Claude vs Copilot vs ChatGPT Enterprise for UK businesses: a selection guide

Reviewed October 2026. Products in this market change monthly; this guide describes the durable differences and gives you a method for choosing on your own evidence. Check current features and terms with each vendor before deciding. We are platform-agnostic and hold no reseller arrangement with any of the three.

Contents: The short version · What they have in common · Where each lives · Documents, reasoning and code · Data terms and governance · Administration and rollout · Building on them · Cost · Why many businesses run two · How to select on your own tasks · Decision table · Rollout after the choice

The three enterprise AI assistants most UK businesses shortlist are Claude for Work (Anthropic), Microsoft 365 Copilot (Microsoft) and ChatGPT Enterprise (OpenAI). All three are capable; the differences that matter are where they live in your working day, how they handle long documents and code, their data and administration terms, and what you can build on them. The wrong way to choose is a vendor demo. The right way is a week of your own tasks, scored by your own people, which this guide shows you how to run.

The short version

• Microsoft 365 Copilot is the default for Microsoft-centred businesses because it sits inside Word, Excel, Outlook, Teams and SharePoint and sees your tenant’s data with its permissions. Its strength is context from your own M365 estate; its weakness is that it is only as good as the tenant’s hygiene and that the underlying model is Microsoft’s choice.
• Claude for Work is the one teams reach for when the work is long documents, careful analysis, writing that has to be right, and code. It is strong on large context and on following complex instructions, and its enterprise platform is the one many developers build agents on. It lives in its own app and integrations rather than inside Office.
• ChatGPT Enterprise is the broadest consumer-familiar option, with a large feature set (voice, image generation, custom GPTs, a wide connector catalogue) and the lowest training curve because many staff already use the consumer product.

Many mid-market businesses end up with two: Copilot for the Office-embedded everyday, plus Claude or ChatGPT for the heavier knowledge work and for building. That is a sensible outcome, not an indecisive one.

What they have in common

All three, on enterprise terms: do not train on your data; offer administrative controls, SSO and audit; support uploaded documents and connected data sources; produce confident, occasionally wrong answers that need a human check; and are improving fast enough that any feature comparison is out of date within a quarter. All three can be deployed to a UK business with data residency and processing considerations that your data-protection lead should review; none is a reason, on its own, to decline enterprise AI.

Where each lives

This is the difference most felt by staff.

Copilot is in the applications: a pane in Word, a prompt in Excel, a summary in Outlook, a recap in Teams. Staff do not go to it; it is there. It also has a standalone chat with access to the tenant’s content. For a business that lives in Microsoft 365, this removes the “remember to use the AI” problem, which is the main cause of low adoption.

Claude lives in its own web and desktop apps, with integrations to common tools and a browser extension, and reaches your data through connectors. Staff open it on purpose. That makes it better for deliberate, longer tasks and worse for incidental ones.

ChatGPT is similar to Claude in this respect: its own apps, plus a wide and growing set of connectors and the ability to build custom GPTs that bundle instructions and data for a team.

The practical consequence: Copilot wins on frequency of use; Claude and ChatGPT win on depth of use. Which matters more depends on where your value is.

Documents, reasoning and code

For long, careful work, the differences are real and worth testing:

• Long documents. Claude’s large context and its handling of long material are the reason legal, engineering and policy teams favour it for contracts, standards, specifications and reports. ChatGPT handles long documents well; Copilot handles them within the Office context it has.
• Analysis and writing. All three write well. Teams that produce documents that must be exactly right (proposals, technical reports, regulated communications) tend to prefer Claude’s instruction-following and tone; teams that produce volume prefer whichever is closest to hand.
• Spreadsheets and data. Copilot in Excel is the most natural for staff who live in spreadsheets; ChatGPT and Claude both run code to analyse uploaded data, which is stronger for ad hoc analysis.
• Code. For software teams, the comparison is Claude Code against GitHub Copilot (a separate Microsoft product) against OpenAI’s Codex tooling. Claude Code is the agentic terminal tool many engineering teams have standardised on; GitHub Copilot is the in-editor assistant with the widest install base. Many teams use both. Our developer tooling page covers this choice separately.

Data terms and governance

The terms are closer than the marketing implies, and your data-protection lead should read them rather than trust a summary. The points to check for each:

• Training. Enterprise tiers of all three commit not to train on your prompts and data. Confirm the tier you are buying is one of those; some lower tiers differ.
• Retention. How long prompts and outputs are retained, whether you can set zero retention, and what the audit log keeps.
• Residency and processing. Where data is processed and stored, and what sub-processors are involved; relevant to UK GDPR transfer assessments.
• Permissions. Copilot inherits M365 permissions, which is a strength and a trap: over-shared SharePoint sites become over-shared answers. A permissions review is part of any Copilot rollout.
• Admin controls. Which features can be switched off, whether connectors can be restricted, whether usage can be reported per user.
• Regulatory posture. For businesses in or near EU AI Act high-risk uses, what documentation the provider gives a deployer; see the EU AI Act guide.

All three can be run in a way that satisfies the governed access model. None should be run without the policy that goes with it.

Administration and rollout

Copilot’s administration is Microsoft’s: licences assigned in the admin centre, policies in the tools you already use, reporting in the usual dashboards. For an IT team already running M365 it is the least new work. Claude and ChatGPT each have their own admin consoles, SSO and SCIM, and are straightforward but separate. The rollout effort that matters is not the administration; it is the training, the prompt library and the champions, which are the same work whichever you choose and which decide adoption. See AI tool implementation.

Building on them

If you intend to build agents, assistants or integrations, the platform behind the assistant matters more than the assistant.

• Anthropic provides the Claude API and a developer platform used for agent development; its models are available directly and through the major clouds. Our Agency51 support agent runs on Claude within our framework.
• OpenAI provides the API and agent tooling behind ChatGPT, also available through Azure.
• Microsoft provides Copilot Studio for building agents on the M365 estate and Azure AI Foundry for building on a choice of models, including OpenAI’s and others.

A business that chooses Copilot for staff can still build its agents on Claude or OpenAI; the assistant choice and the build platform are separate decisions, and keeping them separate avoids locking the engineering to the licence.

Cost

The three are priced per user per month on enterprise terms, with Copilot typically bought as an add-on to existing M365 licences and the other two as standalone seats, often with minimum seat counts or annual commitments at the enterprise tier. Pricing changes often and varies by tier and negotiation; take current quotes rather than published figures, and compare on total cost per active user, because the common failure is paying for seats that are not used. API usage for anything you build is metered separately and is a different budget line.

Why many businesses run two

The honest finding from rollouts is that one assistant rarely covers everything well. The common pairing is Copilot for the embedded everyday plus Claude for long-document, analytical and engineering work, or Copilot plus ChatGPT where the organisation already has ChatGPT habits. The cost of a second tool for the subset of staff who need it is usually smaller than the cost of the wrong single tool for everyone. The governance for two is the same as for one: a sanctioned list, a policy, and the training that says which to use for what.

How to select on your own tasks

The method we use in tool selection engagements, which a business can run itself in two weeks:

1. Collect twenty real tasks from across the functions: a contract to summarise, a customer email to answer, a quote to draft from a spec, a spreadsheet to analyse, a report to tighten, a procedure to turn into a checklist, a bug to fix. Real, recent, with the real inputs.
2. Write the ideal answer for each, or have the domain expert mark what “good” looks like. This is a small evaluation set.
3. Run every task through every candidate, with a reasonable prompt, by the people who would do the task in practice. Keep the outputs.
4. Score blind. The expert scores each output without knowing which tool produced it, on a simple scale: usable as is, usable with edits, not usable.
5. Score the experience separately. How many steps, how much friction, would the person use it again.
6. Add the non-task factors: data terms, administration, build platform, cost per active user.
7. Decide, and expect the answer to be “this one for most people, that one for these teams”.

Two weeks, perhaps ten hours of expert time, and you have a decision grounded in your own work rather than a demo of someone else’s.

Decision table

Rollout after the choice

The tool is a third of the result. The rest is the policy, the training by function, the prompt library, the champions, and the measurement of use. A business that buys Copilot and sends an announcement email gets a third of its staff using it within a quarter; a business that runs a proper rollout gets most of them. The AI tool implementation and training pages describe how we run that, platform-agnostically, for whichever you choose.