In the ever-evolving landscape of Information Technology, Artificial Intelligence (AI) projects stand out as a distinct category. In this post, we would like to discuss about how AI projects are different from other IT projects in terms of Project Management and how to address them.
Uncertainty and Unpredictability
The outcomes of an AI models are often unpredictable, leading to increased uncertainty. Effectively managing this uncertainty as well as the client's expectation became a significant challenge which demands project managers to be agile and ready to pivot when unforeseen challenges arise.
Data Dependency
High-quality data plays a crucial role in any AI project. Therefore, having a comprehensive data pipeline from cleaning to preprocessing is important for AI projects. Moreover, ensuring data privacy, security, and compliance with regulations add an additional layer of complexity. As a result, project managers must establishing effective communication with clients about the data quantity and quality. Simultaneously, ensuring the project team is implementing a robust data processing pipeline.
Repeat Experimentation
Unlike traditional IT projects, AI projects often involve repetitive experimentation to identify the most effective algorithms and approaches. Project teams, especially project managers, must strike a balance between supporting these activities and at the same time, adhering to project goals and timelines.
Compute Resources
The iterative nature of experimentation in AI projects demands high-intensity compute resources for training and evaluating models. Project managers must proactively ensure the availability of such resources for the team. Simultaneously, establish effective communication with clients about these resources' specifications requirements is relatively important to manage expectations.
In conclusion, AI projects present a set of distinctive challenges that set them apart from traditional IT projects. As organizations increasingly leverage AI to enhance decision-making and drive innovation, understanding and addressing these differences becomes crucial for effective project management.
What are your thoughts on this topic? We look forward to hearing your insights!
