When a company decides to adopt an AI assistant, the conversation almost always starts in the wrong place: which one is smartest? The three names that come up —ChatGPT, Claude and Microsoft Copilot— are all capable, improve every few months, and perform at a similar level on most office tasks. Choosing by who “scores higher” on a benchmark is an almost sure way to get it wrong.
The question that decides well is a different one: where does your data live, where does your work live, and which of these assistants respects that boundary while integrating with your operation? This guide compares the three options from the criteria a board cares about —data, integration and adoption—, with information verified against each provider’s official documentation.
The three options in one sentence
- ChatGPT (Enterprise / Business): OpenAI’s general-purpose assistant. Strong for drafting, summarizing, analyzing and exploring any topic; it integrates into your operation through connectors and the API. It lives outside your other platforms: you decide what information you bring to it.
- Claude (for Work / via Amazon Bedrock): Anthropic’s assistant, recognized for its reasoning, its long-context handling and its performance on code and analysis tasks. It now also works inside Microsoft 365: it has native add-ins for Excel, Word and PowerPoint —generally available— and for Outlook in beta, plus a connector to Outlook, SharePoint, OneDrive and Teams. And for the regulated enterprise it can be consumed through Amazon Bedrock, so inference runs inside your AWS account.
- Microsoft 365 Copilot: not a separate assistant, but AI embedded in the tools your team already uses —Word, Excel, PowerPoint, Outlook, Teams— and grounded in your organization’s data through Microsoft Graph. Its strength is context: it knows your documents, emails and meetings because it lives inside them.
The underlying difference is no longer who lives inside your Office —Claude does too now—, but three things: how native each integration is, how much reasoning depth it brings, and where your data boundary lies. Between ChatGPT and Claude, for the regulated enterprise, that last point is the one that decides.
Decision comparison
| Criterion | ChatGPT (Enterprise) | Claude (for Work / Bedrock) | Microsoft 365 Copilot |
|---|---|---|---|
| Nature | General-purpose assistant | General-purpose assistant | AI embedded in Microsoft 365 |
| Where it processes | OpenAI infrastructure | Anthropic or inside your AWS account (Bedrock) | Inside the Microsoft 365 tenant |
| Source of context | Whatever you bring to it | Whatever you bring to it | Your data via Microsoft Graph |
| Training on your data | Not by default on business plans | No, under Commercial Terms | Not on tenant data |
| Natural integration | Connectors and API | Office add-ins + M365 connector, API and RAG on AWS | Office, Teams, Outlook, SharePoint |
| Regulated fit (SBS/CMF, health) | Requires reviewing residency | High via Bedrock (AWS boundary) | High if data already lives in M365 |
| Licensing model | Per user per month | Per user per month / consumption | Per user per month |
The cells summarize; the sections below explain the why of each one against each provider’s documentation.
Where your data lives: the criterion that rules options out first
In a company, the first filter is not answer quality but data governance. Here the three options start from a good place on their business plans, but with nuances that matter.
ChatGPT on its Enterprise, Business and API plans does not, by default, use inputs or outputs to train OpenAI’s models; the organization keeps its rights over what it enters, and deleted conversations are removed from their systems within a set window, unless legally required. The organization only shares data for model improvement if it explicitly opts in.
Claude, under Anthropic’s Commercial Terms that apply to Claude for Work and the API, is not trained on paid customers’ content. Its particularity is the consumption path: when used through Amazon Bedrock, inference runs on AWS-managed infrastructure with regional residency and zero operator access —Anthropic personnel do not access that inference infrastructure—, which lets you build sensitive applications entirely inside the AWS security boundary. We develop that distinction in detail in Claude Platform on AWS vs. Claude in Amazon Bedrock.
Microsoft 365 Copilot processes prompts and responses within the Microsoft 365 service boundary, and per Microsoft’s documentation, neither the prompts, nor the responses, nor the data accessed through Microsoft Graph are used to train the foundation models. Copilot stays within your organization’s tenant, with no cross-tenant visibility.
The read for a CIO: all three protect your data from training on their business plans; the difference is where processing happens and who the processor is. For strict residency requirements, consuming Claude via Bedrock is the most direct path to audit, because data never leaves your AWS account.
Where your work lives: integration rules
An assistant is worth the work it actually saves, and that depends on how close it is to where your team already operates. For a while, “AI inside Office” meant Copilot. Not anymore.
Microsoft 365 Copilot remains the option most deeply woven into the suite: it lives in Word, Excel, PowerPoint, Outlook and Teams, and automatically grounds on Microsoft Graph to know your organization’s context. If your people spend the day inside Office, that embedded, default-available context is its greatest advantage.
But Claude now also works inside Microsoft 365. It has native add-ins for Excel, Word and PowerPoint —generally available— and for Outlook in beta, sharing conversation context across the apps; plus a Microsoft 365 connector that gives it access to Outlook, SharePoint, OneDrive and Teams. It does so respecting the permissions each person already has in Microsoft 365 —no one sees through Claude what they could not see directly— and without taking data out of your tenant: the connector queries on demand and does not store the content. In practice it requires enabling Anthropic as a subprocessor in the Microsoft 365 admin center.
That changes the decision. In our experience with teams in the region, for heavy analytical work —multi-tab financial models in Excel, long-document analysis, judgment-heavy drafting— Claude’s reasoning depth inside the very same spreadsheet, with cell-level citations and care for formula dependencies, makes a difference that many users value over the convenience of the default assistant. Copilot wins on native coverage and automatic context; Claude wins on reasoning and on reaching that same Office with a data boundary you can audit. The choice stops being “Office context versus reasoning”: today you can have both, and the criterion becomes which one fits your operation and your compliance framework better.
For more open cases —research, code generation and review, customer support, agents that execute actions on your systems— ChatGPT and Claude also operate as general-purpose assistants outside Office. On AWS, Claude enables Agentic AI patterns where the assistant not only answers but queries your internal data via RAG and executes actions on your systems. When the goal is an AI agent that acts inside your architecture, integration capability weighs more than the isolated conversation.
How to choose: a simple decision tree
The decision sorts out with a few questions, in order of weight:
- Do you have a residency or single-processor requirement —banking and insurance under the SBS or the CMF, healthcare with patient data? If the answer is yes and your cloud is AWS, consuming Claude through Amazon Bedrock keeps everything inside your boundary. It is the criterion that rules options out first.
- Does your daily operation run on Microsoft 365? Both Microsoft 365 Copilot and Claude work inside Office today. Copilot delivers your organization’s context by default via Microsoft Graph; Claude adds its reasoning depth inside the same apps and an auditable data boundary. Choose by what weighs more: automatic native coverage or reasoning with control over the data.
- Is the use case general-purpose or does it need to integrate your systems and data? For open drafting and analysis, ChatGPT and Claude compete evenly; for agents that integrate your operation on AWS, Claude via Bedrock fits the architecture.
- What is your adoption capacity? The tool your team actually uses wins. Start with a high-volume case, measure it, and scale on evidence.
It is not unusual for the answer to be more than one: Copilot for office productivity and an agent platform on AWS for the cases that integrate your own systems. What you should avoid is duplicating the same use case across two tools or licensing blindly before seeing adoption.
The real cost is not the license
All three options are licensed per user per month, and it is tempting to decide on the price of the seat. That is a framing error. The license cost rarely determines the return: what defines it is how many people adopt the assistant in a sustained way for work that used to take longer or was not being done.
A license without clear use cases, without guidance and over disorganized data yields little, and the spend becomes invisible in results. That is why the return is decided on three fronts that do not appear on the price list: change management, the choice of the first high-volume use cases, and the quality of the information the assistant answers from. Ordering the data —where a cloud data and analytics architecture makes the difference— usually pays off more than switching model providers.
How we approach it at Caleidos
At Caleidos, as an AWS Advanced Tier Services Partner, we help companies make this decision with architecture criteria rather than by trend. We start from two questions —where your data lives and where your work lives—, map your compliance requirements, and when the case calls for it we design the solution on AWS: Claude through Amazon Bedrock inside your data boundary, integration of your sources via RAG, and agents that execute actions on your systems. We do not sell a model: we design the safest and most useful way to put AI to work in your operation. Explore our Agentic AI on AWS practice.
Frequently asked questions
Quick answers to the most common questions are at the top of this guide, in the FAQ section.
Want to choose the right AI assistant for your company?
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