The global generative AI in analytics market size reached USD 931.05 million in 2023 and is projected to hit around USD 10,582.87 million by 2033, growing at a CAGR of 27.41% during the forecast period from 2024 to 2033.
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The U.S. generative AI in analytics market size reached USD 320.86 million in 2023 and is anticipated to be worth around USD 3,821.21 million by 2033, poised to grow at a CAGR of 28% from 2024 to 2033.
North America dominated the market with the largest market share in 2023, the region is expected to sustain its dominance throughout the forecast period. The growth of the market is attributed to the rapid adaptability of the automated technologies in the region. Increasing industrialization across the region will propel the growth of the market in the upcoming years while highlighting the importance of artificial intelligence solution deployment. Continuous development in industries will likely grow the demand for automation and data driven technological solutions. Increasing adoption of generative AI in the countries like United States, Canada, etc. will likely propel the demand for generative AI in the analytics market.
In July 2023, the largest AI accounting company in the United States, “Pilot” launched the Pilot GPT which is made from Open AI the latest generative AI technology. The launch will offer taxes, bookkeeping, and other CFO services to the 1700 businesses. And it is able to enhance the quality and accuracy of Pilot’s accounting services.
Asia Pacific is expected to generate a significant revenue during the forecast period. The increasing population and development in the industries will result in a higher demand for data management by industries. Generative Ai in analytics will help in the management of data storage and be used to increase the profitability of the industries by the better decision-making process. They are increasing industries results in the growth of the market across the region.
In June 2023, the generative AI and machine learning firm “Scribble Data” launch the Hasper, a full stack large language model (LLM)-based engine for the organization to increase in the make of products that are made with the AI-Powered data.
Generative AI is the most promising term or technology which is emerging in almost every sector. Generative AI is used for the creation of new data, images, videos, software, texts, etc., generative AI collects information form the existing database to generate the new one. Generative AI uses various method such as practice analysis for the efficient workflow of the organization. Generative AI in analytics used by the several industries like automation, communication, travel, etc. for the higher productivity and improved quality services.
Generative AI in analytics produce the improvement in productivity, latest innovations, and make an sustainable competitive edge for the organization. Generative AI plays an important role in the development of the industry. Generative AI in analytics increases the visibility in the supply chain, could be useful in fraud detection, predictive analysis, cybersecurity, etc. all these factor had the significant impact on the growth of the generative AI in analytics market.
Generative AI is used in various sectors like supply chain, logistics, travel, communication and automobile, the solutions powered by generative AI are almost impacting the aerospace, medical, automobile, energy and electronics. Likewise, it generates the profit by its predictive analytics it gives accurate prediction about the market fluctuation, so the organization make the competitive strategies according to it. Generative AI helps the organization for the better workflow.
Generative AI is also gaining attraction due to its properties like data accessibility. It is very beneficial for the organization due to the enormous amount of data management. Generative AI manages the data efficiently and in proper manner, in the upcoming period, there is increased need for the generative AI in analytics for the accurate decision-making process which will result in the increase in the growth of the market in the upcoming years.
| Report Coverage | Details |
| Market Size in 2023 | USD 931.05 Million |
| Market Size by 2033 | USD 10,582.87 Million |
| Growth Rate from 2024 to 2033 | CAGR of 27.41% |
| Largest Market | North America |
| Base Year | 2023 |
| Forecast Period | 2024 to 2033 |
| Segments Covered | By Deployment, By Technology, and By Application |
| Regions Covered | North America, Europe, Asia-Pacific, Latin America, and Middle East & Africa |
Driver:
Data generation and forecasting
Generative AI can generate new data fast and affordably. This is beneficial for completing tasks including data organization, processing, augmentation, synthesis, and data output for underrepresented or underrepresented groups. Additionally, it can help with data analysis and complex system comprehension. Some of its well-known applications include transforming satellite images into map views to study new locations, producing marketing data based on consumer behavior or target market research, and rendering medical scans into lifelike visualizations. The aforementioned tools can improve processes for making data-driven decisions. Thanks to generative AI, data processing, synthesis, and analysis are faster. Thus, the data generation and forecasting offered by generative AI is observed to act as a driver for the market’s growth.
Restraint:
Data accuracy and quality concerns
The fact that a given model's generated data or output still must be examined and confirmed is one of the key drawbacks of generative AI solutions applied or deployed in various industries. Certain models of generative AI have frequently provided poor-quality results, including ones that revealed errors, a lack of application, and dubious results. It is crucial to remember that the caliber of the data sets for training or datasets used to train a particular generative model affects the caliber of the results it generates. A particular model could reflect biases found in the training set of data. If the training set is biased, the results may not be accurate. The dependability and output quality are impacted by these findings.
Opportunity:
Technological advancements in AI algorithms
The extensive natural language technology known as Generative Pre-trained Transformer (GPT) uses deep learning to generate writing that resembles that of a person. ChatGPT can compose novels, songs, poems, and even computer code owing to the third generation (GPT-3), which can identify the most probable word to come next in a phrase based on its ingested cumulative training. Moreover, the technological advancements in the algorithms of generative AI can create high-value artifacts, including video, narrative, training data, even designs and schematics. All these discoveries can lead to new insights in the analytics industry. Thus, the deployment of generative AI with technological advancements is observed to open opportunities for the market.
The cloud-based segment dominated the market with the largest market share in 2023. The segment’s growth is attributed to the increasing demand for scalable and easily adaptable solutions by several industries. The application of the cloud can be accessed from anywhere and it can be stored and manage the enormous amounts of data of industries anywhere. Clod analytics is also known for its process of analyzing and storing data and can be useful in accurate business insights. Cloud analytics algorithms are stored a vast amount of data and analyze the future prediction, identify patterns, and produce other information for a better decision-making process in the organization. Organizations create a large amount of data on a daily basis. Cloud analytics is efficient for the management of that data and makes insights from it to take a better decision-making process.
The machine learning segment dominated the market with the largest market size in 2023, the segment is observed to maintain its position throughout the forecast period. The growth of the segment is attributed due to the learning and data-adapting technology without human interventions. Machine learning algorithms create their own logic by the data analysis as a result they create the solution relevant to the field of web searches, fraud detection, price detection, and tumor classification. Modern analytics tools contain ML-powered capabilities that help analysts make decisions faster by reducing the time between data collection and analysis. In order to help analysts quickly construct data stories, these systems include tools that enable automatic data analysis and the generation of contextual visuals with comments. AutoML, which automatically compares several machine learning (ML) techniques to help analysts choose the most accurate model for their use case, is another tool for increasing analyst productivity.
The data augmentation segment dominated the market with the largest market share in 2023. Data augmentation is the process of modifying datasets with pre-existing data in order to artificially expand the training set. Data augmentation is important as it helps to get over the limitations of real-world data. Actual data can be scarce in amount, quality, and diversity, which can impair the accuracy of models and forecasts. The segment’s expansion has increased since data augmentation might improve the stability of your AI/ML models. It allows for the training of models on larger, more diverse datasets, which improves their capacity to generalize more successfully to fresh, unstudied data points.
Segments Covered in the Report:
By Deployment
By Technology
By Application
By Geography
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