Following the widespread popularity of ChatGPT, industries across the board are debating the opportunities and threats that AI technologies and models bring to the application level. There is a lot of tedious and repetitive labor in the field of project management. Project managers in the gaming industry where I used to work, for example, were inundated with task decomposition, scheduling, progress tracking, and risk identification, which did not require a high level of job skills once the pre-process design was complete. Previously, we had established an additional PMA position (A means assistant) in addition to the project manager to do this repetitive labor. Obviously, this practice is not cost-effective, and in addition to the enterprise’s employment costs, there is a high communication cost.
It is possible to make project management work smarter by embedding AI work in project management tools. For example, AI can automatically assign tasks and divide labor based on project knowledge, members’ skills, experience, workload, and so on. The project manager only needs to ask the AI at the start of each milestone, and the AI can capture the project information and quickly generate a working set of schedules based on some simple requirement compilation tools. Simultaneously, the AI can be continuously modified based on user feedback (for example, a program supervisor can provide feedback that a particular program was implemented in a relatively short period of time and request an increase in the duration of the work) until a workable schedule is written(AliCloud, 2023).
After understanding the rules, generative AI can learn and make comprehensive decisions, which can also be applied to risk prediction implementation. Previously, when we used project management tools to perform automated risk identification, we could only make decisions based on pre-set values such as numerical values, attributes, and so on. For example, comparing the current date with the expected completion date, identifying postponed tasks, and marking them in red. However, when AI tools are integrated into project management software, risk assessment can be combined with AI’s knowledge base to provide risk analysis, warning levels, and pre-treatment options.
As AI models become more powerful and extend their services in the future, project management will become more professional and simpler.
Literature
AliCloud. (2023, April 25) Apply “ai model + low code” in project management, the efficiency has increased several times. AliCloud Developer Community. https://developer.aliyun.com/article/1201233
Hi Jin! Thank you for your blog post, I find your blog relatable as I am also a former project management assistant in the mobile game industry. I agree that more AI technologies can be integrated into the project management workflow to optimize it, especially to save project managers’ efforts in handling repetitive and time-consuming tasks. Eliminating these routine tasks should not only reduce costs, but also minimize the potential for errors, thereby increasing the efficiency of the whole process. I can also imagine having AI-generated schedules and task assignments that take into account project knowledge, team skills, and workload for the job (hopefully in the future). Let’s both hope that the synergistic relationship between humans and AI can be realized, so that project managers can focus more on higher-level strategic decisions while AI takes care of the operational details.