The Unique Nature of AI Projects

Julian Krätzmann describes AI projects as iterative and data-driven, unlike traditional IT projects that often pursue a fixed end goal. A success rate of 80–90% is usually sufficient for AI projects, as higher accuracy could overload the system and hinder its learning capabilities. This highlights the fundamental differences between conventional and AI-specific projects.

The Biggest Challenges in Detail

According to Krätzmann, AI projects pose several key challenges:

• Technological Change
  Krätzmann emphasizes the importance of staying flexible while avoiding the temptation to adopt every new technology. The key lies in setting priorities and assessing long-term feasibility.
• Interpersonal Challenges
  The acceptance of AI hinges on well-thought-out change management. Krätzmann highlights that it is essential to help people understand that AI is not intended to replace jobs but to provide support.
• Data Protection and Regulations
  Regarding European standards like GDPR, he explains that the release of business data for training purposes requires a careful cost-benefit analysis.

Strategy, Tools, and the Role of Data

A robust strategy forms the foundation of every AI project. Krätzmann stresses that AI projects should have a meaningful purpose and not be conducted simply for the sake of using AI. He recommends the CRISP-DM framework (Cross-Industry Standard Process for Data Mining) as a valuable tool for structuring processes, from defining the vision to implementation.

Data is also crucial for any AI project. It is essential to first evaluate which data is available, what is missing, and how it can be prepared. He recommends analyzing data selectively and focusing only on relevant information. A data template can help organize and streamline the development process.

How Different Generations View AI

Julian Krätzmann points out that the challenges of engaging with different generations in the workforce vary significantly. For older employees, it is vital to value their experience and involve them actively to foster acceptance of AI. Meanwhile, younger employees, often referred to as Digital Natives, must be sensitized to handle AI systems and sensitive data responsibly. Effective communication and an understanding of the needs and concerns of each generation are critical to ensuring sustainable AI acceptance within the organization.

Change Management: A Key Factor for the Acceptance and Implementation of AI Projects

The implementation of AI tools, such as chatbots in customer service, illustrates the specific requirements of AI projects in project management and change management. According to Krätzmann, such projects require an iterative approach and the early involvement of end users.

AI projects often do not follow a fixed goal but instead evolve iteratively. The initial stages involve data collection and preparation, followed by the development of a first model. This model is tested and refined using new data. This ongoing adjustment requires flexibility in project management to adapt to new demands or changes.

Change management plays a central role in these projects. Krätzmann highlights the importance of addressing user concerns from the outset. To enhance acceptance, he recommends involving employees in decisions such as naming or designing a chatbot. These steps help reduce fears and build trust in the technology. Additionally, training sessions can educate users on how AI technologies can support and ease their workload — such as automating routine tasks — allowing them to focus on more complex responsibilities.

AI as a Support Tool in General Project Management

Beyond AI-specific projects, the use of AI can bring substantial benefits to general project management. Krätzmann explains that AI can be particularly impactful in areas such as data analysis, process automation, and team communication, significantly easing the workload of project managers.

AI-powered tools have the ability to analyze large volumes of data and extract valuable insights. This allows project teams to identify potential risks early and take proactive measures to address them. Additionally, routine tasks such as creating reports, tracking project progress, or monitoring budgets can be automated, freeing up time for strategic and creative activities.

Team communication also stands to benefit greatly from AI. Intelligent tools can help structure and distribute information more effectively, reducing misunderstandings and enhancing transparency across the board.

Another area where AI proves invaluable is decision-making. Using historical project data and current trends, AI tools can generate data-driven recommendations, providing project managers with the insights they need to make informed decisions. Krätzmann emphasizes that this capability can significantly increase the chances of project success.

However, despite its many benefits, Krätzmann underscores that AI remains a supportive tool. The ultimate responsibility for evaluation and decision-making continues to rest with the project team, ensuring that human oversight remains at the center of project management.

Hybrid Project Management as a Recipe for Success

Krätzmann concludes that AI projects often require a hybrid approach that combines classical and agile methods. Flexibility is essential, enabling businesses to think strategically while adapting to technological changes. With the right methods, AI becomes more than just a tool — it transforms into a game-changer for organizations.

The success of AI projects relies on a well-defined strategy, a flexible combination of agile and classical approaches, and the active involvement of all stakeholders. Only by addressing technological, organizational, and interpersonal challenges can businesses fully unlock the potential of AI.