Below, you see my article in the Newsletter of the PMI® Switzerland Chapter.
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22 March 2026
Artificial Intelligence (AI) and Project Management: A Misunderstood Relationship
Author: Can Izgi, PMI-CPMAI, PMP
The aim of this article is to explain how AI is influencing project management. Although I deliver a course related to this subject, I thought preparing an article would be useful as well.
There are two fundamentally different situations when combining AI with project management. I call them “AI-assisted project management” and “AI-creation projects.” Let’s examine these two cases:
Case 1: AI-assisted project management
This case is about using AI tools in project management activities. Many AI tools are available to support project teams in their daily work. These tools can be used to assist with tasks such as analyzing project data, preparing reports, summarizing meetings, generating ideas, evaluating alternatives, etc. These tools can improve efficiency, reduce errors, and free up time for more strategic decision-making. Although their use may depend on organizational policies, they are a standard part of project management in many industries. However, this represents only one side of the story.
Case 2: AI-creation projects
This case is about managing projects that create AI products. In this context, lack of knowledge and various misunderstandings are frequently encountered. Therefore, the rest of this article focuses on this case.
1. Waterfall project management
In this approach, a detailed project plan is created. The plan is approved, establishing a baseline. Then, the project team attempts to follow this plan. On the other hand, AI development is inherently experimental and iterative. It’s more realistic to respond to changes as they occur rather than to generate a detailed plan and attempt to follow it. Therefore, relying solely on the Waterfall project management approach isn’t suitable for this case.
2. Agile project management
In this approach, the product is delivered in small increments, which are delivered continuously. The customer and other stakeholders provide feedback continuously. Based on the feedback, the team makes small adjustments. When the project aims to create an AI product, the product is not delivered in small increments. Therefore, relying solely on the Agile project management approach isn’t suitable for this case.
3. Hybrid project management (combining Waterfall and Agile approaches)
As explained above, trying to establish a stable baseline is unrealistic, and the product is not delivered in small increments. Therefore, combining these two approaches doesn’t provide an effective management strategy for this case. Neither approach addresses the unique characteristics of AI-creation projects.
4. Managing Artificial Intelligence (AI) projects
Projects that create AI products differ from traditional projects in an important way: The resulting solution is probabilistic rather than deterministic. It depends heavily on the available data. Therefore, we need a data-centric approach where the aim is to generate a probabilistic solution. In an iterative manner, the suitable approach will use the CRISP-DM (CRoss-Industry Standard Process for Data Mining) framework or a modified version of it. Here, the project goes through the following phases:
a. Business Understanding
b. Data Understanding
c. Data Preparation
d. Modeling
e. Evaluation
f. Deployment
Conclusion
AI is influencing project management in two distinct ways. First, project teams are increasingly using AI tools to support their daily work. Second, organizations are launching projects whose goal is to build AI products. When a project aims to create an AI product, traditional project management approaches aren’t suitable. We need a different approach because AI projects are data projects.
About the author
Can Izgi, PMI-CPMAI, PMP is an instructor and consultant specializing in Artificial Intelligence (AI) and project management. Since 2005, he has been delivering professional courses and workshops related to these subjects.
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