The Best AI Tools: Every Category Explained and How to Choose Well

Is there really a single «best AI tool»?

Looking for the «best AI tools» is misleading if you expect one universal answer. A tool that produces brilliant images is useless for meeting minutes; an excellent code assistant barely helps you write a newsletter. The more useful question is: what is the best tool for this specific task, within this budget, under these data-protection requirements?

That is why this guide structures the market by category and use case. Thinking in categories leads to a good choice faster, compares like with like, and avoids costly mis-purchases. The products named here are well-known, publicly available examples — not a ranking and not an endorsement over other vendors.

The six most important AI-tool categories

Almost every productive AI application fits into one of six categories. Each has well-known examples and typical uses:

• Text and writing: ChatGPT, Claude, Google Gemini, DeepL Write — drafting, rewriting, summarising, translating.
• Image generation: Midjourney, Adobe Firefly, Stable Diffusion, DALL·E — concepts, illustrations, moodboards.
• Video: Runway, Synthesia, HeyGen, Pika — avatar videos, short clips, subtitles, editing.
• Coding: GitHub Copilot, Cursor, Claude Code — autocompletion, refactoring, tests, debugging.
• Meetings and transcription: Otter, Fireflies, tl;dv and Whisper-based services — minutes, subtitles, searchability.
• Chat assistants: ChatGPT, Claude, Gemini, Microsoft Copilot, Perplexity — research, Q&A, analysis.

Selection criteria: how to spot a good AI tool

Within each category, a consistent checklist helps you compare offers soberly instead of following marketing promises. The following criteria apply across all categories:

• Data protection and hosting: where is data processed and stored? Is there a data-processing agreement and an opt-out from model training?
• Output quality in your language: many tools are optimised for English — test the languages that actually matter to you.
• Integrations: does the tool fit Microsoft 365, Google Workspace, Slack or your existing systems? Is there an API?
• Pricing model: per-user subscription, credits or enterprise licence? Watch for hidden costs and how it scales across a team.
• Usability and learning curve: how quickly does the team become productive? Are there templates, training and documentation?
• Vendor maturity: roadmap, support, reliability and verifiable compliance evidence (e.g. certifications) — not mere marketing claims.

What matters most in each category

• Text: check factual accuracy (models can «hallucinate»), control tone, and keep confidential content out of unvetted services.
• Image: clarify usage rights and licence — is commercial use allowed? What about style control and copyright?
• Video: mind rights to voices and avatars, production effort, and possible watermarks and labelling obligations.
• Code: generated code can carry licence and security risks — human review stays mandatory; never ship it unchecked.
• Meetings: participant consent is decisive under the revFADP; check transcription accuracy and where recordings are stored.
• Chat: look for source citations, how current the answers are, and the context-window size for long documents.

Data protection first: the Swiss perspective

Since September 2023 Switzerland has applied the revised Federal Act on Data Protection (revFADP), overseen by the Federal Data Protection and Information Commissioner (FDPIC). For AI tools this means company and personal data must not flow unchecked into arbitrary cloud services. Before adopting a tool, its data processing belongs under review.

• Clarify hosting location and data flow (Switzerland, EU or third countries) and put a data-processing agreement in place.
• Prefer team or enterprise plans that offer an opt-out from model training and clear data-retention terms.
• Never enter personal data or trade secrets into free consumer tools.
• For meeting tools, be transparent and obtain the consent of all participants.
• Establish an internal AI policy: approved tools, permitted data and clear responsibilities.

Five steps to the right AI tool

• 1. Define the task and use case precisely — what problem should be solved, and what does a good result look like?
• 2. Narrow down to the right category (text, image, video, code, meeting, chat) and shortlist two or three candidates.
• 3. Test in a small pilot with real tasks — not with the vendor's demo examples.
• 4. Check data protection under the revFADP, costs and integrations before deciding.
• 5. Roll out, train the team and anchor usage in a policy — review the impact regularly.