AI Requires Good Data
The impact of Generative AI on productivity is fueling the prioritization of AI and ML initiatives. Models and AI infrastructure are commoditized, and the availability of skilled resources is growing. The last key ingredient for success is the availability of consented, prepped and filtered data for training, tuning, and running the models. Setting up a data collection layer with flexibility, governance, and control is imperative, and remains complex and fragmented. The requirements for compliance and transparency in AI are amplified in regulated industries.
Data Drives Differentiation
Businesses have access to the same models, tools, and resources to fuel their AI initiatives. It is their unique first-party data and how it is used to deliver the right customer experience that sets them apart. Businesses with a flexible, consented, and centralized data layer, have a head start. The only way to take advantage of machine learning and AI is to have the data and related pipelines set up to connect to the AI infrastructure and the rest of the organization. For regulated industries, this helps manage the risk and exposure, while providing a governed path to AI success.