AI based identification of existing knowledge and know-how carriers
Find existing knowledge 27% of the time
IT operations became 16-19% more effective
The search for information has been accelerated by a factor of 50-100
Achieve up to 34% cost savings in the first year
As the complexity and number of topics within a company increases, the knowledge in employees' heads and data storage increases exponentially. Whether in product development, marketing, sales, service, human resources management, finance or many other segments - knowledge is becoming increasingly important for the survival and success of a company. In times of a shortage of skilled workers and less and less redundant filling of positions with knowledge carriers, the failure or elimination of a knowledge carrier very quickly becomes costly and time-consuming.
The company from the automotive supply industry has built up knowledge and product know-how over decades and stored it in documents, databases and wikis. Due to demographic change and a continuous thinning of the workforce, the costs for active knowledge management and product development have increased noticeably because relevant knowledge holders are no longer known or can no longer be contacted, i.e. in the company. In order to avoid duplicate developments and unnecessary costs for reusing existing knowledge, the existing knowledge and its employees must be made easier to identify and use.
Based on the bloofactory Proccess, a prototype for OpenAI AI-supported enterprise knowledge management was implemented within a few weeks. OneDrive data storage, SharePoint sites, Jira ticket systems, Confluence Wikis and other systems were integrated and the existing data was not only made searchable but also understandable for an AI.
Through AI integration, employees can not just search for keywords, but also ask general statements and questions to actively make knowledge visible in the form of data, teams and people.
According to initial findings, the search for information could be accelerated by a factor of 50-100 or, in some cases, made possible in the first place.
During a 6-month trial period, a sophisticated system was developed and a user can find relevant knowledge and related teams and people with one click. The process previously took weeks or knowledge was explicitly purchased externally at a high cost.
Within the testing period of the prototype, 4 months of work could be saved in product development. In other areas, such as IT operations, operations have already been made 16-19% more effective in some areas.
In current product development, the use of AI will save 5-6 months of time to market and products can be brought to market much faster.
With the AI-based approach, employees evaluate existing knowledge and projects and developments that are already underway are identified more easily and the people involved are visible.
This means that duplicate developments are avoided within the company and synergies are identified and used at an early stage. The system will be further refined and, in addition to the know-how, more focus will be placed on the employees behind it. This permanently promotes employee networking and makes the organization increasingly agile and powerful.