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While some argue that AI’s low entry barriers can help break down technological barriers and promote equity in underdeveloped regions (K.L. Scott & Associates, LLC, 2023), AI can also contribute to social inequality in certain situations.

One of the key themes of the Writers Guild of America (WGA) strike in May this year was a rainy-day backlash against the potential impact that AI could have on the screenwriting industry. The major American studios have adopted a form of work called the “mini room”. The type of work requires a number of writers writing at a junior level over an extended period of time, followed by quality enhancement by senior writers to ultimately produce a usable script. The WGA is concerned about two major issues after the surge of large AI models: 1.AI may be used on a large scale in junior writing in the near future, and the minimum number of screenwriting seats a studio can offer, as well as the employment opportunities for WGA members, have been reduced. 2.Previously the scripts they created were used to train AI, which in turn ended up threatening the creators’ very existence (Silicon Valley 101, 2023).

As we can see when we try to create some stories in ChatGpt, the AI is not yet capable of producing a script that can be mass-produced. However, as mentioned earlier, the “writers room” used in the screenwriting industry is to some extent a way of gathering different people and different brains to piece together a more colourful worldview in the early stages of scriptwriting, which is something that AI is capable of producing accurate outputs after learning. There is still room for application in the foreseeable period as some production companies begin to allow AI to replace the work of human screenwriters in the pre-script. Screenwriters who are replaced are frequently newcomers to the industry, earning less and doing relatively repetitive and boring work, and they will miss out on opportunities to practice. This has the potential to exacerbate inequalities in the resources available to young people and creative workers at the bottom of the income scale. AI also involves intellectual property protection. How will the ordering be in the final autograph session if the AI completes the first draft of a script and then a senior human screenwriter touches it up? It is also debatable whether this will further reduce the pay and industry influence of human screenwriters. (StochasticVolatility, 2023)

Literature

• K.L. Scott & Associates, LLC. (2023, March 14) AI Impacts on Social Equity. Linkedin. https://www.linkedin.com/pulse/ai-impacts-social-equity-klsassociates/
• Silicon Valley 101. (2023, August 2) Hollywood actors’ and writers’ unions strike one after another: the AI shockwave and the existential crisis of the practitioners. 36kr. https://36kr.com/p/2370500521207558
• StochasticVolatility. (2023, August 30). From Hollywood to The Voice: Who defines the value of work? StochasticVolatility 120. https://www.stovol.club/120

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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