OpenAI has started linking ChatGPT directly to corporate systems, pulling internal documents, messages and trackers into the assistant’s workflow so it performs like an in-house analyst rather than a generic chatbot.
Executives have long found that generative AI stalls when it cannot reach the right internal information. The facts teams need tend to sit across many places: documents, spreadsheets, shared drives, chat logs, emails, help tickets and project trackers. That spread slows work and weakens decision-making because the best response often lives across multiple sources that don’t talk to one another.
The move places OpenAI in direct competition with major enterprise AI efforts such as Microsoft’s Copilot for Azure and Office 365, Google’s Vertex AI, Salesforce’s Agentforce and AWS Bedrock. The company says ChatGPT will link to apps like Slack, SharePoint, Google Drive and GitHub, and that the feature runs on a version of GPT-5 trained to check several sources before answering. Each response will include citations showing the documents or threads used for verification.
Practical use is broader than simple text editing. A manager preparing for a client meeting could ask ChatGPT for a briefing and receive an integrated memo that draws on recent Slack exchanges, email threads, call notes saved in Google Docs and support tickets in Intercom. The assistant can surface conflicting takes and open questions when asked a broad prompt such as “What are the company goals for next year?”, summarizing conversations and highlighting where teams disagree or left decisions unresolved.
Teams can apply the capability across common workflows:
- Strategy: Compile customer feedback from Slack, survey slides from Google Slides and recurring themes from support tickets to shape product roadmaps.
- Reporting: Produce campaign summaries by pulling performance notes from HubSpot, drafts from Google Docs and key points raised in email threads.
- Planning: Help engineering leads scope releases by checking GitHub for open pull requests, Linear for outstanding tickets and Slack for bug reports or feature discussions.
Security and data control are front of mind for CISOs and data leaders who worry about exposing intellectual property to an external model. OpenAI addresses that with administrative controls and a privacy-first posture. The platform enforces existing company permissions so ChatGPT can only access the enterprise data each user is already allowed to see. Administrators for ChatGPT Enterprise and Edu can manage which apps connect, assign custom roles and gate access across the organization.
OpenAI also says it will not train its core models on a customer’s private data by default. The rollout includes enterprise-grade protections such as encryption, single sign-on, SCIM provisioning, IP whitelisting and a Compliance API that provides logs for audits and review.
Those protections do not remove all trade-offs. At launch the feature is optional: a user must enable company knowledge when they begin a conversation. With company data turned on, ChatGPT will not simultaneously search the public web or generate charts, a limitation OpenAI says it plans to address in upcoming updates. Teams should be aware that the capability’s value depends heavily on the set of integrations available. OpenAI is shipping with a set of major platforms and plans to add connectors for tools like Asana, GitLab Issues and ClickUp, mirroring the connector-driven approach seen in IBM watsonx and SAP Joule.
The change shifts assistants into the private core of organizations by solving a central AI challenge: linking models to the places where day-to-day work actually happens. When an assistant can surface and tie together internal records, it moves from a general productivity tool to a role that supports analysis, synthesis and cross-team coordination.
That shift has clear implications for business leaders:
- Check permissions in place: CISOs and CDAOs should audit SharePoint, Google Drive and other storage to confirm access rules match policy. The assistant will only obey those permissions, so overly permissive settings will be exposed.
- Pilot high-friction workflows: Start small with tasks slowed by fractured information flows. Preparing client briefings and producing cross-department reports are practical candidates to measure impact.
- Set realistic expectations: Teams need to know the feature must be activated per conversation and that web search and charting are restricted while internal data access is enabled.
- Track connectors: CIOs should map the product’s connector list against the company tech stack to judge fit and total value.
- Compare vendor ecosystems: Evaluate this option alongside AI offerings from Microsoft, Google and Salesforce to decide which data ecosystem provides the most secure, integrated and cost-effective route.
OpenAI’s company-knowledge capability makes clear that the decisive ingredient for enterprise generative AI is secure, practical data integration rather than model quality alone. The feature aims to reduce the friction caused by isolated knowledge silos, while raising the need for robust data governance and precise access controls. For leaders, the arrival of this tool is a prompt to organize data and lock down permissions before broader deployment takes place.