In 2023, enterprises across industries invested heavily in generative AI proof of concepts (POCs), eager to explore the technology’s potential. Fast-forward to 2024, companies face a new challenge: moving AI initiatives from prototype to production.
According to Gartner, by 2025, at least 30% of generative AI projects will be abandoned after the POC stage. The reasons? Poor data quality, governance gaps, and the absence of clear business value. Companies are now realizing that the primary challenge isn’t simply building models — it’s ensuring the quality of the data feeding those models. As companies aim to move from prototype to production of models, they’re realizing that the biggest roadblock is curating the right data.
More data isn’t always better
In the early days of AI development, the prevailing belief was that more data leads to better results. However, as AI systems have become more sophisticated, the importance of data quality has surpassed that of quantity. There are several reasons for this shift. Firstly, large data sets are often riddled with errors, inconsistencies, and biases that can unknowingly skew model outcomes. With an excess of data, it becomes difficult to control what the model learns, potentially leading it to fixate on the training set and reducing its effectiveness with new data. Secondly, the “majority concept” within the data set tends to dominate the training process, diluting insights from minority concepts and reducing model generalization. Thirdly, processing massive data sets can slow down iteration cycles, meaning that critical decisions take longer as data quantity increases. Finally, processing large data sets can be costly, especially for smaller organizations or startups.
