How Artificial Intelligence Influences Project Management

Victor, Nwosu Obinnaya Chikezie

doi:10.21203/rs.3.rs-2535611/v1

Cited by 4 publications

(3 citation statements)

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“…"AI in Energy: Overcoming Unforeseen Obstacles" [31] emphasizes the necessity of a coordinated approach, serving as a guide for policymakers, energy enterprises, and scholars. Delving into the broader implications, "How artificial intelligence will affect the future of energy and climate" [35] contemplates AI's influence on energy supply-demand and its ramifications on climate change. Lastly, "AI: the secret to unlocking the potential of renewable energy?"…”

Section: Opportunities For Ai In Clean Energy Technologymentioning

confidence: 99%

Assessing the Potential of Artificial Intelligence in Advancing Clean Energy Technologies in Europe: A Systematic Review

Necula

2023

Energies

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This systematic review investigates the role of artificial intelligence (AI) in advancing clean energy technologies within Europe, based on a literature survey from 2006 to 2023. The assessment reveals that AI, particularly through deep learning and neural networks, enhances the efficiency, optimization, and management of clean energy systems. Noteworthy is AI’s capacity to improve short-term energy forecasts, essential for smart cities and IoT applications. Our findings indicate that AI drives innovation in renewable energy, contributing to the development of smart grids and enabling collaborative energy-sharing models. While the research underscores AI’s substantial influence in Europe’s energy sector, it also identifies gaps, such as varied AI algorithm applications in different renewable energy sectors. The study emphasizes the need for integrating AI with emerging clean energy innovations, advocating for interdisciplinary research to navigate the socio-economic, environmental, and policy dimensions. This approach is crucial for guiding a sustainable and balanced advancement in the clean energy landscape, signifying AI’s pivotal role in Europe’s energy transition.

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Section: Opportunities For Ai In Clean Energy Technologymentioning

confidence: 99%

Assessing the Potential of Artificial Intelligence in Advancing Clean Energy Technologies in Europe: A Systematic Review

Necula

2023

Energies

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“…AI, despite its complexities, has the potential to www.ijacsa.thesai.org increase productivity and reduce errors in various fields, including software development projects. By providing insights into probable outcomes and removing extraneous information, AI improves project management and enables a focus on relevant facts [22].…”

Section: Challenges and Impact Of Ai Implementation In Project Manage...mentioning

confidence: 99%

Exploring the Challenges and Impacts of Artificial Intelligence Implementation in Project Management: A Systematic Literature Review

Hashfi,

Raharjo

2023

IJACSA

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This paper presents a systematic literature review (SLR) investigating the challenges and impacts of implementing artificial intelligence (AI) in project management, specifically mapping them into the process groups defined in the Project Management Body of Knowledge (PMBOK). The study aims to contribute to the understanding of integrating AI in project management and provides insights into the challenges and impacts within each process group. The SLR methodology was applied, and a total of 34 scientific articles were analyzed. The results and analysis reveal the specific challenges and impacts within each process group. In the Initiating Process Group, AI tools and analysis techniques address challenges in risk assessment, cost prediction, and decision-making. The Planning process group benefits from various tools and methodologies that improve risk assessment, project selection, cost estimation, resource allocation, and decision-making. The Execution process group emphasizes the importance of advanced tools and techniques in enhancing productivity, resource utilization, cost reduction, and decision-making. The Monitoring and Controlling process group demonstrates the potential of advanced tools in achieving efficiency, cost reduction, improved quality, and informed decision-making. Lastly, the Closing process group emphasizes the importance of utilizing advanced tools to minimize waste, optimize resource utilization, reduce costs, improve quality, and project closure success. Overall, this research provides valuable insights and strategies for organizations seeking to implement AI in project management, thereby enhancing the potential for success within the PMBOK Process Group.

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“…However, there is a strong perception that project delivery is yet to match the adoption of data analytics seen in other disciplines (Bevort, 2020; Lucas, 2021; Rowland, 2020). Project delivery refers to the comprehensive process of completing and delivering a project to the client or end-users.…”

Section: Introductionmentioning

confidence: 99%

Data analytics for project delivery: unlocking the potential of an emerging field

Shen,

Brookes,

Lattuf Flores

et al. 2024

IJMPB

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PurposeIn recent years, there has been a growing interest in the potential of data analytics to enhance project delivery. Yet many argue that its application in projects is still lagging behind other disciplines. This paper aims to provide a review of the current use of data analytics in project delivery encompassing both academic research and practice to accelerate current understanding and use this to formulate questions and goals for future research.Design/methodology/approachWe propose to achieve the research aim through the creation of a systematic review of the status of data analytics in project delivery. Fusing the methodology of integrative literature review with a recently established practice to include both white and grey literature amounts to an approach tailored to the state of the domain. It serves to delineate a research agenda informed by current developments in both academic research and industrial practice.FindingsThe literature review reveals a dearth of work in both academic research and practice relating to data analytics in project delivery and characterises this situation as having “more gap than knowledge.” Some work does exist in the application of machine learning to predicting project delivery though this is restricted to disparate, single context studies that do not reach extendible findings on algorithm selection or key predictive characteristics. Grey literature addresses the potential benefits of data analytics in project delivery but in a manner reliant on “thought-experiments” and devoid of empirical examples.Originality/valueBased on the review we articulate a research agenda to create knowledge fundamental to the effective use of data analytics in project delivery. This is structured around the functional framework devised by this investigation and highlights both organisational and data analytic challenges. Specifically, we express this structure in the form of an “onion-skin” model for conceptual structuring of data analytics in projects. We conclude with a discussion about if and how today’s project studies research community can respond to the totality of these challenges. This paper provides a blueprint for a bridge connecting data analytics and project management.

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How Artificial Intelligence Influences Project Management

Cited by 4 publications

References 0 publications

Assessing the Potential of Artificial Intelligence in Advancing Clean Energy Technologies in Europe: A Systematic Review

Assessing the Potential of Artificial Intelligence in Advancing Clean Energy Technologies in Europe: A Systematic Review

Exploring the Challenges and Impacts of Artificial Intelligence Implementation in Project Management: A Systematic Literature Review

Data analytics for project delivery: unlocking the potential of an emerging field

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