There are five of them, from a chatbot in a browser tab to a model running on your own hardware. This is what each one is for, what it asks of you, and where the larger gain sits once the first one is in place.
Most companies already have AI in the building. In PagerDuty’s survey of office professionals in June 2026, 66% said they used AI tools at work even though they believed their employer’s policy did not allow it1, and other surveys through 2025 and 2026 put the figure as high as 80%. People went looking on their own because the tools help with the paragraph that will not start, the formula nobody remembers, the first pass at a translation, the forty-page document due tomorrow.
That is demand most organisations spend a year trying to manufacture, and it arrived by itself. It is a good thing to build on. Individual use is genuinely fine for a great deal of work, and for some people it is all they will ever need from AI.
Two practical facts shape what comes next. The first is that consumer tiers train on what goes into them by default. ChatGPT and Gemini do unless the setting is changed2, while Claude’s consumer plans have been opt-in since August 20253. Which tier you are on therefore decides which material suits the tool, and enterprise terms turn training off by contract. The second is that individual use tops out at individual productivity. Forty people each saving an hour a week is forty hours a week, without changing the process any of them work in.
Five shapes cover almost everything a company can actually buy. Each is the right answer for some organisation, and most firms end up running two or three at once. The product names below are passing examples and will change; the categories move more slowly. Hapax resells none of them and takes no commission from any vendor.
These cost nothing, or around €20 a month on someone’s own card. ChatGPT, Claude, Gemini and Copilot sit in a browser tab, bought by the person who uses them. They are good at drafting, summarising, explaining and unblocking, and the person reading the answer is the quality control, which is exactly the right arrangement for a first draft. What they do not come with is anything shared. There is no administrator, no common instructions, and no record of what the organisation is doing with them. As a company’s only shape they stop being enough the moment the same task has to be done the same way by more than one person.
The same tools, bought by the company, are sold as ChatGPT Business or Enterprise, Claude for Work and Gemini for Workspace. Content stays out of training by contract rather than by a setting, an administrator can see and control who has access, and work becomes shareable as projects and standing instructions instead of living in one person’s tab. OpenAI publishes $25 to $30 per user per month for Business as of September 20264, and the comparable tiers are priced in the same region. For most firms this is the shape to buy first. It disappoints in the ordinary way a licence disappoints, when everyone is given one and nobody is shown what to use it for.
Here the AI sits inside software the company already runs, in Microsoft 365 Copilot, Gemini in Workspace, and the assistants now shipped with Salesforce, ServiceNow and SAP. The appeal is context. The assistant can already see the documents, mail and tickets that the person is entitled to see, so its answers are grounded in your business. Microsoft states that prompts, responses and Microsoft Graph data are not used to train foundation models and are processed under its data-processing terms, with the EU Data Boundary covering the EU and EFTA since 1 May 2026; its Flex Routing, on by default for eligible tenants since April 2026, can send inference outside the EU at peak demand, while storage stays inside5. This shape is right when the work genuinely lives in that estate. It is often bought because procurement was easy, one line added to an agreement that already existed, and it then sits next to the line-of-business system where the hours actually are.
You pay per use, by the million tokens, and build the thing around the model yourself. Here a model stops being a destination and becomes a component, which is what makes measurement and checking possible, and it is the shape behind the case studies on this site. The terms are business terms. Anthropic’s commercial terms, as published in September 2026, state that the API does not train on inputs or outputs and holds inputs for seven days by default under the policy it set in September 2025. OpenAI’s API has not trained on business data without opt-in since March 2023, offers a data-processing agreement, and makes EU data residency available on approval, not by default6. An API build is the wrong answer when one person needs prose once a week, which is engineering effort spent rebuilding a chat window.
Open-weight models such as Llama, Mistral and Qwen run on hardware you control, in your own data centre or cloud tenancy, air-gapped if that is what the work requires, with no per-token fee. Licences vary and repay reading. Most Mistral models are Apache 2.0. Meta’s Llama licence permits commercial use but requires a separate licence from Meta above 700 million monthly active users, counting affiliates, and is not OSI open source7. The hardware is arithmetic. A workstation around $2,500, an RTX 4080-class card with 32 GB of system memory, runs 13B to 30B models comfortably at 4-bit quantisation, roughly 0.6 GB of video memory per billion parameters; long context is what breaks the budget, since a 70B model at 128K context needs about 42 GB for the key-value cache alone, and anything beyond that means cluster inference and the people to keep it running. Open-weight buys cost control and complete control of data and model. On deep reasoning, long-context coherence and specialised tasks, our assessment is that the gap to frontier hosted models was still six to twelve months as of mid-2026.
Sovereignty is usually the reason a local model gets raised. Mistral is domiciled in Paris and hosts in the EU by default, with on-premises and private-cloud options; its own French data centre was expected in the second quarter of 2026 and we have seen no confirmation that it is running. Anthropic has no native EU data zone, although EU-region routes exist through AWS Bedrock and Google Vertex, and Claude on Microsoft Foundry is Global Standard only as of September 2026. Under the GDPR the processor terms carry more weight than the map pin, and EU residency strengthens a position without replacing a data-processing agreement. A few organisations genuinely need this shape, and more are told they need it than do. Most companies that ask us about a local model want an enterprise contract and a data-handling policy instead.
One duty applies whichever shape is chosen. Article 4 of the EU AI Act requires every organisation whose staff use AI at work to ensure a sufficient level of AI literacy for their roles and the systems in use. It has been in force since February 2025, with national enforcement powers from August 2026, and it draws no distinction between a browser tab and a model on your own hardware8. The training page sets out what it means in practice.
Two things separate the five in practice. One is how much control you hold over your data and over the model, the other how much effort and money it takes to run. The two rise together, so the five are a choice and not a ranking.
The map shows where the shapes sit relative to one another. Which one fits depends on where your hours go, what your material is, who will maintain the thing afterwards and what your policies already say.
A person asking a chatbot gets a better paragraph; a workflow with AI inside it gets a better process. That is the whole argument of this page, and it is where the compounding happens, because a process improved once stays improved for everyone who uses it afterwards.
Integrated means three things in practice. The model runs inside the step where the hours go, and not in a separate window someone has to remember to open. Its output is checked by design, by a rule that has to pass, a second pass over the same material, or a named person who signs. And the result is measured against what the step used to cost in time, in rework, or in errors that reached a customer.
None of that requires the most expensive shape. An enterprise subscription with a few well-made standing instructions is an integrated workflow if the work runs through it, and an ordinary API key covers most of the rest. Choosing the shape is the smaller decision. Choosing which process to put it inside is the larger one.
The case studies on this site are all this kind of work, run end to end on data built with the answer known in advance, so every result could be marked against that answer. The claim this section makes has since been measured directly, in the tender case, where the same sixteen documents were answered both ways.
Sammalkoski Oy does not exist. It is a 340-person engineering-services firm in Turku where about seventy people had each found their own way to a browser chatbot and were getting real use out of it. The firm bought an enterprise workspace for everyone, with terms that keep its material out of training and an administrator who can see what is licensed. It then picked the two places the hours actually went, tender responses and site-report write-ups, and rebuilt both as workflows with the model inside the step and a check on the output before it moved on. Training ran on the firm’s own tenders.
Six months later a tender response takes 9 hours of a senior engineer’s time against 26 before, and 210 of 340 staff use the tools weekly, up from the seventy who had found them alone. The win rate on tenders sits where it was, at 31%.
Exemplum · hypothetical
We have since measured the step this exemplum describes, from private habits to one workflow, on sixteen engineered tenders answered both ways in the tender case.
If you are working out which shape fits, tell us which tools your teams already use, officially or not, and where their hours go. That is most of what anyone needs in order to give you a useful answer.
Sources
Reviewed · September 2026