Abstract:

The ability to quickly and cost-effectively deploy solutions based on Artificial Intelligence (AI) is a key factor in determining competitive advantage in the modern market. However, traditional AI system development cycles represent a bottleneck for innovation, being a lengthy process that requires highly specialized skills. In response to this challenge, this paper presents a universal business framework that enables the rapid prototyping and deployment of intelligent Decision Support Systems (DSS) using low-code platforms. The paper presents a model of such a generic process template, prepared in the Plus Workflow Editor tool using BPMN notation. The framework's architecture separates the business logic, which analysts can independently model in a graphical environment, from the complex data analysis, which is delegated via API to external AI models. The article demonstrates how the same process template, once prepared, can be adapted within hours to entirely different business problems—from financial planning to operational risk assessment. The key conclusion is the demonstration that a low-code approach drastically reduces implementation time (time-to-market), lowers costs, and democratizes access to advanced technologies, allowing business departments to autonomously implement innovations and enhance operational efficiency.