AI can also be used to enable a much more decentralized data infrastructure by having a centralized intelligence that employs agentic AI to manage the decentralized infrastructure. Hundreds of thousands of agents can enforce standards and ensure data consistency, which, according to Sáiz, is one of the biggest challenges companies face in regard to data infrastructure.
For example, AI can help ensure the systems of records of a particular client are consistent in all systems including CRM, contact center software, and financial applications. “To maintain consistency, whenever there’s a customer interaction with a contact center or with the web, all systems get the change in near real time,” says Sáiz. “Where you used to have more latency and lots of manual checks before, now it’s all driven by AI, which constantly checks on the state and the master data set to determine, based on intelligence, whether a record needs to be updated in the whole system.”
Beatriz Sanz Sáiz, global AI sector leader, EY
EY
According to Sáiz, knowledge is becoming more important than data because it helps interpret the data. A knowledge layer can be built on top of the data infrastructure to provide context and minimize hallucinations. “If somebody in telco runs a forecasting model, the variables, inputs, and results will be different than running the same model for financial forecasting,” she says. “The more you focus on knowledge, the more accurate your AI.”
