Why your AI models stumble before the finish line [Video]

Failed AI projects waste time and resources, damage reputations, and stifle innovation. To succeed with AI, put the necessary practices in place to ensure high-quality data.

Credit: Lukas Gojda / Shutterstock

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 …