AI Process Capability Migration for Faster Enterprise Transformation

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Solution Overview

Problem

Conventional process management techniques are time-consuming, resource-intensive, and prone to errors due to manual handling and subjective evaluations, leading to varying outcomes based on individual skill levels, especially in large-scale transformations.

Innovation Solution

An AI-driven process capability migration engine that includes modules for intake and discovery, agile development, integrated testing, and deployment, utilizing neural networks to automate the assessment, simulation, and migration of process capabilities, reducing manual effort and improving accuracy.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If manual process management techniques are used, then flexibility in handling enterprise needs is maintained, but time consumption and resource intensity increase significantly

Engineering Contradiction:

Improvemanual process management flexibilityVSAvoidtime consumption

Core Design Contradiction:

Ease of operationVSLoss of time

Solution Approach 1:

The patent replaces manual mechanical processes with an AI-based automated system that uses machine learning models, natural language processing, and automated code generation to perform process discovery, requirement analysis, and capability migration tasks that were previously done manually by IT personnel

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables self-service automation where the AI engine autonomously performs process analysis, generates recommendations, creates code migrations, and executes deployments without requiring continuous manual intervention, allowing the system to serve itself in managing enterprise processes

Inventive Principle:

Principle #25Self-service

2Adaptability or versatility

If manual process management techniques are used, then adaptability to changing enterprise needs is maintained, but productivity and speed of transformation decrease

Engineering Contradiction:

Improveadaptability to enterprise needsVSAvoidtransformation speed

Core Design Contradiction:

Adaptability or versatilityVSProductivity

Solution Approach 1:

The patent implements a dynamic system where the AI engine continuously learns from enterprise data, adapts its models based on feedback, and automatically adjusts process migrations to meet evolving enterprise requirements, enabling both high adaptability and rapid transformation

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system performs preliminary actions by using AI to pre-analyze enterprise processes, predict requirements, generate migration plans, and simulate outcomes before actual implementation, enabling faster and more accurate transformations

Inventive Principle:

Principle #10Preliminary action

3Ease of operation

If subjective evaluation methods are used in process design and testing, then human judgment and expertise are utilized, but measurement precision and reliability decrease

Engineering Contradiction:

Improvehuman judgment utilizationVSAvoidevaluation accuracy

Core Design Contradiction:

Ease of operationVSMeasurement precision

Solution Approach 1:

The patent replaces subjective human evaluation with objective AI-based assessment using machine learning models that analyze process data, generate quantitative metrics, and provide consistent, reproducible evaluations free from human bias and variability

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system implements automated feedback loops where AI models continuously evaluate process performance, compare against requirements, and adjust migrations based on measured outcomes, ensuring high precision and reliability through data-driven validation

Inventive Principle:

Principle #23Feedback

4Loss of information

If manual gathering and tracking of requirements is performed, then detailed process analysis is achieved, but device complexity and resource requirements increase

Engineering Contradiction:

Improveprocess information completenessVSAvoidsystem complexity

Core Design Contradiction:

Loss of informationVSDevice complexity

Solution Approach 1:

The patent replaces manual information gathering with automated AI-based process discovery that uses data mining, pattern recognition, and natural language processing to extract and analyze process requirements from enterprise systems, achieving complete information capture with reduced complexity

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS12481872B2Automatically migrating process capabilities using artificial intelligence techniques

Publication Date: 2025.11.25 DELL PROD LP

AI summary

Methods, apparatus, and processor-readable storage media for automatically migrating process capabilities using AI techniques are provided herein. An example computer-implemented method includes determining, for a process of an enterprise, process requirements by analyzing data related to the process and data related to the enterprise; generating a recommendation of process capabilities to be incorporated into the process by processing, using AI techniques, the analyzed data; forecasting behavior of the process in conjunction with portions of the recommended process capabilities by performing simulations of the process incorporating the portions of the recommended process capabilities; developing modified versions of the process, based on the forecasting, by migrating feature sets into portions of code associated with the process; testing the modified versions of the process by performing simulations of the modified versions; and deploying at least one of the modified versions of the process based on the testing.