The AI Project Management – Why It’s Different?

Traditional Project management in Software Development world is all about managing cost, time, scope and most importantly stakeholders! It all comes with multiple challenges but variability in these can be well defined and sometime frozen with a well defined and time bound ‘Definition of Done’!

Its slightly tricky for AI Projects – and project manager’s role become far more important to tie all these together. Lets take a look and dive deeper into it’s nuances.

AI project management has some distinct characteristics that differentiate it from traditional software or IT project management. Here are some of the key differences and considerations:

  1. Uncertainty and Experimentation: Traditional software projects usually have well-defined requirements. In AI projects, it might be unclear if a certain approach or algorithm will yield the desired results until it’s tried. Many AI projects start with a research or experimental phase where the viability of an idea is tested.
  2. Data Dependency: AI projects, especially those involving deep learning, are heavily dependent on data. The quality, quantity, and relevance of this data can significantly impact the outcome. Data acquisition, preprocessing, cleaning, and annotation can be major tasks in AI projects.
  3. Specialised Skills: AI projects often require domain experts, data scientists, data engineers, and researchers. Coordinating between these experts can be challenging.Ensuring that everyone is on the same page and understanding the technical nuances is crucial.
  4. Infrastructure and Compute Needs: Training machine learning models, especially deep learning ones, might require specialised hardware like GPUs. Scalability and deployment considerations can be more complex, especially for real-time applications (exa: Recommendation system for a live-cart, suggesting customer what to buy next)
  5. Iterative Development: AI projects are usually iterative. A model might be trained multiple times with different parameters, data, or architectures to achieve the desired accuracy or performance.Continuous feedback loops are essential.
  6. Ethical and Bias Considerations: AI can inadvertently introduce or amplify biases present in the data. Ensuring that models are fair and don’t perpetuate harmful stereotypes is crucial.There might also be concerns about transparency, accountability, and interpretability of the models.
  7. Validation and Evaluation: Determining the success of an AI project can be trickier than traditional software. Besides accuracy, other metrics like precision, recall, F1 score, or domain-specific metrics might be important. It’s also important to guard against overfitting, where a model performs exceptionally well on training data but poorly on unseen data.
  8. Continuous Learning and Adaptation: Unlike traditional software which might be “done” after release, AI models might need to be retrained or fine-tuned as new data becomes available or as the environment changes.Monitoring model performance in production and having mechanisms to update it is essential.
  9. Stakeholder Communication: This probably is most tricky, managing expectations is crucial. Stakeholders might have unrealistic expectations from AI due to media hype. It’s also important to communicate the limitations and uncertainties associated with AI projects.
  10. Regulatory and Compliance Concerns: Depending on the industry (like healthcare or finance), there might be regulatory requirements for transparency, accountability, or fairness of AI systems.

In essence, while the foundational principles of project management (like scope management, time management, and stakeholder communication) still apply, the nature of AI projects introduces additional complexities and considerations.

Effective AI project management requires a combination of traditional project management skills, a deep understanding of AI and data science, and the ability to navigate the unique challenges and uncertainties associated with AI.

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