OpenText introduces the OpenText AI Data Platform that is designed to meet the evolving needs of organizations across the region by addressing the convergence of data and AI to deliver secure and scalable enterprise capabilities.
The launch of the OpenText AI Data Platform comes at a crucial time for the Middle East. While regional organizations are at the forefront of AI adoption, with an estimated 84% using the technology, only 31% have successfully scaled it across the enterprise, according to the McKinsey State of AI in GCC Countries 2025 survey. This highlights a critical gap between ambition and scaled, reliable implementation. The OpenText AI Data Platform directly solves this challenge by helping organizations manage, secure, and activate their private information with the necessary information governance built in.
“The Middle East is one of the fastest-moving regions in the world when it comes to AI adoption. However, our customers are now entering the critical phase of scaling AI from pilots to enterprise-wide value, and they demand trust and data governance above all else,” said George Schembri, Vice President & General Manager, Middle East at OpenText. “The OpenText AI Data Platform directly addresses this need. It empowers regional organizations to transform their rich, proprietary data into contextual, secure, and accurate intelligence, ensuring their AI investments drive real, audited business outcomes while fully respecting local data sovereignty requirements.”
OpenText also announced an expanded partnership with Databricks, the Data and AI company. Today, OpenText solutions such as OpenText Threat Detection and Response are already built on the Databricks Data Intelligence Platform. Under the enhanced collaboration, the two companies will co-innovate on the OpenText AI Data Platform through deeper technical integrations and Delta Sharing. This will enable customers to seamlessly unify, govern, and analyze their enterprise data, empowering them to unlock trusted AI insights and accelerate innovation at scale.
