AI Maturity Scoring via Segmented Component Analysis
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Solution Overview
Problem
There is a need to accurately assess the AI maturity of organizations to identify potential customers for AI software products, as existing methods lack comprehensive and dynamic scoring systems.
Innovation Solution
A system and process for AI maturity scoring that computes an AI maturity score based on AI technology use, data science expertise, and data maturity, using a database of job titles, locations, and functional areas, with sub-programs to identify AI and data mature products, and generate reports on entity immersion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a comprehensive AI maturity scoring system is implemented, then the accuracy of identifying potential customers is improved, but the complexity of the system increases
Solution Approach 1:
The AI maturity scoring system is segmented into three distinct components: AI component (measuring AI technology adoption), data science component (measuring data science expertise), and data maturity component (measuring data infrastructure). Each component is calculated separately using specific formulas and then aggregated to produce the overall AI maturity score. This segmentation allows the system to comprehensively assess AI maturity while maintaining manageable complexity through modular calculation approaches.
2Reliability
If dynamic AI maturity scoring over time periods is implemented, then the ability to track customer progression is improved, but the data processing requirements increase
Solution Approach 1:
The system performs preliminary actions by dividing database records into time period groups before calculating AI maturity scores. Each record is assigned to a specific time period based on its date, and this pre-grouping allows for efficient batch processing. The AI maturity scores are then calculated separately for each time period, enabling tracking of customer progression over time while optimizing data processing through structured organization of the data beforehand.
Data Source
AI summary
AI maturity scoring implementations that are described herein generally assess the degree of immersion an entity has in AI matters. The AI maturity score for an entity is a combination of three components, namely an AI component, a data science component, and a data maturity component. The AI component quantifies the level of use of AI technologies at the entity. The data science component quantifies the level of an entity's data science expertise on a location basis. And the data maturity component quantifies the degree to which the entity is involved in using data technologies. An AI maturity report is also generated that includes a listing of, for each entity of interest, the AI maturity score computed for that entity.