In 2023, businesses across industries have invested heavily in generative AI proof of concepts (POCs) and are eager to explore the technology’s potential. Fast forward to 2024, and companies face a new challenge: moving AI initiatives from prototype to production.
According to Gartner, at least 30% of generative AI projects will be abandoned after the POC stage by 2025. Reasons? Poor data quality, governance gaps and lack of clear business value. Companies are now realizing that the primary task is not simply to create models, but to ensure the quality of the data that feeds those models. As companies try to move from prototypes to model production, they realize that the biggest hurdle is getting the right data.
More data is not always better
In the early days of AI, the prevailing belief was that more data led to better results. However, as AI systems have become more sophisticated, the importance of data quality has surpassed quantity. There are several reasons for this shift. First, large datasets are often laden with errors, inconsistencies, and biases that can unknowingly bias model results. With an abundance of data, it is difficult to control what the model learns, potentially leading it to fixate on the training set and reducing its effectiveness with new data. Second, the “majority concept” in the data set tends to dominate the training process, diluting the insights from the minority concepts and reducing the generalizability of the model. Third, processing massive data sets can slow down iteration cycles, meaning critical decisions take longer as the amount of data increases. Finally, processing large data sets can be expensive, especially for smaller organizations or start-ups.