Cost: Implementing applications that rely on GenAI can be costly, whether it’s due to licensing costs for advanced models or developing custom solutions in-house. It also takes time and resources to prepare data, craft prompts, and post-process results. organizations will abandon 30% of GenAI projects after proof of concept by the end of 2025 due morocco rcs data to factors like high costs and unclear business value. Therefore, if a simpler, less resource-intensive approach can achieve the same outcomes, it’s probably the smarter choice.
How do these considerations play out in real-world scenarios? Take, for example, the task of categorizing customer feedback for a retailer.
Thanks to GenAI’s ability to understand and process natural language, it excels at categorizing content into topics or sentiments. In this use case, transparency isn’t a major concern, slight variations in correctness are acceptable, and one can manage outputs through relatively simple prompt adjustments. The time and resources saved by automating categorization ultimately outweigh any risk.
On the other hand, consider tasks like retail customer segmentation or fraud detection. GenAI struggles in these use cases, and slight prompt adjustments can lead to unexplainable changes in results. While it’s technically possible to refine GenAI to match the accuracy and reliability of traditional techniques, the time and resource investment can quickly outweigh the simplicity and efficiency of using off-the-shelf methods.