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Managing Project Scope in AI Projects -Key challenges and Solutions

In AI Singapore (AISG), we undertake and run many Artificial Intelligence/Machine Learning (AI/ML) projects where it is not uncommon that the project teams face challenges, both technical and non-technical, while executing the projects. In this article, I am going to share some of the key challenges faced in the area of project scope and how they were addressed by the project managers and the project teams.

PROJECT SCOPE 

Change is constant and scope changes are expected in any projects, but frequent scope change, one of the main challenges faced by the project teams, can potentially derail a project from its planned schedule.

A potential cause for scope changes is the lack of alignment of the project scope between the project sponsor and the product owners or the key stakeholders of the organisation concerned. As the projects executed by AISG have a fixed time duration of seven months, it is particularly important to closely monitor, control and manage scope changes during the project execution.

Challenges faced

  • Frequent scope change requests during project execution
  • Lack of alignment of project scope between the project sponsor and the product owners
  • Project sponsor is not clear of the business requirements from the product owners

 How did we deal with scope changes?

  • Proactively worked with the project sponsor and the product owners to get the exact requirements and understand their expectations, and scoped the work which can be accomplished within the project timeframe.
  • Any scope changes beyond the initial project scope mutually agreed in the project kick-off meeting were evaluated by the project team to assess the feasibility of implementing them within the remaining project timeframe.
  • Prioritised/accepted scope changes which carry more business value to the project sponsor’s organisation and can be accomplished within the project timeframe, and rejected the others.
  • Documented the scope changes and communicated them, together with the revised project plan, to the project sponsor for acknowledgement.

CONCLUSION

 Like all other projects, managing and running AI/ML projects has its set of challenges. The project team needs to adopt a partnership mindset to work hand-in-hand with the project sponsor to overcome the challenges faced. The close collaboration and partnership between the project team and the project sponsor will see long way in overcoming the challenges and ensuring successful delivery of the project. It is also important to constantly review the challenges and lessons learnt from the past projects and look for ways for improvement.

Author

  • Sudha Ravi

    I manage 100 Experiments projects and internal projects at AI Singapore where I help to deliver AI solutions for project sponsors from various industries.