Acceptance by employees

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Data quality
Data quality is the foundation for AI.AI models are only as good as the data they are trained with. If the data is faulty, incomplete, or inconsistent, it leads to inaccurate predictions and decisions. Before data can be fed into an AI system, it often needs to be prepared and cleaned. This process is time-consuming and requires expertise. Data from different sources often needs to be combined, which poses further challenges regarding data quality and consistency.
Many employees fear that AI could make their jobs obsolete. Furthermore, people are often resistant to change, especially when adapting to new ways of working. In addition, AI systems can sometimes make mistakes or make decisions that are difficult to understand. This can undermine employees' trust in the technology.
Data protection and security
Project management often involves processing confidential information such as customer and company data. Protecting this data is of utmost importance. Companies must continue to ensure that the use of AI complies with applicable data protection regulations (e.g., GDPR). AI systems can themselves become targets of cyberattacks. It is therefore crucial to implement appropriate security measures and security software .
Solutions for AI challenges in project management
We asked project managers what they considered the biggest drawbacks of AI in project c level executive list management. Almost half of them (48%) cited false confidence or misconceptions about AI's capabilities. 

It's important to understand what AI can do and where its limitations lie: Results aren't always accurate, are often unreproducible, and AI bias can occur. It's important for companies and project managers to learn how to properly address the problems of AI bias. 

Examples of what biased AI in project management could look like:
If AI-based task allocation is based on historical data, certain tasks could be repeatedly assigned to certain groups, leading to a reinforcement of stereotypes.
AI could also distribute tasks unevenly due to biases against certain teams or departments. 
AI-based risk assessment, which has not adequately considered certain risks based on historical data, could underestimate these or similar risks in future projects.
If AI predicts that certain projects will fail based on historical data, these predictions could lead to fewer resources being invested in those projects, which in turn makes failure more likely.