In March 2025, a total of 75 Generative Artificial intelligence solutions were available to customers on the Google Cloud Platform (GCP) marketplace. Most tools belonged to the software as a service (SaaS) and Vertex AI type, with 35 and 32 tools respectively, followed by the Kubernetes type with 5 tools.

In 2025, the artificial analysis intelligence index ranked AI models based on reasoning capabilities, knowledge, math, and coding. Grok 3 Reasoning Beta led the rankings, followed by o1, DeepSeek R1, and Claude 3.7 Sonnet Thinking. Other high performing models included GPT-4.5 and Gemini 2.0.

Introduction

Generative AI Statistics: In recent years, generative AI has quickly become a game-changer across multiple industries, powered by advancements in machine learning and neural networks. These innovations have greatly improved the efficiency and flexibility of AI systems, enabling them to produce high-quality results.

The growing availability of extensive datasets, along with enhanced computing capabilities, has further accelerated the progress of generative AI, fostering more precise and innovative applications. This shift is particularly evident in sectors such as healthcare, automotive, finance, and entertainment, where AI-driven solutions are revolutionizing business operations and enhancing customer experiences. As digital transformation continues, the demand for generative AI is set to skyrocket, fundamentally altering how businesses function and engage with their audiences.

A broad dataset providing insights into artificial intelligence statistics and trends for 2025, covering market growth, adoption rates across industries, impacts on employment, AI applications in healthcare, education, and more.

The main use, or ** percent, of generative AI was in seeking answers to questions the user did not know or generally brainstorming. Over **** the respondents used generative AI in such cases in 2023. Coding and writing lyrics were the least influential use cases, with barely ** percent of users using generative AI in such tasks.

Introduction

Generative AI statistics: From its early days in research labs to now, a global sensation, generative AI has completely changed the technology landscape. We are past the initial hype and now deep into a phase of actual application and major economic impact.

This article explores more into the data behind the rise of generative AI, giving you a clear, driven look at its current state and what's coming next. I've gathered all the latest information to give you the most detailed and accurate picture available, showing you exactly how this technology is changing everything, so let’s get into it.

During a 2023 survey conducted among professionals in the United States, it was found that 37 percent of those working in advertising or marketing had used artificial intelligence (AI) to assist with work-related tasks. Healthcare, however, had the lowest rate of AI usage with only 15 percent of those asked having used it at work. The rate of adoption in marketing and advertising is understandable, as it is the industry that most weaves together art and creative mediums in its processes.

Generative AI linked to education

Those positions that require a higher level of education are most at risk of being automated with generative AI in the U.S. This is simply because those jobs that require less formal education are rarely digital positions and are more reliant on physical labor. Jobs that require tertiary education, however, are still the least likely to be automated overall, even with the added influence of generative AI.

ChatGPT has competitors

While the OpenAI-developed ChatGPT is the most well-known AI program and the currently most advanced large language model, - other competitors are catching up. While just over half of respondents in the U.S. had heard of or used ChatGPT, nearly half of respondents had also heard of or used Bing Chat. Google’s Bard was slightly behind, with only around a third of Americans having heard of or used it.

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Generative AI In Data Analytics Market Size 2025-2029

The generative ai in data analytics market size is valued to increase by USD 4.62 billion, at a CAGR of 35.5% from 2024 to 2029. Democratization of data analytics and increased accessibility will drive the generative ai in data analytics market.

Market Insights

North America dominated the market and accounted for a 37% growth during the 2025-2029.
By Deployment - Cloud-based segment was valued at USD 510.60 billion in 2023
By Technology - Machine learning segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 621.84 million 
Market Future Opportunities 2024: USD 4624.00 million
CAGR from 2024 to 2029 : 35.5%

Market Summary

The market is experiencing significant growth as businesses worldwide seek to unlock new insights from their data through advanced technologies. This trend is driven by the democratization of data analytics and increased accessibility of AI models, which are now available in domain-specific and enterprise-tuned versions. Generative AI, a subset of artificial intelligence, uses deep learning algorithms to create new data based on existing data sets. This capability is particularly valuable in data analytics, where it can be used to generate predictions, recommendations, and even new data points. One real-world business scenario where generative AI is making a significant impact is in supply chain optimization. In this context, generative AI models can analyze historical data and generate forecasts for demand, inventory levels, and production schedules. This enables businesses to optimize their supply chain operations, reduce costs, and improve customer satisfaction. However, the adoption of generative AI in data analytics also presents challenges, particularly around data privacy, security, and governance. As businesses continue to generate and analyze increasingly large volumes of data, ensuring that it is protected and used in compliance with regulations is paramount. Despite these challenges, the benefits of generative AI in data analytics are clear, and its use is set to grow as businesses seek to gain a competitive edge through data-driven insights.

What will be the size of the Generative AI In Data Analytics Market during the forecast period?

Get Key Insights on Market Forecast (PDF) Request Free SampleGenerative AI, a subset of artificial intelligence, is revolutionizing data analytics by automating data processing and analysis, enabling businesses to derive valuable insights faster and more accurately. Synthetic data generation, a key application of generative AI, allows for the creation of large, realistic datasets, addressing the challenge of insufficient data in analytics. Parallel processing methods and high-performance computing power the rapid analysis of vast datasets. Automated machine learning and hyperparameter optimization streamline model development, while model monitoring systems ensure continuous model performance. Real-time data processing and scalable data solutions facilitate data-driven decision-making, enabling businesses to respond swiftly to market trends. One significant trend in the market is the integration of AI-powered insights into business operations. For instance, probabilistic graphical models and backpropagation techniques are used to predict customer churn and optimize marketing strategies. Ensemble learning methods and transfer learning techniques enhance predictive analytics, leading to improved customer segmentation and targeted marketing. According to recent studies, businesses have achieved a 30% reduction in processing time and a 25% increase in predictive accuracy by implementing generative AI in their data analytics processes. This translates to substantial cost savings and improved operational efficiency. By embracing this technology, businesses can gain a competitive edge, making informed decisions with greater accuracy and agility.

Unpacking the Generative AI In Data Analytics Market Landscape

In the dynamic realm of data analytics, Generative AI algorithms have emerged as a game-changer, revolutionizing data processing and insights generation. Compared to traditional data mining techniques, Generative AI models can create new data points that mirror the original dataset, enabling more comprehensive data exploration and analysis (Source: Gartner). This innovation leads to a 30% increase in identified patterns and trends, resulting in improved ROI and enhanced business decision-making (IDC).

Data security protocols are paramount in this context, with Classification Algorithms and Clustering Algorithms ensuring data privacy and compliance alignment. Machine Learning Pipelines and Deep Learning Frameworks facilitate seamless integration with Predictive Modeling Tools and Automated Report Generation on Cloud

Cloud based services are reported to be the most popular generative artificial intelligence (AI) tool currently in use, with ** percent of those surveyed worldwide reporting that they use it. Far behind are local or offline solutions with a share of ** percent.

Value or potential value created by Generative AI, by North American Industry Classification System (NAICS), business employment size, type of business, business activity and majority ownership, first quarter of 2024.

Discover more about the size and trends of the rapidly expanding generative AI market.

Data sets and generating R code for reproduction of the results in "The Use of Generative AI in Statistical Data Analysis"

Generative AI Market was valued at $14.91 billion in 2023, and is predicted to reach $213.50 billion by 2030.

Most people used generative AI to simply have fun, messing around with the variety of possibilities such as image generation, discussing with chatbots, or asking questions. Few people used it to write notes and emails, but still almost ******* of respondents in 2023.

As large language models (LLMs) such as GPT have become more accessible, concerns about their potential effects on students’ learning have grown. In data science education, the specter of students’ turning to LLMs raises multiple issues, as writing is a means not just of conveying information but of developing their statistical reasoning. In our study, we engage with questions surrounding LLMs and their pedagogical impact by: (a) quantitatively and qualitatively describing how select LLMs write report introductions and complete data analysis reports; and (b) comparing patterns in texts authored by LLMs to those authored by students and by published researchers. Our results show distinct differences between machine-generated and human-generated writing, as well as between novice and expert writing. Those differences are evident in how writers manage information, modulate confidence, signal importance, and report statistics. The findings can help inform classroom instruction, whether that instruction is aimed at dissuading the use of LLMs or at guiding their use as a productivity tool. It also has implications for students’ development as statistical thinkers and writers. What happens when they offload the work of data science to a model that doesn’t write quite like a data scientist? Supplementary materials for this article are available online.

Available data formats for the Generative AI Market Size, Share, Opportunities, And Trends By Offering (Software, Services), By Application (Language, Audio And Speech, Visual, Others), By Model (Generative Adversarial Networks (gans), Transformer-based Models, Others), By End-user Industry (Automotive, Healthcare/drug Discovery, Media And Entertainment, Bfsi, Education, Others), And By Geography - Forecasts From 2025 To 2030 report.

Global Generative AI in Data Analytics Market is segmented by Application (Business intelligence_Forecasting_Customer analytics_Risk analysis_Supply chain analytics), Type (Auto insights_Natural language queries_Predictive models_Data visualization_Synthetic data generation), and Geography (North America_ LATAM_ West Europe_Central & Eastern Europe_ Northern Europe_ Southern Europe_ East Asia_ Southeast Asia_ South Asia_ Central Asia_ Oceania_ MEA)

Introduction

The global generative AI market is experiencing rapid growth, driven by the increasing adoption of AI models across content creation, software development, healthcare, and customer engagement applications. In 2023, the market was valued at approximately USD 13.5 billion and is projected to reach nearly USD 255.8 billion by 2033, registering a strong compound annual growth rate (CAGR) of 34.2% from 2024 to 2033. The surge in demand is being fueled by advancements in large language models, diffusion techniques, and foundation model APIs that enable enterprises to automate complex tasks and personalize user experiences at scale.

The Generative AI market is a rapidly evolving sector focused on AI technologies that create new content such as text, images, video, and code by learning from existing data. It is transforming various industries by enabling automation of content creation, enhancing productivity, and fostering innovation. With applications spanning healthcare, media, automotive, financial services, and more, generative AI helps organizations innovate faster and streamline workflows. This market is growing swiftly as businesses realize the power of AI in reshaping how work and creativity are managed.

The top driving factors for generative AI adoption include advancing AI algorithms like deep learning and neural networks, the demand for operational efficiency, and the growing need to automate repetitive tasks. Companies seek to improve customer experience, speed up product development, and reduce costs. The rise of tools that support text-to-image, text-to-video, and coding automation also fuels adoption. These factors combine with the expanding availability of large datasets and computing power, enabling generative AI to produce increasingly accurate and useful outputs.

https://market.us/wp-content/uploads/2023/10/Global-Generative-AI-Market-1024x595.jpg" alt="Global Generative AI Market" width="1024" height="595">

According to Forbes, the generative AI market is set to expand by USD 180 billion over the next eight years, signaling strong investor confidence and underscoring the vast commercial potential of this technology. This projection reflects the accelerating integration of generative AI across industries seeking to automate workflows, enhance creativity, and unlock new digital services.

A collaborative study by professors from Harvard Business School, Wharton, Warwick Business School, and MIT Sloan found that generative AI can increase employee productivity by up to 40%, indicating its practical value in enhancing operational efficiency. In parallel, a productivity survey reveals that under a midpoint adoption scenario, generative AI could raise U.S. labor productivity by 0.5 to 0.9 percentage points annually through 2030, reinforcing its long-term economic impact.

The banking industry is expected to undergo one of the most significant transformations. The adoption of enterprise-grade generative AI tools is projected to generate an annual value of USD 200 billion to USD 340 billion, revolutionizing risk modeling, client servicing, and back-office functions. This shift positions generative AI not only as a technological innovation but also as a strategic imperative across core financial operations.

As of 2024, around ** percent of organizations expected implementing a governance strategy for generative AI to take more than 2 years to resolve. Additionally, around ** percent of businesses expect to achieve return on investment (ROI) in 1 to 2 years. It is important to note that most challenges in generative AI initiatives are expected to be resolved in 1 to 2 years by almost half of the organizations.

Creativity is core to being human. Generative AI—made readily available by powerful large language models (LLMs)—holds promise for humans to be more creative by offering new ideas, or less creative by anchoring on generative AI ideas. We study the causal impact of generative AI ideas on the production of short stories in an online experiment where some writers obtained story ideas from an LLM. We find that access to generative AI ideas causes stories to be evaluated as more creative, better written, and more enjoyable, especially among less creative writers. However, generative AI-enabled stories are more similar to each other than stories by humans alone. These results point to an increase in individual creativity at the risk of losing collective novelty. This dynamic resembles a social dilemma: with generative AI, writers are individually better off, but collectively a narrower scope of novel content is produced. Our results have implications for researchers, policy-makers, and practi..., This dataset is based on a pre-registered, two-phase experimental online study. In the first phase of our study, we recruited a group of N=293 participants (“writers†) who are asked to write a short, eight sentence story. Participants are randomly assigned to one of three conditions: Human only, Human with 1 GenAI idea, and Human with 5 GenAI ideas. In our Human only baseline condition, writers are assigned the task with no mention of or access to GenAI. In the two GenAI conditions, we provide writers with the option to call upon a GenAI technology (OpenAI’s GPT-4 model) to provide a three-sentence starting idea to inspire their own story writing. In one of the two GenAI conditions (Human with 5 GenAI ideas), writers can choose to receive up to five GenAI ideas, each providing a possibly different inspiration for their story. After completing their story, writers are asked to self-evaluate their story on novelty, usefulness, and several emotional characteristics. In the second phase, th..., , # Dataset and Code for "Generative artificial intelligence enhances creativity but reduces the diversity of novel content"

by Anil R. Doshi and Oliver P. Hauser

Introduction

We recommend downloading the file "GenAI_creativity_data_and_scripts.zip" which contains all data (raw and processed) as well as the analysis code. Then please follow the steps below.

We provide two methods to perform the data analysis.

1. Compile all files. This method processes the raw csv files and performs the analysis. Requires some knowledge of Python and an API key to OpenAI.
2. Processed file analysis. This allows you to run the analysis on the already processed dta files.

1. Compile all files method

If you would like to compile all files, please follow these steps to ensure your machine is set up to run all the necessary scripts.

Machine setup

1. Ensure Python is installed (tested with Python 3)
2. Install the following packages in Python

• numpy
• scipy
• openai

It may be nece...