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OpenText has launched its AI Data Platform (AIDP), designed to unite data management and artificial intelligence under a secure enterprise framework to help organizations activate information with trusted automation at scale. This new platform addresses the growing need for governed, accurate AI results amid rising data volumes and increasing enterprise AI risks.
Contextual AI Agents Built on Trusted Data
Savinay Berry, CPO & CTO at OpenText, highlights that enterprise AI is evolving toward “armies of secure AI agents” orchestrated to handle diverse business processes with auditability rather than guesswork. OpenText’s strategy starts with governed enterprise content and processes, aiming to solve relevancy issues, reduce false confidence, and boost accuracy through contextualized AI.
Leveraging Open Architectures and Partnerships
The platform supports hybrid environments, works with customer-preferred AI models, and integrates tightly with ERP and CRM systems. OpenText is also expanding its partnership with Databricks to co-innovate on data unification, governance, and AI insights. Additionally, the company collaborates with SAP, Microsoft, Google, Salesforce, Oracle, and others to advance an agent-to-agent ecosystem roadmap.
Supporting Practical Enterprise AI Adoption
OpenText builds on its 35-year legacy of information management to help enterprises manage vast data sets—documents, commerce trades, security signals—with metadata tagging for lineage, rights, and retention policies. Its Aviator Studio facilitates AI agent creation with no code, Knowledge Discovery organizes data efficiently, and Data Compliance tools ensure privacy and security. These offerings are designed to make AI practical, secure, and reliable for business adoption.
This launch reflects OpenText’s positioning as a seasoned custodian of enterprise data focused on trustworthy AI deployment, addressing not just technology challenges but critical governance and compliance imperatives across industries. The platform aims to transform AI from an abstract experiment into a trusted, integral component of enterprise operations.
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