Generative AI in Analytics Market By Technology (Natural Language Processing, Machine Learning, Deep Learning, Reinforcement Learning), By Application (Predictive Analytics, Data Visualization, Data Mining, Descriptive Analytics, Prescriptive Analytics), By Deployment (Cloud-Based, On-Premise), By End-User Industry (BFSI, Healthcare, Retail, Manufacturing, IT and Telecommunications, Government), and By Region; Global Insights & Forecast (2024 – 2030)

As per Intent Market Research, the Generative AI in Analytics Market was valued at USD 1.1 billion in 2023 and will surpass USD 10.8 billion by 2030; growing at a CAGR of 38.4% during 2024 - 2030.

The generative AI in analytics market is experiencing a substantial transformation driven by the increasing demand for advanced data analytics solutions that can enhance business decision-making processes. Generative AI, in particular, empowers organizations to utilize large datasets for predictive insights, automation, and optimization. This evolution is being fueled by advancements in machine learning, deep learning, and natural language processing (NLP). As organizations across multiple industries look to streamline operations, gain better customer insights, and improve service offerings, the integration of generative AI in analytics becomes increasingly essential. The widespread adoption of this technology is expected to fuel rapid growth in both developed and emerging markets, making it one of the most dynamic sectors within artificial intelligence.

Machine Learning Segment is Largest Owing to Its Versatility

Within the technology segment of the generative AI in analytics market, machine learning (ML) holds the largest share due to its widespread application across various industries. Machine learning algorithms enable machines to improve automatically through experience, making them highly efficient for data-driven decision-making and predictive analytics. The ability of machine learning to process large volumes of data with minimal human intervention makes it indispensable for applications such as predictive maintenance, customer segmentation, fraud detection, and real-time analytics. In sectors like healthcare, retail, and BFSI, ML models are being used extensively to gain insights into consumer behavior, detect fraudulent activities, and optimize inventory management. This versatility is a primary factor in ML's dominance in the generative AI in analytics space.

Predictive Analytics is Fastest Growing Application

Predictive analytics is emerging as the fastest-growing application within the generative AI in analytics market. As organizations strive to enhance forecasting accuracy and gain a competitive edge, predictive analytics powered by generative AI helps in identifying future trends based on historical data. Businesses are increasingly adopting predictive analytics to make informed decisions in areas like demand forecasting, risk management, and customer behavior predictions. Industries such as BFSI and retail are particularly benefiting from this application, leveraging AI-driven predictions to enhance operational efficiency and customer satisfaction. The rapid adoption of cloud-based AI platforms and the availability of big data are accelerating the growth of predictive analytics as organizations recognize the value of anticipating market changes.

Cloud-Based Deployment is Fastest Growing in Deployment Type

In the deployment segment, cloud-based deployment is the fastest growing due to its scalability, cost-effectiveness, and accessibility. Cloud-based solutions provide businesses with the flexibility to access generative AI tools and analytics from anywhere, thus reducing the need for substantial on-premise infrastructure investments. The ability to scale as per demand, combined with the ease of integration with other cloud-based services, makes this deployment model ideal for organizations across various sectors. As businesses increasingly migrate to cloud environments to enhance operational agility, the demand for cloud-based generative AI tools in analytics is surging. Moreover, cloud providers like Amazon Web Services (AWS), Microsoft Azure, and Google Cloud are contributing to this growth by offering advanced AI capabilities on their platforms.

BFSI Industry is Largest End-User Industry

The BFSI (Banking, Financial Services, and Insurance) industry is the largest end-user sector in the generative AI in analytics market. With the growing need for data security, fraud detection, risk management, and customer relationship management, BFSI organizations are increasingly leveraging AI analytics to automate processes and make data-driven decisions. Machine learning models and predictive analytics are extensively used in fraud detection, credit scoring, investment forecasting, and personalized financial services. The BFSI industry’s vast volume of transactions and complex data sets makes it an ideal sector for the application of generative AI. The industry's commitment to enhancing operational efficiency and ensuring customer satisfaction through intelligent solutions will continue to drive the demand for AI in analytics.

North America is Largest Region in Generative AI in Analytics Market

North America dominates the generative AI in analytics market, primarily due to the region's technological advancements, strong presence of key market players, and early adoption of AI technologies across industries. The U.S., in particular, leads the market, with organizations in sectors like BFSI, healthcare, and retail investing heavily in AI-driven solutions to optimize operations and improve customer experiences. The region also benefits from a supportive regulatory environment and substantial investments in AI research and development. As businesses in North America continue to prioritize digital transformation, the adoption of generative AI in analytics is expected to maintain strong momentum in the coming years.

The generative AI in analytics market is highly competitive, with numerous players offering advanced AI-driven solutions to cater to the diverse needs of various industries. Leading companies such as IBM, Google, Microsoft, Amazon Web Services (AWS), and SAP dominate the market by providing AI and machine learning tools designed to enhance analytics capabilities. These companies continuously invest in research and development to enhance the functionality of their AI solutions and remain at the forefront of innovation. The competitive landscape also includes specialized players like SAS Institute, DataRobot, and RapidMiner, which offer unique AI-driven platforms tailored to specific industry needs. As the market expands, collaboration, acquisitions, and partnerships are expected to play a pivotal role in helping companies strengthen their market position and expand their offerings in generative AI for analytics.

Recent Developments:

• Google announced the launch of its new generative AI-powered tool for analytics, aimed at enhancing business intelligence capabilities for large enterprises.
• IBM entered into a strategic partnership with a leading healthcare provider to integrate generative AI into healthcare analytics, enabling predictive insights for better patient care.
• Microsoft unveiled its Azure AI platform upgrade, which integrates generative AI models to improve business analytics and data-driven decision-making for enterprises.
• Salesforce has acquired a generative AI startup to enhance its analytics platform, enabling users to generate real-time insights and forecasts for customer behavior analysis.
• Amazon Web Services (AWS) launched a new suite of AI-powered analytics tools aimed at improving predictive modeling and analytics capabilities for financial services companies.

List of Leading Companies:

• Accenture PLC
• Amazon Web Services (AWS)
• Cloudera Inc.
• DataRobot
• Google LLC
• IBM Corporation
• Microsoft Corporation
• Oracle Corporation
• Palantir Technologies
• Qlik Technologies
• Salesforce Inc.
• SAP SE
• SAS Institute Inc.
• ThoughtSpot Inc.
• TIBCO Software Inc.

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