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The first time I interacted with Generative AI, I regarded it as merely a novelty, something that could summarize content or produce a rough draft of notes. However, during this course, my perspective shifted. I started considering AI as more than an automation tool, as a partner that augments our thinking, writing, and creation processes.

Employing ChatGPT made it clear how swiftly an outline could be constructed, a set of ideas be organized, a set of arguments be refined, and other viewpoints be proposed. ChatGPT did not replace my work; however, it shifted my focus from drafting to improving and evaluating the output more critically. This corresponds with new findings within the area, which state that generative AI does not remove the human element, but increases productivity by enhancing it. For instance, in controlled studies, access to AI results in a 40% time saving for writing tasks, while also improving the output, particularly for novice users (Noy & Zhang, 2023).

New technologies bring new responsibilities. Because AI models are still biased and limited, human supervision must prevail. In one work environment, AI boosted productivity, but the productivity gains driven by generative AI were most pronounced for novices learning from AI while expert users supplied essential context and corrections (Brynjolfsson, Li, and Raymond, 2023). This indicates that expert human input and AI use are not equivalent. 

The most important lesson this course has taught me is that generative AI is more about reallocation than replacement, it reallocates creative resources toward analysis, emotion, and contextualization. This means the level of AI-assisted work will be determined more by the thoughtfulness of our partnership than the sophistication of the AI tools. One of the guest lecturers answered the question “Will AI replace humans?” very well by responding “No. AI won’t replace humans, but human who use AI will replace humans who don’t use AI.”

References

Brynjolfsson, E., Li, D., & Raymond, L. R. (2023). Generative AI at work (NBER Working Paper No. 31161). National Bureau of Economic Research. https://www.nber.org/papers/w31161

Noy, S., & Zhang, W. (2023). Experimental evidence on the productivity effects of generative artificial intelligence. Science, 381(6654), 187–192. https://doi.org/10.1126/science.adh2586

5/5 (1)

In the past ten years, digital platforms have changed the rules of competition. Unlike the “pipeline” retailers who constructed a competitive edge through long design cycles, investments made in the stores, and controlling the supply chains, these platform-style businesses obtain leverage by coordinating external ecosystems and ‘learning’ from the information flows. Shein is a very clear demonstration of this shift. 

Unlike the more traditional ways of forecasting trends months in advance, Shein tracks and monitors social media and identifies which styles are gaining momentum. Shein produces a limited production batch of test items of a style, supervises their sales, and scales up the items that perform well while the rest are quietly disposed of. This approach supports the notion that inventory is unnecessary and can delay supply. It allows excess inventory to be reduced and for sales to be made faster, while ensuring that these sales are in balance (Uchańska-Bieniusiewicz & Obłój, 2023). This data exemplifies how a platform business would leverage data, showcasing the use of it as a strategic asset as opposed to mere ancillary resources that business can use.

From this viewpoint, the case under discussion supports the thesis that platform strategies thrive on inducing participant interactions and the scaling of learning effects, instead of controlling every step of the value chain (Van Alstyne et al., 2016). Within this context, Shein functions as a coordinator of designers, suppliers, influencers, and customers, a system in which rapid internal feedback loops stimulate growth.

As beneficial and productive as these internal feedback loops are, they also have some negative consequences. After all, the rapid increase in the environmental burden of fast shipping has been attributed to unregulated working hours and unsafe labor conditions in supplier factories. In 2024, Shein’s transport emissions increased by 13.7%, owing to a heavy reliance on air freighting to facilitate worldwide distribution (Reid, 2025). Research in the academic domain suggests that ultra-fast production cycles worsen sustainability issues, which makes the resilient supply chain and forecasting components all the more essential to avoid overproduction (Uchańska-Bieniusiewicz & Obłój, 2023). 

In my opinion, Shein represents the promise and peril of platform-enabled strategies in equal measure. The instant ability to synchronize real-time supply and demand is impressive. The next frontier, however, would be the construction of information systems that prioritize environmental and social positiveness simultaneously with growth.

References:

Reid, H. (2025, June 13). Fast-fashion retailer Shein’s transport emissions jump in 2024. Reuters. https://www.reuters.com/sustainability/climate-energy/fast-fashion-retailer-shein-reports-transport-emissions-up-137-2024-2025-06-13/

Uchańska-Bieniusiewicz, A., & Obłój, K. (2023). Disrupting fast fashion: A case study of Shein’s innovative business model. International Entrepreneurship Review, 9(3), 47–59. https://doi.org/10.15678/IER.2023.0903.03

Van Alstyne, M. W., Parker, G. G., & Choudary, S. P. (2016, April). Pipelines, platforms, and the new rules of strategy. Harvard Business Review. https://hbr.org/2016/04/pipelines-platforms-and-the-new-rules-of-strategy