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Digital 2024, 4, 555–571. https://doi.org/10.3390/digital4030028 www.mdpi.com/journal/digital
Review
Challenges of Integrating Articial Intelligence in Software
Project Planning: A Systematic Literature Review
Abdulghafour Mohammad * and Brian Chirchir
School of Business, Economics and IT, University West, SE-46186 Trollhaan, Sweden
* Correspondence: abdulghafour.mohammad@hv.se
Abstract: Articial intelligence (AI) has helped enhance the management of software development
projects through automation, improving eciency and enabling project professionals to focus on
strategic aspects. Despite its advantages, applying AI in software development project management
still faces several challenges. Thus, this study investigates key obstacles to applying articial intelli-
gence in project management, specically in the project planning phase. This research systematically
reviews the existing literature. The review comprises scientic articles published from 2019 to 2024
and, from the inspected records, 17 papers were analyzed in full-text form. In this review, 10 key
barriers were reported and categorized based on the Technology–Organization–Environment (TOE)
framework. This review showed that eleven articles reported technological challenges, twelve arti-
cles identied organizational challenges, and six articles reported environmental challenges. In ad-
dition, this review found that there was relatively lile interest in the literature on environmental
challenges, compared to organizational and technological barriers.
Keywords: project management; project planning; articial intelligence; machine learning; TOE
framework; software development projects; information technology
1. Introduction
The history of project management is extensive and diverse, spanning many elds.
It has played a crucial role in achieving signicant milestones, from constructing famous
landmarks to breakthroughs in technology and space exploration. According to [1] project
management involves the utilization of knowledge, capabilities, tools, and methods to
meet specic requirements. This comprehensive approach includes identifying needs, en-
gaging stakeholders, and managing resources while navigating the scope, schedule, cost,
quality, and risk constraints [1].
The adoption of articial intelligence (AI) has signicantly transformed project man-
agement, particularly owing to the digital transformations necessitated by the global pan-
demic in 2020 [2]. In addition, AI has helped enhance project management through auto-
mation, improving eciency and enabling project professionals to focus on strategic as-
pects, particularly in IT and software development projects. This transformation includes
the application of AI to resource allocation, risk management, and enhancing communi-
cation within teams. Furthermore, it reveals the need for project managers to develop new
skills and adapt to an AI-driven environment, emphasizing the importance of training
and incorporating AI technologies eectively into organizational cultures [2].
Articial intelligence (AI) can be broadly dened as machine simulation of human
intelligence, aiming to mimic human cognitive processes and behaviours [3]. It includes
the ability to learn, solve problems, and make decisions. According to ref. [3,4], AI encom-
passes both the creation of intelligent machines that can perform tasks requiring human
intelligence and the development of systems that can think and act, whether by emulating
Citation: Mohammad, A.; Chirchir,
B. Challenges of Integrating
Articial Intelligence in Software
Project Planning: A Systematic
Literature Review. Digital 2024, 4,
555–571. hps://doi.org/10.3390/
digital4030028
Academic Editor: Mobyen Uddin
Ahmed
Received: 22 May 2024
Revised: 27 June 2024
Accepted: 27 June 2024
Published: 29 June 2024
Copyright: © 2024 by the authors.
Submied for possible open access
publication under the terms and
conditions of the Creative Commons
Aribution (CC BY) license
(hps://creativecommons.org/license
s/by/4.0/).
Digital 2024, 4 556
human behaviour or through unique, non-biological processes. This eld spans from nar-
row AI, designed for specic tasks, to general AI, capable of handling any cognitive task
like a human [4].
2. Previous Work
Integrating articial intelligence into project management has directly aected the
phases of project management. Project planning is an important phase in this regard. Pro-
ject planning is a crucial stage in managing a project. It aims to dene the project’s goals
and outline the steps to achieve them [1]. This phase involves creating a detailed plan that
includes:
• Scope management plan: How the project’s scope is dened, tracked, and conrmed.
• Requirements management plan: The approach for analyzing, documenting, and
managing project requirements.
• Schedule management plan: Guidelines for developing and overseeing the project
timeline.
• Cost management plan: Strategies for planning and managing project costs.
• Quality management plan: Processes for ensuring project quality meets the objec-
tives.
• Resource management plan: Planning for estimating and managing project re-
sources.
• Risk management plan: Procedures for identifying and addressing project risks.
• Stakeholder engagement plan: Approaches for involving stakeholders and managing
their expectations.
• Communications management plan: Plans for sharing information with stakehold-
ers.
• Procurement management plan: Methods for handling procurement from planning
to contract completion.
Each component is essential for guiding the project team towards successful project
completion. With the advent of the agile framework in project management, planning has
become an iterative process emphasizing adaptability to change and stakeholder feedback
throughout a project’s lifecycle [1]. Agile planning breaks the project into manageable seg-
ments or sprints, enabling exibility and continuous adjustment based on ongoing feed-
back and project evolution [1]. The essential elements of agile planning include agile re-
lease planning, iteration planning, frequent quality and review steps, and active stake-
holder engagement. This approach facilitates a dynamic, value-focused, and collaborative
planning environment, ensuring that projects can swiftly adapt to changes and deliver
incremental value [1].
The launch of PMI Innity by the Project Management Institute (PMI) on 19 January
2024 marked a signicant advancement in integrating articial intelligence into project
management. This AI-powered knowledge base uses OpenAI’s advanced GPT architec-
ture to provide reliable solutions and suggestions for addressing project management
challenges. It features a conversational interface that draws from PMI’s extensive content
library. This development highlights the growing role of AI in project management,
prompting the need for research into the challenges of AI in planning software and infor-
mation technology (IT) projects.
Two literature reviews have aempted to address the challenges of applying AI in
project management. Ref. [5] explored the challenges of AI implementation in project
management. This review identied several challenges of integrating AI in project man-
agement, including the scarcity of data, the high costs associated with AI implementation,
the risk of job displacement, and the need for highly skilled technical personnel. In addi-
tion, this review showed that system integration and interoperability are signicant hur-
Digital 2024, 4 557
dles. However, the study covered generally all project process groups and paid less aen-
tion to the planning phase, which is the most important phase in the project management
process.
Another literature review conducted by [3] highlighted the challenges of integrating
AI in project management, such as creating comprehensive frameworks that include var-
ious project domains, sustainability, and security. They highlighted the lack of research
on successfully adopting AI in these crucial areas. In addition, the need for project man-
agers with skills that complement AI’s capabilities is emphasized, suggesting that human
skills in team and stakeholder management remain vital and are less likely to be replaced
by AI. However, it provides a broader overview of AI’s applicability and benets across
various project management domains and industries, demonstrating an expansive and
multidisciplinary interest in the topic [3].
As a result, this review is essential for oering a cuing-edge review of all the chal-
lenges associated with integrating AI into software development projects during the pro-
ject planning phase. By examining these obstacles, this paper makes a distinctive and
timely addition to the body of knowledge on project management. In addition, this review
focuses mainly on the planning phase of the project management process, which has dif-
ferent activities that can be integrated with AI. Other papers such as [3] have focused gen-
erally on dierent phases with less aention to planning. Additionally, the present review
will assist scholars, decision-makers, and managers who are eager to learn more about
this exciting technology in evaluating AI’s feasibility for the project management eld.
Furthermore, this review categorized the issues that were found into technological, organ-
izational, and environmental contexts using the Technology–Organization–Environment
(TOE) framework [6]. The TOE is an analytical model that helps understand how organi-
zations adopt technological innovations [6]. It examines technological factors which are
the internal and external technologies aecting operations. The organizational factors
which represent the characteristics and resources of the organization. In addition, the en-
vironmental factors describe the broader context in which the organization operates, in-
cluding regulatory policies and market trends [6]. This framework is valuable for analyz-
ing the adoption of new technologies, considering the interplay between technology ca-
pabilities, organizational readiness, and external pressures [6]. This is how the rest of the
paper is organized: The methodology for searching and ltering articles is dened in Sec-
tion 3. Section 4 concentrates on analyzing and presenting the ndings obtained from the
selected articles. The results are followed by the discussion in Section 5. Finally, Section 6
discusses the study’s restrictions and challenges, as well as future research.
3. Materials and Methods
This study set out to answer the following research question: ‘What are the challenges
of articial intelligence in planning IT/software projects?’ This research question guided
the entire review process, including determining its content and structure, designing strat-
egies for locating and selecting relevant studies, critically evaluating these studies, and
analyzing their results. The methodological approach was carefully crafted to ensure a
comprehensive understanding and assessment of the challenges posed by AI in the spe-
cic context of project planning within IT and software project domains. A PRISMA-com-
pliant systematic literature review was carried out. The utilization of PRISMA facilitated
the identication, selection, and critical evaluation of research, thereby mitigating bias and
enhancing the ecacy of the reporting process. Systematic literature reviews oer a
means of observing and assessing the eectiveness of integrating AI in the planning phase
of project management processes. As a result, this review may be useful in determining
any knowledge gaps in this area. Moreover, it facilitates researchers’ understanding of
how AI is applied and advances knowledge of key ideas, investigative strategies, and ex-
perimental approaches in the project management domain.
Digital 2024, 4 558
3.1. The Search and Review Process
The review process was initiated by identifying keywords to structure a search for
relevant scientic articles, utilizing the following boolean combination of keywords:
(“project management” OR “plan*”) AND (“articial intelligence” OR “Machine Learn-
ing”) AND (“challenge*” OR “limitation*” OR “barrier*”) AND (“software development”
OR “Information Technology”). This enabled a structured approach to locating relevant
literature. Following the removal of duplication, the two authors of the current paper care-
fully read the selected papers that were chosen based on the eligibility criteria provided
in Table 1. The following databases were selected for their extensive collection of academic
papers: IEEE Xplore, ScienceDirect, Academic Search Premier, ACM Digital Library, and
Emerald. A backward search methodology was also employed to review references within
the identied articles to uncover further relevant studies. This systematic approach en-
sured that a comprehensive review aligned with the research was conducted. The selected
databases have distinct ltering options. In addition to the predetermined eligibility re-
quirements, each database was individually navigated and ltered based on the specic
criteria relevant to the focus of this study. This approach ensured that the search process
was tailored to facilitate a comprehensive and precise selection of the literature. After ap-
plying all lters, the results for each database were as follows: Academic Search Premier
(137 articles), ACM Digital Library (28), IEEE Xplorer (7 articles), Science Direct (316 arti-
cles), and Springer (861 articles).
Table 1. Inclusion and exclusion criteria.
Inclusion Criteria
Exclusion Criteria
peer-reviewed articles, reviewed conference
papers
book review, magazine, reports, dissertation,
theses, books, audio, video
language: English
non-English articles
limit year: 2019–2024
older than 2019
relevant to the application of articial intelligence
in project management
not relevant to the research question
papers available in full text within the selected da-
tabases
3.2. Data Extraction
Details on various types of challenges, how to address them, and the features of the
study methodology were extracted from the articles. The information collected for each
study was concerned mainly with AI models used, and the limitations of integrating this
technology in the planning phase of the project management process. Additionally, we
have noted the research’s ndings and the authors’ conclusions. Each related paper has
been independently assessed by the authors to verify eligibility and retrieve answers to
the research question. Any disagreements amongst the authors’ perspectives were seled
by conversation and consensus.
4. Results
Throughout each phase of the selection process, the study tracked and reported the
number of studies identied, screened, and either included for further review or excluded,
as shown in Figure 1 below.
Digital 2024, 4 559
Figure 1. Flow diagram for the screening and selection of articles.
Initially, a comprehensive search across ve electronic databases provided 1264 arti-
cles. Of these, 201 were identied as duplicates and subsequently removed. A detailed
review of titles and abstracts led to the exclusion of an additional 1022 articles, primarily
because of their lack of direct relevance to the research question. This left 41 articles for an
in-depth, full-text review. From this subset, 27 articles were excluded for reasons that in-
cluded a focus on AI implementation in projects rather than on project management, dis-
cussions centered on learning software development, and articles addressing general pro-
ject leadership without specic applications of AI. However, through a thorough back-
ward search of references within the identied articles, we added three more studies, for
a nal count of 17 articles deemed suitable for qualitative synthesis (see Table 2). The iden-
tied articles with the models addressed in each article are listed in Table 3. The model is
(n=3) Additional records found identified
through a backward search of references through
the references list
(n=27) Records excluded
10 discussed implementing AI-based
projects and not in Project Management
8 were not focusing on IT-based projects
2 of them discussed learning software
development
7 articles discussed general project
leadership with no artificial intelligence
applications
(n=1066) records screened by title and abstracts
(n=1022) records excluded
(n=44) records assessed for eligibility
(n=17) records included
(n=1264) total articles identified through the
five database:
Academic Search Premier:52
ACM Digital Library:28
IEEE Xplorer:7
ScienceDirect:316
Springer:861
(n=1063) records after duplicates
removed
Digital 2024, 4 560
an algorithm that has been trained on a collection of data to nd particular trends or come
to conclusions on its own without the need for additional human input. Articial intelli-
gence models accomplish the tasks, or outputs, for which they are programmed by apply-
ing various algorithms to pertinent data inputs.
Table 2. List of selected articles and models addressed.
Article
Models Addressed
[7]
Support Vector Machine, Genetic Algorithm (GA), Ant Colony Optimization (ACO), Evolutionary Strategy
(ES), Local Search (LS), Dierential Evaluation (DE), and Practical Swarm Optimization (PSO)
[8]
Neural Network, Random Forest, and Support Vector Regression
[9]
Random Forest
[10]
Meta-heuristic algorithms: GWO, ZOA, MFO, PDO, and WSO
[11]
Decision Tree, K-Nearest Neighbor, Gradient Boosting, Neural Network, Naive Bayes, Support Vector
Machine, and Bayesian Network
[12]
Genetic Algorithm (GA)
[13]
Vector machine, K-Nearest Neighbor, Articial Neural Network, and Random Forest
[14]
SVM, MLP, decision trees, and Random Forest
[15]
Classication model
[16]
Smart AI assistant, conversational AI platform (LLMs)
[17]
Gradient Boosting, Neural Network,
[4]
word2vec, paragraph2vec, Long Short-Term Memory (LSTM), and Convolutional Neural Networks (CNNs)
[18]
Decision Tree, K-Nearest Neighbor
[3]
Naive Bayes
[19]
ChatGPT
[5]
Support Vector Machine, and Bayesian Network
[20]
GPT-2 language models and Transformer architecture
Table 3. AI models addressed and the number of covered papers.
AI model Group
Number of Papers
Models Included
Neural Network
11
(Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM),
Smart AI assistant, conversational AI platform, ChatGPT (GPT-4), GPT-2
language models, Transformer architecture)
Support Vector
Machine
5
(Support Vector Machine, Support Vector Regression)
Random Forest
4
Random Forest
Genetic Algorithm
2
Genetic Algorithm
Decision Trees
2
Decision Tree
K-Nearest Neighbor
2
K-Nearest Neighbor
Other Models
17
Ant Colony Optimization (ACO), Bayesian Network, Case-Based Reasoning
(CBR), Classication model, Dierential Evaluation (DE), Evolutionary
Strategy (ES), GWO, Gradient Boosting, Local Search (LS), MFO, Naive Bayes,
PDO, Practical Swarm Optimization (PSO), WSO, ZOA, paragraph2vec,
word2vec
4.1. Characteristics of Research Articles
It is observed in this study that there has been increased interest in applying AI in
project planning from 2021 to 2023, as shown below. The highest number of articles was
published in 2023 (55%) (see Figure 2), while 27% were published in 2022.
Digital 2024, 4 561
Figure 2. Distribution of articles per year.
The 17 selected articles highlighted dierent challenges. As mentioned above, this
study categorized the challenges using the TOE framework. Table 4 below showcases how
these challenges were divided into sub-themes and what articles referenced them.
Table 4. TOE challenges summary—distribution across articles.
Context
Challenges
Refs
Technological
Data Availability and Quality
[7,9–11,12,14,18]
Model Adaptability and Advancement
[4,7,9–11,14,19,20]
Resources Limitations
[5,12,14,18]
Integration into Existing Project Management
[3,7,12,13,19,20]
Technical Expertise
[3,5,11,16,19]
Organizational
Transparency and Accountability
[8,14,19,20]
Change Management
[7,16]
Generalizability Across Ecosystems
[8,11,12]
Environmental
Project Dynamics
[3,15,16]
AI Ethics and Regulations
[17]
4.2. Technological Challenges
Technological factors within the TOE framework refer to the technological limitations
that aect the implementation of the AI models. This includes assessing the capabilities of
adopting the AI models in project planning. Understanding these factors is crucial for or-
ganizations considering new technologies [6]. Therefore, this review classied data avail-
ability and quality, model adaptability and advancement, and resource limitations as fall-
ing into the technological group (see Table 4).
4.2.1. Data Availability and Quality
This section critically reviews articles discussing data availability and quality limita-
tions for AI models in project planning. Seven of the selected articles explore this type of
limitation (see Table 4).
Ref. [17] conducted a systematic literature review on software project scheduling us-
ing AI and highlighted the constraints presented by the use of synthetic datasets, which,
while useful, may not accurately translate to real-world scenarios where variables are
more dynamic and complex. However, this potentially leads to inaccuracies in task as-
signments and schedule predictions. This concern is echoed by [9] who scrutinized the
Random Forest Model’s training data and identied signicant issues with missing infor-
mation within the libraries.io dataset. The gaps, particularly in repository links, pose a
serious question regarding the model’s ability to accurately reect the current practices
Digital 2024, 4 562
and trends in the Node Package Manager (npm) ecosystem, given the dataset’s exclusion
of data beyond January 2020.
Furthermore, to support the concerns raised by [7,11,12] cited limitations in AI pre-
dictive capabilities owing to the availability and reliability of historical data. In this con-
text, ref. [11] observed that the historical data and variables typically utilized to train AI
models might not contain the particularities of innovative and complex projects, while
[12] emphasized the signicant dependence of the Case-Based Reasoning with Genetic
Algorithm (CBR-GA) model’s accuracy on high-quality historical project data, noting that
any inaccuracies or biases present in the historical datasets could substantially aect the
model’s functionality.
Additionally, ref. [14] reviewed dierent machine learning algorithms for estimating
software development eorts, introduced a dierent dimension to the discussion by high-
lighting the challenges associated with the dataset size. They outlined how larger datasets
could introduce over-generalization issues, potentially leading to models that do not ad-
equately capture specic and detailed project needs. Conversely, smaller datasets might
result in overing, where models are too closely tailored to the training data and fail to
generalize to new data. However, this observation adds a layer of complexity to the da-
taset management challenge, revealing that the dataset’s size, breadth, and depth are as
critical as the quality and completeness of the data itself.
Addressing the methodological considerations of the dataset used in AI model train-
ing, ref. [10] also studied improving software eort estimation using machine learning,
indicated the potential benets of expanding the sample size to obtain a broader and more
representative set of data, which could enhance the validity of the research ndings. In
agreement with this suggestion, ref. [18] proposed standardizing process modeling and
articulating clear risks to avoid issues that might aect the eciency of applications and
the success of machine learning models, such as paern overing, accuracy degradation,
and the threat of overing owing to model misadjustments.
In contrast to the discussions on data limitations, ref. [7] also touched upon the adap-
tive capabilities of dynamic models, suggesting that they may address the uncertainties
and variations in project scheduling, thus dealing with real-world complexities more ef-
fectively. Real datasets can signicantly enhance the eciency of task scheduling, provid-
ing models with precise tangible data for training and validation purposes.
However, ref. [12] advocate studying reference studies that evaluate methods for ad-
dressing historical data gaps to resolve the challenges related to missing and incomplete
data. They highlighted techniques such as deletion and imputation methods, with the lat-
ter being shown to signicantly improve the accuracy of analogy-based eort estimation
models. Such an approach underscores the importance of addressing data shortcomings
to ensure the success and reliability of predictive models such as Case Base Reasoning
with Genetic Algorithm (CBR-GA).
4.2.2. Model Adaptability and Advancement
Ref. [11] stated the limitations of static models in adapting to evolving project land-
scapes, emphasizing their static nature as a barrier in dynamic environments. Concur-
rently, [9] uncovered diculties in accurately predicting restrictive update strategies us-
ing their model, demonstrating high precision and low recall. However, this reects not
only the model’s capability to identify restrictive packages accurately but also its failure
to detect many such instances, a limitation linked to the dataset’s minor focus on restric-
tive strategies, and the contingent nature of these strategies on external factors, such as
breaking changes, rather than intrinsic package characteristics.
Also, in their exploration of enhancing software estimation models, ref. [10] pointed
out a critical oversight: the estimation process overlooked the complexity of software sys-
tems, treating them as monolithic entities rather than compositions of diverse subsystems.
This simplication potentially hampers the precision and relevance of the model to real-
Digital 2024, 4 563
world applications. By contrast, ref. [20] introduced the advantages of applying the aen-
tion mechanism in transformer-based models for agile story point estimation, marking a
novel approach that improves interpretability and accuracy by illuminating the rationale
behind model predictions.
Additionally, ref. [19] critiqued the generative capabilities of AI systems, such as
ChatGPT, noting their tendency to provide singular responses to prompts rather than ex-
ploring the breadth of possible outputs. This limitation could restrict the comprehensive-
ness of project planning as the multitude of potential outcomes and their implications
remain unexplored. However, ref. [7] also highlighted the integration of machine learning
algorithms, such as Support Vector Machine (SVM), with task scheduling and strategies
to augment task assignments’ accuracy and ecacy.
However, ref. [11] advocated additional testing with enriched datasets to beer un-
derstand the dynamics between relevant variables, thereby enhancing model perfor-
mance. This suggests a constructive pathway for overcoming these limitations. Moreover,
ref. [14] proposed developing a hybrid model that combines the strengths of various ma-
chine learning techniques to boost estimation accuracy. This innovative approach can sig-
nicantly rene software eort estimation by optimizing default algorithmic parameters.
In support, ref. [4] introduced the concept of employing deep reinforcement learning to
forge a segment of the planning engine that is adaptable and predictive of potential sprint
execution barriers, aiming for a robust and resilient planning process.
4.2.3. Resources Limitations
Ref. [12] underlined the complexity and resource-intensive nature of the CBR-GA
model’s optimization process, necessitating considerable computational power and time
for tasks like feature selection and determining the optimal number of nearest neighbors.
This complexity poses a signicant challenge for resource-limited seings. Similarly, ref.
[14] shed light on the computational hurdles and scalability issues faced when handling
large datasets, worsened by the problems of dimensionality, noise, and outliers, impacting
the precision of software eort estimations.
Additionally, ref. [5] discussed the substantial initial investments in hardware and
software infrastructure required for AI technology deployment, a formidable obstacle for
many organizations. In contrast, ref. [18] provided a solution by advocating the strategic
selection of cloud services, edge computing devices based on specic computational
needs, and user-friendly reporting tools, including smartphone-compatible dashboards,
to mitigate these barriers.
Furthermore, ref. [18] addressed the operational challenges of maintaining and up-
dating machine learning models to preserve their accuracy and relevance over time. The
continuous evolution of ML models requires regular performance monitoring, model up-
dates, and parameter tuning. To address these issues, they proposed adhering to the
Cross-Industry Standard Process for Machine Learning (CRISP-ML) methodology, em-
ploying Machine Learning Model Operationalization Management (MLOps) practices,
utilizing Predictive Model Markup Language (PMML) for ecient model tracking and
archiving, and integrating CI/CD pipelines to streamline model deployment and use. This
comprehensive approach underscores the importance of strategic management and oper-
ational eciency in eectively leveraging AI and machine learning technologies in infor-
mation technology projects.
4.3. Organizational Challenges
The obstacles arising from the internal operations of the organizations managing the
software project are referred to as organizational barriers. Therefore, the organizational
challenges must be considered concerning the organization’s intention to use AI technol-
ogy as part of its project management methodology. This review revealed four organiza-
tional obstacles to the integration of AI in project management: integration into existing
Digital 2024, 4 564
project management, technical expertise, transparency and accountability, and change
management.
4.3.1. Integration into Existing Project Management
Integrating articial intelligence (AI) models into existing project management and
planning frameworks presents complexities that can obstruct seamless adoption, as noted
by [7]. This challenge is further compounded in the context of agile development meth-
odologies, where the conventional design of models such as CBR-GA may not align well
with agile’s dynamic nature and reliance on metrics such as story points, according to [12]
However, advancements such as the Transformer-based Agile Story Point Estimation
(GPT2SP) approach, leveraging the GPT-2 architecture, have shown promise in accurately
estimating story points in agile environments, as ref. [20] have demonstrated. This tool
enhances the accuracy of estimations and the interpretability of decision-making pro-
cesses by highlighting inuential keywords and providing relevant historical examples.
Additionally, ref. [19] revealed that AI-driven and human-crafted project plans pos-
sess distinct advantages and limitations, suggesting that a synergistic approach could pro-
vide eciency and depth of project planning. This perspective advocates integrating hu-
man expertise with AI-generated insights to elevate the quality and thoroughness of the
project plans. Moreover, the rise in the adoption of scrum methodologies in software pro-
jects has highlighted the absence of risk management practices within such frameworks,
a gap highlighted by [13]. This omission underscores a critical challenge in eectively
foreseeing and mitigating project risks using AI models for risk prediction. However,
Gouthaman and Sankaranarayanan proposed incorporating risk management into agile
methodologies through a continuous feedback loop to strengthen the success rates of agile
projects by fortifying risk-management practices.
To address the operational challenges of AI in agile project management, ref. [4] ex-
plained the complexities of formulating the AI planning problem. The complex process
requires dening the initial state, which includes the project status before a sprint and the
objectives of the sprint as the goal state. The multifaceted decision-making involved in
transitioning from the initial to the goal state is compounded by the need to account for
various inputs, such as product backlog items, team capacity, and previous sprint perfor-
mances. The transformation of these often informally expressed factors into vector repre-
sentations for AI planning necessitates advanced representation learning engines and for-
mal encoding.
4.3.2. Technical Expertise
Ref. [11] underscored the challenges that arises from the intricate nature of some AI
models, particularly those built on sophisticated algorithms, which can be opaque and
dicult for project managers without deep technical knowledge to interpret them. This
opacity complicates the application of AI in informed project management decisions be-
cause of the inability to understand the basis of the model’s forecasts.
Ref. [3] also acknowledge AI’s potential to boost project outcomes, especially in the
information technology (IT) domain. However, they emphasize the undiminished need
for adept project managers capable of leveraging their expertise to incorporate AI tools
eectively into project workows. Ref. [5] pointed out the specialized skills and experi-
ence required to develop, deploy, and maintain AI systems within project planning, not-
ing the recruitment and retention of such skilled personnel as a signicant challenge. Ad-
dressing the training gap, ref. [16] recommended the adoption of AI-powered tutoring
systems endowed with natural language processing abilities to facilitate training. These
systems enable interactive, conversational learning sessions, thereby democratizing access
to training for customers and sta at their convenience.
Additionally, ref. [19] introduced the concept of prompt engineering, a skill learned
by project managers. This novel skill involves the strategic formulation of inputs to steer
Digital 2024, 4 565
AI towards generating outputs more aligned with project-specic requirements. In addi-
tion, this skill is pivotal for optimizing the utility of AI in project planning, enabling the
tailoring and enhancement of AI-generated proposals to suit the unique demands and
limitations of projects.
Furthermore, ref. [5] raised concerns regarding the potential for AI systems to dis-
place human jobs, suggesting the risk of increased unemployment as AI assumes roles
traditionally lled by humans. However, ref. [3] argued that domains reliant on human
intellect and interpersonal skills, such as team development and stakeholder manage-
ment, are likely to remain less impacted by AI. This viewpoint recognizes AI’s limitations
in fully replicating human cognitive and social interactions.
Moreover, ref. [19] acknowledged the capabilities of AI in generating components of
project plans but stressed the indispensable role of human project managers. Their exper-
tise is crucial for rening AI-generated outputs, ensuring plans are realistically executable
and closely aligned with overarching project objectives. This collaboration between hu-
man expertise and AI innovation is essential for realizing the full spectrum of benets that
AI oers to project planning, underlining the symbiotic relationship between technology
and human insight in navigating the complexities of project management.
4.3.3. Transparency and Accountability
In their investigation into employing AI to estimate the functional size of software,
ref. [8] drew aention to the inherent “black-box nature” of many machine learning algo-
rithms. This characteristic complicates the documentation, tracing, and elucidation of the
processes, results, and logic underpinning machine learning algorithms, rendering them
less transparent and dicult to interpret. Such opacity becomes a critical issue in scenarios
demanding clear and accountable decision-making, notably within the public sector,
where outcomes shrouded in ambiguity and lack of reliability can hinder stakeholder ac-
ceptance and trust. Despite these challenges, ref. [19] suggested that harmonizing human
insight with AI in project planning can forge more credible and eective project plans. By
blending human expertise with AI’s analytical process of AI, project planning can achieve
greater eciency, innovation, and eectiveness.
Additionally, ref. [20] revealed that AI-driven story-point estimations, when accom-
panied by explanations, are deemed more valuable and trustworthy by users than those
without justication. Furthermore, a signicant number of survey participants (69%) ex-
pressed a willingness to adopt AI-enhanced agile story-point estimates, especially if these
systems were integrated into widely used software development platforms such as JIRA.
This nding underscores the industry’s growing recognition of and potential readiness to
embrace explainable AI solutions for story-point estimation.
Ref. [14] pointed out another dimension of complexity in the real-world application
of AI models, emphasizing the inuence of situational factors and company-specic
standards, such as the Capability Maturity Model (CMM) levels. These variables can sig-
nicantly impact the eectiveness and suitability of AI solutions across dierent organi-
zational contexts, underscoring the need for adaptable and exible AI applications tai-
lored to meet diverse operational standards and project environments.
4.3.4. Change Management
Ref. [16] highlighted an implicit challenge in ensuring that all team members nd AI
tools neither too hard nor too easy to use. This indicates a need for training or an adapta-
tion period for employees to become accustomed to new software and tools. Ref. [7] also
note that adopting AI models in planning involves signicant changes in processes and
workows. Therefore, organizations may encounter resistance from employees who are
accustomed to traditional methods. Thus, eective change management strategies are es-
sential to address these concerns.
Digital 2024, 4 566
4.4. Environmental Challenges
This review discusses three important environmental challenges that aect the use
of AI in project management planning, as follows: generalizability across ecosystems, pro-
ject dynamics, and AI ethics and regulations.
4.4.1. Generalizability across Ecosystems
Ref. [8] pointed out that their ndings and the model’s eciency are conned to the
npm ecosystem. This suggests that its applicability might not extend seamlessly across
various software ecosystems, each characterized by its practices, cultural norms, and de-
pendency management techniques. Nonetheless, they proposed that the study’s method-
ology and approach could be applied in other ecosystems with similar types of depend-
ency data.
Ref. [11] pointed out the inherent complexity in using AI models to select the most
ing project management methodology, primarily due to the diversity of projects that
lack a universal solution. They emphasize the critical need for a detailed evaluation of
each project’s specic characteristics, context, and seing, and a step frequently bypassed
in favor of intuitive or discriminatory decisions. Such oversight can profoundly aect the
project outcomes. Furthermore, the study acknowledges that AI models, including those
based on machine learning, are trained on historical project data, which may hinder their
ability to generalize eectively to projects with distinct or unprecedented features absent
from the training data, thus aecting the precision of identifying the optimal project man-
agement approach for these cases.
Similarly, ref. [12] highlighted variations in model performance across dierent da-
tasets, noting that although the model demonstrates an improvement in accuracy com-
pared to traditional CBR methods, its ecacy may diminish with smaller datasets. This
variation indicates a potential challenge in the model’s capacity to generalize across di-
verse software project datasets, suggesting that its utility might be constrained in specic
scenarios.
4.4.2. Project Dynamics
Ref. [15] highlighted a signicant challenge in the task-planning model used for soft-
ware process planning, the prerequisite for early and precise estimations of the project’s
size and timeline. Given the uid nature of software development projects, where require-
ments and scopes are subject to change, this model’s rigidity in needing upfront estima-
tions is a notable drawback.
Additionally, ref. [3] emphasized the critical need for developing all-inclusive frame-
works for AI-enhanced project management that cover various project life cycle perfor-
mance domains, including sustainability and security, and facilitate project managers’
adoption. They underlined the research gap in these essential areas, which are pivotal for
seamlessly integrating AI into project management routines.
Similarly, ref. [16] observed the frequent introduction of additional requirements by
clients in the nal stages of a project, which complicates the project management process
by necessitating adjustments to the project timeline and reallocating resources. They also
highlight client availability and engagement challenges, such as missing meetings or un-
availability for crucial decisions. However, they suggested implementing intelligent AI
assistants to schedule regular meetings and automate the preliminary collection of client
information. This strategy includes ensuring the availability of at least one decision-maker
to prevent delays, streamlining the project management process, and enhancing eciency
despite potential hurdles.
4.4.3. AI Ethics and Regulations
Ref. [17] identied a notable gap between AI ethics guidelines and industrial practice,
particularly in societal and environmental well-being, diversity, nondiscrimination, and
Digital 2024, 4 567
fairness, which are not adequately addressed. In addition, companies largely ignore the
societal and ecological well-being requirements of software development [21,22]. How-
ever, they suggested using methods or tools for implementing AI ethics as a practical im-
plication of their ndings. One such method is ECCOLA, which can address identied
gaps by providing a structured approach to ethical considerations in AI development.
5. Discussion
This review examined AI models in IT project planning and unveiled a series of chal-
lenges classied within technological, organizational, and environmental domains [6].
This discussion delineates these challenges, leveraging insights from the literature to pro-
pose future research directions and practical applications. According to this review, tech-
nological and organizational challenges received more aention than environmental chal-
lenges. This review showed that eleven articles reported technological challenges, twelve
articles identied organizational challenges, and six articles reported environmental chal-
lenges (see Figure 3). This is due to the technological novelty of AI technology in the eld
of project management. However, this also reects a research gap, in terms of environ-
mental barriers that might have a negative impact on the success of the integration of AI
in project planning. According to the Project Management Institute [1], a project is “a tem-
porary endeavor undertaken to create a unique product, service or result”. In this context,
the uniqueness of a project’s environmental factors makes it dicult to generalize AI so-
lutions to dierent projects, especially when the project’s distinct or unprecedented char-
acteristics were absent from the training data, thus aecting the precision of project man-
agement planning such as resource, cost, and schedule planning. Therefore, further re-
search on this perspective is needed.
Figure 3. Number of articles per TOE category.
Another category of challenges reported by the selected articles were the technolog-
ical obstacles that primarily concern data availability and quality, model adaptability and
advancement, and resource limitations (see Figure 4). In this regard, this review showed
that seven of the selected articles reported the challenge of data availability and quality,
revealing issues from dierent angles such as the reliance on synthetic datasets, missing
data, and the inadequacy of historical data for training AI models [7,9,11]. However, this
review also showed that the literature neglected the condentiality and integrity of the
sensitive data collected and input into the machine language models, especially since
these data exists in most projects if not all of them. This means ignorance of the two es-
sential pillars of the information security triad. As a result, the privacy of the data is com-
promised, which also represents neglecting to adhere to data protection regulations and
Digital 2024, 4 568
laws such as the General Data Protection Regulation (GDPR) and The California Con-
sumer Privacy Act (CCPA). In this regard, this review showed that there is no article has
discussed this issue. Therefore, to ll this gap, further research on this topic is needed.
Figure 4. Number of articles in each technological challenge identied.
Additionally, this review revealed that eight of the selected articles reported the lim-
itations of the static models and the need for models that can adapt to evolving project
landscapes [7,9,11]. Thus, there is still a gap in exploring the development of dynamic,
self-evolving AI models, which was only addressed by two papers [7,9] exploring models
that need to be continuously updated based on new project data and interactions, provid-
ing a pathway to overcome these limitations. Furthermore, this review showed that four
articles pointed out the computational and resource barriers to AI model development
and deployment. In this context, it is clear that the literature lacks an in-depth analysis of
computational resources like cloud computing and algorithmic eciencies that could mit-
igate these constraints.
Additionally, this review showed that six articles illustrated the complexities of inte-
grating AI within traditional project management frameworks (see Figure 5). In this re-
gard, the need for specialized skills for AI deployment, which was reported in ve articles,
was considered a substantial challenge. However, there is also a notable gap in strategies
for upskilling project management professionals and the role of AI-powered tutoring sys-
tems in democratizing access to training. Furthermore, this review showed that four arti-
cles raised concerns about AI’s lack of transparency and the need for explainability in AI-
driven project management decisions. This issue becomes critical in scenarios requiring
transparent and accountable decision-making, particularly within the public sector, where
outcomes wrapped in ambiguity and lack of reliability can impede stakeholder acceptance
and trust. Finally, change management is one of the challenges that has not received much
aention in the selected articles; only two articles emphasized this barrier. It is, therefore,
necessary to further research this area.
Furthermore, this review showed that the applicability of AI models across various
software ecosystems is a signicant concern (see Figure 6). As reported in three articles,
this is due to the rigidity of AI models in accommodating the uid nature of project re-
quirements and timelines. Furthermore, this review showed that the discrepancy between
AI ethics guidelines and their application in practice is one of the key challenges, as re-
ported by [17], who indicated the need for structured approaches to ethical AI develop-
ment. This point is in line with the previously discussed argument about the importance
of compliance with data protection regulations and laws.
Digital 2024, 4 569
Figure 5. The number of articles in each organizational challenge identied.
Figure 6. Number of articles in each environmental challenge identied.
6. Challenges and Future Directions
The study only focused on ve databases (IEEE Xplore, ScienceDirect, Academic
Search Premier, ACM Digital Library, and Emerald); as a result, relevant articles in other
scientic databases might have been overlooked. In addition, the study is limited to the
models listed in Table 3. Hence, developing new models or models that are not addressed
might present challenges. Furthermore, the study restricted the number of irrelevant pa-
pers based on eligible criteria (i.e., those that were published a long time ago, were overly
generic, or did not specically address the research issue). Moreover, only English-lan-
guage items were included; as a result, relevant articles wrien in languages other than
English may have been excluded by these criteria. These limitations might have aected
the retrieval of important records and had an impact on the number of records collected.
Consequently, the number of articles investigated and the relevance of dierent research
papers limited our research. They might have had an impact on our data extraction and
analysis as well. These limitations, however, had no appreciable eect on the discussion
and conclusions.
This study can guide future researchers in this area with a focus on environmental
challenges, especially how region regulations, i.e., GDPR, will aect the implementation
of the AI models in project planning as they have been underexplored. Additionally, a
focus on data handling and privacy should also be explored.
Digital 2024, 4 570
7. Conclusions
The future of articial intelligence (AI) in the project management eld is very prom-
ising. Therefore, the key contribution of this review was to identify the challenges of im-
plementing AI in the project planning phase, particularly within IT and software projects.
This review answered the research question “What are the challenges of Articial Intelli-
gence in project planning for IT/Software Projects?” by identifying and categorizing these
challenges according to the Technology–Organization–Environment (TOE) framework.
The technological barriers are as follows: data availability and quality, model adaptability
and advancement, and resource limitations. In the organizational context, there is integra-
tion into existing project management, technical expertise, transparency and accountabil-
ity, and change management. In the environmental context, there is generalizability across
ecosystems, project dynamics, and AI ethics and regulations.
This review showed that environmental challenges received less aention from the
reviewed articles than the other two contexts. This reects a research gap in terms of en-
vironmental challenges. This will have a negative impact on the success of the integration
of AI in software project planning. Therefore, more research is needed from this perspec-
tive, especially the eect of applying regional regulations like the GDPR. This review also
showed how the condentiality and integrity of sensitive data gathered and input into
machine language models were overlooked in the literature, particularly given that these
data are included in the majority of projects, if not all of them. Furthermore, this review
disclosed that the inconsistency between AI ethics guidelines and their use in practice is
one of the key challenges.
Author Contributions: Conceptualization, A.M. and B.C.; methodology, A.M. and B.C.; validation,
A.M. and B.C.; formal analysis, A.M. and B.C.; investigation, A.M. and B.C.; resources, A.M.; data
curation, A.M. and B.C.; writing—original draft preparation, A.M.; writing—review and editing,
A.M. and B.C; visualization, A.M. and B.C.; supervision, A.M. All authors have read and agreed to
the published version of the manuscript.
Funding: This research received no external funding.
Data Availability Statement: The data that support the ndings of this study are available on re-
quest from the corresponding author. The data are not publicly available due to privacy or ethical
restrictions.
Conicts of Interest: The authors declare no conicts of interest.
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