The AI-Model Risk Management Market is being driven by Intensifying global regulatory scrutiny and compliance mandates
The AI-Model Risk Management Market is expected to grow at a CAGR of 14.8% during 2024 and 2029. During this period, the market is also expected to show a growth of USD 5477.3 million. The global AI-model risk management market is witnessing a significant evolution, moving from standalone, purpose-built solutions to integrated, end-to-end governance platforms. As businesses expand their AI initiatives from a limited number of models to a diverse and extensive portfolio, the requirement for a centralized, unified system has become indispensable. Modern organizations seek comprehensive platforms capable of managing the entire model lifecycle, encompassing pre-development ethical assessments and data validation, development and pre-deployment testing, ongoing production monitoring, and eventual model retirement. This transition signifies the market's progression from a collection of specialized tools for tasks like bias detection or explainability to a full-fledged enterprise software category.
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The market is segmented based on
According to Technavio, There are several factors that are causing the market to flourish during the forecast period, which are as follows:
However, the market also witnesses some limitations, which are as follows:
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Market Scope |
|
Report Coverage |
Details |
Page number |
246 |
Base year |
2024 |
Historic period |
2019-2023 |
Forecast period |
2025-2029 |
Growth momentum & CAGR |
Accelerate at a CAGR of 14.8% |
Market growth 2025-2029 |
USD 5477.3 million |
Market structure |
fragmentation |
YoY growth 2024-2025(%) |
14.1 |
Key countries |
US, China, UK, Canada, Germany, Japan, South Korea, India, France, and Australia |
Competitive landscape |
Leading Companies, Market Positioning of Companies, Competitive Strategies, and Industry Risks |
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The AI-Model Risk Management Market encompasses various techniques and tools for ensuring the reliability, security, and ethical use of artificial intelligence systems. Key components include drift detection methods for identifying model performance changes, model validation processes for assessing risk and ensuring compliance, and model monitoring tools for tracking performance and detecting issues. Other critical elements include bias quantification, explainable AI techniques, system integrity checks, and data governance policies. Regulatory guidelines, model security audits, and ethical considerations are also essential for effective risk management. Additionally, performance benchmarks, reliability assessments, model uncertainty, data integrity checks, error analysis, system resilience, data privacy, model validation metrics, and audit trail management are crucial for maintaining the integrity and reliability of AI models throughout their lifecycle. Risk mitigation controls, model retraining, and the model deployment process are also important aspects of AI-Model Risk Management.
The AI Model Risk Management market is a segment of the larger IT software industry, focusing on companies providing solutions for detecting model bias, employing explainability techniques, monitoring data drift, and measuring model accuracy metrics. These firms specialize in developing and producing software designed to mitigate risks associated with artificial intelligence and machine learning models in business applications. Technavio's market analysis includes revenue generated by companies offering such solutions, encompassing both on-premises and cloud-based offerings. This market size calculation excludes firms primarily engaged in interactive home entertainment, database management software, or systems software.. Industries are leveraging the products belonging to the market for customer engagement, transactional notifications, and promotional offers.
Technavio Research
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