Improve digital processes with analytical AI

Digitizing manual workflows is an important step for every company. But even after digitization, there is still room for further improvements and optimization. Continuous monitoring and analysis of processes plays a crucial role here.

By relying on central monitoring and automatic training, you can continuously improve the efficiency of your digital workflows . By centrally collecting and evaluating performance data, you gain valuable insights into the flow and performance of your processes. This enables you to not only understand the current state, but also identify potential opportunities for improvement.

The processing and analysis of this data makes it possible to identify dependencies and patterns within digital workflows. This allows you to take targeted measures to eliminate weaknesses and improve efficiency. This makes your company more agile and responsive to changes in the business world.

How does analytical AI ensure the quality of digital processes?

💡 Accompany digital processes with central performance monitoring to identify the current status and changes over time.

A continuous improvement process (CIP) is a powerful tool for companies to make their digital processes more effective and to continuously optimize them .
By monitoring digital processes in parallel with central control and linking them to central performance monitoring, you can identify the current state and changes over time . This gives you insights that enable you to identify weak points and take measures to improve them.

A concrete example of the application of a CIP in digital processes is the continuous improvement of pattern recognition through automatic training .

Imagine you are using an analytical AI component for pattern recognition in your process. By continuously training on the processed data, the AI ​​component can be continuously improved to produce more accurate results and achieve greater efficiency.

What constitutes good AI quality assurance?

Focus on the following three implementation topics to achieve a good digital process:

• Identify deviations and patterns in the business process: Find errors in the digital process.
• Interpret the results : Use analytical artificial intelligence to find the causes of deviations and errors.
• Automate quality assurance : Use the results from the analysis to automatically improve your digital processes and better train your monitoring.

We have written a short impulse article on LinkedIn for the use of analytical artificial intelligence in quality assurance:

reading tip

• How can AI help with RPA debugging?

Our conclusion

By continuously improving your digital processes, you not only remain competitive, but can also be successful in the long term. An efficient and optimized way of working makes a significant contribution to reducing costs, increasing productivity and increasing customer and employee satisfaction.

Tip: Use a local LLM for critical processes. This means that the analysis of your data takes place directly in your company.

Therefore, use the opportunities offered by central monitoring and automatic training to continuously improve and develop your digital workflows. This will lay the foundation for a competitive future in the digital world.

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