NEW! Using Artificial Intelligence in Strategic Analysis and Decision-Making Processes with Esra Tepeli

What You Will Learn

Upon completion of this training, learners will be able to:

  • Understand the basics of artificial intelligence and its applications in strategic analysis and decision making.
  • Evaluate the potential benefits and risks associated with using artificial intelligence in strategic analysis and decision making.
  • Develop strategies and plans for integrating artificial intelligence into existing decision-making processes.


Decision making in projects is a complex and dynamic process that depends on various project parameters and the project environment. Since each project is unique and has specific factors, a thorough analysis of the project context is necessary to develop an objective and reliable decision-making model. Complex projects often hold strategic importance for stakeholders in terms of their business strategy, brand image, and financial and economic opportunities. While front-end planning is essential, not all events and scenarios can be foreseen, especially for long-term projects involving multiple stakeholders. Therefore, strategic analysis is critical in the early phases of the project to identify and analyze major opportunity and risk factors for making informed strategic decisions. However, in the early phases the amount of information available is limited, and predictive tools may be necessary to forecast the project's future.

Artificial Intelligence (AI) can be a powerful tool in strategic analysis and decision-making processes. It can analyze vast amounts of data, develop predictive models, analyze text data, extract valuable insights, provide decision support, and automate decision making. This is particularly important in projects, where numerous variables and factors need to be considered to make informed decisions.

AI can analyze data and identify patterns, correlations, and trends that may not be apparent to humans, providing insights into market trends, customer behavior, and competitor activity. It can develop predictive models that forecast future outcomes based on past performance, identifying potential risks and opportunities, and developing strategies to address them. AI can provide decision support by analyzing multiple scenarios and providing recommendations based on objective criteria, leading to more informed and data-driven decisions. Additionally, AI can automate decision-making processes based on predefined rules and criteria, streamlining decision making, and reducing the time and resources required to make decisions.

In this training, we will explore how AI can be used in strategic analysis and decision-making processes throughout the project life cycle. We will provide a framework for combining the AI model into strategic analysis and decision-making methods, tailored to project characteristics, complexity, environmental factors, project life cycles, project management goals, stakeholders' objectives, project governance, project teams, and corporate strategies.



  • I. Introduction to Artificial Intelligence (AI)

    • Definition and history of AI
    • Applications of AI in various industries
    • AI tools and techniques for strategic analysis and decision-making

    II. Benefits and Risks of AI in Strategic Analysis and Decision-Making 

    • Potential benefits of using AI for decision-making
    • Risks and limitations of AI in decision-making
    • Ethical and legal considerations

    III. Types of AI Tools and Techniques 

    • Predictive analytics and modeling
    • Neural networks and deep learning
    • Visualization tools and techniques

    IV. Real-World Examples of AI in Strategic Analysis and Decision-Making 

    • Case studies from various industries, such as finance, healthcare, and marketing
    • Success stories and best practices for implementing AI in decision-making

    V. Integrating AI Into Existing Decision-Making Processes 

    • Strategies for incorporating AI into current processes
    • Planning for the implementation of AI
    • Change management and training for stakeholders

    VI. Evaluating and Communicating Results 

    • Analyzing and interpreting the results of AI-based analysis and decision-making
    • Communicating findings and recommendations to stakeholders
    • Evaluating the effectiveness of AI-based decision-making

    VII. Future Trends and Directions in AI for Strategic Analysis and Decision-Making     

    • Emerging AI tools and techniques
    • Potential future applications of AI in decision-making
    • Conclusion, discussions

PDU Allocation Table

The table below displays the number of professional development units (PDUs) awarded for each PMI® credential, as they align to the PMI Talent Triangle®. Power Skills and Business Acumen PDUs apply evenly across all credentials and Ways of Working PDUs apply only to specific credentials. PDUs will be added in full to all eligible credentials. *Please note that the asterisked row below applies to the PMI® Agile Certification Journey and includes Disciplined Agile® Scrum Master (DASM), Disciplined Agile® Senior Scrum Master (DASSM), Disciplined Agile® Coach (DAC), and Disciplined Agile® Value Stream Consultant (DAVSC) certifications.
Ways of Working Power Skills Business Acumen Total
CAPM® / PMI-CP™ / PMP® / PgMP® 2 2 3 7.00
PMI-ACP® / Agile* 0 2 3 5.00
PMI-SP® 0 2 3 5.00
PMI-RMP® 0 2 3 5.00
PfMP® 0 2 3 5.00
PMI-PBA® 0 2 3 5.00
Ways of Working Power Skills Business Acument Talent Triangle



Esra Tepeli PMP

Dr. Esra Tepeli has a civil engineering background with a double major in project and risk management. She obtained her PhD in the field of construction project management and risk management at the University of Bordeaux. During her PhD, she worked on project and risk management for complex and strategic construction projects, especially Public-Private-Partnership (PPP), design- build-maintenance and transportation projects, in cooperation with a major French construction company. Her PhD thesis consists of the development of a formalized and systematic risk management process in a complex project environment. After her PhD, she started working as an academic member and she has taught a variety of construction management and project management courses for undergraduate and graduate students. Her research and teaching activities deal with project and risk management, construction management and building information modeling. She is an author and co-author of several peer-reviewed scientific articles, conference papers, and books including Risk Analysis in Early Phase of Complex Infrastructure Projects, and Risk Management Keys for Complex Construction Projects. Besides her academic career, Dr. Tepeli is working as a project and risk management consultant in the private sector. Dr. Tepeli is also an active PMI member, instructor in the PMI Training program, having conducted project management mentoring and outreach projects, and has presented at global conferences and various events held by numerous chapters.

Training Information

16 September 2024
Start Time
8:00 AM
1 Day


Member Price
(US$705.00 before 5 August)
Nonmember Price
(US$910.00 before 5 August)
Government Price
Student Price

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