ChatGPT Enterprise versus Microsoft Copilot: A 2025 Comparison

Today, we want to compare ChatGPT Enterprise and Microsoft Copilot. Specifically, we want to understand the limitations of these extremely popular tools to help explain a trend.

In 2025 alone, enterprises will invest over $600B into generative AI. However, this number draws an enigma: three out of four companies can’t demonstrate tangible business value from AI. The gap isn’t in the models—they’re incredibly effective at summarizing text, analyzing numbers, and other business-related tasks. So with ChatGPT Enterprise and Microsoft Copilot being two of the most popular AI tools, with over 70% adoption from Fortune 500s, it begs the question: why are these tools falling short?

Today, let’s understand why these orchestration systems are limited in producing results and discuss an alternative approach.

Why are companies actually adopting Microsoft Copilot and ChatGPT Enterprise?

Let’s first understand why Microsoft Copilot and ChatGPT Enterprise are so popular, with nearly three-fourths of major enterprises adopting them. The reasoning relies on how both tools are just enterprise search AI platforms.

Let’s start with theory—the advertised advantages of Microsoft Copilot and ChatGPT Enterprise. Hypothetically, both tools could help nearly every team in an organization for a wide breadth of tasks. For example, both products could be used to (i) synthesize meeting notes, (ii) analyze operational data, (iii) write code scripts, (iv) flag inaccuracies in billing, (v) write sales emails etc. This broad nature of both products complements the fact that many enterprises don’t know exactly how they want to use AI yet, figuring things out on a case-by-case basis. And because Microsoft Copilot and ChatGPT Enterprise are fancy chat interfaces, they could be shoehorned into any workflow that involves answering questions or analysis.

However, companies often purchase either tool due to a top-down request from leadership or a bottoms-up consolidation of subscriptions. In the former scenario, the CEO or COO might want to embrace AI across the board; here, the IT department is instructed to procure Microsoft Copilot or ChatGPT Enterprise to satiate a big-picture goal, not a set of explicit use cases. In the latter scenario, multiple teams might be using smaller subscriptions put on department-scoped credit cards; eventually, to save costs and unify the subscription, the IT department purchases a bulk plan for the entire organization.

Despite having general-purpose popularity, Microsoft Copilot and ChatGPT Enterprise aren’t identical tools. Let’s discuss the strengths of each, starting with Microsoft Copilot.

Microsoft Copilot: An Enterprise Search Solution Built for Microsoft 365

Microsoft Copilot is a chat interface that is accessible both from a dedicated interface and directly inside Microsoft tools such as Excel, PowerPoint, or VS Code. Microsoft Copilot is strictly designed for the Microsoft 365 stack, with immediate availability in Microsoft’s tools. In other words, Microsoft Copilot is designed for teams that use Microsoft tools across the board.

Given tight integration with other enterprise software, Microsoft Copilot is also designed with security in mind. It leverages Microsoft’s Zero Trust security framework, where other endpoints are considered outsiders that undergo the same authentication guardrails. It also only integrates with data within Microsoft 365 boundaries, preventing employees from accidentally leaking information to AI models that are not already protected by Microsoft’s stack. These protections mean that Microsoft Copilot is GDPR, HIPAA, and SOC 2 compliant, with comprehensive auditing available through Microsoft Purview.

However, despite having tight integrations with Microsoft’s tooling, Microsoft Copilot is also quite limited. For instance, in Excel, Microsoft Copilot can tackle table-formatted data, but cannot analyze embedded or linked content. It also has limited memory, where context is lost whenever it is refreshed (just like a browser tab). Content generation is no longer customized beyond the base models, and Microsoft hasn’t built any middleware that fine-tunes the models beyond the AI binaries available out of the box.

Additionally, Microsoft Copilot is inaccessible to teams that don’t use Microsoft products and is a fragmented solution for teams that use a few, but not many, Microsoft tools. For teams looking for a similar product that’s not tightly bound to Microsoft’s ecosystem, ChatGPT Enterprise might be a better pick.

Why do businesses procure Microsoft Copilot over a tool like ChatGPT Enterprise?

While ChatGPT is an ubiquitous tool that most professionals have tried, companies procure Microsoft Copilot because they heavily rely on the Microsoft 365 stack. For these organizations, relying on an external platform like ChatGPT Enterprise would forces employees to constantly context-switch between two tools, creating friction. Instead, Microsoft 365 enables AI across the board—a better solution to rolling out company-wide AI access, whether inspired by a top-down decision by corporate or a bottoms-up shadow IT reason.

ChatGPT Enterprise: Cutting-Edge AI with Surface-Level Integrations

Most users are familiar with ChatGPT, OpenAI’s flagship AI product. ChatGPT Enterprise is the highest tier of ChatGPT; it eclipses the Pro and Business plans. Given that ChatGPT is free, we’ll mostly compare ChatGPT Enterprise to Microsoft Copilot by focusing on the features exclusive to ChatGPT Enterprise, as Microsoft Copilot users also have access to ChatGPT’s free tier.

ChatGPT Enterprise is centered around a few tenets: massive context windows, broad integrations, enterprise-friendly security promises, and uncapped API limits.

While ChatGPT Enterprise isn’t pegged to a product suite, and therefore exists as an external program/interface, it does integrate with popular tools, including GitHub, Google Workspace, Salesforce, Microsoft 365, and Box. It also integrates with Zapier, which can bridge ChatGPT Enterprise to 7,000+ integrations. These integrations enable ChatGPT users to automatically pull information without having to copy and paste data; this is especially useful for tasks that involve synthesizing or analyzing company or customer data. For instance, with ChatGPT Enterprise, users could:

• Design a brief about selling to mid-market buyers, integrating the company’s sales documentation that lives in Google Drive, to properly understand the team’s playbook
• Create a report for IT leadership on spikes in traffic, integrating with the company’s Excel sheets in Microsoft 365 to identify trends
• Issue a pull request to patch a CSS syntax issue in the last pull request, integrating with the company's GitHub.

All of these tasks are possible with a 400K token context window and 2x faster processing than cheaper ChatGPT tiers. Generally speaking, ChatGPT Enterprise is OpenAI’s most performant AI chat system.

Additionally, because users can create custom GPTs in ChatGPT Enterprise—custom, fine-tuned language models for custom tasks—it’s easy to create chats focused on specific integrations. For instance, ChatGPT Enterprise users could create a custom GPT that has context to the company’s Salesforce schema, making it easy to ask questions about customer accounts.

ChatGPT Enterprise checks off many security checkboxes, something that’s necessary since it’s introducing a new application outside the confines of an already secure environment (e.g. Microsoft 365). ChatGPT Enterprise has support for SAML SSO, SCIM provisioning, RBAC, user groups, configurable data retention, regional residency, and usage auditing. These practices make ChatGPT Enterprise compatible with compliance standards like GDPR, CCPA, SOC 2, ISO 21001, and CSA STAR.

However, while ChatGPT Enterprise isn’t confined to a single suite of tools, it’s still very limited in what it can do. Like Microsoft Copilot, ChatGPT Enterprise is primarily an AI Chat interface. Outside of a few bespoke outputs like a pull request, the primary output of ChatGPT Enterprise is the equivalent of a text message. That’s a massive constraint for businesses that want to use AI to actually affect their workflows, where actions are more likely to be data writes than exported documents. This introduces the biggest limitation of both ChatGPT Enterprise and Microsoft Copilot: poor support for AI agents.

Why do businesses procure ChatGPT Enterprise over a tool like Microsoft Copilot?

Microsoft Copilot is specific to the Microsoft 365 stack and not useful or accessible to companies that use other tooling; ChatGPT Enterprise, meanwhile, is generalist and integrates with other products like Salesforce, Google Drive, Dropbox, and Hubspot.

Additionally, ChatGPT is an incredible popular and accessible tool due to its free and affordable pro tiers. Many employees use ChatGPT even if not purchased by corporate; something known as Shadow IT creep. By purchasing ChatGPT Enterprise, companies that adopt the solution as a holistic platform with shared GPTs, context, and integrations across the entire team.

What are AI Agents? Does Microsoft Copilot or ChatGPT Enterprise support them?

AI Agents are autonomous programs that leverage AI to make decisions to carry out actions. Being able to execute actions is what separates AI Agents from AI chat. For example, an AI chat application could integrate with Salesforce to identify customers who are churn risks; meanwhile, an AI Agent could identify those customers, mark a field in Salesforce, and queue an email to go out to re-engage.

Both Microsoft Copilot and ChatGPT Enterprise have pre-built agents. However, these agents are limited to specific tasks. For example, Microsoft Copilot has an agent to create visuals via the Visual Creator agent. Or, employees could use the Jira Cloud agent to update tasks in Jira for them. Meanwhile, in ChatGPT Enterprise, the Codex agent could be used to write and make coding changes.

While these agents are certainly helpful, they are designed for specific, siloed tasks with specific data requirements. Accordingly, these agents operate more like fancy integrations than a true AI agent. Rather, a fully autonomous AI agent should be able to:

• Decide what data sources to pull context from in order to make a decision. It should also keep pulling context until a confident decision could be made (or context was exhausted)
• Make a decision on what to do next independently. This might involve altering data in Salesforce, creating a Jira task, updating a Google doc, sending an email, or anything else (or a combination of outputs)

In other words, truly autonomous agents are like effective employees—they are curious, learn things, and figure out what to do next without requiring explicit directions. However, Microsoft Copilot and ChatGPT Enterprise don’t have support for truly autonomous agents.

Credal: A Third Alternative

The reason that we’re strongly versed in ChatGPT Enterprise and Microsoft Copilot is that many Credal customers were former users of those platforms, but needed a more configurable agentic platform. Credal is an orchestration platform for AI agents. While ChatGPT Enterprise and Microsoft Copilot are just AI chat platforms with pre-built integrations, Credal handles a lot more

• With Credal, you can integrate with data sources and inherit permissions so that access is granular to the users invoking agents
• Provide agents with tools to carry out actions in integrated programs, including Salesforce, Office 365, Jira, and Google Drive.
• Set up human-in-the-loop governance to safeguard agents from doing destructive actions, which is possible when actions can independently carry out non-deterministic actions

With Credal, you can still ship an AI chat platform that is able to tap fully-custom agents under-the-hood to execute on actions. For example, with Credal, you could create a brief about selling to mid-market buyers and include that brief in Salesforce notes for each mid-market account.

An enterprise might have Copilot drafting documents in Word or ChatGPT Enterprise summarizing customer calls, but without AI agents that have read and write access, these are siloed tasks that still depend on humans, severely hampering the potential time-savings of AI. Our goal with Credal is to build an AI agent platform with security and integrations suited for enterprises without limiting the capacity of AI.

Credal has a proven track record, where most companies have +75% AI adoption, and nearly 50% of employees save 20-40% of their daily work. Credal is used by companies such as Wise, MongoDB, Checkr, IFRS, Lattice, and Gecko.

Pricing: ChatGPT Enterprise versus Microsoft Copilot

ChatGPT Enterprise and Microsoft Copilot both have per-seat pricing, but the minimum seats enforced for each is not disclosed.

For Microsoft Copilot, the pricing is just $30 per user per month. The pricing for ChatGPT Enterprise is meanwhile $60 per user per month, with rumors that they require a minimum of 150 users on an annual commitment.

Generally speaking, ChatGPT Enterprise is more expensive than Microsoft Copilot because it is a standalone solution; Microsoft Copilot, meanwhile, is expected to be bundled into Microsoft 365.

A Closing Thought: The Multi-Agent Future

ChatGPT Enterprise and Microsoft Copilot have very rudimentary AI agents. They are limited, not autonomous, and require a lot of human rigging. Long-term, this design pattern will continue to be constraining as more workflows move towards multi-agent workflows.

At Credal, we’re invested in building tooling for multi-agents. Multi-agents are akin to microservices—each agent focuses its context window on a specific set of tasks. For instance, one agent might be focused on deploying emails and managing flow. Another agent might be focused on managing the team’s Confluence. Another might be focused on the company’s internal policies. Ideally, agents work together to accomplish a task; that way, no single agent needs the entire company’s context, and agentic work better imitates how humans work together to accomplish things.