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AI & Enterprise

Execution, not AI models, is what separates finance through data platforms

AI is now widespread across the financial services industry, but results vary by institution. Some firms that have embedded AI in operations are speeding up decision-making and cutting operating costs by as much as 20 percent. The key constraint is not model performance but the ability to scale AI into real workflows amid fragmented data infrastructure and strict security and governance requirements. The article argues that integrated data and AI platforms, with built-in governance, underpin stronger execution.

AI & Enterprise

KT builds companywide AI analytics environment, wins global innovation award

KT said it won the Stratygy Digital Transformation Award at Stratygy World Seoul 2026, a global data and AI analytics conference held in Seoul on July 9. The award recognises KT’s ability to combine its data platform and AI technology to modernise companywide data use and extend it to AI transformation work for corporate clients. KT said it modernised its analytics environment using its MAGMA platform built on Azure Databricks.

AI & Enterprise

Databricks unveils unified architecture for operational and analytics data, targeting AI agents

Databricks on Monday unveiled a new data architecture that integrates transactional databases and analytics systems. At its Data+AI Summit in San Francisco, the company introduced its lake transactional/analytical processing (LTAP) architecture and a real-time analytics engine designed to deliver millisecond responses without separate systems. Databricks said LTAP uses a single dataset in a data lake for both operational and analytics workloads, removing the need for CDC pipelines, ETL processing and replica databases.