Embedded in the heart of the software

The application pressure is enormous: in practically all industrial segments, the use of AI promises previously unimaginable leaps in efficiency, modernization options and cost benefits. However, it is not a sure-fire success and must be used in a targeted and coordinated manner. A patchwork of AI tools or even AI silos in the company is not only counterproductive, but also raises critical questions regarding security and compliance. The logical consequence is the embedding of AI tools in the company's IT as 'embedded AI'. And in an industrial context, this can only be in the software heart of every productive company. Through native integration, the company software with its ERP, FSM and EAM systems mutates into the central platform for AI use.

Seamless integration instead of isolated tools

Unlike generic AI tools, this makes it possible to tailor contextual intelligence to the specific requirements of industrial processes. The application scenarios are virtually inexhaustible. AI can be used in all its various forms: as analytical, predictive, generative or agentic AI. By embedding all these functions centrally in the business software, the IT confusion that often occurs in companies is systematically eliminated and the security risks posed by the uncontrolled use of AI tools are minimized. This also prevents the latent danger of shadow IT, which is becoming more topical and explosive in the AI environment.

Embedded AI functionalities also lower the entry barrier for users. Instead of searching for the right tool, they can immediately access the AI functions they need within their familiar environment, which are integrated directly into the industry-specific solution. Thanks to the connection to the cloud, they are also highly scalable. This makes it quicker and easier to provide additional resources. And the standardized user interface simplifies operation and makes work easier. This benefits effectiveness and efficiency.

Embedded AI in industrial practice

Typical application scenarios for embedded AI in the industrial environment include product development, demand and sales forecasting, production control and dynamic workflows, service control and predictive maintenance. Their potential use begins in advance with the identification and forecasting of new business opportunities. For this purpose, the historical data of previous transactions is analyzed and the probability that a new transaction can be successfully concluded is calculated. Users have the option of retraining the model using their own data. This results in a customized model that fits far more precisely. Not only can sales prioritize prospects with a higher probability, they also gain more transparency on key factors that influence sales performance. The data-driven insights enable better strategic planning and more effective decision-making. Sales potential is recognized more accurately through AI-supported demand and sales forecasts.

As a result, this has the advantage that inventory and resource management can be adapted more precisely and superfluous warehousing can be reduced. The more precise resource planning that this enables in turn results in a more efficient use of resources. Historical data is also used for this and analyzed using machine learning to provide more precise time data for the various work tasks. This supports the operational business through greater planning efficiency.