Data for AI training is often hard to come by, especially when organizations use third party AI and machine learning solutions. Only 15-20% of customers consent to using their data for analytics, the rest of the data and the insights contained are locked away. Due to privacy reasons, sensitive data is often off-limits both for in-house data science teams and for external AI or analytics vendors.
Even when data is available, data quality is an issue. Historic biases and model drift complicate AI/ML development and negatively impact performance. Machine learning accuracy suffers when training data quality is insufficient. This is due to imbalanced training data. Recalibrating models without easy access to fresh, balanced training data is impossible. Models need to be able pick up on rare or completely new events buried in sensitive data, such as transaction records in banking or patient care journey in healthcare. No matter how good a model is, if the training data is not intelligent.
Injecting new domain knowledge into models is also problematic. Due to regulations, customer data often cannot be linked to other, even publicly available data sources. Without the ability to add new knowledge into models, their intelligence will be limited.