Data Strategy

As AI use widens, enterprise-level impact is still uneven, so the edge now is careful design and accountable oversight

The real data quality breakthrough happens when companies transition to the third mode, where errors are prevented at the source. But this shift requires a major change in mindset, in which every employee recognizes that they are both a data creator and a data customer and starts acting like it.

Data-efficient AI techniques are emerging — and that means you don’t always need large volumes of labeled data to train AI systems based on neural networks.

Across the vast range of real-world usage scenarios, there have been far more instances of augmentation of human work by smart machines than of full automation. That scenario is expected to continue for the foreseeable future.

Data-driven personalization is coming to businesses across all industries, not just tech giants.