OpenAI links company data to make ChatGPT an analyst

OpenAI is wiring ChatGPT Atlas into enterprise systems so it can surface internal knowledge and move from a general assistant to a task-ready analyst. The company says the model, powered by a new GPT-5 variant, can pull from sources such as Slack, SharePoint, Google Drive, GitHub and more to answer questions with citations and run cross-tool analyses. That shift tackles a long-standing barrier: vital information is scattered across documents, emails, tickets and trackers that rarely connect.

With unified access, managers could request a client briefing that blends recent Slack threads, email details, notes in Google Docs and Intercom tickets; ask about next year’s goals and get a summary that highlights alignment and disagreements; or assemble strategy, reporting and release plans by combining feedback, slides, issues and bug reports from apps like HubSpot, Linear and GitHub.

The push places OpenAI in direct competition with Microsoft’s Copilot, Google Vertex AI, Salesforce Agentforce and AWS Bedrock, and mirrors integration plays from IBM watsonx and SAP Joule. To address security concerns, OpenAI says ChatGPT respects existing permissions and gives admins fine-grained controls for app access and roles. It doesn’t train on customer data by default and supports encryption, SSO, SCIM, IP whitelisting and a Compliance API for logs.

There are early limits: users must opt in at the start of a conversation, and when company knowledge is enabled, the system can’t browse the web or generate charts, capabilities OpenAI plans to restore. Success will hinge on connectors, with launch partners live and additional integrations such as Asana, GitLab Issues and ClickUp on the way.

Business leaders are advised to validate data permissions, pilot the tool on workflows constrained by silos, set expectations around current trade-offs, track the integration roadmap and compare offerings across their existing stack. The broader message is clear: in enterprise AI, secure data integration now matters as much as model quality, and organizing data is becoming a competitive necessity.