Navigating AI Transformations: Key Competencies for Project Managers

AI projects heavily rely on large, high-quality datasets for training and testing AI models. Ensuring data quality, quantity, and relevance is crucial. Data must be cleaned, labelled, and preprocessed adequately to train models effectively.

AI projects require an iterative, cyclical approach (e.g., Agile or CRISP-DM methodology) where continuous experimentation, model tuning, and validation occur throughout the project. Models are continuously trained, tested, and refined based on feedback and new data, allowing for ongoing improvement and adaptation.

Evaluating AI model performance focuses on specific metrics such as accuracy, precision, recall, and F1 score. Model validation is crucial to ensure the AI solution meets the desired criteria and generalizes well to new data. This process requires ongoing model maintenance, monitoring for performance drift, retraining with new data, and handling evolving requirements and environments.

AI projects raise unique ethical concerns around bias, fairness, transparency, and accountability in AI decision-making. Compliance with regulations (e.g., GDPR) regarding data privacy and algorithmic transparency is crucial. Ensuring that AI models do not perpetuate or exacerbate biases is a significant concern.

Post-deployment AI models require ongoing maintenance, including monitoring for model degradation or bias, retraining with new data, and updating algorithms to keep pace with evolving requirements and environments.

AI projects require specialized roles such as data scientists, machine learning engineers, and AI researchers who have expertise in algorithms, statistical modelling, and data manipulation.