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Aditya -
Given the high degree of uncertainty and complexity with an A.I. product development project, I'd assume that it would follow an adaptive approach and as such, your WBS could likely only be developed to one or two levels deep. It would also depend heavily on the nature of the AI product. If it is a machine learning-based approach, that will drive a certain set of deliverables and activities whereas a different type of AI would require others. Kiron
In my case, I am constantly running this type of project and I am using Agile based approach most of the time using Kanban or sometimes Scrum as the support method/framework. Please, do not think that I am saying that Kanban or Scrum are "agile methods/frameworks". Not at all. Just one fits for our flow based initiatives while the other fits for our batches based initiatives and one or another fits for AI and Data Science initiatives just in our case.
Here's ChatGPT's suggestioni:
I. Project Initiation A. Define project scope and objectives B. Conduct market research C. Gather requirements and constraints D. Develop project plan II. Requirements Analysis and Design A. Perform feasibility study B. Develop high-level system architecture C. Define system requirements D. Design AI algorithms III. Development A. Implement AI algorithms B. Develop User Interface (UI) C. Integrate AI components with UI D. Implement data storage and management IV. Testing A. Unit testing B. Integration testing C. System testing D. User acceptance testing V. Deployment A. Deploy to production environment B. Provide training to end-users C. Monitor and maintain system performance VI. Project Closeout A. Evaluate project results B. Document lessons learned C. Close project Note: This is a high-level example and may need to be adapted and refined based on the specific requirements and constraints of the AI product development project. |
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