high performance. These elements have served as a foundational basis for scholars [ 9 , 43 , 44 ] and institutions [ 12 , 13 ], who have further expanded the scope to develop frameworks tailored explicitly to the domain of PM.

PM institutions, including the Project Management Institute (PMI) and the International Project Management Association (IPMA), have played a crucial role in the definition and development of various standards and frameworks that pertain to the competencies of PMGs [ 45 ]. Several studies [ 16 , 46 , 47 ] have employed these institutional standards to define competencies. The next paragraph provides a concise overview of these institutional frameworks.

According to the IPMA [ 13 ], competencies comprise the practical application of knowledges, skills, and abilities to achieve desired outcomes. This framework recognizes the interconnectedness of these elements, as proficiency entails acquiring relevant knowledge and developing skills that, when put into practice, enable professionals to manage projects effectively and successfully. Similarly, the PMI [ 12 ] defines competencies as the capability to carry out activities within a portfolio, program, or project setting that yield anticipated results based on established and accepted standards. This definition builds upon Boyatzis’ [ 35 ] elements and aligns with the IPMA [ 13 ] perspective, but it also emphasizes compliance by acknowledging the significance of adhering to current regulations and guidelines to meet stakeholders’ expectations. More recently, the PMI [ 48 ] introduced the concept of power skills , which refers to the abilities and behaviors that facilitate working with others and enable project professionals to succeed in the workplace, align projects to organizational objectives, and motivate teams to contribute value to the organization and its customers.

The scholarly literature [ 8 , 37 , 43 , 44 ] has significantly contributed to the conceptualization of the competencies required by PMGs by incorporating key elements from the PM discipline. For instance, Hanna et al. [ 43 ] emphasized the evolving nature of projects. They argued that competencies entail the demonstrated ability to perform project activities within a dynamic environment, leading to expected outcomes based on established standards. Building upon this perspective, Bashir et al. [ 44 ] defined competencies as a meta-ability that integrates skills, aptitudes, and abilities to perform throughout the project life cycle, from initiation to closing, intending to achieve expected results. Moreover, Crawford [ 49 ] posited a close relationship between PMGs’ competencies and PS. Recent literature has underscored the pivotal role of PMGs’ competencies in attaining higher levels of success, enhancing efficiency and effectiveness, and consequently increasing the likelihood of PS [ 8 ].

2.2. Project success

This section provides an overview of the historical development of the conceptualization of PS, tracing its progression from a unidimensional to a more comprehensive and multidimensional concept [ 50 ]. It also aims to identify the dimensions and criteria incorporated into the concept in recent years. Furthermore, it defines PS and examines its distinctions from related concepts, such as project performance and efficiency.

Traditionally, scholars [ 39 , 51 – 53 ] have viewed PS as a combination of success factors and criteria. On the one hand, success factors refer to the significant elements that enhance the probability of achieving success. On the other hand, success criteria comprise a set of measures used to evaluate if the project can be judged as successful [ 39 ]. This SLR specifically focuses on PS criteria.

The measurement criteria for assessing PS have undergone significant evolution to encompass the complex and dynamic nature of projects, resulting in the development of more comprehensive models [ 52 , 54 ]. Initially, PS frameworks primarily focused on efficiency criteria, commonly referred to as the “golden triangle,” “iron triangle,” or “holy trinity,” which encompassed elements such as time, cost, and quality [ 54 ]. Subsequent models expanded to incorporate dimensions of client and project team satisfaction [ 55 ]. From the year 2000 onwards, the emergence of integrative models took into account additional dimensions, including realized benefits to the business or organization [ 56 , 57 ], satisfaction levels of internal and external stakeholders such as end-users, suppliers, and other relevant parties [ 58 ], the impacts on the community and environment [ 59 ], long-term effects like the creation of new markets or product lines [ 56 , 60 ], and investment returns [ 61 ].

The conceptual boundaries between PS, project performance, project efficiency, and PM success have often been blurred. On the one hand, PM success represents a conventional measure of PS that primarily focuses on time, cost, and quality, assessed upon project completion [ 62 , 63 ]. These criteria are also called project efficiency [ 64 ]. On the other hand, project performance refers to the degree to which management practices and processes contribute to the achievement of goals and objectives, as well as the fulfillment of stakeholders’ expectations. It is typically evaluated throughout project execution and upon completion [ 54 , 65 ]. In contrast, PS represents a broader and multidimensional concept encompassing the achievement of goals and objectives determined by key stakeholders after project completion [ 63 , 64 ], as well as the long-term impacts of the project [ 66 ].

The SLR was undertaken to investigate the abovementioned research questions and followed the guidelines outlined in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). The protocol employed for conducting this SLR is elaborated next.

3.1. Search strategy

The Web of Science, Scopus, ScienceDirect, and ProQuest electronic databases were selected for this SLR. The databases were first consulted in September 2021, with an update in August and October 2022, by searching the following keywords in the title of the article: “competence,” “competency,” “competences,” “competencies,” “skill,” “skills,” and “project success,” without any additional constraint. The search was performed by two of the authors using the following search strings:

• Scopus database : TITLE ((competence) OR (competency) OR (competences) OR (competencies) OR (skill) OR (skills)) AND TITLE ((project AND success))
• Web of Science database : TI = (competence OR competency OR competences OR competencies OR skill OR skills) AND TI = (project success)
• ScienceDirect database : Title: (competence OR competency OR competences OR competencies OR skill OR skills) AND (project success)
• ProQuest database : title((competence OR competency OR competences OR competencies OR skill OR skills)) AND title((project success))

The metadata of the records (title, authors, document type, source title, author keywords, abstract, publication year, volume number, issue number, and DOI) was exported, compared, and saved on Microsoft Excel spreadsheets to remove duplicated studies and conduct the screening process.

3.2. Study selection

The study selection process comprised several stages to find relevant articles for the review. The initial research resulted in 232 articles. After removing duplicated records, 172 articles were considered for the next stages. The procedures followed by the authors are described below.

3.2.1. Inclusion and exclusion criteria.

The inclusion and exclusion criteria for document selection in this review were based on various factors, including publication timeline, document type, language, study type, population, and context. To be included in this review, documents had to meet the following criteria: (1) they had to be peer-reviewed scholarly research articles, (2) they had to be published between January 2010 and October 2022, (3) they had to be written in English, (4) they had to have a quantitative approach measuring PMG’s competencies as independent variable and PS as a dependent variable, (5) the study population had to consist of PMGs or similar positions (e.g., project director, project leader, senior PMG, department manager, functional manager, team leader), and (6) the research work had to be conducted in professional settings. The study selection process did not impose restrictions on industry, project type, or project size to ensure a broader scope and encompass various perspectives. This approach allowed for the retrieval of peer-reviewed scholarly articles that addressed the research questions of this SLR. Initially, 172 articles were evaluated, and after applying the inclusion criteria, 131 records were removed. Subsequently, 41 research articles remained for the screening process.

3.2.2. Article screening process.

After applying inclusion and exclusion criteria, the retained articles were screened by title, abstract, and full text. This process was conducted by two of the authors independently. The reasons for excluding articles were reported in each step. The exclusion criteria were objectively applied. Studies were excluded if the relationship between PMGs’ competencies and PS was not examined. Each reviewer’s number and list of excluded articles were compared after the screening. In those cases where there was disagreement between reviewers, a third author reviewed the article and discussed it with the other two authors to reach a consensus. Eligible articles were included in the final review. Ineligible articles were formally excluded, with the reasons for exclusion noted.

Out of 41 articles, seven were excluded based on the title. In this step, the main reasons for exclusion were: (a) the study was related to project-based learning ( n = 4), (b) the article was a literature review ( n = 3), and (c) the article was a case of study ( n = 1). The retained 34 articles were screened by abstract. After analyzing the abstract of each article, eight were removed because of the following: (a) the study had a qualitative design ( n = 1), (b) the article was a case of study ( n = 1), (c) the article analyzed only leadership styles ( n = 1), (d) the article was theoretical ( n = 2), (e) the study was not conducted in a PM professional setting ( n = 1), and (f) the article did not analyze the relationship between PMGs’ competencies and PS ( n = 2). Finally, the full-text screening was carried out on 26 articles. Thirteen records were excluded based on the following reasons: (a) PMGs’ competencies were not measured ( n = 3), (b) the article was theoretical ( n = 3), and (c) the study did not analyze the relationship between PMGs’ competencies and PS ( n = 7). After the whole screening process, 13 articles were considered for quality assessment.

3.2.3. Quality assessment.

The quality assessment focused on ten quality criteria statements: (1) The research questions, objectives, or hypothesis were appropriately established; (2) The study design was well described and appropriate for answering the research questions; (3) The sample and population of the study were clearly described, and its size was sufficient to carry out the proposed analysis; (4) The response rate was reported and above 50%; (5) The instruments used for measuring PMGs’ competencies were well described and design-based; (6) The instrument used for measuring PS was well described and design-based; (7) The statistical method was appropriate and sufficiently described to enable them to be repeated; (8) The research questions were adequately answered; (9) The statistical significance of associations was tested and reported; (10) The conclusions were clearly described and based on the results.

The abovementioned criteria were adapted from the Newcastle-Ottawa Quality Assessment Scale (adapted for cross-sectional studies), the Appraisal Tool for Cross-Sectional Studies (AXIS), and similar studies [ 67 ]. Each statement had three rating options coded as “Yes” (1 point), “No” (0 points), and “Partial” (0.5 points). Articles with a score of 7.5 points or higher were included in the final sample. The quality assessment was carried out by two authors independently. The results were compared, and the differences found were discussed to make a final decision. In this phase, three articles were removed. Ten articles were selected to conduct the analysis and answer the research questions of this SLR. Fig 1 summarizes the data extraction procedure through a PRISMA flow.

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https://doi.org/10.1371/journal.pone.0295417.g001

3.3. Data extraction and analysis

Three authors analyzed the articles for data extraction, including sample characteristics, country, setting, independent and outcome variable(s), data analysis procedures, and main findings. These data were synthesized in Table 4 .

A thematic analysis was conducted to identify the dimensions of PMGs’ competencies and PS criteria used in the included articles, following the procedures proposed by Nowell et al. [ 68 ]:

• Familiarization with the data . The authors read and analyzed the content of each article.
• Generation of initial code s. Each author generated a list of competencies and PS criteria extracted from each article. The resulting lists were compared and matched to get a final version.
• Creation of themes . Categories were created by grouping similar competencies and PS criteria. Each of the authors carried out this process individually. The resulting lists were compared and matched to get a final version as in the previous step.
• Definition and naming of themes . Once the final list of competencies and PS were obtained and the main categories were defined, each category was named based on theoretical foundations. This process was carried out jointly by the three authors.

When studying topics such as PMGs’ competencies, an important issue is how authors support their choice regarding what competencies to include in their work. This decision is important since it shapes the structure of the research field. Thus, a citation network analysis (CNA) was carried out to map the structure of the PMGs’ competencies research field. In CNA, research documents serve as nodes, and the connections between them are represented by citations [ 69 ]. CNA is a practical approach for identifying contributions to a specific topic and uncovering relationships within the scholarly literature, thereby revealing patterns of influence and collaboration [ 70 ]. In this SLR, the ten included articles relied on citations of prior works to select the pertinent PMGs’ competencies. These cited references were used to build a network representing the relevant frameworks in the included articles. The citation network was generated using the visNetwork package in RStudio.

4.1. Study characteristics

The main characteristics of the articles included in this SLR are shown in Table 1 . Data were collected from 11 countries across five regions: Asia, Europe, North America, Oceania, and South America. Notably, Pakistan emerged as the most prolific country, with five papers published between 2010 and 2022, followed by the USA ( n = 2) and Brazil ( n = 2). Most studies were published within the last five years ( n = 9). Out of the ten articles, eight were published in journals categorized in the Q1 ( n = 5) and Q2 ( n = 3) impact quartiles. In terms of study design, most articles employed a purely quantitative approach ( n = 8), while two utilized mixed methods. For instance, Sampaio et al. [ 71 ] conducted a systematic review to identify the competencies to be included in their subsequent questionnaire, while Podgórska and Pichlak [ 72 ] employed a mixed-method approach comprising semi-structured interviews and a survey questionnaire.

https://doi.org/10.1371/journal.pone.0295417.t001

4.2. Project managers’ competencies

4.2.1. the most influential theoretical frameworks..

The majority of articles ( n = 9) included in the SLR employed an existing framework to identify the PMGs’ competencies that were examined in their empirical analyses. However, in the study conducted by Sampaio et al. [ 71 ], a comprehensive literature review was undertaken to determine the specific competencies that should be considered for testing their impact on PS.

A CNA was conducted to explore the interrelationships among the ten articles included in this SLR and to identify the most influential frameworks for defining and determining the PMGs’ competencies. Fig 2 visually represents the articles included in the SLR as square nodes and the studies that have contributed to conceptualizing PMGs’ competencies as circle nodes. The size of each node reflects the number of citations it has received. The diagram layout was arranged such that the most frequently cited studies are positioned in the center, while less frequently cited ones are placed towards the periphery. A summary of the most influential works in PMGs’ competencies is provided below.

Notes. Square nodes represent the articles in the SLR ( n = 10), while circle nodes denote the studies that contributed to conceptualizing PMGs’ competencies. The number of citations gives the size of the node.

https://doi.org/10.1371/journal.pone.0295417.g002

Four articles included in this SLR [ 39 , 72 , 73 , 75 ] employed a common framework developed by Dulewicz and Higgs [ 79 ]. This framework encompasses 15 leadership competencies categorized into three dimensions: intellectual competencies (critical analysis and judgment, vision and imagination, strategic perspective), managerial competencies (managing resources, engaging communication, empowering, developing, achieving), and emotional competencies (self-awareness, emotional resilience, intuitiveness, interpersonal sensitivity, influence, motivation, conscientiousness). Additionally, two articles [ 15 , 77 ] drew upon Clarke’s [ 80 ] study, which identified four main PMGs’ competencies: communication, teamwork, attentiveness, and managing conflict. Other frameworks utilized in the SLR articles were proposed by Sunindijo [ 81 ], Katz [ 82 ], Nguyen and Hadikusumo [ 83 ], and Ofori [ 84 ]. These frameworks shared common elements, emphasizing the significance of communication, leadership, managing emotions, and interpersonal relationships as essential competencies for PMGs. Notably, the Project Manager Competency Development Framework [ 12 ] and the Individual Competence Baseline for Project Management [ 13 ] were among the most cited institutional frameworks employed in the SLR articles.

4.2.2. Categorization of project managers’ competencies.

Several common competencies were identified based on the review of competencies reported in each article. These competencies were categorized into four dimensions based in previous studies [ 11 , 31 , 85 ], as presented in Table 2 : cognitive, personal, social, and sustainability. It should be noted that not all competencies were consistently referred to by the same name across the included articles. Therefore, the names used to denote a specific competence in each article are listed in the third column of Table 2 .

https://doi.org/10.1371/journal.pone.0295417.t002

4.3. Project success criteria

Previous literature has traditionally focused on PS measures related to cost, time, and quality. However, the findings of the SLR indicate a growing tendency to incorporate a broader range of success criteria. Table 3 presents a categorization of the different success criteria reported in the included articles.

https://doi.org/10.1371/journal.pone.0295417.t003

The first dimension pertains to the impact on stakeholders, encompassing clients, users, providers, the project team, and other relevant parties. While stakeholder impact is commonly assessed through satisfaction measures, some studies consider alternative indicators such as the acceptability of the product, perceived benefits [ 73 ], or the fulfillment of stakeholder expectations [ 74 ]. Less frequently addressed are measures related to the impact on the organization, for which two criteria were identified: (i) visible short-term improvements in organizational outcomes or performance [ 73 , 75 , 76 ], and (ii) long-term improvements, such as the development of new technologies or the initiation of future projects [ 39 , 72 , 75 ]. Additional criteria related to the project management process were identified, encompassing project performance, achievement of the project’s primary objectives, other self-defined criteria related to project management, and compliance with procedures, safety regulations, and environmental standards. Project performance indicators include the traditional metrics of cost, time, and quality of the project’s deliverables [ 39 , 72 ].

4.4. Empirical analysis of the relationship between project managers’ competencies and project success

Table 4 presents a comprehensive overview of the research methods and results employed in the included studies. Several studies conducted correlational analyses to examine the relationship between competencies and various PS criteria [ 71 , 72 , 75 ], as well as overall PS [ 15 ]. Regression analysis was a common method to assess the predictive impact of PMGs’ competencies on PS criteria in the selected articles [ 39 , 72 , 74 , 75 ]. Additionally, some studies [ 73 , 74 , 76 , 78 ] employed structural equation modeling (SEM) or partial least squares (PLS) to analyze the predictive effect of PMGs’ competencies, modeled as second-order constructs, on PS.

https://doi.org/10.1371/journal.pone.0295417.t004

4.4.1. Relationship between cognitive competencies and project success.

Cognitive competencies encompassed creativity, decision-making, and strategic perspective. Findings from two studies revealed a positive correlation between creativity and various PS criteria, such as accomplishing project objectives, project efficiency, user satisfaction [ 71 ], and suppliers’ satisfaction [ 72 ]. While creativity significantly predicted project efficiency, its effect on achieving project objectives and user satisfaction was not statistically significant [ 71 ]. Two studies included in the SLR [ 39 , 72 ] provided evidence concerning the relationship between strategic perspective and PS. Firstly, Müller and Turner [ 39 ] found that this competence influences project efficiency and self-defined success criteria. Secondly, Podgórska & Pichlak [ 72 ] reported that strategic perspective is significantly associated with all the analyzed PS criteria except for project efficiency and self-defined success criteria. Regarding the decision-making competence, Müller and Turner [ 39 ] did not identify any significant predictive effects of this competence. However, Podgórska and Pichlak [ 72 ] observed significant positive correlations between decision-making and all the PS criteria, with the highest coefficients observed for self-defined success criteria, end-user satisfaction, and satisfaction of other stakeholders.

4.4.2. Relationship between personal competencies and project success.

Personal competencies included emotional intelligence, results orientation, and conscientiousness. Among these competencies, emotional intelligence has received significant attention in the included studies. Out of the ten studies, eight explored the relationship between emotional intelligence and PS. The evidence revealed direct and significant predictive effects of emotional intelligence on various PS criteria, such as end-user satisfaction [ 71 ], achievement of project objectives [ 39 ], and overall PS [ 15 ].

Regarding results orientation, Sampaio et al. [ 71 ] demonstrated that this competence had a predictive effect on project efficiency. Correlational analysis revealed a higher correlation between this competence with PS criteria related to user satisfaction [ 71 ] and satisfaction of other stakeholders [ 72 ]. The conscientiousness competence was examined in the studies conducted by Müller and Turner [ 39 ] and Podgórska and Pichlak [ 72 ]. This competence emerged as a significant predictor of team satisfaction and the achievement of project objectives [ 39 ]. Furthermore, the effects of conscientiousness could vary depending on the type and complexity of the project [ 72 ].

4.4.3. Relationship between social competencies and project success.

Based on the articles included in this review, social competencies, such as communication, leadership, interpersonal relations, conflict management, and teamwork, tend to be associated with PS. Leadership has been extensively studied in the project management literature and was addressed in seven out of the ten articles included in this review. Correlational analysis revealed that leadership shows significant associations with nearly all PS criteria, being its highest correlation with the user satisfaction criterion [ 71 , 72 ]. Regarding its predictive effect on PS, Sampaio et al. [ 71 ] reported a non-significant effect of this competence on some criteria, such as achieving project’s purpose, project efficiency, and stakeholders’ satisfaction, while other studies found a significant effect on team satisfaction criterion [ 39 ] and an overall PS measure [ 15 , 74 ].

Regarding communication, correlational analysis showed that this competence is highly correlated with stakeholders’ satisfaction criterion [ 71 , 72 ]. Maqbool et al. [ 15 ] found that communication had the strongest correlation with a general measure of PS among all competencies included in their study. The predictive effect of this competence on PS was confirmed by Lima and Quevedo-Silva [ 77 ], Khan et al. [ 78 ] and Podgórska and Pichlak [ 72 ]. However, Sampaio et al. [ 71 ] and Müller and Turner [ 39 ] reported non-significant effects of communication of PS criteria.

Interpersonal relations showed significant positive associations with PS criteria, with the strongest coefficients on achieving project’s purpose [ 72 ]. Müller and Turner [ 39 ] found that this competence has a significant predictive effect on other stakeholders’ satisfaction criteria, while two studies [ 77 , 78 ] reported its predictive effect on a general PS measure. Finally, significant positive associations of conflict management and teamwork with PS were reported by Maqbool et al. [ 15 ]. However, its predictive effect on individual PS criteria were not estimated on any of the included articles.

4.4.4. Relationship between sustainability competencies and project success.

According to Elmezain et al. [ 74 ], the capacity to demonstrate integrity, sincerity, and authenticity, and to inspire confidence and trust in others, is relevant for achieving PS. The authors emphasized that PMGs who possess integrity play a crucial role in the advancement of any project. Similarly, Sampaio et al. [ 71 ] highlighted ethics, conceptualized as transparency, integrity, and honesty, as the most significant competence for achieving PS in terms of goal attainment.

5. Discussion

This SLR examined the evidence pertaining to the relationship between PMGs’ competencies and PS. The analysis of the included studies yielded three key findings. Firstly, six distinct clusters of authors were identified, each contributing to the conceptualization and identification of PMGs’ competencies. Secondly, the conceptualization of PS has evolved from a traditional approach centered around criteria such as time, cost, and quality, to a more comprehensive, holistic, and multidimensional perspective. Lastly, through thematic analysis, a total of 12 competencies, organized into four dimensions, were identified as potential determinants of PS. Notably, the most significant competencies associated with PS were found within the personal and social dimensions. A brief discussion of these findings is presented below.

In relation to the first finding, this SLR identified six distinct clusters of authors whose work influenced the competence frameworks utilized in the included articles. These clusters represented conceptualizations proposed by scholars and reputable PM institutions. The in-depth content analysis revealed that the frameworks proposed by Dulewicz and Higgs [ 79 ] and Clarke [ 80 ] were the most prevalent among the examined articles. Conversely, frameworks developed by Sunindijo [ 81 ], Ofori [ 84 ], and Nguyen and Hadikusumo [ 83 ] were comparatively less frequently employed. Additionally, the PMI [ 12 ] emerged as a key institutional point of reference for identifying the competencies required in a PMG. For instance, Lima and Quevedo-Silva [ 77 ] and Maqbool et al. [ 15 ] studies adopted Clarke’s [ 80 ] framework, which was based on the PMI’s [ 12 ] (2017b) list of competencies. Elmezain et al. [ 74 ], who cited Sunindijo et al. [ 81 ] as their framework source, incorporated several competencies defined by the PMI [ 5 ], although the majority of these were technical.

Regarding the second finding, the articles examined in this SLR provided support for the view that PS should be understood as a multidimensional construct. This finding aligns with a recent study by Ika and Pinto [ 54 ] that revisited the conceptualization of PS. The results of this review indicate that project performance, encompassing time, cost, and quality, emerged as the most commonly considered criterion of success across all the articles. However, a significant number of the included articles also acknowledged additional criteria, leading to the identification of three dimensions of PS. The first dimension refers to the impact on stakeholders and includes criteria related to the satisfaction of various project stakeholders, including clients, users, suppliers, and the project team, among others. The second dimension focuses on the impact of the project on the organization, comprising both short- and long-term improvements. Lastly, the third dimension is related with the general management of the project. This dimension encompasses aspects such as project performance, which includes the traditional "iron triangle" of time, cost, and quality, as well as the achievement of project objectives, adherence to project-defined criteria, and compliance with safety and environmental protocols and regulations. This conceptualization supports the multidimensional nature of PS. However, as noted by Ika and Pinto [ 54 ], it is important to highlight that the majority of the included articles overlooked the inclusion of sustainability criteria. Among the entire sample of studies, only one [ 76 ] out of ten explicitly addressed compliance with safety and environmental regulations as a criterion of success.

The findings of this SLR have provided insights into the competencies that exhibit a significant relationship with PS. Specifically, the articles included in this review extensively examined competencies associated with the personal and social dimensions, such as leadership, communication, and emotional intelligence. These competencies have been extensively studied in previous literature [ 18 , 86 ], and their impact on PS was explored in the majority of the reviewed articles. Conversely, the influence of other competencies, such as ethics, received less attention and was not extensively explored. Moreover, the empirical evidence gathered in this review suggests that the effect of project management competencies on PS may vary depending on several factors. For instance, the type of project was found to be a significant factor influencing the relationship between competencies and PS [ 72 ]. Furthermore, individual and organizational factors were identified as potential mediating variables that could affect the relationship between competencies and PS [ 73 ]. These findings highlight the complexity and contextual nature of the relationship between competencies and PS. Next, a brief discussion will be presented to shed light on how these identified competencies can contribute to enhancing PS.

Leadership competence was one of the most studied competencies that improve PS. Although a few of the studies included in this SLR [ 71 , 73 ] reported that it does not have a significant effect on PS, a great number of the studies [ 15 , 39 , 72 , 74 , 76 ] suggested that PMGs’ leadership, conceived as their capacity to influence, empower and develop others, has a positive effect on PS. This finding agrees with the existing literature that has examined its influence on PS [ 87 – 90 ]. The development of competencies such as leadership allows PMGs to motivate their teams to be more productive [ 91 ], to show outstanding performance beyond expectations [ 89 ], to enhance team cohesion and engagement [ 92 ], to foster knowledge transfer across project teams [ 89 ], among other positive behaviors that would impact on projects’ outcomes.

The articles included in this SLR demonstrate a significant and positive relationship between communication and PS [ 15 , 72 , 77 , 78 ], in agreement with previous research findings [ 93 , 94 ]. The significance of this competence lies in its impact throughout various stages of a project [ 94 ]. Effective communication between PMGs and the project team’s members allows better collaboration [ 95 ], encourages knowledge sharing [ 96 ], and enhances the team’s motivation a sense of inclusivity [ 94 ], which contribute to the overall achievement of PS.

The influence of emotional intelligence on PS was assessed in most of the articles included in this SLR. Although some studies reported a non-significant relationship between emotional intelligence [ 71 , 73 , 77 ], there was evidence supporting a positive association between these two variables [ 15 , 39 , 72 , 75 ]. PMGs with high emotional intelligence are more likely to establish stronger relationships with their teams, thereby improving communication, clarity of mission, and support, ultimately enhancing PS [ 21 ]. In addition, the development of this competence allows PMGs to adequately regulate their emotions in complex situations, promoting positive behaviors such as empathy, respect, and leadership. These behaviors contribute to their ability to address challenges successfully and ensure higher PS [ 97 , 98 ].

Regarding the influence of PMGs’ ethics, a positive relationship was identified between this competence and PS criteria, particularly goal achievement [ 71 ]. Ethics has been acknowledged as a driving force for the advancement of the PM profession [ 48 ] and an essential competence that PMGs should possess [ 99 , 100 ]. However, empirical evidence on the impact of ethics on PS remains limited. Some related terms, such as honesty, integrity, and transparency [ 71 , 74 ], or ethical thinking [ 100 ], ethical decision-making, and ethics sensitivity have been addressed in previous studies. However, its effect on PS has rarely been estimated and reported. The evidence found on ethics in this SLR was obtained from information systems and construction projects. Future studies could explore the influence of this competence in different industries and countries.

6. Limitations and strengths

While this review contributes with some insights to the PM literature, it is important to mention its limitations. Firstly, the time frame of the review from 2010 to 2022 may have resulted in the exclusion of relevant articles that explore the relationship between PMGs’ competencies and PS. It is possible that some studies conducted outside this timeframe may provide further insights into the topic. Secondly, the use of specific search terms such as “competence,” “competency,” “competences,” “competencies,” “skill,” and “skills” may have excluded other studies [ 86 , 88 ] that examined the impact of different competencies individually. Including an exhaustive list of competencies in the search strings could have introduced significant heterogeneity into the reviewed articles, potentially limiting the ability to provide a comprehensive review of the existing literature.

Despite these limitations, this SLR makes several notable contributions to the PM discipline. First, it fills a gap in the existing literature by synthesizing available empirical evidence on the relationship between PMGs’ competencies and PS. Second, the review conducts a thematic analysis and adopts a holistic perspective to categorize the PMGs’ competencies that are associated with PS. Third, this review highlights the primary authors and PM institutions that have significantly influenced the conceptualization of PMGs’ competencies. Four, the review examines the criteria used to measure PS in the included articles and organizes them into three dimensions, offering a nuanced understanding of the multifaceted nature of PS measurement.

7. Conclusions

The present SLR extends the literature in project management concerning the influence of PMGs’ competencies on PS. Despite the growing interest in addressing the role of PMGs’ competencies to achieve higher success, to the best of our knowledge, there is a lack of systematic reviews that present an analysis of the available evidence on the relationship between PMGs’ competencies on PS. To fill this gap in the literature, this SLR analyzed the existing evidence regarding this relationship. Three main conclusions can be derived from the findings of this review. First, the existing literature has primarily explored the influence on PS of PMGs’ competencies from the personal and social dimensions, such as leadership, communication, and emotional intelligence. Second, PS is a multidimensional construct that comprises three main dimensions: impact on stakeholders, impact on the organization, and general project management. Third, the available data suggested that greater levels of PMGs’ competencies are associated with improved PS. These findings may support scholars and managers to understand the mechanisms through which individual characteristics, such as competencies, may allow PMGs to achieve better outcomes.

This SLR contributes to the existing literature in the PM discipline by offering a comprehensive synthesis of empirical evidence, providing a thorough overview of the current state of knowledge regarding the relationship between PMGs’ competencies and PS. In addition, this SLR identifies key contributors and sources of knowledge in the field, offering a valuable reference point for further research and exploration. The study also offers a review on how PS is conceptualized and measured. Moreover, it presents a classification of PMGs’ competencies that influence PS. Through a thematic analysis of the competencies examined in the included articles, this categorization provides valuable insights into the emphasis placed on different types of competencies. It highlights the significant attention given to personal and social competencies, while pointing out the relatively limited exploration of sustainability, cultural, or digital competencies [ 85 ].

Supporting information

S1 checklist. prisma 2020 checklist..

https://doi.org/10.1371/journal.pone.0295417.s001

S1 Table. Inclusion and exclusion criteria used in the SLR.

Notes: PMG = Project manager, PS = Project success.

https://doi.org/10.1371/journal.pone.0295417.s002

S2 Table. Quality assessment criteria scoring guide.

https://doi.org/10.1371/journal.pone.0295417.s003

S3 Table. Quality assessment results.

Notes: QC1 = Research questions; QC2 = Study design; QC3 = Sample representativeness; QC4 = Response rate; QC5 = PMG’s competencies measurement; QC6 = PS measurement; QC7 = Statistical analysis; QC8 = Results; QC9 = Statistical significance; QC10 = Conclusions; SLR = Systematic literature review.

https://doi.org/10.1371/journal.pone.0295417.s004

S4 Table. Brief description of the Project Managers’ competencies in included articles.

https://doi.org/10.1371/journal.pone.0295417.s005

S5 Table. Brief description of the project success criteria in included articles.

https://doi.org/10.1371/journal.pone.0295417.s006

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What Successful Project Managers Do

Traditional approaches to project management emphasize long-term planning and a focus on stability to manage risk. But today, managers leading complex projects often combine traditional and “agile” methods to give them more flexibility — and better results.

• Project Management

An analysis of three Mars missions undertaken by NASA’s Jet Propulsion Laboratory concluded that a key success for the Mars Pathfinder project (shown here) was a high level of collaboration.

Image courtesy of NASA.

In today’s dynamic and competitive world, a project manager’s key challenge is coping with frequent unexpected events. Despite meticulous planning and risk-management processes, a project manager may encounter, on a near-daily basis, such events as the failure of workers to show up at a site, the bankruptcy of a key vendor, a contradiction in the guidelines provided by two engineering consultants or changes in customers’ requirements. 1 Such events can be classified according to their level of predictability as follows: events that were anticipated but whose impacts were much stronger than expected; events that could not have been predicted; and events that could have been predicted but were not. All three types of events can become problems that need to be addressed by the project manager. The objective of this article is to describe how successful project managers cope with this challenge. 2

Coping with frequent unexpected events requires an organizational culture that allows the project manager to exercise a great amount of flexibility. Here are two examples of advanced organizations that took steps to modify their cultures accordingly.

A group of 23 project managers who had come from all over NASA to participate in an advanced project management course declared mutiny. They left the class in the middle of the course, claiming that the course text, based on NASA’s standard procedures, was too restrictive for their projects and that they needed more flexibility. With the blessing of NASA’s top leadership, the class members then spent four months conducting interviews at companies outside of NASA. This led to a rewriting of numerous NASA procedures. Among other things, NASA headquarters accepted the group’s recommendation to give NASA project managers the freedom to tailor NASA’s standard procedures to the unique needs of their projects. A similar movement to enhance project managers’ flexibility occurred at Procter & Gamble, where the number of procedures for capital projects was reduced from 18 technical standards and 32 standard operating procedures to four technical standards and four standard operating procedures.

Concurrent with these changes at NASA and P&G, a heated debate emerged within the wider project management profession regarding the need for flexibility, as opposed to the traditional approach, which emphasizes that project success depends on stability. According to the traditional approach, project success can be achieved by focusing on planning and on controlling and managing risks. Although the popularity of this approach has sharply increased across industries, research covering a wide variety of projects consistently reveals poor performance. A large percentage of projects run significantly over budget and behind schedule and deliver only a fraction of their original requirements. 3

The Four Roles of the Project Manager

Our research found that today’s successful project managers assume four roles that help them cope with unexpected events.

The other side in this debate is best represented by a newer project management approach popular within the software industry. Called the agile method, it asserts that project success requires enormous flexibility throughout the project’s life. However, even proponents of the agile approach acknowledge that this approach is best suited to small projects and teams. 4

Our studies, employing experiential data collected from more than 150 successful project managers affiliated with more than 20 organizations, indicate that today’s successful project managers cope with unexpected events by a combination of the traditional and agile approaches, assuming four roles. (See “About the Research.”) Two of the roles are intention-driven and two are event-driven, with each role assumed on its own time schedule throughout the life of the project. The first role, developing collaboration, is performed early on during the project. The second role, integrating planning and review with learning, is performed periodically. The third role, preventing major disruptions, is performed occasionally. The fourth role, maintaining forward momentum, is performed continuously. 5 (See “The Four Roles of the Project Manager.”)

1. Develop Collaboration

Since project progress depends on the contribution of individuals who represent different disciplines and are affiliated with different parties, collaboration is crucial for the early detection of problems as well as the quick development and smooth implementation of solutions. The importance of collaboration can be demonstrated by the following two examples in which projects failed.

Tim Flores analyzed the causes for the different outcomes of three Mars exploration missions initiated by NASA’s Jet Propulsion Laboratory: Pathfinder, Climate Orbiter and Polar Lander. Although all three projects were conducted under the same guiding principles, were of comparable scope and shared many elements (even some of the same team members), Pathfinder was a success, whereas the other two missions failed. Flores expected to find that the Pathfinder project differed from the other projects in a variety of factors, such as resources, constraints and personnel. Although this was true to some extent, he found that the primary factor distinguishing the successful mission from the failed missions was the level of collaboration. The Pathfinder team developed trusting relationships within a culture of openness. Managers felt free to make the best decisions they could, and they knew that they weren’t going to be harshly punished for mistakes. That trust never developed in the other two projects. 6

A different NASA project, the Wide-Field Infrared Explorer (WIRE) mission, was designed to study the formation and evolution of galaxies. Its telescope was so delicate it had to be sealed inside a solid hydrogen cryostat. When, shortly after launch, a digital error ejected the cryostat’s cover prematurely, hydrogen was discharged with a force that sent the Explorer craft tumbling wildly through space, and the mission was lost.

Jim Watzin, a project manager at NASA and a member of the WIRE project team, had this to say regarding the official report that NASA issued following the WIRE failure: “WIRE failed because people could not or would not communicate well with each other. … Individuals … simply were uncomfortable allowing others to see their work.” Watzin added: “The real [lesson] from this loss is that any team member that does not participate as a true team player should be excused [from the project].” 7

In the next two examples, project success can be attributed to the project manager’s deliberate attempt to develop collaboration. (Note that in the discussions that follow, we use only the project managers’ first names.)

Allan, the payload manager for NASA’s Advanced Composition Explorer project at the Jet Propulsion Laboratory, has described how he developed trust between his team and the 20 groups of scientists developing instruments for the project, who were based at universities throughout the United States and Europe. Allan devised a three-stage plan. First, he selected team members who could operate in a university environment — people who knew when to bend or even break the rules. Second, he relocated his JPL team to a university environment (California Institute of Technology), recognizing that it might be difficult to develop an open, flexible culture at JPL. Third, he came up with an uncommon process for interacting with the scientists. 8

The challenge, with regard to interaction, was getting the scientists to regard his JPL team as partners. Having dealt with NASA before, they tended to believe that someone coming from JPL would demand a lot of paperwork, lay out sets of rules to be followed and expect things to be done a certain way. In fact, many of the scientists weren’t sure they should share with Allan’s team the problems they were encountering along the way — problems that could slow down the project’s progress.

When unexpected events affect one task, many other interdependent tasks may also be quickly impacted. Thus, solving problems as soon as they emerge is vital for maintaining work progress.

The primary role of Allan’s team was to review the development of the instruments, and Allan believed that the best way to do this was by focusing on trust and convincing the scientists that his team was there to help them solve their problems. To facilitate this, Allan and his team of five to eight members traveled to each university and stayed on site for an extended period of time. By spending days and nights with the scientists and helping them solve their problems — not as auditors but as colleagues — the JPL team gradually became accepted as partners. 9

Most projects are characterized by an inherent incompatibility: The various parties to the project are loosely coupled, whereas the tasks themselves are tightly coupled. When unexpected events affect one task, many other interdependent tasks are quickly affected. Yet the direct responsibility for these tasks is distributed among various loosely coupled parties, who are unable to coordinate their actions and provide a timely response. Project success, therefore, requires both interdependence and trust among the various parties. 10

However, if one of the parties believes that project planning and contractual documents provide sufficient protection from unexpected problems, developing collaboration among all the parties may require creative and bold practices.

This was the case in a large construction project that P&G launched at one of its European plants. After the contractor’s project manager, Karl, brushed off numerous team-building efforts, Pierre, the P&G project manager, finally found an opportunity to change Karl’s attitude. Three months into construction, the contractor accidentally placed a set of foundations 10 inches inside the planned periphery and poured about 600 lineal feet of striped foundation in the wrong place. Instead of forcing the contractor to fix his mistake and start over — a solution that would have damaged the contractor’s reputation and ego — Pierre chose a different approach. Through several intensive days of meetings and negotiations with the project’s users and designers, he was able to modify the interior layout of the plant, thereby minimizing damage to the users without having to tear down the misplaced foundations and hurt the project’s schedule. The financial cost of making the changes incurred by the contractor’s mistake was significant, but the loss in reputation was minimal. As a result, Karl gradually embraced Pierre’s working philosophy — namely, “If they fail, we fail.” The realization that the organizations involved in the project are all interdependent led to the development of a collaborative relationship.

2. Integrate Planning and Review With Learning

Project managers faced with unexpected events employ a “rolling wave” approach to planning. Recognizing that firm commitments cannot be made on the basis of volatile information, they develop plans in waves as the project unfolds and information becomes more reliable. With their teams, they develop detailed short-term plans with firm commitments while also preparing tentative long-term plans with fewer details. To ensure that project milestones and objectives are met, these long-term plans include redundancies, such as backup systems or human resources. 11

One key difference between the traditional planning approach, in which both short- and long-term plans are prepared in great detail, and the rolling wave approach becomes evident when implementation deviates from the plan. In the traditional planning approach, the project team attempts to answer the question: Why didn’t our performance yesterday conform to the original plan? In the rolling wave approach, project managers also attempt to answer the question: What can we learn from the performance data to improve the next cycle of planning? In particular, they attempt to learn from their mistakes — to prevent an unexpected event from recurring. 12

Successful project managers do not limit the learning process to the planning phase but also use it for project reviews. For example, after a review session in the midst of a project at NASA’s Goddard Space Flight Center, Marty was a frustrated project manager. The existing review process may have fulfilled upper management’s need to control its operations, but Marty felt it did not fulfill his team’s need to learn. Therefore, he modified the process to give his team the best input for identifying problems and the best advice for solving them. This meant doing away with the usual “trial court” atmosphere at NASA review sessions, where team members’ presentations were often interrupted by review board members’ skeptical comments and “probing the truth” questions. In its place, Marty developed a review process that provided feedback from independent, supportive experts and encouraged joint problem solving rather than just reporting.

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The first thing Marty did was unilaterally specify the composition of the review panel to fit the unique needs of his project, making sure that the panel members agreed with his concept of an effective review process. The second thing he did was change the structure of the sessions, devoting the first day to his team’s presentations and the second day to one-on-one, in-depth discussions between the panel and the team members to come up with possible solutions to the problems identified on the first day. This modified process enabled Marty to create a working climate based on trust and respect, in which his team members could safely share their doubts and concerns. At the end of the second day, the entire panel held a summary meeting. It was agreed that the review session had been a big success. In fact, other NASA project managers quickly adopted Marty’s process, including it in their managerial tool kits. 13

Successful managers of more traditional projects, such as designing and building manufacturing facilities, also practice learning-based project reviews. P&G has replaced review panels composed of external experts or senior managers with peer-review panels. These last four to eight hours and follow a simple protocol: First, the project team concisely communicates its technical and execution strategies, and then the floor is opened to all the invited peers for comments, critique and clarifying questions. Out of the numerous notes documented throughout the review process, five to 10 “nuggets” usually emerge that the project team uses to improve the technical, cost and scheduling aspects of the project. Sometimes, the invited peers even take one or two of the “nuggets” back to their own projects. 14

3. Prevent Major Disruptions

In their book Great by Choice , Jim Collins and Morten T. Hansen describe one of the core behaviors of great leaders as “productive paranoia.” Even in calm periods, these leaders are considering the possibility that events could turn against them at any moment and are preparing to react. 15 Similarly, successful project managers never stop expecting surprises, even though they may effect major remedial changes only a few times during a project. They’re constantly anticipating disruptions and maintaining the flexibility to respond proactively. 16 The following two examples illustrate that, when convinced that a change is unavoidable, a successful project manager acts as early as possible, since it is easier to tackle a threat before it reaches a full-blown state.

NASA’s Advanced Composition Explorer project, discussed earlier, was plagued from the start with severe financial problems arising from internal and external sources. Internally, the development of the nine scientific instruments led very quickly to a $22 million cost overrun. Externally, the project, which was part of a larger NASA program, inherited part of a budget overrun in an earlier project. As a result of these internal and external factors, the ACE project experienced frequent work stoppages, forcing the manager to constantly change his contractors’ and scientists’ work priorities.

Don, the project manager, believed that without immediate changes the project would continue down the same bumpy road, with the likely result that cost and time objectives would not be met. To prevent this, he made an extremely unpopular decision: He stopped the development of the instruments, calling on every science team to revisit its original technical requirements to see how they could be reduced. In every area — instruments, spacecraft, ground operation, integration and testing — scientists had to go back and ask such questions as: How much can I save if I take out a circuit board — and how much performance will I lose if I do take it out?

At the same time, Don negotiated a new agreement with NASA headquarters to secure stable funding. To seal the agreement, he assured them that, by using descoping tactics, the project would not go over budget. With the newly stable budget and the project team’s willingness to rethink its technical requirements, the ACE project gradually overcame its technical and organizational problems. Completed early and below budget, the spacecraft has provided excellent scientific data ever since.

The second example of preventing a major disruption from occurring took place during the Joint Air-to-Surface Standoff Missile, or JASSM, project. In this case, the Pentagon had decided to make another attempt to develop JASSM after the first attempt was aborted due to a cost overrun of more than $2 billion. The original project manager for the second attempt was dismissed in midcourse due to poor performance, and a new project manager, Terry, replaced him.

To keep costs under control, Terry decided to have two contractors compete for the final contract. Terry quickly realized that both contractors were approaching the development too conservatively and that unless he took a more radical approach, the project would be canceled again. Therefore, he told the contractors to completely disregard the military standards and adhere to only three key performance parameters. One of the contractors, Lockheed Martin, took this directive seriously and changed its approach dramatically. It decided to build the missile fuselage not out of metal but out of composites. And to accomplish this, it found a company that made baseball bats and golf club shafts. The company had never built a military product, but it knew how to weave carbon fiber and was open-minded. Following trials with several prototypes, this company was able to manufacture a product of the highest quality. Lockheed Martin transformed this small company from a baseball bat provider to a cruise missile supplier, which led to Lockheed Martin winning the contract — as well as to remarkable cost reductions.

4. Maintain Forward Momentum

As noted earlier, when unexpected events affect one task, many other interdependent tasks may also be quickly impacted. Thus, solving problems as soon as they emerge is vital for maintaining work progress. As Leonard R. Sayles and Margaret K. Chandler wrote in their 1971 book Managing Large Systems , “In working to maintain a forward momentum, the manager seeks to avoid stalemates. … Another penalty for waiting is that in a good many situations, corrective action is possible only during a brief ‘window.’ … The heart of the matter is quickness of response.” In a study of project managers on construction sites, it was found that they addressed (not necessarily solved) 95 percent of the problems during the first seven minutes following problem detection. 17

In a recent knowledge development meeting, a group of 20 project managers at The Boldt Company, a construction services company based in Appleton, Wisconsin, focused on how best to cope with unexpected events. It became evident that most of the managers employed three complementary practices: hands-on engagement; frequent face-to-face communication; and frequent moving about.

Regarding hands-on engagement, one project manager, Charlie, said that to solve problems he often engaged in activities such as making phone calls, convening urgent meetings and taking trips to local retail stores to purchase missing parts. Documenting the time it took him to resolve 10 recent problems, Charlie reported that three were resolved within 30 minutes, three within 60 minutes, and three in less than one day; one problem took two days until it was resolved. Charlie also said that, because of his quick responses, he made one mistake. However, he was able to quickly repair its damage the following day. The entire group at Boldt agreed that maintaining forward momentum was more important than always being right. 18

The second practice, frequent face-to-face communication, was described by Matt, one of the project managers, in terms of “daily 10-minute huddles” with all the on-site team members (the superintendent, field engineers, project coordinator and safety officer). Matt used these informal morning meetings to share the latest instructions from the client and to ensure that team members understood one another’s current workloads and constraints and understood how they could help one another. Very often, the meetings enabled the team to identify and resolve conflicting priorities before they became problems. Matt noted that, while the primary purpose of the huddle was to update everyone, it also reinforced a spirit of camaraderie and a sense of shared purpose. As a result, these meetings turned out to be very valuable for sustaining teamwork. 19

As for the third practice, frequent moving about, one project manager, Tony, described the three primary outcomes of spending 30 minutes a day roaming around the project site. First, he was able to develop rich and open communication with his team members. Tony explained that while many workers did not feel safe asking him questions during various formal meetings, they felt very comfortable interacting with him freely during his on-site visits, which had a great impact on their motivation. Second, receiving immediate information, and in particular a greater range of information, enabled him to identify problems early on. At times, he was able to detect conflicts before they actually became an issue. Third, Tony developed a much better understanding of where the project was with respect to the schedule, rather than having to take someone’s word for it. He found that coming to the weekly and monthly planning and scheduling meetings equipped with firsthand, undistorted information allowed him to address questions and solve problems much better. The Boldt project managers did not agree on the preferred timing for moving about and, in particular, whether one should schedule the visits, as Tony did, or leave their timing flexible. However, they all agreed that moving about is a most effective practice that should be applied as often as possible. 20

These three practices are not limited to construction projects. For example, in the previously mentioned JASSM project, which was geographically dispersed, all three practices necessary to maintain forward momentum were employed by the various project managers at each production site. Additionally, Terry, the customer’s project manager, spent much of his time moving about between all the different production sites.

Implications for Senior Managers

Although every project manager tries to minimize the frequency and negative impact of unexpected events, in today’s dynamic environment such events will still occur. Acknowledging the emergence of a problem is a necessary first step, allowing the project manager to respond quickly and effectively. Some organizations assume that almost all problems can be prevented if the project manager is competent enough — resulting in project managers who are hesitant to admit that they are facing an emerging problem. In fact, a recent study indicates that project managers submit biased reports as often as 60 percent of the time. 21 When upper management fosters an organizational climate that embraces problems as an inherent part of a project’s progression, project managers are able to detect and resolve problems more successfully.

Management scholar Henry Mintzberg argues that today’s managers must be people-oriented, information-oriented and action-oriented. In contrast, the two prevailing project management approaches, the traditional approach and the agile approach, do not require project managers to encompass all three orientations. The traditional approach (primarily intention-driven) stresses information, whereas the agile approach (primarily event-driven) stresses people and action.

By assuming the four roles discussed in this article, the successful project managers we studied are both intention- and event-driven and embrace all three orientations. Developing collaboration requires them to be people-oriented. Integrating planning and review with learning requires them to be information-oriented. Preventing major disruptions requires them to be action-oriented. Finally, maintaining forward momentum, which is pursued throughout a project, requires them to adopt all three orientations. Senior managers must ensure that all three orientations are considered when selecting project managers and developing project management methodologies. 22

About the Authors

Alexander Laufer is the director of the Consortium for Project Leadership at the University of Wisconsin-Madison. Edward J. Hoffman is NASA’s chief knowledge officer. Jeffrey S. Russell is vice provost for lifelong learning, dean of the Continuing Studies Division and executive director of the Consortium for Project Management at the University of Wisconsin-Madison. W. Scott Cameron is the global project management technology process owner at Procter & Gamble.

1. Geraldi et al. concluded: “No matter how good risk management processes are, projects will invariably face unexpected events. … Front-end thinking alone is not going to be enough to develop successful projects.” See J.G. Geraldi, L. Lee-Kelley and E. Kutsch, “The Titanic Sunk, So What? Project Manager Response to Unexpected Events,” International Journal of Project Management 28, no. 6 (August 2010): 547-558. See also I. Holmberg and M. Tyrstrup, “Managerial Leadership as Event-Driven Improvisation,” chap. 3 in “The Work of Managers: Towards a Practice Theory of Management,” ed. S. Tengblad (Oxford, U.K.: Oxford University Press, 2012); A. Söderholm, “Project Management of Unexpected Events,” International Journal of Project Management 26, no. 1 (January 2008): 80-86; M. Hällgren and E. Maaninen-Olsson, “Deviations and the Breakdown of Project Management Principles,” International Journal of Managing Projects in Business 2, no. 1 (2009): 53-69; and K. Aaltonen, J. Kujala, P. Lehtonen and I. Ruuska, “A Stakeholder Network Perspective on Unexpected Events and Their Management in International Projects,” International Journal of Managing Projects in Business 3, no. 4 (2010): 564-588.

2. S. Piperca and S. Floricel, “A Typology of Unexpected Events in Complex Projects,” International Journal of Managing Projects in Business 5, no. 2 (2012): 248-265.

3. For examples of the poor statistics of project results, see T. Williams, “Assessing and Moving on From the Dominant Project Management Discourse in the Light of Project Overruns,” IEEE Transactions on Engineering Management 52, no. 4 (November 2005): 497-508; and B. Flyvbjerg, M.K. Skamris Holm and S.L. Buhl, “How Common and How Large Are Cost Overruns in Transport Infrastructure Projects?” Transport Reviews 23, no. 1 (2003): 71-88.

4. B. Boehm and R. Turner, “Balancing Agility and Discipline: A Guide for the Perplexed” (Boston, Massachusetts: Addison-Wesley, 2004).

5. Tengblad, “The Work of Managers,” 348-350; and A. Styhre, “Leadership as Muddling Through: Site Managers in the Construction Industry,” in Tengblad, “The Work of Managers,” chap. 7.

6. T. Flores, “Earthly Considerations on Mars,” Ask Magazine 51 (summer 2003): 5-8.

7. J. Watzin, “Response #2,” in “WIRE Case Study,” NASA Academy of Program and Project Leadership, 12; also, Geraldi et al. studied the way 22 project managers responded to unexpected events and found that “the heart of successful responses … lies with people assets.” Geraldi et al., “The Titanic Sunk, So What?”

8. For the idea that building trust requires deliberate and careful choice, see R.C. Solomon and F. Flores, “Building Trust: In Business, Politics, Relationships, and Life” (Oxford, U.K.: Oxford University Press, 2001), 13-15, 153-4; the NASA and U.S. Air Force examples presented in this article are based on case studies discussed in A. Laufer, “Mastering the Leadership Role in Project Management: Practices That Deliver Remarkable Results” (Upper Saddle River, New Jersey: FT Press, 2012). Building trust was a key to the success of all eight case studies documented in this book.

9. Zand found that trust is a significant determinant of managerial problem-solving effectiveness; see D.E Zand, “Trust and Managerial Problem Solving,” Administrative Science Quarterly 17, no. 2 (June 1972): 229-239.

10. Styhre, “Leadership as Muddling Through”; and D.P. Baker, R. Day and E. Salas, “Teamwork as an Essential Component of High-Reliability Organizations,” Health Services Research 41, no. 4, part 2 (August 2006): 1576-1598.

11. A. Laufer, “Breaking the Code of Project Management” (New York: Palgrave Macmillan, 2009), 46-48; and P.G. Smith, “Flexible Product Development: Building Agility for Changing Markets” (San Francisco, California: Jossey-Bass, 2007), 186-188.

12. A.C. Edmondson, “The Competitive Imperative of Learning,” Harvard Business Review 86, no. 7-8 (July-August 2008): 60-67.

13. A.C. Edmondson, “Teaming: How Organizations Learn, Innovate, and Compete in the Knowledge Economy” (San Francisco, California: Jossey-Bass, 2012), 115-148.

14. M.P. Rice, G.C. O’Connor and R. Pierantozzi, “Implementing a Learning Plan to Counter Project Uncertainty,” MIT Sloan Management Review 49, no. 2 (winter 2008): 19-22.

15. J. Collins and M.T. Hansen, “Great by Choice: Uncertainty, Chaos and Luck — Why Some Thrive Despite Them All” (New York: Harper Collins, 2011), 26-30; and G. Klein, “Streetlights and Shadows: Searching for the Keys to Adaptive Learning” (Cambridge, Massachusetts: MIT Press, 2009), 147-163. On the importance of discovering a problem early on, see W.A. Sheremata, “Finding and Solving Problems in Software New Product Development,” Journal of Product Innovation Management 19, no. 2 (March 2002): 144-158.

16. Organizational researcher Karl E. Weick stresses that the ability to notice disruptions early on is not detached from the ability to cope with these disruptions. As he puts it: “When you develop the capacity to act on something, then you can afford to see it.” K.E. Weick, “Drop Your Tools: On Reconfiguring Management Education,” Journal of Management Education 31, no.1 (February 2007): 5-16.

17. L.R. Sayles and M.K. Chandler, “Managing Large Systems: Organizations for the Future” (New York: Harper and Row, 1971), 218-219; B.K. Muirhead and W.L. Simon, “High Velocity Leadership: The Mars Pathfinder Approach to Faster, Better, Cheaper” (New York: Harper Business, 1999), 76-77; Styhre, “Leadership as Muddling Through”; and Laufer, “Breaking the Code of Project Management,” 104-105.

18. For the importance of fast response to implementation problems, see C. Sicotte and G. Paré, “Success in Health Information Exchange Projects: Solving the Implementation Puzzle,” Social Science & Medicine 70, no. 8 (April 2010): 1159-1165.

19. A.J. Nurick and H.J. Thamhain, “Developing Multinational Project Teams,” chap. 5 in “Global Project Management Handbook: Planning, Organizing and Controlling International Projects,” second ed., eds. D.I. Cleland and R. Gareis (New York: McGraw-Hill, 2006). Nardi and Whittaker concluded that engaging attention is crucial for effective communication, and that it can be facilitated by face-to-face communication; see B.A. Nardi and S. Whittaker, “The Place of Face-to-Face Communication in Distributed Work,” in “Distributed Work,” eds. P. Hinds and S. Kiesler (Cambridge, Massachusetts: MIT Press, 2002), 95-97.

20. In a study of project managers on construction sites, it was found that moving about at the on-site production areas occupied 28 percent of their time. See A. Laufer, A. Shapira and D. Telem, “Communicating in Dynamic Conditions: How Do On-Site Construction Project Managers Do It?” Journal of Management in Engineering 24, no. 2 (April 2008): 75-86.

21. A.P. Snow, M. Keil and L. Wallace, “The Effects of Optimistic and Pessimistic Biasing on Software Project Status Reporting,” Information & Management 44, no. 2 (March 2007): 130-141.

22. H. Mintzberg, “Managing” (San Francisco, California: Berrett-Koehler Publishers, 2009), 89-91; H. Mintzberg, “Managers, Not MBAs: A Hard Look at the Soft Practice of Managing and Management Development” (San Francisco, California: Berrett-Koehler Publishers, 2004), 238-275; and Boehm and Turner, “Balancing Agility and Discipline,” 25-57.

i. For examples of the poor statistics of project results, see Williams, “Assessing and Moving on From the Dominant Project Management Discourse”; B. Flyvbjerg, M.K. Skamris Holm and S.L. Buhl, “How Common and How Large Are Cost Overruns?”; and K.A. Brown, N.L. Hyer and R. Ettenson, “The Question Every Project Team Should Answer,” MIT Sloan Management Review 55, no. 1 (fall 2013): 49-57. For examples of discussions regarding the gaps between research and practice, see M. Engwall, “PERT, Polaris, and the Realities of Project Execution,” International Journal of Managing Projects in Business 5, no. 4 (2012): 595-616; S. Lenfle and C. Loch, “Lost Roots: How Project Management Came to Emphasize Control Over Flexibility and Novelty,” California Management Review 53, no. 1 (fall 2010): 32-55; S. Cicmil, T. Williams, J. Thomas and D. Hodgson, “Rethinking Project Management: Researching the Actuality of Projects,” International Journal of Project Management 24, no. 8 (November 2006): 675-686; and L. Koskela and G. Howell, “The Underlying Theory of Project Management Is Obsolete,” in “Proceedings of PMI Research Conference 2002: Frontiers of Project Management Research and Application” (Newtown Square, Pennsylvania: Project Management Institute, 2002), 293-301.

ii. M.S. Feldman and W.J. Orlikowski, “Theorizing Practice and Practicing Theory,” Organization Science 22, no. 5 (September-October 2011): 1240-1253; and S. Tengblad, ed., “The Work of Managers,” 337-354. Our research approach was influenced in many respects by management scholar Henry Mintzberg’s approach. That includes viewing management as a practice (not as a profession) and stressing the use of systematic observations of managers. In particular, it involves the use of “rich description,” about which Mintzberg writes: “I need to be stimulated by rich description. … Tangible data is best … and stories are best of all. …Anecdotal data is not incidental to theory development at all, but an essential part of it.” See H. Mintzberg, “Developing Theory About the Development of Theory,” in “Great Minds in Management: The Process of Theory Development,” eds. K.G. Smith and M.A. Hitt (New York: Oxford University Press, 2005): 355-372.

iii. See, for example, E. Wenger, R. McDermott and W.M. Snyder, “Cultivating Communities of Practice” (Boston, Massachusetts: Harvard Business School Press, 2002), 49-64; and J.S. Brown, “Narrative as a Knowledge Medium in Organizations,” in J.S. Brown, S. Denning, K. Groh and L. Prusak, “Storytelling in Organizations: Why Storytelling Is Transforming 21st Century Organizations and Management” (Burlington, Massachusetts: Butterworth-Heinemann, 2005), 53-95.

iv. D. Lee, J. Simmons and J. Drueen, “Knowledge Sharing in Practice: Applied Storytelling and Knowledge Communities at NASA,” International Journal of Knowledge and Learning 1, no. 1-2 (2005): 171-180.

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What Successful Project Managers Do

By: Alexander Laufer, Edward J. Hoffman, Jeffrey S. Russell, W. Scott Cameron

This is an MIT Sloan Management Review article. In today's dynamic and competitive world, a project manager's key challenge is coping with frequent unexpected events. Such events can be classified…

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This is an MIT Sloan Management Review article. In today's dynamic and competitive world, a project manager's key challenge is coping with frequent unexpected events. Such events can be classified according to their level of predictability as follows: events that were anticipated but whose impacts were much stronger than expected; events that could not have been predicted; and events that could have been predicted but were not. Coping with frequent unexpected events requires an organizational culture that allows the project manager to exercise a great amount of flexibility. The traditional approach to project management emphasizes that project success depends on stability. According to this approach, project success can be achieved by focusing on planning and on controlling and managing risks. Although the popularity of this approach has sharply increased across industries, research covering a wide variety of projects consistently reveals poor performance. The authors collected data from more than 150 successful project managers affiliated with more than 20 organizations and concluded that today's successful project managers cope with unexpected events by a combination of traditional and "agile"approaches to project management. Using business examples drawn from their research at organizations such as Procter & Gamble, NASA and the construction services company Boldt, the authors identified four key roles that successful project managers play: •The first role, developing collaboration, is performed early on during the project. •The second role, integrating planning and review with learning, is performed periodically throughout the project. •The third role, preventing major disruptions, is performed occasionally. •The fourth role, maintaining forward momentum, is performed continuously. Today's managers must be people-oriented, information-oriented and action-oriented.

The authors argue that by assuming the four roles discussed in this article, successful project managers will embrace all three orientations.

Apr 1, 2015

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General Management

MIT Sloan Management Review

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How artificial intelligence will transform project management in the age of digitization: a systematic literature review

• Open access
• Published: 09 April 2024

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You have full access to this open access article

• Maria Elena Nenni 1 ,
• Fabio De Felice 2 ,
• Cristina De Luca 2 &
• Antonio Forcina 2  

Among the causes of the low success rate of the projects (around 35% of the total) is the low level of maturity of the technologies available for the management of the projects themselves. However, today many researchers, startups and innovative companies are starting to apply artificial intelligence (AI), machine learning and other advanced technologies to the field of project management. By 2030 the industry will undergo significant changes. By using the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) protocol this paper explores the intersection of project risk management and AI. The study highlights how AI-driven methodologies and tools can revolutionize the way project risks are managed throughout the project lifecycle. Specifically, 215 papers have been analysed to explore how the scientific community has been moving so far on the topic. Besides, a cross-sectional investigation of the PM processes and AI categories/tools was carried out to identify any path that is prevalent, where the prevalence comes from, and for which PM process or sector it is most successful. Finally, from this study several gaps emerged that scientific research would have to fill to effectively implement AI in PM and that have been turned into opportunities for future research in the form of a research agenda.

Avoid common mistakes on your manuscript.

1 Introduction

Project management has deep roots in history and has evolved over the centuries. The earliest forms of project management can be traced back to antiquity, with the construction of great works such as the pyramids of Egypt and the Great Wall of China. One of the key early developments in modern project management was the “Gantt Chart” method, developed by Henry L. Gantt in 1917 (Pacagnella and da Silva 2023 ). In the 1950s and 1960s, scholars such as Peter Drucker and Frederick Taylor helped develop theories on the organization and management of production processes (Winkler-Schwartz et al. 2019 ). During the Cold War, the aerospace and defense industry in the United States invested heavily in complex project management. With the advent of computers and project management software in the 1980s and 1990s, project management has become increasingly automated and sophisticated (Friedrich 2023 ). Today, project management has become a key discipline in a wide range of industries, from construction to information technology, healthcare to manufacturing (De Felice et al. 2022 ; Makarov et al. 2021 ; Vollmer et al. 2020 ). It is supported by a wide range of tools, methodologies and standards that continue to evolve to meet the ever-changing needs of modern organizations (Listikova et al. 2020 ; Wang and Chen 2023 ). In this context it is worth mentioning that pproximately $48 trillion is invested in projects every year. However, according to the Standish Group, only 35% of projects are considered successful. The waste of resources and unrealized benefits of the remaining 65% are staggering. One of the reasons why success rates are so low is the low level of maturity of the available technologies. For project management, most organizations and project managers still use spreadsheets, slides and other applications that have not evolved much in recent decades. These tools are adequate when it comes to measuring the success of a simple project based on deliveries and deadlines met, but they are not up to par in a complex environment where projects and initiatives are constantly adapting and evolving (Weber et al. 2012 ). In recent years, Artificial Intelligence (AI) has begun to radically transform the way projects are planned, managed and executed (Shukla Shubhendu and Vijay 2013 ). Due to its numerous applications and even more so due to its enormous benefits (Castro and New 2016 ), AI has been able to spread rapidly in many areas (Health, Finance, Business, etc.) and generate considerable social and economic value, as testified by the large amount of research in the scientific library (Nguyen et al. 2022 ; Nishimwe et al. 2022 ; Sangeetha et al. 2022 ; Xue et al. 2020 ; Zhao and Saeed 2022 ). Even the main international standardization body for project management, the Project Management Institute (PMI), supports the application of AI (PMI 2021 ). Observing that conventional project management tools often fall short in accurately predicting project success, Martínez and Fernández-Rodríguez ( 2015 ) conducted a study to explore alternative tools. Their research particularly emphasizes the value of artificial intelligence in managing the uncertainties and complexities inherent in project environments. The literature was also analyzed by Afzal et al. ( 2021 ) to investigate the connection between complexity and risk and to identify the main AI technologies for risk management in construction projects. Still in Fridgeirsson et al. ( 2021 ) have worked on identifying the potential areas of greater success of AI (project cost, planning and risk). Finally, many studies and research have already been conducted to explore the benefits and risks of applying AI to risk management processes (Jiang et al. 2013 ; Naim 2022 ; Žigienė et al. 2019 ; Nimmy et al. 2023 ). Despite all this abundance of literature, the research is mostly focused on the technology solutions and still sparse and confusing when considering managing project risks with digitized systems such as AI (Gejke 2018 ; Pande and Khamparia 2023 ). In this scenario, it seemed appropriate to provide a comprehensive Systematic Literature Review (SLR) to (i) investigare the state of the art of project management; (ii) how AI can support the project management; (iii) which specific aspects of project management will be innovated, and (iv) to understand which AI tools are best suited for any processes of project management and why. SLR emphasizes the most important authors, research institutes and countries where important contributions to the field have been made. It is a fundamental method used in academic and scientific research to summarize and analyze the current state of knowledge in a particular field (Kitchenham 2004 ). Thus, the present research aims to clarify the subject from a scientific point of view to offer a starting point for future studies that intend to concretely pursue this idea of using AI for risk management. With this purpose, this document also provides a proposal for the future research agenda with a specific focus of producing in a very near future a framework to outline the critical decision of a successful process for introducing AI in PM and to promote a rapid maturation of AI in this field.

The rest of the paper is organized as follows: Sect.  2 explains the research methodology used to conduct the literature review; Sect.  3 discusses the main findings of the research. While, in Sect.  4 an overview of future development and research agenda is given. Finally, Sect.  5 provides the main implications of research.

2 Research methodology

A Systematic Literature Review (SLR) is applied to identifying, evaluating, and synthesizing existing research studies relevant to the specific research questions (Moher et al. 2009 ). Figure  1 shows the main phases followed to carried out the SLR.

Methodology applied to the development of the Systematic Literature Review (author’s elaboration)

In detail, SLR starts by identifying the research questions (Phase#1). Then, Phase#2 continues defining the analysis sample. The search results are selected and evaluated considering well-defined exclusion, inclusion and quality criteria. The process of screening articles up to the definition of the final analysis sample is summarized graphically by using the PRISMA Protocol (Rethlefsen et al. 2021 ). Next, the Phase#3 develops a bibliometric analysis of the papers forming the sample by classifying the papers by year, country and journal of publication. Furthmeore, the Phase#4 shows an analysis of bibliometric networks obtained using the VOSviewer software (Li et al. 2022 ). In this phase, firstly, objective data, as such as the PM processes addressed—as reported in the PMBOK (Faraji et al. 2022 )—and the Project Management (PM) processes connected with them, AI categories and tools were extracted from papers. Besides, in order to deepen the study, a cross-analysis is performed to assess the coexistence and relationship of the PM processes, AI categories and tools. Afterward, the main results of the process are classified and analyzed. Then, the discussion follows the results, and the definition of challenges and limits.

2.1 Phase#1: research question definition

In a SLR, research questions guide the entire review process. These questions help define the scope of the review and provide a clear focus for selecting, analyzing, and synthesizing relevant studies. Well-formulated research questions ensure that the review is systematic, structured, and goal-oriented. Typically, SLRs have one or more research questions that address specific aspects of the topic under investigation. Research questions should be relevant to the research area or field being studied. Thus, it is important to it is important to remember the assumptions underlying our SRL:

By their nature, projects are characterized by unpredictability, which makes them highly susceptible to risk (Trier and Treffers 2021 ). Risk management is therefore fundamental to the effective development and implementation of the project itself.

The purpose of PM is to continuously identify, analyze and control all project-related uncertainty factors to minimize the probability of occurrence and the impact of risks (Cervone 2006 ).

PM is also viewed as a systematic and proactive approach that aim to increase the probability of project success (Willumsen et al. 2019 ). Today, this activity is carried out by the Project Manager (PM), or Project Risk Manager (PM), sustained often by a team of experts.

This research stems from the idea of providing managers with a specific support in risk management coming from the AI and aims at promoting and backing an effective application of AI.

For this purpose and based on what pointed out, we identified a few Research Questions (RQ) that are summarized in Table  1 .

2.2 Phase#2: identification of the analysis sample

2.2.1 database search identification.

A systematic review involves a comprehensive search of various academic databases, journals, conference proceedings, and other sources to identify all relevant studies. The databases taken into consideration for the search are the Web of Science and Scopus, currently the largest bibliographic database of scientific literature. To perform the subject-specific search, thirteen keywords, listed in the Table  2 , were selected, and appropriately combined using the Boolean operators of union and intersection. Only articles in which the string was found in (1) article title, or in (2) abstract or in (3) keywords were analyzed.

The bibliometric analysis considered articles completely in English published from 1996 to 2023 .

2.2.2 Eligibility criteria definition: exclusion ad inclusion criteria

A systematic review follows a well-defined and systematic process, including clear criteria for study selection, data extraction, and analysis. This process is designed to minimize bias and ensure transparency. Therefore, it is necessary to define inclusion and exclusion criteria to select the studies that will be included in the analysis sample. In detail, the exclusion criteria considered are:

E1. Incomplete documents;

E2. Documents such as books, proceedings and thesis;

E3. Documents such as literature reviews, surveys and repots;

E5. Duplicate documents;

E6. Documents outside the reference time;

E7. Documents out of scope.

While the inclusion criteria are:

I1. Papers published in English only;

I2. Papers only;

I3. Documents referring to AI according to a precise definition.

2.2.3 Quality criteria definition

The quality and validity of each included study are assessed using predetermined criteria. This assessment helps in evaluating the reliability of the evidence. This study established three criteria:

Q1: Papers that include risk management processes and artificial intelligence;

Q2: Papers exploring the fields of application of artificial intelligence categories and tools;

Q3: Papers with a significant impact factor, SCImago Journal Rank or CiteScore.

2.2.4 Identification of the analysis sample (PRISMA)

According to the above sections, the documents were identified and checked for eligibility and relevance to form an inclusion set using the PRISMA Protocol. The initial investigation returned a sample of 1820 bibliographic records. However, considering the assumed selection criteria the analysis identified 666 out-of-scope documents, returning a sample of 524 eligible documents. It is worth pointing out that the selected studies were analyzed checking in each of them the effective use of AI was analyzed. This check is due to the fact that there is no single definition of AI. A few papers include basic algorithms that are, however, far from the true essence of AI. To perform this kind of check, the definition of Kaplan and Haenlein ( 2019 ) was used: Artificial intelligence is the ability of a system to correctly interpret external data, to learn from that data, and to use that learning to achieve specific goals and tasks through flexible adaptation. The final list of the analyzed documents is shown in Online Appendix A . Figure  2 summarizes the represents flow diagram for the selection of documents based on PRISMA.

Flow diagram for the selection of documents based on PRISMA

2.3 Phase#3: bibliometric sample analysis

2.3.1 publication by years.

The analysis of the trend of the number of publications per year highlights that from 2020 to 2023 there is an increase in the number of documents of documents developing researche on integration of PM with AI. The trend does not surprise us considering the evolution of digitalization in project management (as shown in Fig.  3 ). The trend does not surprise us considering the evolution of digitalization in project management. In addition, it is reasonably conceivable that this trend is also due to the spread of the Covid-19 pandemic that caused certainly a moment of discontinuity. Presumably, the research on this topic was experiencing a stalemate when the economic and social crisis triggered from pandemic worked as a “catalyst” factor that accelerated digital and technological change. This undoubtedly encouraged the adoption, and consequently research, of innovative technologies such as AI. The tred suggests that the next few years will be characterized by the ever-increasing publication of scientific publications on this research frontier.

Publications per year from 1996 to 2023 (source: Scopus)

2.3.2 Country analysis

Figure  4 shows that research is concentrated outside Europe. The first country in terms of number of publications on the integration of AI in PM is China , which is credited with 17% (33 documents) of the publications. This is followed by the United Kingdom (19;10%), Iran and the United States (15;8%) and Taiwan (10;5%). However, these results are due to government policies implemented to foster AI research and application; especially in countries such as China and the US which are the main competitors in the global landscape for the development of AI technology. Over the past 5 years, the two countries have recorded the highest rate of adoption of this technology in government and business. They contributed 94% of global funding of new companies employing 70% of the best global researchers in this field (Kratochwill et al. 2020 ). In the United States, in 2019, the “ American AI Initiative ” presidential degree was signed, inviting federal agencies to increase funding for AI research by allowing scientists and researchers to access government data as well. More recently, the Department of Defense formulated a $2 billion strategic plan with the aim of overcoming the limitations of current AI technologies (Wiltz 2019 ). In China, in 2017, 2 billion dollars was allocated for research and development and 2.1 billion for the creation of a research park dedicated to AI topics in 2018 (Kumar 2021 ).

Publications by country (source: Scopus)

2.3.3 Documents by types

The analysis of the documents by type revealed the following breakdown: articles (149; 88%), conference papers (14; 8%), and brief surveys (7; 4%). The 215 articles published between 1996 and 2023 that make up the analysis sample were published in 71 journals. A total of 62% of the papers were published in 12 journals, with the remainder recording no more than two publications on the topic of interest for this study. The main journals and publication details are shown in Table  3 .

Thus, it can be deduced that the research interest in this topic is diversified from the field of innovation, through management to sustainability.

2.3.4 Analysis of research trends

This section aims to highlight the bibliometric network to understand the magnitude of the phenomenon. Specifically, VOSviewer software was used to analyse bibliographic records from a collection of scientific literature, including keywords and citations, and to generate co-occurrence networks of significant terms. Through the same software, a keyword co-occurrence clustering view was generated. The total number of identified keywords is approximately 1315, which include both keywords provided by the authors of the articles and those assigned by Scopus. However, regarding how many times a keyword is repeated, the maximum value calculated by VOSviewer is 87. A total of 55 keywords with a frequency of at least 5 were selected and a co-occurrence analysis was performed on them, as shown in Fig.  5 .

Mapping of index keywords used in articles

Furthermore, the larger is the node, the higher is the frequency of that specific keyword. Accordingly, the most frequent word is “ risk assessment ”, located in the yellow cluster, with an occurrence value of 78. This is followed by the words “risk management” in the red cluster with occurrence 57 and “project management” with occurrence 56 belonging to the purple cluster. The thickness and proximity of the lines connecting the keywords in the visualization denotes the frequency of co-occurrence between two keywords across publications. Therefore, smaller distances between elements and thicker lines signify a strong relationship between them. It means that words that are close or connected by thick lines are more frequent, while a high distance or thin connection between two keywords indicates that they do not occur. The colour of each cluster is not random but determined by the complex score considering occurences, links and link strength. Colours range from purple (indicating a very low score) to yellow (indicating a low score), blue (indicating a medium–low score), green (indicating a medium score) and red (indicating a high score). To carry out the analysis of research trends followed below, it is importat to consider that our research focuses on the integration of AI into PM, particularly in risk management. The research also highlights an integrated approach in which AI is used for data collection and analysis in risk management, showcasing AI-powered decision support tools. Additionally, the role of AI extends to other PM processes such as Portfolio Analysis, General Framework, and Decision Support Systems (DSS), where it aids in risk identification and decision-making. The study positions techniques like AHP, Fuzzy Logic, Machine Learning, and Optimization within an AI framework, suggesting that their use in AI-driven decision support systems.

2.3.4.1 Red cluster

Table 4 lists the keywords that belong to the cluster, including the number of occurrences of these keywords and the number and strength of links they have.

The red cluster is the largest and most significant, presenting 12 keywords referring mainly to the decision-making aspect of risk management in projects. A new method for decision support in project risk response is introduced by Zhang et al. ( 2020 ). The method is comprised of two key steps: the creation of alternative risk response actions (RRAs) using case-based analysis, and the identification of the optimal set of RRAs through a fuzzy optimization model. In addition to these, fuzzy set theory is applied to assess risk probability, risk impact and risk similarity in the RRA selection process. For the prediction of project control, Wauters and Vanhoucke ( 2017 ) introduce the Nearest Neighbors (NN) technique. This technique is used as a predictor and compared with existing EVM/ES and AI methods. It is therefore referred to as hybridization as the prediction process requires the use of NN and an AI method. Liu et al. ( 2020 ) present a new intelligent model for project risk management during the construction of large, prefabricated building projects. The model is a hybrid of two algorithms: the Backpropagation (BP) neural network-based feedforward multilayer deep learning algorithm and the Modified Teaching–Learning-Based-Optimization (MTLBO) algorithm. To enhance the traditional Teaching–Learning-Based Optimization (TLBO) algorithm, information entropy was incorporated to create the MTLBO algorithm, resulting in the MTLBO-BP neural network prediction model. This risk management model provides faster convergence and a more precise solution to the problem of reliability and cost allocation in engineering projects. Recently, Bilgin et al. ( 2023 ) propose a process model and a tool, COPPMAN (COnstruction Project Portfolio MANagement), were developed to support project portfolio decisions in construction companies.

2.3.4.2 Green cluster

The green cluster is the second most significant cluster and relates mainly to the identification of risk and its classification. Table 5 summarizes all characteristics.

In real development, the success of a project can be jeopardized by a multitude of risk factors, which are not necessarily uniform and can vary widely. In most projects, risk impacts are often difficult to identify, relying largely on subjective assessments rather than hard data. Dikmen et al. ( 2007 ) proposes a methodology for quantifying risk assessments of construction projects. Specifically, the influence diagram method is used to construct a risk model. This is integrated with a fuzzy risk assessment approach to estimate a cost overrun risk assessment. While, it was the absence of empirical models for project risk planning and analysis that motivated Hu et al. ( 2013a ) research in the context of software projects. To mitigate risk impacts and improve predictability of project outcomes, an integrative framework for intelligent software project risk planning (IF-ISPRP) was proposed. The research team employed the random forest algorithm to develop a risk analysis model and introduced a many-to-many actionable knowledge discovery method (MMAKD) for risk planning purposes.

To minimize the overall risks associated with a project, however, Albogami et al. ( 2021 ) proposed a new approach using a hybrid method of Analytic Hierarchy Process (AHP) and Dempster-Shafer theory of evidence. The study involved several phases: firstly, quantitative research was conducted to identify potential risk factors that could impact a project. Then, a hybrid unsupervised machine learning algorithm based on Principal Component Analysis (PCA) and agglomerative clustering was used to classify projects according to ownership, operational and technological, financial, and strategic risk factors. Finally, a hybrid AHP and Dempster-Shafer evidence theory was developed to select the best alternative with the lowest overall risk. The results of their study highlighted four primary risk categories: Property Risk Factors, Technology and Operational Risk Factors, Financial Risk Factors, and Strategic Risk Factors. More recently, Ansari et al. ( 2022 ) aim to take a significant step to improve the efficiency of projects by identifying and ranking the causes of claims and analyzing their effects on key efficiency indicators using AHP-TOPSIS technique.

2.3.4.3 Blue cluster

The blue cluster , the characteristics of which are shown in Table  6 , concerns the modelling and programming of project risk.

Mokhtari and Aghagoli ( 2020 ) propose a technique for selecting risk-responsive actions in the project portfolio. A Bayesian belief network is used to model the portfolio risks, their impacts and responses. Whereas, to select the response, an optimization model is used to minimize the sum of the residual risk impacts on the objectives of the portfolio components and the costs of implementing the responses. Solving the model is a genetic algorithm whose results support project managers in providing an appropriate combination of actions consistent with available resources. While, Lee et al. ( 2012 ) introduce a framework for assessing and simulating outsourcing risks in the supply chain. The framework aims to incorporate not only established but also emerging risks and is guided by sound risk management practices, including risk identification, analysis and mitigation actions. The proposed methodology involves both qualitative and quantitative risk assessment. For the former, the use of Failure Mode and Effects Analysis (FMEA) is suggested to construct a risk map. For the second, Monte Carlo simulation (MCS) is used. In a complex and dynamic environment with high uncertainties amidst limited resources, project risk assessment is crucial for project success. Isah and Kim ( 2021 ) propose a stochastic multiskilled resource planning model (SMSRS) for resource-constrained project scheduling problems (RCSPSP) that considers the impact of risk and uncertainty on task duration. The SMSRS model is developed by integrating a planning risk analysis (SRA) model with an existing algorithm (MSRS) to create feasible and realistic planning. In particular, triangular probability distribution and Monte Carlo simulation using MS Excel are applied to assess the risks and uncertainties associated with the duration of construction activities. Recently, Prieto and Alarcón ( 2023 ) develop new approaches regarding AI systems, using fuzzy sets and multiple linear regression for managing waste in construction project delivery in the metropolitan area of Santiago, Chile.

2.3.4.4 Yellow cluster

The yellow cluster is mainly related to project-related economic risk, as can be seen from the cluster characteristics shown in Table  7 .

An industrial case concerning large projects in the energy industry is used to illustrate the application of the method proposed by Sanchez et al. ( 2020 ) to reduce the risk of project cost (or budget) overruns. The case study outlines the development of a rigorous and repeatable method for estimating the impact of Project Management Maturity (PMM) on project performance. Bayesian networks are employed to formalize the knowledge of project management experts and extract information from a database of previous projects, allowing for a better understanding of performance failures (such as the risk of cost overruns) caused by insufficient PMM maturity. Project portfolios are strategic tools for the implementation of corporate strategy. Therefore, Dixit and Tiwari ( 2020 ) proposed a way to reduce the likelihood of experiencing significant losses by implementing a risk-averse strategy. The approach involves using the conditional value-at-risk (CVaR) measure as an objective function to construct a portfolio of projects that has the least risky profile. There are three models that researchers have developed for selecting and planning project portfolios, namely the risk-neutral (max_E), risk-averse (max_CVaR), and combined compromise (max_E_CVaR) models. These models enable organizations to choose and plan project portfolios based on their appetite for risk and the importance they assign to risk-averse and risk-neutral objectives. A flexible and rational approach is proposed by Idrus et al. ( 2011 ) who proposed a method for estimating cost contingency based on risk analysis and a fuzzy expert system. The method involved the development of a cost contingency model in construction projects. The fuzzy expert system is incorporated in the use of the risk analysis technique as a general technique applied as a method for estimating project cost contingency. Recently, de Oliveira et al. ( 2023 ) propose an interesting case study on self-organizing maps and Bayesian networks in organizational modelling.

2.3.4.5 Purple cluster

Finally, the smallest is the purple cluster with 8 keywords. This groups those articles from the literature that deal with risk assessment, response and management; as shown in Table  8 .

Project risks are commonly treated as separate entities in the field of risk management. However, not considering the potential interrelationships among them can lead to an inadequate assessment of potential risks and decrease the overall effectiveness of the management process. To address this problem, Guan et al. ( 2021 ) developed a new risk interdependence network model with the aim of helping decision-makers accurately assess project risks and develop more effective risk mitigation strategies. The novel model incorporates both Interpretive Structural Modeling (ISM) and Monte Carlo simulation (MCS) techniques to effectively model the stochastic nature of project risk occurrence, considering interdependencies and analyzing the potential consequences of risk propagation. This integration allows for a more comprehensive and accurate assessment of project risks, providing decision-makers with valuable insights to inform their risk management strategies.

Risk management (RM) is a process that heavily relies on knowledge, necessitating the effective management of risk-related information. However, it is common to overlook the importance of integrating various stages of the process, such as risk identification, analysis, response, and monitoring. This can result in suboptimal risk management outcomes and missed opportunities to identify and address potential risks. Fan and Yu ( 2004 ) show that Bayesian belief networks (BBNs) provide visible and repeatable decision support under conditions of uncertainty in software design risk management. A BBN-based procedure has been developed using a feedback loop to predict potential risks, identify sources of risk and advise on the dynamic adjustment of resources. Okudan et al. ( 2021 ) proposed a knowledge-based RM tool called CBRisk, which utilizes case-based reasoning (CBR) techniques. CBRisk is web-based, enables the cyclic RM process and incorporates an efficient case finding method using a comprehensive set of design similarity features in the form of fuzzy linguistic variables. The tool utilizes a database of past projects to provide a risk register model, which identifies them, calculates their probability and impact, and generates response plans for each risk. By understanding all RM processes and supporting the project team throughout the project lifecycle, CBRisk proves to be a valuable resource for managing risks effectively. While, recently, Waqar et al. ( 2023 ) explore the integration of AI into construction safety management, highlighting its potential to improve risk management. It identifies key factors for successful AI implementation, based on industry expert analysis.

2.4 Data analysis

In this section we provide an analysis of data from the170 primary studies with the aim of giving a clear overview of how the scientific community has moved to date and highlight any gaps to fill. The analysis is organized accordingly to the research questions listed above.

2.4.1 RQ #1 “Are there industries where the spread of AI for PM processes has been wider and faster so far?”

Analysis of the selected sample from 1996 to 2023 shows that a large majority of studies are focused on a specific sector. Specifically, out of 215 articles, as many as 70 focus on construction (50.3%). This sector is followed by the field of IT, software development (13.4%). The remaining articles are spread over different sectors as shown in Fig.  6 .

Application fields of AI for PM processes

The reasons that probably make these two sectors so relevant are essentially related to the fact that both sectors contribute to economic growth worldwide (Ma et al. 2019 ). However, it should be noted that although both the construction sector and the IT sector heavily utilize project management techniques, but for different reasons and in different ways as explained below:

Construction Sector : Project management is extensively used in the construction sector due to the inherently complex nature of construction projects. Here is why: a) Large-Scale Projects (Project management is crucial to manage the various aspects of these large-scale projects effectively); b) Multidisciplinary Teams (Project management ensures that these multidisciplinary teams work cohesively toward a common goal); c) Resource Management (Project management techniques help optimize resource usage, minimize waste, and keep projects on track); d) Budget and Cost Control (Project management methodologies assist in controlling budgets, tracking expenses, and preventing financial setbacks); and e) Risk Management (Project management provides strategies to identify, assess, and mitigate these risks).

IT Sector: The IT sector heavily relies on project management to navigate the complexities of technology-driven initiatives. Here is why project management is crucial in the IT sector: a) Rapid Technological Advancements (Project management provides a structured approach to adopting new technologies and managing complex software development projects); b) Software Development Lifecycles (Project management helps in organizing and streamlining these phases); c) Budget Constraints (Project management methodologies ensure efficient allocation of resources and cost control); d) Risk and Change Management (Project management techniques aid in managing changes, addressing risks, and maintaining project stability); e) Agile Methodologies (Project management helps implement these methodologies effectively).

Thus, Project management is indispensable in ensuring the successful execution of complex, resource-intensive, and time-sensitive projects in both sectors.

2.4.2 RQ #2 “What is the support for effectively applying the topic of AI in PM, in terms of frameworks, models, tools, etc., already provided in literature?”

The second research question is asked to understand in a quantitative way the real extent of AI research for PM in terms of gained knowledge for an effective application. To obtain in-depth and detailed results, the research question is divided into sub-questions such as:

RQ2.a: Which PM processes are most likely to be managed through AI?

RQ2.b: Which AI categories are most involved in the research?

RQ2.c: What are the most used AI tools for PM??

RQ2.a : According to PMBOK sixth Edition, there are seven main PM processes: Plan risk management , Identify Risk , Perform Qualitative rick Analysis Perform , Quantitative rick Analysis , Plan Risk Responses , Implement Risk Responses and Monitor Risks . The 215 in the analysis sample were investigated to understand the integration of AI into PM. The results show that only one process occurs frequently (43 items) while the rest occur insignificantly. Table 9 shows these results in detail.

Most of the articles dealt with PQRA processes. This is probably due to the quantitative and data-driven characteristics of these processes for which AI offers better advantages. The selected documents, however, revealed that several articles dealt with not just one process, but an integrated approach involving several processes in sequence. In particular, three main combinations of PM processes were intercepted (Table  10 ). However, it should be clarified that the “ Perform risk analysis ” process combines Quantitative Analysis and Qualitative Analysis.

The integrated approach in project management (PM) leverages AI for data collection, particularly in the quantitative phase of risk analysis. AI is utilized to analyze large datasets, identifying potential risk triggers and providing decision support. This includes recommendations for risk response based on historical data and project context, as demonstrated in Ebrahimnejad et al.’s ( 2010 ) research, which outlines a new risk structure using methods like FTOPSIS and FLINMAP. Similarly, Isaac and Navon ( 2014 ) highlight the use of AI-powered dashboards for semi-automated project monitoring and risk management, including an Automated Project Performance Control (APPC) system. The research sample includes studies where AI is applied in PM processes, especially in areas closely related to risk management, such as Portfolio Analysis , General Framework , Prediction/Estimation/Forecasting , Optimization , and Decision Support Systems (DSS). These studies often use AI for risk identification and decision-making. The analysis is specific to papers where AI directly influences risk management decisions. Papers not directly utilizing AI for risk management, comprising 28% (49 papers) of the sample, are categorized under “other PM processes”. This approach ensures a focused examination of AI’s role in project risk management. Table 11 show the details.

For example, Han et al. ( 2008 ) introduced a web-based decision support system for managing risks in international construction projects, offering global accessibility and addressing the high risk of failure due to uncertainties. The paper emphasizes the significance of risk identification and analysis in project management. Ghasemzadeh and Archer ( 2000 ) discussed the challenges in selecting a project portfolio, proposing the PASS for structured decision-making. Idrus et al. ( 2011 ) developed a method for objectively estimating project costs using a fuzzy expert system, enhancing traditional subjective judgments in risk analysis. Furthermore, research on optimization in project management, though less prevalent, has been noted. Honari Choobar et al. ( 2012 ) applied optimization to classify risks in power plant projects using fuzzy analytical network and the fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) for a comprehensive risk assessment. Lastly, Fang and Marle ( 2012 ) created an integrated Decision Support System (DSS) framework for managing complex risk networks in projects. This framework combines the design structure matrix (DSM) for dependency modeling with the analytical hierarchy process (AHP) to evaluate risk interactions, supplemented by simulation techniques for dynamic risk assessment. This approach provides an in-depth understanding of risk behavior and supports effective risk management decision-making in project management. Additionally, simulation techniques are used to analyze the propagation of risks and reassess them as needed. By integrating these various approaches, the proposed framework provides comprehensive understanding of risk behavior and enables more effective risk management decision-making. To see if some PM processes have been systematically supported by AI in a specific industry, the relationship between sector and processes is analysed, as illustrated in Fig.  7 . Radar charts show two series representing the sectors that, from the analysis conducted so far, they seem to have applied AI more to PM processes: construction and IT. For each sector, the number of documents reporting the PM processes of the PMBOK is reported, including the integrated ones, (Fig.  7 a) and the PM processes that in this research have been indicated as “others PM processes connected to PM ones” (Fig.  7 b).

Relationship between industry and processes: A: PM processes of PMBOK; B: “other” processes

As shown in Fig.  6 , there is not a specific pattern, as both considered industries, construction and IT, present a higher number of papers dealing with performing quantitative risk analysis both as singular as well as integrated approach. Numbers are also consistent with the general trend not depending thus on any industry.

RQ2.b: Continuing the investigation, the next step is to link PM processes with specific AI applications. There are a great number of AI applications and many ways to group them. In this research, eight possible AI applications are distinguished, the characteristics of which are shown in Table  12 .

Also in this analysis, a distinction is made between works in which only one AI category is applied and others that mix several categories. A total of 132 papers (77.1%) were found to apply only one category of AI. There are three categories with the highest number of applications in the surveyed sample, such as: 1) Advising ; 2) Prediction and 3) Classification. The other categories have fewer than 10 associated documents. The importance of these 3 categories is also demonstrated by the Pareto Analysis shown in Fig.  8 .

Pareto analysis for AI categories

From the sample study, the category “ Advising” is the most frequently used application of AI. For example, in Nie et al. ( 2009 ) research, advising is utilized to develop a mechanism for evaluating a company’s eligibility for launching a data mining project. Similarly, Hu et al. ( 2013a , b ) proposes an integrative framework called IF-ISPRP for intelligent software project risk planning, which leverages advising techniques. The second most applied AI category is “ Predictive ”. A modelling framework using AI methods is applied by Wu et al. ( 2015 ) to develop an integrated interpretative structure modelling (ISM) and Bayesian network system in a risk assessment context. In the research of Ghasemi et al. ( 2018 ) forecasting is used to define a framework for analysing the risk of a portfolio to achieve sustainability. The last most applied category of AI is Classification. This category is applied in Ebrahimnejad et al. ( 2010 ) research to create risk classification methods that can identify critical factors and evaluate associated risks. In a similar vein, Fan and Yu ( 2004 ) employs AI classification to forecast potential risks, detect risk sources, and recommend real-time resource adjustments.

As far as the mix of categories is concerned, the results obtained from the sample study are shown in Table  13 .

As shown, the “ Classification/Prediction ” pair is by far the most used. This is shown in the research of Zhang et al. ( 2013 ) that introduce a novel assessment framework that combines the analytical interval hierarchy process (AIHP) and the extension of the technique for order preference by similarity to ideal solution (TOPSIS). This approach aims to enhance the accuracy and dependability of risk identification in hydropower projects. Classification/Prediction is also applied in the context of cost, benefit and return on investment (ROI) risks of projects by Yet et al. ( 2016 ). A Bayesian network modelling framework capable of modelling various risk events is proposed, allowing users to assess project costs and benefits under different risk scenarios.

RQ2.c: This research question aims to find out what are the most used AI for PM and why? Specific artificial intelligence tools are defined for each category of AI. Specifically, 104 tools are identified, which in turn are grouped into clusters, as shown in Fig.  9 . This classification took place in two steps in an empirical way. First, clusters were identified through a study aimed at identifying the main categories of AI mentioned in articles focusing on the topic available in the scientific literature. Then, for each article in the analysis sample, the AI tool applied was identified and placed in one of the categories identified in the previous stage.

Clusters of AI tool

Three of them account for 52% of the total tools, as shown in Table  14 .

2.4.3 RQ #3 “Is there a pathway that matches AI categories/tools and PM processes?”

The investigation conducted so far has shown the importance of searching for pathways linking AI categories and PM processes. Therefore, several “cross-over” analyses are given below. The first analysis is shown in Table  15 , where AI categories are cross-referenced with PM processes to evaluate if a relationship exists.

The analysis, as depicted in Table  15 , indicates that out of various combinations of PM and AI, two are particularly significant. First, the Perform Quantitative/Qualitative Risk Analysis combined with Prediction, utilized in 18 research papers. Notably, Hu et al. ( 2013b ) employed Bayesian networks with causality constraints (BNCC) in this category, developing a framework for risk causality analysis in software projects. This framework focuses on identifying causal links between risk factors, using historical data to discover new connections and validate existing ones impacting project outcomes.

The second significant combination involves Advising in the context of Perform Quantitative/Qualitative Risk Analysis and Risk Response processes, featured in 14 studies. This indicates that risk analysis is instrumental in supporting decisions related to risk responses. Lachhab et al. ( 2018 ) showcased this through a multi-criteria decision support tool integrating Project Management and System Engineering (SE) sub-processes. They introduced a novel multi-objective ant colony (ACO) algorithm, MONACO, which uses a unique learning mechanism allowing ants to learn from past decisions, thereby optimizing future choices efficiently. Furthermore, Plan Risk Response combined with Advising was highlighted in the research of Yavari et al. ( 2013 ), which developed an effective method for measuring software risk using fuzzy logic. These results illustrate the evolving role of AI in enhancing various aspects of PM, particularly in risk analysis and response planning. A second analysis, shown in the Table  16 below, cross-references the AI categories with other PM processes to clarify the existing relationship.

The analysis reveals that only a few combinations of AI categories and PM processes yield significant results. The most prominent combination, featuring in 7 articles, is Consulting with Portfolio Analysis . In this context, Khalili-Damghani et al. ( 2013 ) introduced a multi-objective approach for selecting sustainable project portfolios, proposing a hybrid framework that merges data mining, Data Envelopment Analysis (DEA), and an evolutionary algorithm (EA) to construct a Fuzzy Rule-Based (FRB) system. Another notable combination, found in 6 articles, is Decision Support Systems (DSS) with Consulting . This involves modeling, identifying, and interacting with risks, particularly in complex and uncertain project environments. Fang and Marle ( 2012 ) proposed an integrated DSS framework that includes risk network identification, evaluation, and analysis, combining the design structure matrix for dependency modeling with the analytical hierarchy process for risk interaction evaluation. This simulation-based model aids project managers in planning risk response actions systematically. However, the impact of Advising in developing a General framework is less apparent, with only three articles addressing this aspect. Notably, Dey ( 2012 ) integrated all risk management processes, including risk identification and assessment, using a combined multi-criteria decision-making technique and decision tree analysis. This approach employs the cause-and-effect diagram for risk identification, the analytical hierarchy process for analysis, and a risk map for developing responses. Decision tree analysis is then applied to model various risk response options and optimize risk mitigation strategies. Another analysis was conducted by cross-referencing the AI categories with the integrated approaches of the PMBOK stages intercepted in a previous analysis. The results are shown in Table  17 .

Considering the integrated PM processes, the most significant results are noted for Identify Risks  +  Perform Quantitative Risk Analysis associated with Prediction and Advising ; and Risk  +  Perform Quantitative Risk Analysis  +  Planning Risk Responses associated with Advising .

The latter solution is given by Khodakarami and Abdi ( 2014 ) with their research aiming to fill a gap in the literature. According to his studies despite a causal relationship between sources of uncertainty and cost items; this causality is not modelled in current state-of-the-art project cost risk analysis techniques (such as simulation techniques). Therefore, it proposes a quantitative evaluation framework by modelling the uncertainty of common characteristics and performance indicators affecting cost items. The model integrates the Bayesian network inference process to probabilistic risk analysis. The data analysis indicates that AI research in project management (PM) primarily focuses on technological aspects and specific applications. The literature suggests the field is not yet mature from a managerial perspective, lacking sufficient knowledge for widespread AI integration in PM. Future research directions are discussed next.

3 Discussions

This discussion is structured, as the data analysis in the previous section, according to the posed research questions and findings are used to advance, in the last paragraph, future research directions that need to be explored further by scholars.

3.1 Main application fields

The analysis shows that AI is applied for the risk management of projects mainly in two sectors: construction and IT sector. Concerning the construction industry , reasons behind the interests for AI seem to be twofold: first of all the economic and social role played from this industry (Moradi et al. 2022 ). The Global Construction 2030 estimates in fact that global spending on construction and engineering projects may reach over $212 trillion by 2030 (Robinson 2015 ). Not surprisingly, the construction industry is the mainstay of the economy in many countries, accounting for a significant percentage of the nation's Gross Domestic Product (GDP). For example, in China, in 2016, the total value of construction output was 6.5% of GDP (Ayoub and Mukherjee 2019 ). In Italy, in 2021, the construction sector had a percentage value of 4.9% of GDP (Norkus and Markevičiūtė, 2021 ), while in Malaysia, in the same period, the percentage was 4.5% (Mustafa et al. 2021 ). Besides, this industry faces also organizational and management difficulties due to the complexity and dynamism of these projects that always show a strong propensity to risk even when characterized by pre-calculated and calibrated project details (Pinto et al. 2011 ). Consequently, the dominant feature of this environment is the risk due to processes that are very difficult to manage as they are characterized by many decisions, often spread over a long period of time with many interdependencies in a highly uncertain environment (Chen et al. 2022 ). The challenge is to create a risk management system that adopts tools and methodologies suitable for the construction industry, which motivates research interest in the application of AI in this sector. One more reason why the world of research has directed its interest towards this field is the spread in recent times of Building Information Modeling (BIM). The term BIM finds a synonym in “digital twin”, which indicates the integral digital transposition of the model of a material work (López et al. 2018 ). During its life cycle, each building constantly generates a set of data that represent the genetic code of an asset's digital twin (Azhar et al. 2012 ). If the data transmitted from one stage of the life cycle to another is accurate and truthful, all stakeholders can be reliably informed through appropriately verified digital transactions entrusted to a distributed accounting system (Sacks et al. 2010 ). Considering that AI feeds on data, these systems can be the promoters and accelerators of the use of AI in project risk management.

Concerning the IT sector , in particular the software development industry it is important to say that in just a few years, software has “eaten” both traditional markets (bookstores, advertising, music distribution, recruiting, communications) and individual processes or portions of value chains (logistics and distribution, price optimization, satellite image management, transport) (Pontikes 2022 ). This spread is evidenced by the Compound Annual Growth Rate (CAGR) growing by 14.3% in 2022, resulting from the market value increasing from USD 1141.43 billion in 2021 to USD 1304.74 billion in 2022. In the short term, the market is estimated to grow to $2040.37bn in 2026 at a CAGR of 11.8% (Tang et al. 2022 ). However, to date, software design and development are high-risk activities. The success rate of global software projects is only about 32% (Sharma and Kumar 2022 ). The cause of this failure is the risks associated with the software development project (Butler et al. 2020 ; Mahmud et al. 2022 ; Fazli et al. 2020 ). Research shows that AI and machine learning may be the solution to the problem as they can revolutionize each stage of the software development life cycle (SDLC) (Wallace et al. 2004 ). Given the more advanced state of development in the two industries, they are candidates to become the subjects of a more searching study and testing with the aim to code the knowledge and best practices for a more effective application of AI in PM.

3.2 Integration between AI categories and PM processes

The analysis of project management (PM) processes, as per the PMBOK guidelines, shows that “ Identify Risks ”, “ Perform Quantitative Risk Analysis ”, and “ Plan Risk Responses ” are the most frequently used processes. A significant trend observed in the sample is the integration of multiple PM processes. For instance, “ Performing Risk Analysis ” is often combined with “ Monitoring Risks ” or “ Identifying Risks ”, with the latter also being integrated with “ Perform Risk Analysis ” and “ Plan Risk Responses ”. This integration forms a complex “ black box ” of interconnected processes.

In this scenario, AI plays a pivotal role by providing “ augmented intelligence ”. AI assists project managers in handling the complexities of these integrated systems. Interestingly, the application of AI extends beyond typical PM processes to include “ other processes ” such as Portfolio Analysis, General Framework, Prediction/Estimation/Forecasting, Optimization, and Decision Support Systems (DSS). These other processes are covered in 28% of the sampled articles, indicating a broadening scope of AI application in PM that goes beyond conventional risk categories.

The study also categorizes various AI applications, including Advising , Classification , Clustering , Guiding, Knowledge Extraction, Modelling , Optimization , Prediction , and Regression . Among these, Advising , Prediction , and Classification emerge as the most utilized in the sample. This trend persists even in integrated approaches, with Classification/Prediction being the dominant combination, followed by Classification/Advising and Classification/Clustering/Prediction. These findings underscore AI’s role in enhancing, not replacing, the project manager’s functions. AI facilitates cognitive capabilities with high accuracy and performance, transforming the PM's role to collaborate with AI systems, monitor their performance, analyze outcomes, and complete tasks beyond the scope of autonomous systems (Hribernik et al. 2021 ). The analysis also reveals that significant AI categories like Advising , Classification , and Prediction are consistently integrated into common PM processes. However, when it comes to “ Other Processes ”, Advising is predominantly applied, particularly in Portfolio Analysis and General Framework . The study suggests that no single AI category is exclusively suited for specific PM processes. Thus, it highlights the potential benefit of developing a matching system between PM processes and AI categories to better support their application in project management activities.

3.3 Artificial intelligence tools and recommendations for practitioners

The most significant impact of AI in PM processes is observed in the construction and IT sectors, accounting for a substantial portion of the research focus. In the construction sector , AI plays a pivotal role in managing large-scale, complex projects, facilitating cohesive work among multidisciplinary teams, optimizing resource usage, controlling budgets, and mitigating risks. In the IT sector , AI aids in handling rapid technological advancements, streamlining software development lifecycles, managing budgets, and implementing agile methodologies (Al-Mhdawi et al. 2023 ). These sectors are crucial due to their substantial contributions to global economic growth and the complexity of projects they encompass. Recent research demonstrates how new AI models (e.g. deep neural networks and reinforcement learning) are increasingly used in project management to improve risk prediction and resource optimization (Altan and Işık 2023 ). Innovations in AI are revolutionizing project management through tools such as predictive analytics platforms, which predict future outcomes by improving decisions and optimizing resources. AI-powered risk management software identifies risks early, while advanced decision support systems provide data-driven recommendations. AI-enhanced collaboration platforms improve communication and workflows, and robotic process automation (RPA) tools free up human resources for high-value tasks. Additionally, generative AI facilitates content creation, increasing efficiency and reducing manual efforts (Fridgeirsson et al. 2023 ; Zabala-Vargas et al. 2023 ).With these premises, the study of the analysis sample identified more than a 100 AI tools . Given the large number, these were grouped into clusters, such as: Case Based Reasoning (CBR), Network Analysis, DSS, Algorithm, Fuzzy Logic, Simulation, Machine learning, Support vector machine, FMEA and Others . Each paper in the sample, based on the AI tool reported in the research, was placed in one of the identified clusters which resulted in a clear prevalence of three clusters, such as: Fuzzy Logic, Network Analysis and Decision Support System . However, this result is not surprising but in line with previous findings. These tools, like the most applied AI categories and PM processes, are characterized by their supporting capability. The term “fuzzy” refers to the ability to handle imprecise or vague input. Fuzzy logic in fact comes very close to human reasoning by applying linguistic descriptions to define the relationship between input information and output actions (Ahmed et al. 2022 ). Network Analysis tools use a network as a decision support technique to handle probabilistic events. Whereas Decision Support Systems improve the quality of decisions by providing powerful analysis capabilities that enable the exploration and comparison of a set of mutually incompatible alternatives (Hak et al. 2022 ). In addition, to the clear trend owards the application of solutions capable of supporting and improving project risk management activities, from the analysis of the 215 papers it was impossible to individuate a clear-cut divide among PM processes, AI categories and AI tools. It means that research is still in a primordial state comparing to application of AI in other topics (i.e. medicine, etc.), as well as that there is an important gap to fill. In fact the lack of a trajectory to take makes the application of AI in PM very chaotic and probably uneffective. Table 18 summarizess the challenges for the construction sector and for the IT sector with AI integration in project management.

Definitely, the study allows us to affirm that AI’s role in project management is multifaceted and expanding. Its ability to handle large datasets, provide insightful analysis, and support complex decision-making processes significantly enhances the effectiveness of PM, especially in sectors characterized by high complexity and rapid technological change. The insights from AI applications in PM not only aid current practices but also pave the way for future innovations and research directions in this field. As AI continues to evolve, its integration within PM processes is expected to become more profound, offering new opportunities and challenges for project managers and organizations. For the previous considerations, practitioners should select AI tools that align with their specific project needs, team size, industry, and the complexity of the tasks at hand. The key is to leverage these tools to enhance efficiency, decision-making, risk management, and overall project success. Table 19 offers a concise overview of various AI tools and platforms, highlighting their primary functions and key features. It serves as a guide for project management practitioners to select appropriate tools based on their specific needs and project requirements.

It is equally important to keep some considerations in mind for choosing AI Tools, as follows:

Compatibility with Existing Systems: Ensure the tool integrates well with current project management software and systems.

Scalability: Choose tools that can scale with the growth of projects and organization.

User-Friendly Interface: Prioritize tools with intuitive interfaces to facilitate quicker adoption by the team.

Cost-Effectiveness: Consider the cost–benefit ratio, especially for small and medium-sized projects.

Data Security and Privacy: Ensure the tool adheres to data security and privacy standards.

4 Future development and research agenda

This present research provided a global picture of AI in project risk management and a few concerns arose to constitute the base for future developments and to structure a research agenda:

Construction and IT sectors made the most progress in applying AI to project risk management processes: most of research in the literature addressed the issue specifically or, if presenting an application, it was in either of the two industries. Such concentration of papers asks for a deep investigation to identify a scientific rationale that could boost AI in project risk management even in other industries.

There exists no pathway in applicating AI to project risk management: despite the large amount of research developed in the last years, scientifically substantiated choices in selecting an AI category/tool under specific circumstances don’t seem to occur, as well as best practices to guide the effective application. This leads to the conclusion that the topic remains relatively disintegrated theoretically.

Project risk management processes are mostly involved by AI in an integrated way: the majority of papers address AI not only in a specific project risk management process, but by developing a tool or a procedure to cover more processes. This trend sends a clear message that researchers and practitioners intend to recognize a pervasive role played by AI or even it might mean the pursuing of a fully automated approach to project risk management.

Throughout this review, we critically evaluated the literature related to our research questions and highlight potential gaps for further study. We outlined a four-pronged agenda for future AI in PM research that build from the identified gaps:

Providing general frameworks for introduction of AI in PM: in the previous section we highlighted as the theoretical knowledge on the topic is large yet unorganized and it leads to chaotic implementation trajectories. In other fields, as such as medical application (He et al. 2019 ), researchers and practitioners have been able to develop guidelines, good practices, check lists, and even regulatory documents to support and make more effective AI implementation. Clearly, the project risk management would also benefit of frameworks to guide choices towards the most suitable methods/tools adjusted to the characteristics of the context or the project. Throughout this review we also glimpsed possible drivers to structure knowledge as such as number and type of processes involved, aim to be pursued, proposed role for AI. In the next future we hope for researchers focussed on exploring those drivers or even finding new ones.

Developing criteria for evaluating AI performance: this area of research is connected and serving the previous point as well. In fact, despite a large amount of KPIs aimed at evaluating technical performance of AI, criteria to assess instead the effectiveness of AI implementation methods are missing. Addressing this issue might be very helpful also to provide effective frameworks for introduction of AI in PM.

Selecting enabling factors for implementing AI in PM: factors as such as a better organizational culture and a higher level of maturity in project risk management, as well as availability of data and processes of knowledge management might easily represent enabling factors and for this be explored together with new ones coming from analysing current implementation. A good starting point for this line of research is the analysis of construction and IT, as industries with the greatest number of applications AI to PM.

Hard Skills Development: Training and skill development are crucial for project managers in the digital age, allowing them to understand and effectively apply AI technologies. This not only improves efficiency and innovation in projects, but also ensures competitiveness and adaptability in a rapidly changing business environment.

Table 20 provides a structured overview of the key recommendations for both practitioners in the field of project management and researchers focusing on the future of AI in this domain. It highlights actionable steps for current practice and areas of interest for academic and practical research.

5 Conclusion

Project risk management is a crucial phase for project success, especially in dynamic environments where traditional tools are proving inadequate due to their lack of planning, collaboration, automation, and smart functionalities. To address these gaps, recent research has increasingly focused on the application of AI in project risk management. A literature review was conducted on 215 articles published between 1996 and 2023 to understand the scientific community's progress in this area. The review aimed to answer research questions about AI’s application areas in project risk management and to explore any patterns between AI categories or tools and PM processes. The research indicated that AI in PM is predominantly applied in the construction and IT sectors. The AI categories most employed in these sectors are those with capabilities for analysis, processing, and learning, which are necessary to support project managers in handling large amounts of data. The most popular AI tools are those that can manage ill-defined and seemingly disconnected information, predict events, compare alternatives, and deliver accurate results quickly. AI algorithms, complemented by human skills and experience, are essential for rapidly evaluating data and providing effective responses to achieve project objectives. The role of AI is seen as strategic, not to replace humans but to enhance human capabilities with data analysis power. An interesting trend identified in the study is the integrated approach to addressing PM processes, aiming to provide a unified and automated solution for a substantial portion of PM. Despite analyzing a large number of studies, the research could not identify clear trajectories, patterns, or best practices for AI application in PM. This gap, along with the trend of integrating PM processes and the advancements in IT and construction sectors, forms the basis of the proposed research agenda. This research is among the first to quantitatively evaluate AI’s application in PM through a literature review. However, it reveals that this solution is still in its early stages, lacking a well-defined path for successfully integrating PM processes and AI categories. Future studies are encouraged to develop ad hoc tools and methods for applying AI in specific industries, with potential for replication in other fields. The findings suggest an urgent need for future research to focus on identifying clear trajectories and best practices for the integration of AI in PM processes (such as Identify Risks + Perform Quantitative Risk Analysis associated with Prediction and Advising, and Risk + Perform Quantitative Risk Analysis + Planning Risk Responses associated with Advising). This future research should aim to develop tailored AI tools and methodologies for specific industries, potentially establishing a well-defined framework that can be replicated across various sectors. Such studies could significantly contribute to bridging the current gap and advancing the field of AI in project management. Based on the latest scientific research, it is clear that adopting AI in project management presents significant challenges, such as data privacy and security issues, which require robust protocols to protect sensitive information. Ethical considerations, such as algorithmic bias and impact on work, need special attention to ensure fairness and transparency. Barriers to adoption include resistance to change, lack of technical expertise and the cost of initial investments. Overcoming these challenges requires ongoing training, stakeholder engagement to develop trust in AI technologies, and strategic investments in security and skill development, ensuring that AI is used responsibly and effectively.

Data availability

All data and resources discussed in this document are freely available and accessible to the public.

Abbreviations

• Artificial intelligence

Analytic hierarchy process

Interval analytic hierarchy process

Application programming interfaces

Bayesian networks with causality constraints

Decision support system

Fuzzy logic

Information technology

Literature review

Project analysis and selection system

Perform quantitative/qualitative risk analysis

Preference reporting items for systematic reviews and meta-analyses

Project management body of knowledge

Project management institute

Project manager

Research questions

Systematic literature review

Technique for performing order by similarity to ideal solution

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Maria Elena Nenni

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Fabio De Felice, Cristina De Luca & Antonio Forcina

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Conceptualization, M.E.N., and F.D.F.; methodology, M.E.N. and F.D.F.; software, C.D.L.; writing—original draft preparation, M.E.N., A.F., C.D.L.; writing—review and editing, M.E.N. F.D.F. and A.F.; supervision, M.E.N. and F.D.F. All authors have read and agreed to the published version of the manuscript.

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Nenni, M.E., De Felice, F., De Luca, C. et al. How artificial intelligence will transform project management in the age of digitization: a systematic literature review. Manag Rev Q (2024). https://doi.org/10.1007/s11301-024-00418-z

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DOI : https://doi.org/10.1007/s11301-024-00418-z

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