Microsoft 365 Copilot, ChatGPT Enterprise, or a custom AI assistant? A comparison for a large enterprise (2027)

Most large companies in Poland have already run their first generative AI pilots. The question now is: how do you bring AI to the entire organization? You can choose ready-made tools like Microsoft 365 Copilot and ChatGPT Enterprise, or build a custom AI assistant on your company’s data and processes. The right answer depends on where your data lives, how specific your processes are, and how much control you want.

Below is a comparison of the three options: costs, data security, integrations, GDPR and AI Act compliance, plus four questions worth asking before you decide. Product status: October 2026.

Key takeaways

• Microsoft 365 Copilot is the natural choice for companies already on Microsoft 365: it works across email, calendar, Teams, and SharePoint without a deployment project.
• ChatGPT Enterprise gives you access to OpenAI’s flagship models with an admin console, SSO, and connectors to company systems, with pricing negotiated individually.
• A custom AI assistant gives you control over data, response logic, interface, and model choice, plus the ability to embed AI in your products and processes that off-the-shelf tools don’t cover.
• The “build or buy” decision is a 3-year TCO analysis, data sensitivity, and whether AI is a tool for employees or part of your product.
• These options aren’t mutually exclusive. Many companies use Copilot for office work and build a custom assistant for one critical process.

Three options at a glance

Off-the-shelf tools are software as a service: the model, infrastructure, and interface belong to the vendor; you buy licenses. A custom AI assistant is built on model APIs (OpenAI, Anthropic, Google) or open-weight models (e.g., Llama, Mistral), deployed in your infrastructure and connected to your systems.

When to choose an off-the-shelf tool

• Your priority is quickly giving employees AI for office work: email, documents, meeting summaries, ad hoc analysis.
• Your processes are standard and your data is in systems the tool already supports.
• You don’t have an AI engineering team and don’t plan to build one.

When to build your own assistant

• AI must run on data you won’t send outside your infrastructure, or a regulator requires it.
• AI should be part of a product or process that differentiates your company (customer support, production planning, customer portal).
• You need to integrate with legacy systems that lack standard connectors.
• You want to control which model responds and be able to swap it without changing the rest of the system.

Microsoft 365 Copilot: AI in the tools your company already uses

Microsoft 365 Copilot is an AI layer on top of Microsoft 365: Outlook, Teams, Word, Excel, PowerPoint, SharePoint. Its strength is access to the user’s work context (email, calendar, files) while honoring existing permissions: Copilot only surfaces what the user already has access to. It uses OpenAI models and, from 2025, select features also use Anthropic models.

What it delivers in practice: meeting notes and summaries in Teams, drafts of documents and emails, natural-language data analysis in Excel, and search across company files. For organizations with thousands of knowledge workers, it delivers quick impact without an implementation project.

Data: processed within your organization’s Microsoft 365 service boundary; does not train models; EU Data Boundary applies for EU customers. This usually satisfies compliance teams for office workloads.

Limitations: outside the Microsoft ecosystem, Copilot needs connectors and agents built in Copilot Studio; agents running inside Microsoft 365 are covered by the license, while other scenarios are billed with credits (a 25,000-credit bundle is about $200/month or pay-as-you-go). You can only customize model behavior within Microsoft’s guardrails. At 1,000+ users, license costs ($30/user/month plus base E3/E5) become a fixed budget line regardless of actual usage.

Before rollout: review permissions in SharePoint and OneDrive. Copilot can surface files a user can access, including those shared by mistake.

ChatGPT Enterprise: OpenAI’s flagship models with enterprise administration

ChatGPT Enterprise is ChatGPT for organizations: access to OpenAI’s current flagship models, an admin console with roles and usage analytics, SSO (e.g., Okta, Entra ID), SOC 2 Type II, and a GDPR data processing agreement (DPA). Your data doesn’t train the models, and the company owns inputs and outputs. Pricing isn’t public; it’s negotiated, typically with a minimum seat count.

What it delivers in practice: data analysis with code execution (uploaded files, charts, forecasts), coding and code review, creative work, and custom GPTs for teams. From 2025, connectors to SharePoint, Google Drive, Slack, Jira, Confluence, GitHub, and more, plus “company knowledge,” which answers based on company data with source citations. You can build custom connectors via the Model Context Protocol (MCP).

Limitations: the interface and response logic belong to OpenAI; you can’t embed it into your own product as ChatGPT (use the API—that’s already a custom build). Data is processed in OpenAI’s infrastructure; confirm data residency options in your contract. Single model provider.

Custom AI assistant: control over data, logic, and model

A custom AI assistant is built for a specific process or product: customer support, a customer portal, an internal knowledge base, production planning. It uses models via API (OpenAI, Anthropic, Google) or open-weight models running in your infrastructure, but everything around the model (data, permissions, logic, interface, logs) belongs to you.

RAG architecture

A typical architecture is RAG (retrieval-augmented generation): the assistant first searches trusted company sources (documents, wiki, databases, systems), then passes the retrieved snippets to the model as context. The model answers from company facts and cites sources, reducing hallucinations. Permissions flow through from source systems: users only get answers from data they’re authorized to see.

What a custom solution gives you

• Data under control: deploy in your cloud, choose region and model provider, business terms excluding training; with open-weight models you can also run in a closed network.
• Legacy integration: AI as a layer on top of your existing ERP, CRM, or custom system without rewriting it. That’s how our AI Interface Layer works.
• Your own product: the assistant can be part of your customer-facing product with your brand and interface.
• Swappable model: when a better or cheaper model appears, swap a single component; data, integrations, and UI stay the same.
• Cost tied to usage, not seats: with many users and moderate usage, model costs can be lower than per-seat licenses; with heavy usage they can be higher. Do the math—don’t assume.

What a custom solution requires

A team or partner to build and maintain it; monitoring answer quality; a budget for model usage at scale; and a business owner. A pilot on one process takes weeks; a production rollout with integrations takes months.

Four questions before you decide to build or buy

You can feed these questions into your current AI assistant along with your company data to prepare material for a decision workshop:

1. Data sensitivity. “List the categories of data the AI assistant will process (customer data, source code, price lists, employee data). For each, assess the legal and business risk of processing in an external SaaS versus company infrastructure, and note any regulatory requirements.”
2. 3-year TCO. “Compare the 3-year cost of N Microsoft 365 Copilot licenses ($30/user/month plus base license) with a custom assistant: build, model usage at the estimated query volume, maintenance and evolution. Account for how many employees will actually use it.”
3. Integrations. “Name 5 systems the AI must draw data from. Which have ready-made connectors for Copilot or ChatGPT Enterprise, and which require a custom connector? What’s the cost of those connectors versus a custom assistant connected directly to these systems?”
4. Business value. “Is the assistant meant to improve office productivity, or become part of a product or process that differentiates the company? If the latter, what does lack of ownership and dependence on a single vendor imply?”

Security and compliance: GDPR and the AI Act

The AI Act takes effect in phases from August 2024; transparency obligations (disclosing AI interactions, labeling generated content) apply from August 2, 2026, and workforce AI competencies from February 2025. This affects all three options.

With off-the-shelf tools you rely on the vendor’s contracts and certifications (Microsoft: EU Data Boundary; OpenAI: SOC 2 Type II, DPA). With a custom assistant you decide data location, have full logs of prompts and responses, and can implement your own guardrails—e.g., blocking PESEL numbers, card data, and contract amounts—before anything reaches the model. In high-risk AI Act uses (e.g., recruiting, credit scoring), this control over logs and human oversight is a deployer obligation, not a nice-to-have.

Example: a custom AI assistant inside an existing customer portal

Client: a US cybersecurity platform serving Fortune 500 customers. Problem: onboarding a new customer in the portal took 45 minutes, and the data was used once a quarter. An off-the-shelf tool was not an option because the assistant had to run inside the client’s product, on its data, for three audiences: the board, CFO, and CISO.

Solution: a customer portal with an embedded AI assistant (Anthropic models, LangGraph, MCP) that presents ready-made insights. Result: onboarding cut by 95% (to under 2 minutes), and usage went from quarterly to daily. In a second project, the platform was rebuilt into an AI orchestration layer so a user can launch a new AI agent in minutes with no engineering effort.

Which option fits your company

Microsoft 365 Copilot, if: you run on Microsoft 365, want fast impact in office work, and don’t need AI inside your own processes.

ChatGPT Enterprise, if: teams need data analysis and ad hoc model work, employees already use ChatGPT (shadow IT), and your data is in systems with connectors.

A custom AI assistant, if: data cannot leave your infrastructure, AI must be part of a critical product or process, you need to integrate legacy systems, or per-user licensing at scale no longer makes sense.

Most often: a combination. Copilot or ChatGPT Enterprise for office work, plus a custom assistant for one or two processes where AI creates customer value.

How to start: discovery, not licenses

1. Pick one high-impact, low-risk process (e.g., knowledge search across documents, handling repetitive inquiries).
2. Measure the current state: time, query volume, cost.
3. Build a pilot on real data in a few weeks, either with an off-the-shelf tool or a custom assistant.
4. Compare results to the baseline and calculate 3-year TCO for both paths.
5. Scale what proves itself in the numbers.

Startup House builds custom AI assistants, agents, and RAG knowledge bases for mid-sized and large companies, including regulated industries (fintech, healthcare, cybersecurity), deployed in the customer’s cloud and without training public models on their data. Ready-to-adapt products (KnowHub, InProduct AI, SmartSearch, AI Interface Layer, AI Demand Planner): KnowHub (a knowledge base on company documents), InProduct AI (an assistant embedded in a SaaS product), AI Interface Layer (an AI layer on top of an existing platform). Technology partners: Anthropic, OpenAI, Google Cloud, Microsoft Azure, ElevenLabs, Okta.

See also: TOP 10 companies implementing AI in Poland

FAQ

Is data in Microsoft 365 Copilot safe?

Data is processed within your organization’s Microsoft 365 service boundary, doesn’t train models, and is subject to the EU Data Boundary for EU customers. Copilot honors existing permissions, so it’s worth tidying up file access before rollout.

How much does ChatGPT Enterprise cost?

Pricing isn’t public and depends on seat count and contract scope. Team and Business editions have public pricing but aren’t equivalent to Enterprise in admin features and contractual terms.

How much does a custom AI assistant cost?

It depends on the number of integrations and knowledge sources. A pilot for a single process on off-the-shelf models takes weeks; a production rollout with integrations takes months. Estimates should separate build cost, model usage, and maintenance.

Can a custom assistant run without internet access?

Yes, with open-weight models running in your infrastructure. ChatGPT Enterprise and Microsoft 365 Copilot run only in the provider’s cloud. A middle ground is OpenAI models on Azure in an EU region within your own subscription.

Can ChatGPT Enterprise connect to internal files and systems?

Yes: connectors for SharePoint, Google Drive, Slack, Jira, Confluence, and more; the “company knowledge” feature; and custom connectors via MCP. The difference versus a custom assistant is who controls response logic, permissions, interface, and model choice.

Can you combine these options?

Yes, and it’s common: an off-the-shelf tool for office work plus a custom assistant for processes that require company data, system integrations, or run inside a customer-facing product.

Microsoft Copilot vs ChatGPT — what’s the biggest difference?

Microsoft Copilot works inside Microsoft Office apps via Microsoft Graph. ChatGPT is a more general-purpose AI assistant for data analysis, content creation, and Deep Research.

Which AI assistant best supports a company’s day-to-day work?

Copilot excels in email and office apps, ChatGPT in analysis, and GitHub Copilot in technical documentation and programming.

How should you compare business plans for AI tools?

Compare ChatGPT Business, Claude Team, Microsoft 365 Business Premium, Business Standard, and Google AI Pro for integrations, costs, and data protection.

When is it worth deploying a custom AI assistant in a large enterprise?

When business processes require deep integrations, custom language models, an admin panel, and control over training data.

Product and pricing status: October 2026, based on public materials from Microsoft and OpenAI and market analyses. Prices may vary by region and contract.