Generative AI In business: where to start and what to avoid

What exactly can generative AI do?

Generative AI, models such as GPT-4o, Claude, Gemini or open-source alternatives like Llama, can create new content based on a prompt. Text, code, images, audio, video. This represents a fundamental shift compared to earlier AI, which mainly classified or predicted based on historical data.

From the company's perspective, this opens up specific opportunities in four main areas: customer communication, content creation, coding and data analysis. Each offers real savings and real risks.

Where Generative AI Delivers Real Value

1. Customer communication and support

LLM-based chatbots (large language models) handle customer queries significantly better than previous rule-based solutions. They can read context, formulate responses in natural language and work with company documentation as a knowledge base.

Reality in practice: an e-commerce company with a catalogue of 50 000 SKU can deploy an AI assistant to handle 60-70 % of repetitive queries without human intervention, and pass the remaining cases to a live agent with conversation context. Result: shorter waiting times, lower costs, satisfied customer.

2. Content creation and editing

Copywriting, translations, product description creation, generating ad copy variants for A/B testing, and report summarisation, AI handles all of this faster and cheaper than a purely human team. Output quality depends on the quality of the prompt and how much knowledge about the company and industry you feed into the system.

Festival partners BeeSkills.cz In their company workshops they regularly demonstrate how to reduce the preparation time for marketing materials from hours to minutes while maintaining a consistent brand voice.

3. Assisted software development

GitHub Copilot, Cursor, Claude and other tools have become standard equipment for developers. Surveys consistently show a productivity increase of 20-40% in routine coding tasks, writing boilerplate, documentation, unit tests, and refactoring. They do not replace programmers; they enable them to focus on more complex problems.

For companies, this means a development team of the same size can handle more projects or bring existing projects to production faster.

4. Analysis and summary of data and documents

Contracts, research reports, customer feedback, financial statements, AI can process these documents, extract key information and answer questions about them. What takes an analyst a day, AI handles in minutes. The key is correctly configuring the RAG (Retrieval-Augmented Generation) pipeline and ensuring sensitive data does not leave the company's infrastructure.

Most common errors in implementing AI

Hallucinations: AI makes things up

Generative models sometimes state falsehoods with absolute certainty. They fabricate citations, figures, dates or facts. In the context of customer communication or report generation, this can cause serious problems.

Solution: Never deploy AI without a human in the loop for critical outputs. For internal tools, design processes where a human verifies the final output. Technically, this is supported by grounding, working with specific, verified sources instead of "free-form" generation.

Data privacy and security

Sending company documents, customer data or source code to external APIs (OpenAI, Anthropic) can compromise GDPR compliance, breach NDAs or disclose trade secrets. Companies do this without fully realising it.

Solution: a clear policy on what employees may submit to external AI tools. For sensitive processes, consider self-hosted open-source models or enterprise versions with guaranteed data isolation. The topic is covered at the AI Festival (ai-festival.cz). flylight.ai with every implementation of AI automation.

Over-automation: too much AI, too soon

Companies get carried away and try to automate everything at once. The result is a complex, fragile system that no one truly understands, and when it breaks, no one knows why.

Solution: start with one specific, well-defined use case with a clear success metric. Demonstrate the value, learn from it, and then proceed further.

Unrealistic ROI expectations

AI will save us 80% on customer support costs." We hear promises like this too often. The reality is more complex. Implementation takes time and money (integration, testing, training), results arrive gradually, and they depend on the quality of your data and processes.

Realistic figure for the first year: 15-30% time saving in the target area, if the project is done correctly. This remains a significant value. It simply requires setting expectations properly.

How to proceed: recommended steps

1. Process audit: Identify 3-5 recurring, time-consuming processes that are based on text or data. These are your candidates.
2. Proof of concept: Select one process and create a simple PoC, without major infrastructure investment. ChatGPT Enterprise or the Claude.ai API will suffice to validate the concept.
3. Measurement: Define metrics before launch (time, error rate, customer satisfaction). Without measurement, you cannot tell if AI is helping.
4. Iteration: PoC reveals where the gaps are. Iterate the prompt, workflow and integration, and only then invest in scaling.
5. Team training: AI tools are only as good as the team's ability to use them. Investment in training pays off much faster than investment in additional software.

Tools to get started with

Before investing in your own AI infrastructure, try out available SaaS tools:

• ChatGPT Team/Enterprise - most well-known and easily accessible. Suitable for experimentation and quick PoC.
• Claude.ai (Anthropic) - excels in handling long documents, coding and nuanced writing. Strong for corporate use cases requiring consistency.
• Microsoft Copilot 365 - direct integration with Teams, Word, Excel and Outlook. The smoothest adoption for companies on the Microsoft stack.
• Make.com + AI modules - for automating workflows without coding. Ideal for medium-sized companies without their own dev team. More at flylight.ai.
• Cursor / GitHub Copilot - for development teams. Pays off from day one.

Conclusion: AI is a marathon, not a sprint

Generative AI is not a magic wand that will transform a company in a month. It is a powerful tool that, when deployed correctly, delivers real, measurable value, but it requires patience, a systematic approach and willing people.

The best Czech companies we have during preparation AI Festival Prague They have one thing in common: they started with a small, well-defined project, learned from practice and gradually expanded. None of them waited for a perfect plan.

Also read our article about Where stands the Czech AI scene in 2025 - context may assist in deciding which directions to pursue.