Cognitive Intelligent Autonomous Transformation System for Business Intelligence
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Solution Overview
Problem
Existing enterprise systems lack comprehensive transformation to best-in-class business systems, failing to leverage AI, IoT, and other advanced technologies for real-time strategic and operational decision-making, and do not provide pervasive business intelligence or automation, making them slow to react to emerging threats and opportunities.
Innovation Solution
The Cognitive Intelligent Autonomous Transformation System (CIATSFABI) autonomously or semi-autonomously transforms existing COTS systems, such as SAP, into a self-generating, iterative, best-in-class information system using AI, RPA, ML, IoT, and blockchain, providing actionable business intelligence and real-time advice through digital assistants and automation scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If comprehensive transformation to best-in-class business system is implemented, then operational excellence and long-term profitability are improved, but system complexity and transformation difficulty increase
Solution Approach 1:
The transformation system is divided into modular components including data migration modules, AI integration modules, RPA automation modules, and analytics modules. Each module handles specific transformation tasks independently, allowing comprehensive system transformation while managing complexity through modular architecture.
Solution Approach 2:
The system performs preliminary analysis of the existing business system architecture, data structures, and processes before transformation. This includes assessing current AI capabilities, identifying automation opportunities, and planning the transformation roadmap in advance to reduce implementation complexity.
2Productivity
If AI and advanced technologies are integrated into existing systems, then real-time strategic and operational decision-making is improved, but implementation complexity and cost increase
Solution Approach 1:
The transformation framework is designed to be universally applicable across different COTS systems (SAP, Oracle, Microsoft) and industry sectors. It provides multi-functional capabilities including data migration, AI model integration, RPA automation, and analytics, reducing implementation complexity through a standardized approach.
Solution Approach 2:
The system introduces intermediary layers including API gateways, data transformation services, and AI model orchestration platforms that mediate between existing legacy systems and advanced AI technologies. This abstraction layer simplifies integration complexity while enabling real-time decision-making capabilities.
3Loss of information
If existing COTS systems are transformed to latest versions with full automation, then business intelligence pervasiveness is improved, but transformation time and resource requirements increase
Solution Approach 1:
The transformation system includes self-service capabilities that automatically assess the current system state, generate transformation roadmaps, and execute migration tasks with minimal human intervention. AI-driven automation detects and corrects transformation issues autonomously, reducing both transformation time and resource requirements while ensuring comprehensive business intelligence coverage.
Solution Approach 2:
The system implements continuous feedback mechanisms during transformation that monitor progress, identify bottlenecks, and dynamically adjust transformation strategies. Real-time analytics track data integrity and business intelligence completeness, enabling rapid course correction to minimize transformation time while maintaining comprehensive intelligence coverage.
4Reliability
If piece-meal transformation is performed instead of comprehensive transformation, then transformation risk is reduced, but business intelligence completeness and operational excellence deteriorate
Solution Approach 1:
The transformation approach is dynamically adaptable, allowing organizations to start with pilot transformations in specific modules or business units and progressively expand to comprehensive transformation. The system dynamically adjusts the transformation scope and pace based on risk tolerance, resource availability, and business needs, enabling both risk management and comprehensive business intelligence achievement.
Data Source
AI summary
A Utility patent with new concepts, methods, a comprehensive step-by-step procedure/system to produce semi-autonomous, self-curing customizable Cognitive Intelligent Autonomous Transformation System aided by Digital Assistants based on AI, Machine Learning enhanced RPA, natural language processing, speech recognition and image recognition with Deep Learning and Neural networks, that will transform an existing business system to the latest version supported by vendor for the industry with superior process automation. By Combining AI and cognitive computing in a single operating environment using the same sets of data—configuration data, Master Data, Transaction Data and historical transaction data, we propose to revolutionize existing customer's information systems to be a self-evolving cognitive intelligent automation systems where it not only knows the ultimate target information systems but also how to get there every step of the way seamlessly, similar to autonomous cars taking to destination, except in this case, information systems that run your business.