Generative AI in Analytics Market – Global Industry Size, Share, Trends, Opportunity, and Forecast, Segmented By Deployment (Cloud-based, On-premises), By Technology (Natural Language Processing, Machine Learning, Deep Learning, Others), By Application (Forecasting and Predictions, Automated Reporting, Anomaly Detection, Personalization), By Region & Competition, 2021-2031F

Forecast Period	2027-2031
Market Size (2025)	USD 1.39 Billion
CAGR (2026-2031)	27.81%
Fastest Growing Segment	Cloud Based
Largest Market	North America
Market Size (2031)	USD 6.06 Billion

Market Overview

The Global Generative AI in Analytics Market will grow from USD 1.39 Billion in 2025 to USD 6.06 Billion by 2031 at a 27.81% CAGR. Generative AI in analytics comprises advanced machine learning models that autonomously interpret datasets to generate insights and code through natural language interfaces. The main drivers supporting market growth include the demand for data democratization allowing business users to access information without technical expertise and the necessity to analyze unstructured data for faster strategic planning. These elements differ from passing trends by addressing the core operational need to reduce the time required to derive value from enterprise information.

A significant challenge that could impede market expansion involves the accuracy of model outputs and data governance risks which can diminish organizational trust. Companies face difficulties in ensuring that automated insights are reliable enough for critical business operations. Despite these obstacles, commitment to the technology is strong. According to the IEEE, in 2024, 65% of technology executives ranked artificial intelligence, including generative AI, as a top priority. This data underscores the robust intent to overcome barriers for future implementation.

Key Market Drivers

Democratization of Data Access through Natural Language Interfaces is fundamentally reshaping the market by lowering the barrier to entry for advanced analytics. By enabling users to interact with complex datasets using conversational prompts, organizations are empowering non-technical staff to derive actionable intelligence without relying on specialized data science teams. This shift accelerates decision-making processes and fosters a data-driven culture across all business functions. According to Google Cloud, April 2024, in the 'Data and AI Trends Report 2024', 84% of surveyed data decision-makers believe that generative AI will help their organization access insights faster, validating the push towards more accessible analytics frameworks.

The Enhanced Ability to Analyze and Extract Value from Unstructured Data is another critical driver, as it allows enterprises to tap into previously inaccessible information sources like customer feedback, emails, and contracts. Generative AI models can process these vast reserves of qualitative information to identify patterns and anomalies that traditional structured analytics tools overlook, directly translating into operational improvements. According to the Capgemini Research Institute, September 2024, in the 'Harnessing the value of generative AI: 2nd edition' report, organizations implementing these technologies reported a 7.8% increase in productivity over the previous year. This efficiency gain underscores the technology's tangible impact. Furthermore, according to IBM, in 2024, 42% of enterprise-scale organizations surveyed have actively deployed AI in their business operations, reflecting the wider operational integration of these advanced capabilities.

Download Free Sample Report

Key Market Challenges

The primary impediment hindering the expansion of the Global Generative AI in Analytics Market is the inherent uncertainty regarding the accuracy of model outputs combined with significant data governance risks. In the domain of business analytics, where precise data is critical for strategic decision-making, the tendency of generative models to produce plausible but erroneous information—known as hallucinations—creates a substantial trust deficit. This unreliability negates the technology's core value proposition of speed and automation, as organizations are forced to implement rigorous, time-consuming human validation layers. Furthermore, the risk of data leakage and the absence of transparent governance frameworks deter enterprises from integrating these tools into sensitive operational workflows, thereby restricting the market to pilot programs rather than full-scale deployment.

This apprehension is substantiated by industry data reflecting widespread caution among professionals. According to ISACA, in 2024, 81% of digital trust professionals identified misinformation and disinformation as the most significant risks associated with artificial intelligence. This high level of distrust compels companies to delay the adoption of generative analytics for critical business functions. Consequently, market growth is directly throttled as organizations prioritize risk mitigation over innovation, waiting for higher standards of model reliability and security to be established before committing to enterprise-wide implementation.

Key Market Trends

The emergence of autonomous agentic AI is transforming analytics from a passive reporting function into an active, self-correcting operational layer. Unlike traditional models that require human prompts for every step, these agents can autonomously formulate plans, write and execute code to clean datasets, and iteratively refine their outputs to ensure accuracy. This capability directly addresses the need for reliable automated workflows, reducing the manual oversight previously required for complex data tasks. The rapid integration of this technology is evident in recent enterprise activity. According to Salesforce, September 2025, in the 'Agentic Enterprise Index', the creation of AI agents among early adopters surged by 119% during the first half of the year, signaling a major shift towards automated decision-making systems.

Concurrently, the widespread adoption of synthetic data is resolving critical bottlenecks related to privacy preservation and model training limitations. As enterprises face stricter governance protocols, synthetic datasets allow organizations to train robust analytical models without exposing sensitive customer information or intellectual property. This approach not only mitigates compliance risks but also fills gaps in historical data, enabling more comprehensive scenario simulations. The financial sector has become a leading adopter of this privacy-enhancing technique. According to NVIDIA, February 2025, in the 'State of AI in Financial Services' report, the adoption of synthetic data generation among surveyed organizations reached 46%, reflecting its growing importance in validating analytical strategies securely.

Segmental Insights

The Cloud Based segment currently demonstrates the most rapid expansion within the Global Generative AI in Analytics Market due to the inherent scalability required by resource-intensive generative models. Enterprises prefer cloud deployment to access high-performance computing power without incurring heavy upfront hardware costs. This delivery model supports continuous data updates and remote accessibility, which are essential for maintaining accurate analytical outputs. Additionally, the availability of pre-configured platforms from major technology providers reduces barriers to entry, enabling businesses to integrate generative artificial intelligence into their operations efficiently while ensuring data security and compliance.

Regional Insights

North America maintains the leading position in the global generative AI in analytics market due to the strong presence of established technology companies and cloud infrastructure providers. The region benefits from substantial investments in artificial intelligence research, particularly within the United States, which accelerates the adoption of automated data interpretation and predictive modeling tools. This market dominance is supported by a mature digital ecosystem that allows enterprises to scale operations efficiently. Furthermore, early acceptance of intelligent business intelligence solutions across diverse sectors continues to drive steady demand and reinforces the regional competitive advantage.

Recent Developments

• In May 2025, SAS announced new capabilities for creating and deploying autonomous AI agents within its flagship Viya platform. During its annual innovation conference, the company introduced features that blended human and AI autonomy, emphasizing embedded governance and the explainability of decisions. The release included productivity assistants designed to help users build enterprise logic faster and reduce manual steps in the analytics lifecycle. This strategic update focused on agentic AI, enabling systems to not only predict outcomes but also act on them, thereby multiplying the productivity of organizations leveraging generative AI for complex decision-making.
• In November 2024, Snowflake introduced significant advancements to its AI and machine learning platform during its annual developer conference. The updates included broader options for large language models, serverless fine-tuning capabilities, and new features for building conversational applications against both structured and unstructured data. The company focused on enabling enterprises to create trusted AI agents with built-in governance and safety guardrails. These innovations were aimed at accelerating the production of reliable AI-powered analytics applications, allowing data teams to optimize cost and performance while maintaining strict security standards required for enterprise-grade generative AI deployments.
• In September 2024, Oracle released the general availability of its AI assistant for the Oracle Analytics Cloud. The update provided a conversational interface that allowed analysts and business users to query data, generate visualizations, and perform complex calculations using natural language commands. The assistant was designed to understand user intent and context, thereby reducing the manual effort required for data preparation and analysis. This launch positioned Oracle as a key player in embedding machine learning and generative AI directly into enterprise analytics platforms to enhance decision-making speed and accuracy for diverse business functions.
• In June 2024, Microsoft announced the general availability of its generative AI assistant for Power BI, expanding access to customers with specific premium capacities. This release enabled users to generate full report pages, summaries, and visual insights using simple conversational language. The tool offered capabilities to answer questions about data across the entire report and facilitated semantic model exploration by automatically identifying relevant tables and measures. This development aimed to streamline the creation of visual analytics and democratize data insights for users without deep technical expertise, directly integrating generative AI into the daily analytics workflow of enterprises.

Key Market Players

• OpenAI OpCo, LLC
• IBM Corporation
• Microsoft Corporation
• Google LLC
• Amazon Web Services, Inc.
• NVIDIA Corporation
• Salesforce, Inc.
• SAP SE
• Oracle Corporation
• Palantir Technologies Inc.
• DataRobot, Inc.
• H2O.ai, Inc.

By Deployment	By Technology	By Application	By Region
Cloud-based On-premises	Natural Language Processing Machine Learning Deep Learning Others	Forecasting and Predictions Automated Reporting Anomaly Detection Personalization	North America Europe Asia Pacific South America Middle East & Africa

Report Scope:

In this report, the Global Generative AI in Analytics Market has been segmented into the following categories, in addition to the industry trends which have also been detailed below:

• Generative AI in Analytics Market, By Deployment:

• Cloud-based
• On-premises

• Generative AI in Analytics Market, By Technology:

• Natural Language Processing
• Machine Learning
• Deep Learning
• Others

• Generative AI in Analytics Market, By Application:

• Forecasting and Predictions
• Automated Reporting
• Anomaly Detection
• Personalization

• Generative AI in Analytics Market, By Region:

• North America
• United States
• Canada
• Mexico
• Europe
• France
• United Kingdom
• Italy
• Germany
• Spain
• Asia Pacific
• China
• India
• Japan
• Australia
• South Korea
• South America
• Brazil
• Argentina
• Colombia
• Middle East & Africa
• South Africa
• Saudi Arabia
• UAE