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Project Management

AI in Open Innovation Project Management

  • Introduction

Artificial Intelligence (AI) is reshaping industries, and its intersection with open innovation (OI) and project management (PM) is creating new opportunities for revenue growth and business performance. Open innovation has become a key driver of revenue growth and business performance. While ideation is important, effective project management ensures ideas are transformed into business strategies. AI enhances project management by integrating diverse knowledge sources and improving collaboration. It also provides strategic insights for decision-making. This literature review explores AI's applications in project management, particularly within open innovation contexts, and identifies key AI technologies, their contributions, adoption across industries, and the requirements for successful integration.

  • Overview of AI Technologies in Project Management Machine Learning (ML) is a widely used AI technology in project management, especially for predictive analytics, resource optimization, and risk management. ML, a subset of AI, teaches computers to learn from data without explicit programming. Generative AI, another subfield, creates new content like text and images by learning patterns from existing data. Generative AI enhances efficiency, decision-making, and automation across industries.

Common AI techniques include data mining, natural language processing (NLP), mobile learning, case-based reasoning, fuzzy logic, genetic algorithms, cognitive computing, computer vision, robotics, and big data/data analytics. Eleven of these techniques were found in selected studies, in addition to generative AI. ML and NLP can extract valuable insights from datasets, including patents and social media. NLP can forecast trends and match problems with solutions, while ML improves project risk management.

III. AI's Impact on Project Management Tasks

AI significantly enhances various project management tasks. Based on the Project Management Institute (PMI), these tasks include planning and managing scope, assessing and managing risks, planning and managing schedules, planning and managing budgets and resources, planning and managing the quality of products/deliverables, engaging stakeholders, managing project artifacts, managing project issues, ensuring knowledge transfer for project continuity, executing project with urgency to deliver business value, determining appropriate project methodology/methods and practices, and managing communications.

  • Planning and managing budgets and resources is the most frequently mentioned task, underscoring its importance in project success and AI's transformative potential in this area. AI can analyze large datasets to identify patterns, optimize resource allocation, and provide predictive insights into potential budget overruns or resource shortages.
  • Assessing and managing risks is another critical area where AI is applied. AI's ability to predict risk levels based on historical data enhances project success rates. AI techniques are also being applied to risk management in open innovation projects.
  • Planning and managing schedules benefits from AI's ability to estimate progress and identify potential delays, facilitating effective timeline management.
  • Planning and managing the quality of products and deliverables is enhanced through automated quality checks and stability predictions, leading to higher customer satisfaction and reduced rework.
  • Generative AI enhances project management by improving efficiency, creativity, and team coordination. It breaks down projects into tasks, monitors progress, manages dependencies, and automates workflows. Generative AI also facilitates communication and collaboration by providing contextual insights and streamlining tasks.
  • Industry-Specific Adoption of AI in Project Management The industrials sector leads in AI adoption in project management. This sector benefits from AI's ability to enhance efficiency, precision, and safety in large-scale projects. In construction and manufacturing, AI automates tasks, optimizes resource use, and improves risk management. The energy industry also shows significant involvement, particularly in risk mitigation, quality management, and financial oversight.
  • Industrials extensively utilize AI for risk management and scheduling.
  • The energy industry focuses on risk mitigation, quality assurance, and financial management with AI.
  • In the consumer staples sector, AI-based tools streamline decision-making by providing insights from trend analysis, historical data, and predictive models. This fosters better collaboration among diverse stakeholder groups, enhancing decision-making efficiency and resource allocation.
  • Requirements for AI Adoption in Open Innovation Project Management

Successful AI adoption depends on technical and non-technical requirements.

  • Technical requirements include a robust technical infrastructure, seamless integration with existing systems, and data readiness. Technical infrastructure is frequently cited as the backbone of AI deployment, encompassing the necessary hardware, software, and networking capabilities that support AI operations. Data management and readiness are crucial because AI systems rely heavily on high-quality, well-organized data.
  • Non-technical requirements include leadership support, strategic alignment, financial resources, skills development, and organizational culture. Organizational culture is important for embracing new technologies. Leadership support is essential for championing AI initiatives. Financial resources are crucial for covering the costs of AI implementation and maintenance. In open innovation projects, an open and collaborative organizational culture is vital.
  • Challenges and Limitations

Adopting AI in project management presents several challenges and limitations. Rapid technological evolution can quickly make literature outdated. The interdisciplinary nature of AI complicates the search and synthesis process. Potential biases in literature selection may lead to an incomplete understanding of AI's role. The focus on commonly known AI technologies may overlook emerging or niche technologies. The diversity of included studies with varying contexts and methodologies limits the generalizability of findings.

A limitation of systematic literature reviews (SLR) is that it doesn't allow for quantitative data synthesis. This limits the ability to draw definitive conclusions about the comparative effectiveness of various AI applications in project management.

VII. Future Research Directions

Future research should focus on the long-term effects of AI on project management. Longitudinal studies are needed to understand how AI affects project results over time. Research should also highlight AI applications in sectors with less research, such as public administration, healthcare, and education.

VIII. Conclusion

AI integration into project management improves efficiency, decision-making, and overall project outcomes across industries. In open innovation, AI's advanced analytics help manage complexity by identifying patterns, optimizing resource allocation, and supporting informed decision-making. AI enables organizations to respond quickly to changing market demands and collaborate effectively with external contributors. The industrials sector, especially construction, benefits significantly from AI technologies. Organizations should prioritize building the right technical infrastructure and managing data effectively to ensure successful AI adoption.

  • How are AI and Machine Learning (ML) defined in the context of open innovation project management?

AI is a broad field focused on mimicking human intelligence, while ML is a subfield of AI. ML focuses on enabling computers to learn from data without explicit programming. Generative AI, a further subset, creates new content (text, images, audio, video) by learning from existing data patterns.

  • What are the key criteria used to assess the quality of research studies included in the systematic literature review?

The study quality assessment is based on five main topics, each weighted equally: clear research objectives and research questions, a clear statement of scientific contribution, an explanation of the technical application or role of AI, relevance through study cases, practical applications, or implications, and a clear statement of findings. Each criterion is scored as "yes," "partly," or "no."

  • Which AI technologies are most frequently used in project management, and for what purposes?

Artificial Neural Networks (ANNs) are strongly preferred, primarily for prediction tasks. Fuzzy Logic is increasingly recognized for handling uncertainties and improving estimation accuracy. Computer Vision is used to automate and enhance visual tasks like quality control and site monitoring. Random Forests are used for handling complex datasets and providing interpretable results. Support Vector Machines (SVM) perform linear classification.

  • In what specific project management tasks is AI and ML most frequently applied?

AI/ML is prominently used in risk assessment and management (predicting risk levels), schedule planning and management (estimating progress, identifying delays), and quality management (automated quality checks, stability predictions).

  • Which industries are benefiting the most from the adoption of AI in project management?

The industrial sector leads in AI adoption for project management, closely followed by Information Technology and Energy. The consumer staples sector is also actively implementing AI solutions.

  • What are the technical requirements for successful AI adoption in project management?

Adequate technical infrastructure is crucial, including the availability and suitability of hardware, software, and network resources needed to effectively support AI tools.

  • What non-technical dimensions are critical for the successful integration of AI in project management?

Key non-technical dimensions include strong leadership support to champion AI initiatives, a supportive organizational culture that embraces AI, and comprehensive training and skill development programs to equip staff with the necessary AI skills.

  • What are some of the ethical considerations that need to be addressed when using AI in project management?

Ethical concerns include data privacy issues, the potential for errors in automated processes, and ethical considerations in AI decision-making. In specific sectors, ethical issues in AI deployment are also a potential concern, particularly in healthcare

(Prasetyo et al., 2025)

Prasetyo, M. L., Peranginangin, R. A., Martinovic, N., Ichsan, M., & Wicaksono, H. (2025). Artificial intelligence in open innovation project management: A systematic literature review on technologies, applications, and integration requirements. In Journal of Open Innovation: Technology, Market, and Complexity (Vol. 11, Issue 1). Elsevier B.V. https://doi.org/10.1016/j.joitmc.2024.100445