Machine Learning Initiative Planning for Multi-Expert Bottlenecks

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Solution Overview

Problem

Generating initiative plans is a time-consuming and resource-intensive process requiring input from multiple experts across various business disciplines, leading to inefficiencies and potential errors in decision-making.

Innovation Solution

A planning system utilizing machine learning models to analyze client data, determine current and future states, identify initiatives, and generate initiative plans, including a multi-phase approach with discovery, validation, and executive readout stages, leveraging predictive and recommendation engines to automate the planning process.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If multiple experts across various business disciplines are involved in generating initiative plans, then the quality and comprehensiveness of the plan is improved, but the time consumption and resource intensity increase significantly

Engineering Contradiction:

Improvequality of initiative planVSAvoidtime consumption

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The patent introduces an AI assistant as an intermediary that mediates between the need for comprehensive expert input and the need for efficient plan generation. The AI assistant automatically synthesizes information from multiple sources and disciplines, acting as a mediator that produces high-quality initiative plans without requiring direct involvement of multiple human experts in the drafting process.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system creates simplified copies or representations of expert knowledge through trained AI models. These models capture the essence of multiple business disciplines and replicate expert-level analysis and planning capabilities, enabling the system to generate comprehensive plans quickly without actually involving multiple human experts.

Inventive Principle:

Principle #26Copying

2Reliability

If multiple experts across various business disciplines are involved in generating initiative plans, then the comprehensiveness of the plan is improved, but the resource intensity and potential for errors increase

Engineering Contradiction:

Improvecomprehensiveness of initiative planVSAvoidpotential errors in decision-making

Core Design Contradiction:

ReliabilityVSObject-generated harmful factors

Solution Approach 1:

The AI assistant performs self-service by automatically gathering, analyzing, and synthesizing information from multiple business disciplines without requiring human experts to manually coordinate their inputs. The system independently manages the complex task of creating comprehensive plans, reducing the chances of human error in data collection, analysis, and synthesis.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces the mechanical system of human expert collaboration with an automated AI-based system. This substitution eliminates human factors such as miscommunication, bias, and manual errors while maintaining the comprehensiveness achieved through multi-disciplinary input.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Adaptability or versatility

If a manual process is used to generate initiative plans with input from multiple experts, then the ability to incorporate diverse perspectives is improved, but the productivity and efficiency decrease

Engineering Contradiction:

Improveincorporation of diverse perspectivesVSAvoidefficiency of plan generation

Core Design Contradiction:

Adaptability or versatilityVSProductivity

Solution Approach 1:

The AI assistant is designed with multi-functionality to handle various business disciplines and perspectives simultaneously. It can process and synthesize information from finance, marketing, operations, and other domains through a single unified system, maintaining adaptability to diverse perspectives while dramatically improving productivity compared to manual multi-expert processes.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The system changes the parameter of information processing capacity by using AI models that can rapidly analyze and synthesize large volumes of data from multiple sources. This parameter change enables the system to incorporate diverse perspectives at speeds impossible for human experts while maintaining the quality and comprehensiveness of the analysis.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS12493819B2Utilizing machine learning models to generate initiative plans

Publication Date: 2025.12.09 ACCENTURE GLOBAL SOLUTIONS LTD

AI summary

A device may receive and process client data, with a first machine learning model, to determine current state data identifying a current state of a client. The device may process the current state data and prior client data, with a second machine learning model, to determine a problem statement for the client and future state data of the client. The device may utilize the second machine learning model to identify initiatives for the client, and costs of the initiatives, based on the problem statement, the current state data, and the future state data, and to assign benefits and priorities to the initiatives. The device may process the initiatives, the benefits and priorities of the initiatives, and the costs of the initiatives, with the second machine learning model, to generate an initiative plan for solving a problem of the problem statement, and may perform actions based on the initiative plan.