Artificial Intelligence is rapidly moving from experimentation to real-world application. In sectors like healthcare and public services, however, adopting AI goes far beyond technology — it raises critical questions around governance, regulation, ethics, and data sharing.

This shift is at the heart of the upcoming event “Inside the AI Hub at Campus Biotech: Exploring AI in Highly Regulated Environments” on April 14, 2026 (Tuesday, 18:00). Hosted within Geneva’s AI Hub, the session offers a unique opportunity to explore how AI is being developed where safety, transparency, and accountability are essential.

A Collaborative AI Ecosystem in Action

The AI Hub (Pôle IA) Geneva — created by Geneva University Hospitals (HUG), the Wyss Center for Bio and Neuroengineering, and the Canton of Geneva — brings together clinicians, researchers, and AI specialists in a shared environment at Campus Biotech.

Over the past year, the Hub has focused on building infrastructure, partnerships, and early approaches to AI and data sharing. It represents one of Europe’s newest collaborative ecosystems for applied AI in healthcare, where innovation is closely tied to real clinical use. Speakers from the AI Hub — Coralie Fournier, Project Manager at the Geneva University Hospitals (HUG), and Wolf-Julian Neumann,MD, Head of AI Innovation and Therapeutic Neurotechnology at the Wyss Center for Bio and Neuroengineering in Geneva — will offer firsthand insights from within this rapidly evolving ecosystem.

Why AI Is Different in Regulated Environments

In healthcare, AI must be designed with regulation in mind from the start. Issues such as patient data protection, clinical validation, and ethical oversight are integral — not optional.

By embedding development within Geneva University Hospitals (HUG), the Hub ensures that AI solutions are shaped by real-world constraints. This makes it a powerful example of how innovation can align with governance and operational realities.

What Participants Will Gain

Hosting the event inside the Hub allows participants to experience firsthand where clinical expertise, research, and digital innovation intersect. They will gain:

• Insight into how a new AI ecosystem in healthcare is being built
• Perspectives on early challenges of AI and data sharing in regulated settings
• Reflections on collaboration between clinicians, researchers, and technology experts
• Inspiration for applying AI in other regulated sectors

Conclusion

The AI Hub Geneva illustrates a crucial point: successful AI in highly regulated environments depends not just on advanced technology, but on collaboration, trust, and integration into real systems.

For attendees, the April 14th event offers a rare chance to learn directly from this emerging ecosystem, and to understand what it truly takes to deliver AI projects where the stakes are highest. The session will be followed by a networking apéro, providing an opportunity to connect with speakers and fellow participants, exchange perspectives, and continue the conversation in an informal setting.

For more information, visit the event page and register to take part in this engaging and insightful session: Event LINK.

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. A suitable approach is to iteratively 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, used in their standard form, are often not sufficient. 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 on these subjects.

Dear PMI Switzerland Chapter Community

In a few days, we will gather to celebrate a very special milestone: 25 years of PMI Switzerland. As we count down to this unforgettable evening, we would like to take a moment to recognize the people who have made it possible: our volunteers.

Behind the scenes, for six months, 11 dedicated members of our community have been planning, coordinating, designing, organizing, and refining every detail of this celebration. Their commitment, professionalism, and passion are the reason this anniversary event will be such a meaningful and memorable experience.

For 25 years, volunteers have been the heart of PMI Switzerland. They organize events, support initiatives, strengthen partnerships, and continuously create value for our members. The upcoming anniversary is not only a celebration of our history, it is also a celebration of their dedication.

To every volunteer involved in making this milestone possible: Thank you for your time, your energy, and your belief in our community.

Your engagement is what transforms PMI Switzerland from an association into a vibrant, living community.

We look forward to celebrating this week together and to honouring the spirit of collaboration that has defined us for 25 years.

With sincere appreciation,

Martin Härri, Linda Vasquez

25th Anniversary Team