AI KPI Tree for Autonomous Process Optimization
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Solution Overview
Problem
Existing decision automation systems are insufficient in adapting to dynamic environments, relying on pre-defined rules and failing to provide real-time insights and tailored action recommendations, leading to suboptimal performance and difficulty in troubleshooting anomalies in process workflows.
Innovation Solution
A computer-implemented method and system utilizing AI and ML to construct a KPI tree structure, autonomously monitor anomalies, determine root causes, recommend targeted actions, and evaluate their effectiveness through controlled experiments, integrating actions into the process workflow while tracking their impact.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If pre-defined rules are used to automate decisions, then automation of routine tasks is achieved, but adaptability to dynamic environments deteriorates
Solution Approach 1:
The system transitions from static pre-defined rules to dynamic AI/ML models that continuously learn from user behavior data. The decision automation system incorporates machine learning algorithms that adapt to changing user responses and environmental conditions in real-time, enabling the system to automatically adjust its decision-making logic without requiring manual rule updates.
Solution Approach 2:
The system implements feedback loops where user behavior data is continuously collected, analyzed by AI/ML models, and used to refine decision-making algorithms. This feedback mechanism allows the system to learn from actual user responses and improve its automation performance over time, resolving the contradiction between initial automation capability and long-term adaptability.
2Device complexity
If process workflow is not updated with changing end-user behavior, then system simplicity is maintained, but service quality deteriorates
Solution Approach 1:
The system performs self-optimization by automatically analyzing user behavior data and adjusting its own decision-making logic without requiring manual intervention to update workflows. The AI/ML components continuously learn from new data patterns, enabling the system to maintain high service quality while avoiding the complexity of manual process reconfiguration.
Solution Approach 2:
The system dynamically changes its operational parameters based on analyzed user behavior patterns. Instead of requiring workflow restructuring, the AI/ML models adjust decision thresholds, routing logic, and automation parameters in real-time, maintaining service quality while minimizing structural complexity changes.
3Loss of information
If KPIs are used to monitor process performance, then high-level performance insights are obtained, but root cause analysis capability deteriorates
Solution Approach 1:
The system segments KPIs into hierarchical levels with drill-down capabilities, allowing users to navigate from high-level performance metrics to granular operational data. The AI/ML components automatically perform root cause analysis by segmenting and analyzing underlying data patterns, transforming KPI monitoring from superficial insights to deep diagnostic capability.
Solution Approach 2:
The system introduces AI/ML analytics as an intermediary layer between KPI metrics and root cause analysis. This intermediary automatically processes raw data, identifies patterns, and connects KPI anomalies to their underlying causes, eliminating the need for manual investigation while maintaining both high-level insights and deep diagnostic capability.
4Adaptability or versatility
If iterative test and learn approach is adopted, then decision-making adaptability is improved, but decision speed deteriorates
Solution Approach 1:
The system performs preliminary learning and model training in advance using historical data and simulated scenarios. By pre-training AI/ML models on diverse situations, the system builds decision-making capability beforehand, enabling rapid real-time decisions without requiring iterative testing during critical moments.
Solution Approach 2:
The system maintains continuous learning operations that run parallel to decision-making processes. AI/ML models continuously refine their parameters in the background using incoming data streams, ensuring that decision speed remains high while adaptability improves over time through ongoing, non-disruptive learning.
Data Source
AI summary
The present invention relates to a system and method for optimizing performance of a process by constructing a KPI tree structure modelling workflow of the process using data received from a client device; autonomously monitoring a plurality of interconnected metrics by analysing data from the KPI tree structure; determining one or more root causes for an issue affecting the performance based on the autonomous monitoring of the plurality of interconnected metrics; recommending one or more actions correlating with one or more levels in the workflow to remediate the one or more root causes, supported by controlled experimentation as a sub-step; enabling performance of the one or more actions in the process workflow by integrating the one or more actions with execution of process workflow; and tracking impact of the one or more actions using continually received feedback on implemented actions and analysis of data from the KPI tree structure.