Is artificial intelligence revolutionizing everything? This question is currently causing a stir in many companies - especially in marketing and product communication. New technologies such as Large Language Models (LLMs) or Agentic AI are opening up completely new ways of digital consulting and interaction. But despite all the enthusiasm, one thing is often overlooked: Without a clean database, any solution, no matter how intelligent, will fall far short of its potential.
The starting point: product communication in the age of AI
Before the use of artificial intelligence, it was usually trained specialists - retailers and advisors - who were able to provide customers with detailed information on complex products. They knew the differences, explained features and categorized the offer according to need. This advisory situation was personal, contextual - and generally of a high quality.
With the advent of digital touchpoints and the increasing use of AI systems, this advisory situation is changing fundamentally. Websites, chatbots, comparison portals and voice assistants are increasingly taking over the interaction with customers.
It is important to note that companies retain control over messages and content on their own channels, such as their website or chatbot. However, as soon as information is used from outside - for example through ChatGPT - this control ends. The AI then draws its conclusions from the publicly available information. If there is a lack of clearly structured, up-to-date data, the risk of misinterpretation or hallucinations increases - and therefore of poor advice without the company being able to intervene.
Thus, only those who provide product information in a consistent, comprehensible and structured manner - regardless of the channel - can ensure high-quality, brand-appropriate advice, even in the age of AI.
The challenge: From raw data to intelligent consulting
Many companies initially believe that it is sufficient to enter existing product data into an LLM in order to automatically receive smart consulting results. However, the reality is that incomplete or inconsistent data can quickly lead to incorrect recommendations. For example, the system can wrongly assume that only one product has a certain feature - simply because it appears in the marketing description but not in the product compared.
The solution: a cross-product content strategy
This is precisely where a strategic project with STIHL came in. The aim was not only to accompany the change in product communication in the short term, but also to create a future-proof basis for all digital consulting scenarios. The solution: a comprehensive product content strategy that works across all touchpoints - regardless of whether the information is used on a website, in an app, by a chatbot or via a voice assistant.
The core of the solution is a modular structure: Content is structured along defined building blocks that are based on clear user needs, brand values and CX guidelines. These modules can be flexibly combined depending on the use case - for different target groups, contexts and channels. In this way, the same content is used to create consistent, high-quality and customized advice.
Digital consulting at a consistently high level
This structure makes it possible to create digital consulting situations that ensure consistently high consulting quality across all touchpoints.
The major advantage of modular, structured product content also lies in the quality and efficiency of administration: content can be centrally recorded, systematically maintained and flexibly expanded. This makes it much easier to keep the information complete, correct and up-to-date - even over long product lifecycles.
Maintenance also becomes more efficient: Duplications and inconsistencies can be avoided, editorial work is reduced and the overview of all product information is noticeably improved.
This also brings considerable advantages for the use of AI: Precise, trustworthy recommendations are only possible if the model has access to high-quality, clearly structured data. This basis also allows hyper-personalized consulting scenarios to be implemented, for example for specific customer inquiries in key account management or for individual product recommendations in e-commerce.
If you want to use AI, you have to start with quality
Artificial intelligence is revolutionizing the way customers communicate with companies. But above all, it shows what has always been important and is now becoming even more crucial: Structured, strategically structured product information is the prerequisite for leveraging new potential - be it for personalized advice, omnichannel communication or automated content creation.
For companies, this means that now is the right time to create the database on which future AI systems can be built. Quality in - quality out. A principle that has always applied and is more relevant than ever in the age of AI.