Unfortunately, AI is failing everywhere. The abandonment rate of projects reflects a broader trend of resource misalignment and strategic oversights. The rapid advancements in AI capabilities have been matched by increased complexity and specificity of data requirements. Many organizations need help sourcing and managing high-quality data for successful AI deployments, which has become an obstacle that most enterprises must overcome.
Data is the problem
Poor data quality is a central factor contributing to project failures. As companies venture into more complex AI applications, the demand for tailored, high-quality data sets has exposed deficiencies in existing enterprise data. Although most enterprises understood that their data could have been better, they haven’t known how bad. For years, enterprises have been kicking the data can down the road, unwilling to fix it, while technical debt gathered.
AI requires excellent, accurate data that many enterprises don’t have—at least, not without putting in a great deal of work. This is why many enterprises are giving up on generative AI. The data problems are too expensive to fix, and many CIOs who know what’s good for their careers don’t want to take it on. The intricacies in labeling, cleaning, and updating data to maintain its relevance for training models have become increasingly challenging, underscoring another layer of complexity that organizations must navigate.