FIWelcome to the first part of our blog series.
The purpose of this series is to explore what large language models need around them in order for organizations to benefit from them in the best possible way. In the upcoming articles, we will look at practical solutions and aim to answer how and where organizations can start using AI more effectively in their daily processes. In this first part, we will briefly discuss why AI alone does not create value for organizations.
Since the release of large language models, many organizations have rushed to experiment with AI. The initial excitement has been enormous, as AI has been able to answer questions in clear language and produce largely correct responses. Still, for many organizations the real impact of language models on business has remained limited.
But why?
A language model generates text and produces responses based on the material it was trained on, making its best possible prediction. On their own, however, the texts and predictions produced by a language model do not create value for an organization.
Many companies treat AI as a slightly better search engine or a more efficient text editor. But companies do not run on sentences and paragraphs. They run on systems, ways of working, processes, data flows, and responsibilities. If AI does not have access to the necessary information and functionality, it cannot participate in real processes. In that case, AI inevitably becomes just another tool among many and can only create positive impact in isolated situations.
A common misconception is to see the language model itself as a finished product. In reality, a language model is only one component in a larger whole. On its own it is disconnected: it lacks context and cannot act or continue work when needed. In business, however, value is created through outcomes—when something actually changes in systems, decisions, or the customer experience.
