ChatGPT by OpenAI has rapidly made a name for itself in the tech world. Its platform-independent and user-friendly interface has enabled both IT professionals and non-experts alike to engage with Large Language Models (LLMs) for the first time in a truly accessible way and to experience their potential firsthand.
According to a survey by Bitkom, 74% of surveyed companies plan to invest in artificial intelligence over the coming years. Generative AI opens up groundbreaking opportunities for revenue growth, cost reduction, productivity gains, and improved risk management. In the near future, it will become a decisive competitive advantage and a key differentiator.
A recent webinar survey conducted by Gartner among more than 2,500 executives revealed that 38% view customer experience and customer retention as the primary goals of their investments in generative AI. Additional objectives include revenue growth, cost optimization, and business continuity. For decision-makers, the message is clear: generative AI is becoming a core technology across nearly all areas of the enterprise.
What Is Generative AI?
Generative AI represents the next major wave in artificial intelligence. While earlier systems focused primarily on classification or recognition tasks, generative models are capable of creating entirely new content from scratch, including text, images, audio, and video. From a technological perspective, generative AI is based on deep learning and, in particular, on Large Language Models.
Models such as LaMDA, LLaMA, or GPT rely on vast amounts of data and transformer architectures to model language probabilistically. This approach can, however, lead to so-called hallucinations—statements that are factually incorrect but linguistically very convincing.
Prompt Design and Grounding
One of the key success factors when working with generative AI is prompt design. The way a request is phrased has a significant impact on the relevance, accuracy, and quality of the response. Well-crafted prompts guide the model effectively and help reduce ambiguity and errors.
Equally important is grounding. In this context, grounding refers to anchoring the model to trusted data sources such as documents, knowledge bases, or structured datasets. Grounding ensures that responses are not based solely on probabilities but are supported by verifiable information.
Enterprise Use Cases with Company Data
When applied correctly, generative AI can deliver substantial value in customer communication. This requires a secure, integrated, and data-driven architecture. With sufficient data volume, generative AI can identify patterns, generate new content, automate processes, and elevate customer interactions to an entirely new level.
Software Agents and Virtual Assistants
A practical example is the use of AI-powered software agents. These can automatically summarize customer conversations, store insights in CRM systems, and combine information from internal knowledge bases. As a result, employees are immediately in context during follow-up interactions and can respond faster and more effectively.
A well-known real-world example is “Lilli” by McKinsey. This internal platform accesses proprietary research documents, surfaces relevant content, summarizes key insights, links original sources, and even identifies subject-matter experts for a given topic.
The Cost of Generative AI
The cost of implementing generative AI varies significantly depending on the chosen approach. Training Large Language Models from scratch is by far the most expensive option, requiring massive investments in data, infrastructure, and expertise. A prominent example is BloombergGPT.
More cost-efficient alternatives include fine-tuning existing models with domain-specific data. The most economical option is prompt tuning, which relies solely on optimized prompts. While easy to implement, this approach often delivers limited long-term value in enterprise scenarios.
Conclusion
Generative AI is not a short-lived trend but a strategic lever for sustainable competitiveness. Organizations that adopt a structured, secure, and economically viable approach early on will gain a lasting advantage. Liongate supports companies in deploying generative AI responsibly, effectively, and with measurable business value.
