News AnalysisOpenAI Makes a Play for the Enterprise

OpenAI is doubling down on the enterprise. With its latest update to ChatGPT, the company unveiled Connectors, a suite of native integrations that enable the artificial intelligence (AI) assistant to securely connect with business platforms such as Gmail, Google Drive, Microsoft Outlook, SharePoint and GitHub. Connectors is available for ChatGPT Enterprise, Team and Edu customers.

This marks a step toward embedding AI into everyday workflows, so ChatGPT can find insights, generate summaries and offer real-time recommendations from proprietary enterprise data.

Alongside this, OpenAI introduced a Record mode intended for meetings, updated its pricing models for ChatGPT Teams and ChatGPT Enterprise users and continues to emphasize robust security and compliance — key concerns for corporate adoption. 

The company also reportedly has document collaboration capabilities and communicate via chat within the app in the works, according to the Information.   

These features come amid a broader push across the tech industry to add generative AI to productivity tools. Companies such as Microsoft (with Copilot in Microsoft 365), Google (via Gemini in Workspace) and Salesforce (through Einstein GPT) are all competing in the AI productivity space. 

OpenAI as Enterprise Productivity Software

Yet, despite OpenAI's technical prowess and early mover advantage in consumer AI, the enterprise software market is a different battlefield, dominated by incumbents with established ecosystems, deep integration and longstanding client relationships. The question now is whether OpenAI can transcend its chatbot origins to become a foundational layer of enterprise productivity software.

While this looks like a smart move to gain traction in the enterprise market, some industry veterans are questioning whether the strategy is enough to make it a serious player in enterprise productivity. The stakes go beyond technical integration across the technology stack, said Peter Swimm, former principal program manager at Microsoft Copilot Studio and founder of Toilville.

“OpenAI’s connectors enhance ChatGPT’s ability to interact with services like Gmail, Google Drive and Outlook,” Swimm acknowledged. “But integration alone isn't enough to establish dominance in enterprise AI.”

Instead, Swimm emphasized the importance of clear oversight, governance, and trust — crucial factors as enterprises hand over increasing control to AI systems. “Without clear accountability mechanisms, businesses may find themselves relying on AI without fully understanding its impact,” he warned.

The Productivity Potential of ChatGPT Enterprise

This is especially pressing in regulated industries such as healthcare and finance, where compliance requirements are strict. While OpenAI says its connectors respect existing permissions and maintain data silos, Swimm urges enterprises to dig deeper. Questions about data residency, encryption, access control and AI output verification must be addressed, or companies risk exposing sensitive information.

“AI-driven access to email chains, financial data and customer records poses serious security risks,” Swimm warned. “Who has access to AI-generated insights? Are outputs verifiable and auditable?”

Swimm also drew attention to the potential misuse of tools such as Model Context Protocol (MCP), which allows for custom AI integrations. While its flexibility is welcome, it raises the risk of manipulation if safeguards aren't in place. “Customization alone isn’t a safeguard,” he said. “Organizations need independent verification before trusting AI-generated outputs.”

Swimm is skeptical, too, of so-called differentiators such as Deep Research and Record mode, which many competitors now offer. The differentiator is not the feature set, but the integrity and reliability of the output, he said. “If AI-generated research lacks fact-checking and source validation, enterprises risk making decisions based on false or manipulated data.”

Even with these challenges, Swimm doesn’t discount ChatGPT’s enterprise potential. The learning curve for users is minimal, and once AI demonstrates real productivity gains, adoption tends to follow, he said. However, employees must be trained to question AI outputs and avoid blindly following automation.

OpenAI’s biggest hurdle is earning enterprise trust, and doing so at scale, Swimm said. Scaling AI without strong ethical protections risks turning productivity gains into unmanaged vulnerabilities. For OpenAI to thrive in the enterprise, it must deliver more than convenience, he said. “It must deliver confidence.” 

Is OpenAI Right for Your Enterprise?

What impact is this having on organizations? Organizations should look at these six issues when considering OpenAI as part of their productivity strategies:

1. Integration Without Lock-in

For organizations juggling heterogeneous software environments, integration depth is where the real differentiation begins. While Microsoft Copilot and Google Duet integrate deeply into their own ecosystems, they often stop short of accommodating multi-platform stacks.

“ChatGPT has a competitive advantage because it can integrate with multiple other solutions,” said Llia Badeev, head of data science at Trevolution Group. “We use Microsoft products, but we don’t use GitHub — we use GitLab, Atlassian, Jira, etc. Microsoft Copilot most likely won’t work with these platforms.”

In contrast, ChatGPT’s product-agnostic design allows it to plug into a patchwork of enterprise tools, giving it an edge among companies that don’t subscribe wholesale to one cloud ecosystem.

2. Regulation Isn’t Just a Checkbox

Enterprises — especially those in finance, healthcare and legal sectors — aren’t interested in whether connectors simply “respect permissions.” They want legal clarity, technical rigor and auditable assurance.

“Even with permission-aware connectors, companies need to think beyond surface-level access control,” said Steve Zisk, senior product marketing manager at Redpoint Global. “Legal safeguards start with data lineage and auditing. Traceability, role-based access and governance must come first.”

“Companies in highly regulated industries are expecting to run local versions of foundation models,” agreed Yelena Ambartsumian, an AI governance and privacy attorney and founder of Ambart Law. “If they can’t, they are very selective about data minimization, and they’re deploying auditing tools to confirm compliance regardless of vendor promises.”

3. Security and Sensitivity

Generative AI’s usefulness is often in conflict with its unpredictability. Giving these systems access to internal data introduces both value and volatility. “Yes, it absolutely could raise compliance concerns — and it should,” Badeev said. “Sometimes it’s just forbidden to share data outside of your system, no matter how securely it’s handled. And then there’s the hallucination problem.”

This risk amplification underscores why integration alone isn’t enough. Companies need mechanisms to prevent generative models from acting on incorrect assumptions — or worse, outputting legally risky information.

4. Customization, Not Just Connectivity

MCP’s rise illustrates a shift in AI architecture from static integrations to context-rich interactions.

“MCP isn’t just about exposing functionality — it’s about exposing meaning,” said Tal Lev-Ami, CTO and co-founder of Cloudinary. “When you give LLMs both access and an understanding of the system’s intent and structure, you reduce the need for brittle prompt engineering and unlock more reliable interactions.”

While OpenAI’s enterprise stack benefits from this flexibility, other vendors such as Anthropic and Claude are driving the MCP conversation forward. As more companies stand up their own MCP servers, interoperability may become a defining factor of the next wave of enterprise AI.

5. Price, Roles and Practicality

Despite these technical advances, practical barriers remain. “The first problem is price,” said Badeev. “And then there’s access control. Different levels of employees will need different levels of access. Plus, if you want to connect to in-house tools, you’ll likely have to build your own MCP server.”

Yet the learning curve may not be the issue. “Once ChatGPT allows you to be more productive, employees will start using it. It’s that simple,” Badeev added.

6. Dual Strategy: Feature or Flaw?

Some critics warn that OpenAI’s dual focus on consumer and enterprise might dilute its impact. Badeev disagreed. “Everyone is consumer-first. Enterprise and consumer are complementary. Like deep research or a Gmail connector — these features are valuable across the board.”

But Badeev cautions against mistaking feature lists for competitive edges. “Most platforms are building the same things,” she said. “What really matters is the quality of the model. A feature like deep research might exist everywhere, but the implementation is what sets it apart.”

As enterprise AI tools proliferate, the burden of proof shifts from promise to practice. OpenAI’s ChatGPT has taken a flexible, product-agnostic route that holds appeal for complex businesses. But true adoption will depend on how well it balances trust, control and performance at scale.

Editor's Note: Read more from the Generative AI productivity front:

• Microsoft Prepares for Life After OpenAI. What's Next for Copilot? — Microsoft and OpenAI's partnership, forged in 2019 and expected to last until 2030, is showing signs of strain. A look at Copilot's future, without OpenAI.
• Glean Adds New Functionality, Scale and Reach to Agent Platform at Glean:GO — Glean launched over 40 new features and functionality, most of which revolved around its Agent offering, at its Glean:GO event in San Francisco on May 20.
• Zoho Enters Agentic AI Fray With Zia Agents — Zoho's new AI agent platform, Zia Agents, is built for its own productivity suite. The agentic AI integrates seamlessly across the over 50 Zoho workplace apps.