Which AI tool for which task - a sober comparison

As of April 2026. Model names, context windows and pricing shift monthly. The use-case fit described here changes more slowly - we update this post when substantial model changes occur.

Most teams use a single AI tool for every task. Usually ChatGPT. That is efficient for training, inefficient for the result: an all-round model is rarely the best choice for a contract review, an Excel pivot or a marketing campaign. This post maps typical SME tasks to the tools that hold up today - and notes where a Swiss hosting option exists.

If you are looking more for the strategic selection along data-protection tiers: the decision matrix for AI tools in Swiss SMEs is available for that. Here we move to the next step: the concrete use-case mapping.

Use case 1: Text creation (emails, reports, marketing)

Task	Recommended tool	Why
Email drafts	ChatGPT Plus, Copilot in Outlook	Fast, solid default structure
Longer report	Claude Pro/Team	Consistent tone across multiple pages
Marketing copy (DE-CH)	ChatGPT, Claude	Both need the instruction “Swiss High German, ss instead of Eszett”
Translation	DeepL, Claude	DeepL for clean wording, Claude when context adaptation is needed
Multilingual newsletters	ChatGPT with GPTs, DeepL Write	GPTs as format template; DeepL for language variants

The most common mistake: ChatGPT for long reports. After about three pages the style breaks down and repetitions accumulate. Claude holds its tone longer.

Use case 2: Office automation (Word, Excel, PowerPoint)

Task	Recommended tool	Why
Word templates	M365 Copilot	Direct access to existing documents and templates
Excel formulas, pivot tables	M365 Copilot, ChatGPT (Code Interpreter)	Copilot when data lives in M365; ChatGPT for complex one-off analyses
PowerPoint slides from a brief	M365 Copilot, Gamma.app	Copilot when corporate design exists; Gamma for pitch decks from scratch
Outlook email triage	M365 Copilot	Only works if the tenant is Copilot-enabled
Teams meeting summaries	M365 Copilot	Recording must live in the M365 tenant

The value of Copilot depends on whether the team already works in M365. A separate Copilot license for a team that lives primarily in Google Workspace rarely pays off.

Use case 3: Research and source verification

Task	Recommended tool	Why
Current market data	Perplexity Pro	Real-time search with source citations
Academic sources	Perplexity (Academic Mode), NotebookLM	NotebookLM for in-depth work with your own PDFs
Competitive analysis	Perplexity, ChatGPT with browsing	Both require cross-checking of sources
Fact-checking	Perplexity	Direct source linking

ChatGPT and Claude without a browsing function are unsuitable for real-time research - their training cut-offs lie in the past. Anyone asking for “current numbers” gets plausible answers, not verifiable ones.

Use case 4: Document analysis (contracts, reports, codebases)

Task	Recommended tool	Why
Contract review	Claude Pro/Team	Long context window, precise clause extraction
Study synthesis	NotebookLM	Multiple PDFs as a knowledge base, own notes can be added
Code review	Claude, GitHub Copilot Chat	Claude for architectural reviews; Copilot Chat for focused code questions
Summarizing long reports	Claude, ChatGPT (with file upload)	Claude preferred for >50 pages

For contracts and HR documents the compliance dimension is decisive. Anyone regularly feeding personal data into document analysis should consult the Swiss Data Protection Act (DSG) guide - Claude API with a DPA or Azure OpenAI Switzerland are the robust options then.

Use case 5: Coding support

Task	Recommended tool	Why
Inline completion in the IDE	GitHub Copilot, Cursor	Both work in the common IDEs
Code refactoring	Claude (via editor plugins), Cursor	Claude often more precise on larger refactorings
Bug hunting	Claude Code, Cursor, ChatGPT	Claude Code particularly strong on multi-step debug sessions
Architecture sparring	Claude, ChatGPT	Discussion of complex designs
Test generation	GitHub Copilot, Claude	Copilot in the IDE, Claude for more complex test strategies

For pure coding teams, GitHub Copilot Business is the standard. For teams that combine engineering and strategy, Claude is worthwhile as a sparring partner.

Use case 6: AI agents and workflows

Task	Recommended tool	Why
No-code workflows	OpenAI Agent Builder, Make.com with LLM modules	Visual editor, fast iteration
Microsoft-centric automation	Copilot Studio	Native M365 integration
Browser automation	Computer Use (Anthropic), OpenAI Operator	Both still in active development - caution with personal data
Data pipelines with an LLM step	n8n, Zapier with OpenAI/Claude connector	Established workflow tools, LLM as a building block

In depth: OpenAI Agent Builder and AI agents overview.

Swiss hosting options by tool

Tool	CH hosting available	Note
ChatGPT Team/Enterprise	No (US, DPF-certified)	With a DPA sufficient for regular personal data
Microsoft 365 Copilot	Yes (Switzerland tenant)	Standard for Swiss M365 contracts
Azure OpenAI Service	Yes (Switzerland North/West)	Highest CH compliance tier
Claude API	EU region available	EU sufficient for disclosure
Mistral La Plateforme	Yes (EU/France)	EU hosting by default
GitHub Copilot Business	No (US)	With training-use opt-out acceptable for most code use cases

The Azure Switzerland regions (Zurich North, Geneva) are currently the only option for ChatGPT-equivalent models with data residency in Switzerland.

Three recurring mistakes

Tool stacking without a use case. Four tool licenses per employee because “everything looks useful”. The result: high costs, low depth per tool. Rule of thumb: at most two generalists plus one or two specialists.

Only the free tier. Free plans are fine for internal tests, not for production workflows with personal data. Training use is active by default, DPA is missing.

Training as a one-time event. Tool updates arrive weekly. Without continuous refreshers, internal knowledge decays faster than it is built up.

How to apply this post in 30 minutes

1. List the five most frequent tasks of your team.
2. Map them to the tables above - which tool covers which task?
3. Compare with the tools currently licensed. Where are the gaps, where the overlaps?
4. Prioritize the two most important adjustments for the next quarterly planning.

We run this exercise together with you in the workshops for Swiss SME teams - including a pilot plan and a training roadmap.

---

Further reading on ai-edu.ch:

• AI tools for Swiss SMEs: the decision matrix
• Prompt templates: 8 templates for everyday work
• DSG and AI in Swiss SMEs

Sources:

• OpenAI Models - current model overview
• Anthropic Claude Models
• Microsoft 365 Copilot - What it can do
• Azure OpenAI Service - Switzerland Regions