ChatGPT, agents, automations, bots, LLMs, Midjourney – anyone exploring AI today is confronted with a flood of terminology. It’s difficult to get a clear overview. What’s the difference between an AI agent and an automation? When does a specialised tool make sense, and when is a proven LLM sufficient?
The confusion is understandable. These terms overlap, marketing often uses them loosely, and what seemed like science fiction yesterday is already a concrete product today. This article offers an overview of AI tools for business – not through technical definitions, but with a practical question: which tool fits which task? Because when it comes to choosing the right AI tool, context matters.
To use our favourite analogy: if the only tool you have is a hammer, every problem looks like a nail. Or, applied to this topic: the best hammer is useless when you actually need a screwdriver. And not every nail can be driven in with the same hammer.
The Key AI Tool Categories
Before we dive deeper, here’s an overview of the common categories and tools in this AI tool comparison:
| Category | Suited for | Typical Tools |
|---|---|---|
| General Language Models (LLMs) | Text, research, analysis, sparring | ChatGPT, Claude, Gemini |
| AI Agents | Multi-step, autonomous tasks | Custom GPTs, Claude Projects, Langchain |
| AI-powered Automations | Recurring workflows | n8n, Make, Zapier AI |
| Image Generation | Creating visual content | Midjourney, DALL-E, Stable Diffusion |
| Video Generation | Moving images and animation | Sora, Runway, Pika |
| Knowledge Management | Unlocking large document volumes | NotebookLM, Perplexity |
| Coding Assistants | Programming and prototyping | GitHub Copilot, Cursor, Claude |
| Speech Tools | Transcription and voice output | Whisper, ElevenLabs |
With all this tool diversity, one fundamental principle applies: every tool is only as good as the context you give it. Every result is only as precise as the input and the prompt you use.
This isn’t a shortcoming of the tools – it’s their nature. They’re tools, not mind readers. The better you communicate what you need, the better the results.
General Language Models (LLMs): The Generalists
What they are: Large Language Models like ChatGPT, Claude, or Gemini are trained to understand and generate human language. They can write texts, answer questions, analyse, translate, and serve as sparring partners for ideas.
What they’re suited for:
- Text creation and editing
- Research and summaries
- Brainstorming and strategic sparring
- Document analysis
- Translations
What they’re less suited for:
- Tasks requiring current information (without web search)
- Highly specialised expert tasks without appropriate context
- Fully automated processes without human review
Real-world example: For an SME that regularly produces proposals, reports, or customer communications, an LLM can be valuable. Instead of starting from scratch, the LLM delivers a structured draft that can then be refined.
Important to understand: LLMs are generalists. They can do many things well, but they’re not perfect. The more context they can draw upon, the better the results.
AI Agents: The Autonomous Problem-Solvers
What they are: Unlike a simple chatbot, AI agents can execute multi-step tasks independently. They break down complex requests into sub-steps, use various tools, and work through a problem step by step.
The difference from an LLM: An LLM answers your question. An agent handles your task – potentially across multiple steps, with intermediate results and its own decisions.
What they’re suited for:
- Complex research drawing on multiple sources
- Tasks that combine different tools
- Processes requiring decision logic
What they’re less suited for:
- Simple, one-off queries (an LLM is sufficient here)
- Tasks where every step needs human approval
- Critical processes without clear control mechanisms
Real-world example: An agent is deployed to create a weekly market overview: it automatically collects relevant industry news from defined sources, analyses new publications from trade associations, summarises the key developments, and prepares a structured report for the management meeting.
Important to understand: Agents are powerful, but they need clear guardrails. Without good instructions, an agent can head in the wrong direction – very efficiently.
AI-Powered Automations: Intelligent Workflows
What they are: Classic automation tools like Zapier, Make, or n8n connect different software applications. Through AI integrations, these workflows become more intelligent: instead of just “if X, then Y”, they can also analyse text, make decisions, or generate content.
The difference from agents: Automations follow predefined paths – albeit with intelligent branches. Agents can adapt their approach themselves.
What they’re suited for:
- Recurring processes with clear structure
- Email processing and categorisation
- Data transfer between systems
- Content creation following fixed templates
What they’re less suited for:
- One-off, complex tasks
- Processes requiring deep contextual understanding
- Situations needing flexible responses
Real-world example: An SME could have incoming customer enquiries automatically categorised: urgent complaints are forwarded immediately, standard queries are answered with prepared responses, complex cases are routed to the appropriate department – all without manual sorting.
Important to understand: Automations shine with volume and repetition. The setup effort only pays off when a process runs regularly.
Image Generation: From Text to Picture
What they are: Tools like Midjourney, DALL-E, or Stable Diffusion create images from text descriptions. They’re trained on millions of images and can translate styles, compositions, and concepts into visual results.
What they’re suited for:
- Concept images and mood boards
- Social media graphics
- Illustrations for presentations
- Quick visualisation of ideas
What they’re less suited for:
- Precise technical drawings
- Images with exact text (lettering is often flawed)
- Corporate design with strict guidelines
- Photos of specific people or products
Real-world example: A company needs a header image for a blog post about “Digital Transformation in SMEs”. Instead of spending hours searching stock photos, you describe the desired image in a few sentences and receive several variations within seconds.
Important to understand: Quality depends heavily on the prompt. “A picture of an office” delivers generic results. The more precise the description, the closer the result comes to your vision.
Choosing the Right AI Tool
Three questions to help you find the right tool for your task.
How it works: For each question, select the answer that best matches your current situation.
1 What is the task?
2 How much autonomy is appropriate?
3 What context is available?
Video Generation: Moving Images from Text
What they are: Tools like Sora, Runway, or Pika can generate short videos from text descriptions or still images. The technology is developing rapidly but isn’t yet quite at the level of image generation.
What they’re suited for:
- Short clips for social media
- Animated product visualisations
- Concept videos and prototypes
- B-roll material
What they’re less suited for:
- Longer, coherent videos
- Precise brand representation
- Content requiring absolute consistency
- Professional productions with high quality standards
Real-world example: For a LinkedIn campaign, a company needs short, attention-grabbing clips. Instead of commissioning a video team, simple animations or scenes can be quickly generated and tested.
Important to understand: Video generation is still relatively young. The results are impressive but not always predictable. For professional productions, human post-production remains essential.
Knowledge Management: Unlocking Large Document Volumes
What they are: Tools like NotebookLM or Perplexity help unlock large volumes of documents, reports, or research material quickly. They can create summaries, answer questions about the content, and establish connections between documents.
What they’re suited for:
- Getting up to speed on complex topics
- Analysing complex information, reports, studies
- Quick orientation in unfamiliar specialist areas
- Preparing meetings and presentations
What they’re less suited for:
- Real-time information (limited – only uploaded documents or web research)
- Tasks that go beyond pure analysis
Real-world example: A managing director needs to prepare for a workshop on a new regulatory topic. Instead of reading 200 pages of documents themselves, they upload them to NotebookLM and have it summarise the key points, highlight contradictions, and identify open questions. The results can be prepared as an explainer video, podcast, presentation, or infographic.
Important to understand: These tools are brilliant at making existing knowledge accessible. They don’t create new knowledge – they unlock what already exists.
Coding Assistants: Programming for Everyone
What they are: Tools like GitHub Copilot, Cursor, or Claude can write, explain, debug, and improve code. They’re relevant not just for professional developers – also for quick prototyping and smaller automations.
What they’re suited for:
- Accelerating software development
- Explaining existing code
- Quick prototypes and proof-of-concepts
- Creating simple tools and scripts
- Generating data visualisations
What they’re less suited for:
- Complex system architectures without human oversight
- Security-critical applications
Real-world example: A company without its own IT department needs a simple overview of sales figures. With a coding assistant, a working dashboard prototype can be created in minutes – without programming knowledge, just with a description of what you want to see.
Important to understand: Coding assistants democratise software development. But generated code should always be reviewed – especially when deployed in production.
Speech Tools: From Speech to Text and Back
What they are: Tools like Whisper (transcription) or ElevenLabs (speech synthesis) translate between spoken and written language. Whisper can convert interviews or podcasts into text. ElevenLabs can transform text into natural-sounding speech.
What they’re suited for:
- Automating meeting minutes
- Transcribing podcasts and videos
- Creating multilingual content
- Producing accessible content
What they’re less suited for:
- Poor audio quality (heavy noise, overlapping voices)
- Strongly dialect-influenced speech
Real-world example: A company conducts regular customer conversations. Instead of taking manual notes, the conversation is recorded (with consent) and automatically transcribed. The summary can then be transferred directly to the CRM.
Important to understand: Transcription quality depends heavily on audio quality. Clear recordings deliver significantly better results.
The Common Thread: Context is Crucial
With all this tool diversity, one fundamental principle applies: every tool is only as good as the context you give it. Every result is only as precise as the input and the prompt you use.
An LLM without background information delivers generic answers. An image generator without a precise prompt produces interchangeable pictures. An agent without clear instructions loses itself in irrelevant tasks.
This isn’t a shortcoming of the tools – it’s their nature. They’re tools, not mind readers. The better you communicate what you need, the better the results.
The Strength Lies in Combination
Often, AI tools only unfold their full potential when combined. Some examples:
Speech → Text → Analysis: A customer conversation is transcribed with Whisper. The text is then fed into an LLM, which extracts the key points and identifies open issues.
Many conversations → Pattern recognition: Dozens of interview transcripts are uploaded to NotebookLM. The tool identifies consistencies and deviations across all conversations – revealing details and insights that would easily be missed when reviewing them individually.
Research → Deepening: Perplexity provides a broad overview of a topic. The results are then deepened in Claude with your own company context and applied to your specific situation.
Concept → Visualisation: An LLM helps articulate an idea. The description is then passed to an image generator, which visualises the concept.
Automation + Intelligence: An email comes in, is automatically categorised, an appropriate response is generated and submitted for approval – all in one workflow.
The art lies in combining the right tools for the respective sub-tasks – and knowing where human review remains indispensable.
Framework: Selecting the Right Tool
Three questions help with the selection:
1. What is the task?
- One-off or recurring?
- Creative or structured?
- Text, image, video, code, or analysis?
2. How much autonomy makes sense?
- Do I need a draft to refine? → LLM
- Should a clearly defined process be automated? → Automation
- Should the AI make decisions itself? → Agent (with caution)
3. What context is available?
- Do I have clear examples and specifications?
- Are there documents that can be incorporated?
- Can I improve the process iteratively?
When in doubt: just start. With a general LLM, you can at least begin almost any task – and then decide whether a specialised tool would be more effective.
Conclusion: The Toolbox is Growing
The variety of AI tools can seem overwhelming. But at its core, it’s like any toolbox: you don’t need to have and master every tool. Rather, you need to know which tool suits which task – and when you need it.
Our advice: start with the basics. A good language model, an understanding of automations, perhaps an image generator for visual content. The rest reveals itself as the need arises or a use case emerges.
What’s Often Forgotten: Tools Alone Aren’t Enough
With all the enthusiasm for what these tools can do – three things are regularly underestimated:
Context is crucial: We’ve emphasised this repeatedly in our articles, but it cannot be said often enough: without relevant context, even the best tool delivers only mediocre results. This applies to the individual prompt just as much as to strategically embedding AI in your organisation. Those who simply “bolt on” AI tools without providing them with the necessary knowledge about the company, processes, and goals will be disappointed.
AI implementation is change management: Introducing AI tools isn’t an IT project. It’s a change process that affects people, ways of working, and structures. Which tasks are changing? Which competencies are becoming more important? How do teams handle the new possibilities? These questions are at least as important as selecting the right tool.
Without clear processes, no sustainable results: AI can accelerate and improve processes – but only if these processes exist and are understood. Those who don’t know how a workflow functions today won’t make it better with AI. Working on your own processes is the foundation for any successful AI deployment.
This may sound less exciting than the latest tool announcements. But it’s the difference between “We tried ChatGPT once” and “AI has become part of how we work”.
Want to know which AI tool suits your organisation?
Do you have questions about which tool is right for your situation – or how to embed AI sustainably in your organisation?
Tool Overview with Links
General Language Models (LLMs)
Automations
Image Generation
- Midjourney
- DALL-E (OpenAI)
- Stable Diffusion
Video Generation
Knowledge Management
- NotebookLM (Google)
- Perplexity
Coding Assistants
- GitHub Copilot
- Cursor
- Claude Code (via Claude)
Speech Tools
- Whisper (OpenAI, transcription)
- ElevenLabs (speech synthesis)