Integrating Artificial Intelligence Into Business Processes

Integrating Artificial Intelligence Into Business Processes

Many companies rely on AI-based knowledge management to quickly obtain an overview of complex issues without lengthy research activities or are using the technology to create "digital twins." A fundamental prerequisite for this is the appropriate preparation of the data available in a company.

The Intelligent Networking of Data as the Basis for Knowledge Management

The simple collection of data causes the volume of data to increase exponentially. Only by refining data, such as linking, analyzing, and interpreting the available data, can "deeper" insights into the company be gained and the company's knowledge be better utilized. However, this requires appropriate solutions. Based on well-known technologies from the enterprise search field, insight engines can analyze and link existing corporate data, recognize correlations and extract factual information tailored to the user and context. Doing so provides management and those responsible in a specific functional area with a valuable overview of all business-relevant information on various topics such as customers, projects, suppliers, or processes. From this, concrete recommendations for action can be derived, thus creating ideal conditions for further business success.

Artificial intelligence as a Game Changer

The strength of artificial intelligence lies in discovering and understanding patterns in complex, difficult-to-understand data sets and analyzing and interpreting them. Insight engines harness the power of technologies such as deep learning, machine learning, natural language processing (NLP), and natural language question answering (NLQA).

NLP (Natural Language Processing) and NLQA (Natural Language Question Answering) enable the Insight Engine to interpret structured metadata and unstructured text content correctly. This means that users can type their search query into the search bar. Today, these are usually already concrete questions and not individual words. NLP analyzes and understands the query posed in natural language, while NLQA ensures that the user's concrete request is identified. Through linguistic interpretation, the technology can adequately respond to questions with "where," "who," "why," "when," etc.

360-Degree Views Provide an Overview

To give decision-makers and employees a complete picture of the available information, insight engines display results in personalized dashboards to quickly identify correlations between information. For each query, they account for the individual needs of the respective departments, the position, users' access rights, and the query's context. This gives users a detailed and personalized 360-degree view. 

All available information is then readily available for business-critical decisions.

Digital Twins

In the production environment, the 360-degree view is also increasingly used to display a "digital twin" of a product or process. This virtual image can lead to new, far-reaching insights, for example, when carrying out simulations of planned changes to a process or a product. This tool can check transformations in advance or can find the appropriate adaptation in different scenarios. Once everyone involved is satisfied with the result, actions can transfer to the real world.

Contact a Mindbreeze Expert today for more information about our game-changing solution.