Generative AI is artificial intelligence. Artificial intelligence is the umbrella term for digital systems that recognize patterns in data and take over tasks that would otherwise require human perception, assessment, or decision-making... Click to learn more , generating new content such as text, images, audio, video, or code by learning patterns from training data and applying them to new tasks. For SMEs, generative artificial intelligence is useful when it solves specific bottlenecks: faster structuring, better formulation, making knowledge more easily discoverable, or reducing repetitive work. The most important limitation remains clear: Generative AI does not replace strategy, expert review, or human responsibility.
In my work with small businesses, I frequently see the same issue: AI is either overestimated or not used at all due to uncertainty. Both lead to poor decisions. When used correctly, generative AI is not an automatic quality improvement, but rather a tool for clarity, positioning, and streamlined processes.
Generative AI is not a replacement for thinking. Generative AI makes visible how good your strategy, your data, and your quality processes already are.
Generative AI explained simply
Generative AI generates new content based on learned probabilities. A system recognizes patterns in training data and calculates a suitable output: a paragraph, an image concept, a line of code, a summary, a voiceover, or a video script.
It's important to distinguish between the two: Classical AI often recognizes, evaluates, or decides based on existing data. Generative AI produces new content. Machine learning is the methodological foundation of many AI systems, while deep learning, using neural networks, processes particularly complex patterns.
An example from everyday business: Classic AI can segment customer data . Segmentation is the systematic division of a market, target group, customer base, or data set into smaller segments with common characteristics. Segmentation makes decisions more precise: ... Click and learn more and calculate a probability of purchase interest. Generative AI can then create drafts for emails, offer modules, FAQ answers, or social media versions. However, the benefit only arises when the target group definition (A target group (also target audience) is a specific group of people or buyer groups (such as consumers, potential customers, decision-makers, etc.)... Click and learn more ) , tone of voice, and approval process are clear.
How does generative AI work?
Modern generative AI works with different model families. For SMEs, you don't need to understand every technical formula, but the basic logic helps with proper classification.
- Large Language Model, LLM for short: A large language model generates and processes language. Large Language Models like the model class behind ChatGPTChatGPT is a dialog-based application from OpenAI that processes input and generates text or other content. ChatGPT is often equated with GPT. The... Click to learn more calculates which words, sentences, or structures are likely to fit in a given context.
- Transform: Transformer architectures are a central foundation of many modern Language modelsWhat is a language model? A language model is a type of artificial intelligence (AI) trained to understand and generate human language.... Click to learn moreThey help models to weigh the relationships between words, sentences, and contexts.
- Diffusion Model: Diffusion models are particularly important for image generation and increasingly also for video and audio generation. They generate content step by step from noise and refine this output until the target image or signal is achieved.
- GAN: A Generative Adversarial Network works with two networks trained against each other: A generator creates content, a discriminator evaluates whether this content appears genuine.
- VAE: A variational autoencoder (VAE) condenses data into an internal representation and generates new variants from it. VAEs are used, among other things, for creative variant generation and data modeling.
- Multimodal AI: Multimodal modelsMultimodal models: What are they and why should you be interested in them? In the field of artificial intelligence (AI) and machine learning, they are constantly emerging... Click to learn more They combine multiple input and output formats, such as text, images, audio, video, and code. This allows a system to, for example, describe an image, analyze a graphic, or develop a video script from a briefing.
ChatGPT is a well-known example of generative AI in the field of text generation. Natural Language Generation (NLG) is a data-to-text technology that generates understandable text from structured or prepared data. For your business, this means: ... Click to learn more and engage in dialogue. According to a UBS study reported by Reuters, ChatGPT was estimated to have reached around 100 million monthly active users in January 2023, about two months after its launch. This figure demonstrates its rapid spread but should be understood as an estimate based on UBS and Similarweb data, not an official measurement. You can find the source at the end of this article.
What distinguishes generative AI from classical AI?
The main difference lies in the result. Classical AI often supports analysis, forecasting, classification, or decision-making. Generative AI creates new content.
- Classic AI It recognizes patterns, assesses risks, sorts requests, forecasts demand, or classifies data.
- Generative AI writes texts, creates images, develops code, outlines campaign ideas, formulates answers, or produces audio and video content.
- Machine Learning describes the learning approach that many of these systems use to derive patterns from data.
- Deep Learning is a sub-area of Machine LearningMachine learning (ML) is one of the most exciting fields in computer science and has gained enormous importance in recent years. In short, it's... Click to learn more and uses deep neural networks for particularly complex tasks.
For entrepreneurs: An entrepreneur is someone who founds, manages, and is responsible for the success of a business. This role can be challenging, but it also offers... Click to learn more. This distinction is more practical than many tool comparisons. If you want to analyze a problem, you might need classic AI or data analysis. If you want to generate content, designs, or variations, generative AI is the more suitable type of tool.
Typical application areas for SMEs
In small businesses, generative AI is particularly beneficial where there is a wealth of knowledge but a lack of time, structure, or routine. I wouldn't start with the tool itself, but rather with the question: What bottleneck is draining your energy every week?
Text generation and communication
- Drafts for blog articles, service pages, NewsletterA "newsletter" is essentially nothing more than a digital message that is regularly sent to subscribers. Imagine you have a favorite magazine... Click to learn more and social media posts
- Variants for headings, introductions, product descriptions and advertisements
- Translations, summaries and simplifications of complex content
- Suggested answers to frequently asked customer questions
- Structuring interview notes, workshop results or protocols
AI content is just one application of generative AI. AI-generated text is not automatically good brand communication. Good communication requires positioning, an understanding of the target audience, and a clear tone. If you want to delve deeper into this topic, our article on AI in content marketing for SMEs is suitable further reading.
Image generation, video generation and audio generation
- Image ideas for campaigns, mood boards and visual concepts
- Storyboard designs for reels, explainer videos, or presentations
- Video scripts, voiceover texts and subtitles
- Sound ideas, audio variations, or synthetic voices for internal prototypes
- Visualizing ideas before a shoot or design process begins
Tools like Midjourney, DALL·E, Stable Diffusion, Adobe Firefly, Runway, or Sora-like video AI demonstrate how drastically image and video production is changing. Nevertheless, one thing remains true: an AI-generated image idea cannot replace a brand image. Especially in visual communication, it's not technical feasibility that matters, but rather whether the visual language—the consciously defined visual rulebook of a brand—is appropriate. Visual language determines how photos and images are used on a website, in social media, in recruiting... Click to learn more . The brand 's attitude and recognition are also important . The definition of a brand: A brand (also called a brand) comes from English and stands for brand. A brand is a distinctive identifier that identifies products or services... Click to learn more.
Code generation and internal processes
- Code help for simple scripts, AutomationsAutomation is the execution of recurring tasks and rule-based processes by software, systems, or machines, ensuring that a process continues reliably without constant manual intervention. The... Click to learn more or prototypes
- Documentation of processes, checklists and workflows
- Drafts for offers, project descriptions and briefingsIf you want to create a briefing, you primarily need clarity: A briefing is a structured document or a coordinated conversation that outlines the goal, framework, etc. Click to learn more
- Structuring internal knowledge databases
- Support responses based on verified company information
Tools like GitHub Copilot demonstrate how code generation can support developers. However, for SMEs, the biggest lever is often not the code itself, but rather the process clarity that precedes it. If recurring tasks, data sources, and responsibilities are disorganized, AI will initially exacerbate the disorganization.
When generative AI makes sense for your business
Generative AI makes sense when there's a clear bottleneck and the quality of the result remains verifiable. At Berger+Team, we don't use AI because it sounds trendy, but when it saves time, creates order, or facilitates better decisions. If you need support with strategy, tool selection, or integration, you can find our services related to AI & digitalization.
Generative AI is useful when…
- You have many recurring text, research, or structuring tasks.
- You want to get from an idea to a first draft faster.
- You want to make better use of existing knowledge.
- You have clear brand rules, approval processes, and responsibilities.
- You can professionally review AI expenditures.
- You no longer ContentContent encompasses all intentionally published digital content on websites, in online shops, on social media channels, in newsletters, and in other digital environments. If you want to know more... Click to learn more You want, but better structure and more consistent communication.
Generative AI is risky if…
- You enter sensitive customer data or trade secrets into external tools without checking them.
- You publish AI output without source verification.
- You don't have a clear Brand positioningBrand positioning describes the targeted place your brand should occupy in the minds of a clearly defined target group compared to relevant alternatives. If you want to... Click to learn more and none Style guideA brand style guide is a binding working document that defines the visual and linguistic rules of your brand. The brand style guide defines how logo usage,... Click to learn more have.
- You have legal questions about Privacy PolicyData protection safeguards the personal data of natural persons from unlawful processing, misuse, and loss of control. For SMEs, data protection therefore means: You consciously decide which data you collect,... Click to learn more, ignoring copyright or consents.
- You're confusing quantity with quality.
- You expect a tool to take on strategic responsibility.
Risks: Hallucinations, data protection and copyright
Generative AI can formulate convincing arguments and still be wrong. Such errors are often called hallucinations . Research on AI hallucinations describes the phenomenon as the generation of false or distorted information by AI systems; this content can seem plausible even though it is not supported by reliable evidence. You can find the source at the end of this article.
For companies, these risks are particularly relevant:
- Hallucinations: The system invents facts, sources, names, numbers, or connections.
- Outdated information: The model may not be aware of current developments or may rely on incomplete context.
- Privacy Policy: Personal data, customer data, internal figures or confidential documents should not be entered into AI tools without careful consideration.
- Copyright: For texts, images, music, code and training data, usage rights, licenses and labeling requirements may become relevant.
- Brand dilution: Too many unverified AI-generated texts make communication interchangeable.
- Loss of quality: When AI-related publications are released without expert review, trust suffers.
AI is also becoming more subject to regulatory requirements. The EU AI Act came into force on August 1, 2024, and stipulates risk-based obligations for certain AI systems as well as rules for general-purpose AI models, including transparency, copyright, and risk management requirements. Many obligations are phased in, so SMEs should examine specific use cases individually. You can find the source at the end of this article.
Quality assurance: No professional use of AI without testing.
Quality assurance is not an add-on, but a requirement. Generative AI can provide raw material, but the responsibility remains with the company. Especially with published content, offers, legal issues, health, finance, or sensitive customer topics, every issue needs a thorough review.
- Clear prompts: Describe the role, target audience, context, tone of voice, format, source base, and exclusions.
- Source verification: Verify figures, studies, names, quotes and legal statements against reliable sources.
- Four-eyes principle: Have important content reviewed by a second expert.
- Style guide: Define how your brand speaks, which terms it uses, and which statements it avoids.
- Approval process: Define who is allowed to edit, approve, and publish AI output.
- Data rules: Clarify which data must never be entered into external AI tools.
- Versioning: Document important prompts, sources, and changes so that decisions remain traceable.
This assessment is crucial, especially for brand communication. A brand isn't built on maximizing output, but on recognition, relevance, and trust. Therefore, generative AI should always be integrated into a broader system encompassing brand strategy, positioning, and tone of voice.
Examples of modern generative AI
The most popular tools change rapidly. However, examples still help to better understand the categories.
- ChatGPT: Dialogue, text generation, idea development, summaries, structuring and simple analysis.
- Midjourney, DALL·E, Stable Diffusion and Adobe Firefly: Image generation, visual concepts, mood boards and variations.
- GitHub Copilot: Code generation, code suggestions and support for developers.
- Runway and Sora-like systems: Video generation, moving image concepts and visual prototypes.
- Multimodal AI systems: A combination of text, image, audio, video and data analysis in one workflow.
For SMEs, it's not crucial which tool is currently trending. What matters is whether the tool fits your specific bottleneck, your data, your team, and your quality standards.
My recommendation from practical experience
If you want to use generative AI in your company, start small and concrete. Take a recurring process that takes time every week: draft proposals, support responses, content structure, meeting minutes, or internal documentation. Then define your goal, data source, prompt ( the term "prompt (AI)" might sound like technical jargon at first, but it actually represents a fascinating world that deals with the nature and... Click to learn more ), quality control, and accountability.
From my perspective as a branding strategist, the most important point is this: AI must not make your brand more generic. If generative artificial intelligence generates your texts, images, or responses, your stance must be strengthened, not weakened. This is precisely where meaningful use of AI differs from unchecked mass production.
At Berger+Team, we therefore don't view AI in isolation. We connect strategy, website, content, processes, and technical implementation. For example, if company knowledge needs to be reliably discoverable and controllably usable, a structured knowledge catalog for AI agents is often more valuable than the next standalone tool.
FAQ about generative AI
Is ChatGPT generative AI?
Yes, ChatGPT is an example of generative AI, more precisely a dialogue-oriented language model. ChatGPT generates new texts based on patterns from training data and additional context that you provide in the prompt.
When is generative AI useful?
Generative AI is useful if you want to accelerate recurring tasks, create designs, or structure knowledge. Its use is particularly worthwhile if you have clear goals, good source data, and a review process.
Can generative AI replace employees?
Generative AI can support individual tasks, but it doesn't replace responsibility or expertise. What does "know-how" mean? Quite simply: it's the ability to know and do something. It's less about theoretical knowledge and more about... clicking to learn more , not building relationships with customers. In SMEs, AI works best as a tool for employees, not as a replacement for people.
Am I allowed to publish AI-generated content?
In principle, you can publish AI-generated content if you respect data protection, copyright, fact-checking, and brand quality. Before publishing, you should check sources, statements, image rights, and tone.
What data should I not enter into AI tools?
Do not enter sensitive customer data, personal data, passwords, internal financial figures, confidential contracts, or trade secrets into untested AI tools. Establish clear data rules for your team before generative AI is used operationally.
What is the difference between AI content and generative AI?
AI content is a result of generative AI, for example, a text, an image, or a video script. Generative AI is the overarching technology behind it and also includes audio generation, code generation, knowledge structuring, and multimodal applications.
