Enhancing Decision-Making with Artificial Intelligence in Project Management
Uloženo v:
| Vydáno v: | European Conference on Knowledge Management vol. 1 (Sep 2025), p. 1007-1016 |
|---|---|
| Hlavní autor: | |
| Vydáno: |
Academic Conferences International Limited
|
| Témata: | |
| On-line přístup: | Citation/Abstract Full Text Full Text - PDF |
| Tagy: |
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3270512425 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2048-8963 | ||
| 022 | |a 2048-8971 | ||
| 035 | |a 3270512425 | ||
| 045 | 2 | |b d20250901 |b d20250930 | |
| 084 | |a 183536 |2 nlm | ||
| 100 | 1 | |a Tenhunen, Marja-Liisa |u Dimitrie Cantemir Christian University, Bucharest, Romania | |
| 245 | 1 | |a Enhancing Decision-Making with Artificial Intelligence in Project Management | |
| 260 | |b Academic Conferences International Limited |c Sep 2025 | ||
| 513 | |a Conference Proceedings | ||
| 520 | 3 | |a In an era of rapid innovation and complex business challenges, the integration of knowledge management (KM) and project management (PM) has emerged as a critical key driver of strategic success. Project management is a complex discipline that requires effective decision-making to ensure the successful completion of projects within scope, time, and budget constraints. Traditional decision-making methods often rely on human judgment, which can be subject to biases, inefficiencies, and limitations in data processing. The rapid advancement of Artificial Intelligence (AI) has introduced innovative solutions to enhance decision-making in project management by leveraging machine learning, predictive analytics, and intelligent automation. This paper explores how AI-driven technologies are transforming decision-making processes in project management by improving risk assessment, resource allocation, and project forecasting. AI-powered tools can analyze vast amounts of historical project data to identify patterns, predict potential bottlenecks, and recommend optimal strategies. Additionally, artificial intelligence enables real-time monitoring of project performance, providing project managers with data-driven insights that enhance their ability to make proactive and informed decisions. By integrating AI into project management workflows, organizations can reduce human errors, optimize productivity, and improve overall project success rates. Despite its numerous benefits, AI implementation in project management presents challenges such as data privacy concerns, reliance on high-quality datasets, and the need for organizational adaptation. This paper discusses these challenges and provides recommendations for overcoming them to maximize the benefits of AI-driven decisionmaking. By analyzing real-world case studies and industry applications, this paper highlights the potential of AI in revolutionizing project management and offers insights into future trends. The findings underscore the importance of AI adoption in modern project environments and emphasize the need for continuous learning and ethical AI deployment. Attendees will gain insights into practical approaches for incorporating KM into project management workflows and fostering a culture of continuous learning and improvement. | |
| 653 | |a Accuracy | ||
| 653 | |a Data processing | ||
| 653 | |a Predictive analytics | ||
| 653 | |a Collaboration | ||
| 653 | |a Investigations | ||
| 653 | |a Success | ||
| 653 | |a Trends | ||
| 653 | |a Resource allocation | ||
| 653 | |a Knowledge management | ||
| 653 | |a Automation | ||
| 653 | |a Ethics | ||
| 653 | |a Machine learning | ||
| 653 | |a Research & development--R&D | ||
| 653 | |a Decision making | ||
| 653 | |a Efficiency | ||
| 653 | |a Risk assessment | ||
| 653 | |a Innovations | ||
| 653 | |a Technology adoption | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Project management | ||
| 653 | |a Optimization | ||
| 653 | |a Industrial applications | ||
| 653 | |a Natural language processing | ||
| 653 | |a Error reduction | ||
| 653 | |a Algorithms | ||
| 653 | |a Real time | ||
| 653 | |a Critical path | ||
| 653 | |a Human error | ||
| 773 | 0 | |t European Conference on Knowledge Management |g vol. 1 (Sep 2025), p. 1007-1016 | |
| 786 | 0 | |d ProQuest |t ABI/INFORM Global | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3270512425/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3270512425/fulltext/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3270512425/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |