AI Agent for Enterprise Process Automation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current process automation technologies in enterprises are inefficient and resource-intensive, particularly in generating financial reports and other complex business processes, relying heavily on human input and requiring extensive manual updates and multiple automation tools, which becomes costly and time-consuming as tasks become more complex.
Innovation Solution
The system employs process mining techniques to generate end-to-end process models, transforms unstructured data into searchable structured data, and configures machine learning tools for automation, enabling the validation and generation of financial reports with improved efficiency and scalability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If traditional robotic process automation is used to automate simple tasks, then automation coverage increases, but device complexity and maintenance burden increase due to proliferation of bots
Solution Approach 1:
The patent merges multiple separate bot functionalities into a single centralized AI agent that can perform diverse tasks including data extraction, validation, and report generation. Instead of deploying thousands of specialized bots, one unified agent handles multiple functions, reducing system complexity while maintaining automation coverage.
Solution Approach 2:
The AI agent is designed with universal capabilities to perform multiple functions across different business processes. It can extract data from various sources, validate different types of financial reports, and generate diverse outputs, replacing the need for multiple specialized bots with a single multi-functional agent.
2Reliability
If manual updates are performed to maintain automation bots when processes change, then automation reliability is maintained, but loss of time and productivity decrease due to constant manual intervention
Solution Approach 1:
The AI agent performs self-updating by automatically adapting to process changes through continuous learning from new data. When business processes or data formats change, the agent autonomously adjusts its extraction and validation rules without requiring manual reconfiguration, eliminating the need for constant human intervention while maintaining reliability.
Solution Approach 2:
The system implements feedback loops where the AI agent continuously learns from validation results and process outcomes. This feedback mechanism enables automatic adaptation to changing processes, allowing the system to maintain reliability while reducing the time spent on manual updates by constantly improving its performance based on actual data.
3Adaptability or versatility
If multiple automation tools are implemented to cover full business processes, then functionality increases, but device complexity and ease of operation worsen due to managing multiple tools
Solution Approach 1:
The patent consolidates multiple automation tools into a single integrated AI agent platform that provides end-to-end process automation. The unified platform combines data extraction, validation, and report generation capabilities that previously required separate tools, simplifying the system architecture while maintaining comprehensive process coverage.
Solution Approach 2:
The AI agent is designed as a universal platform capable of handling diverse business processes and data types. It provides adaptable functionality for different financial reports and business scenarios without requiring separate specialized tools, thereby reducing the number of automation tools needed while maintaining versatility.
4Extent of automation
If traditional automation bots are used for complex tasks requiring higher cognition, then automation capability increases, but productivity and ease of operation decrease due to difficulty in managing and updating bots
Solution Approach 1:
The AI agent autonomously manages its own configuration and updates through self-learning mechanisms. For complex tasks requiring high cognition, the agent automatically adjusts its parameters and learning models based on performance feedback, eliminating the need for manual management and updates that previously reduced productivity.
Solution Approach 2:
The system implements continuous feedback loops where the AI agent learns from validation results and task outcomes. This enables the agent to automatically improve its performance on complex cognitive tasks over time, increasing productivity by reducing the need for manual intervention and reconfiguration while handling increasingly sophisticated automation requirements.
Data Source
AI summary
Disclosed are systems and methods providing for automation of enterprise and other processes. The systems and methods involve receiving historical process data, applying process mining techniques and generating process models. The process models can be used to identify automation candidates. One or more automation tools designed and configured for the identified automation candidates can be deployed to automate or to increase the efficiency of the process. In one embodiment, automation tools include artificial intelligence networks, which can label a set of input data according to determined or preconfigured domain-specific labels. An aggregator module can combine the similarly labeled data as part of automating a process or to increase the efficiency of a process.