Recent developments include: December 2023: TELUS International, a digital customer experience innovator in AI and content moderation, launched Experts Engine, a fully managed, technology-driven, on-demand expert acquisition solution for generative AI models. It programmatically brings together human expertise and Gen AI tasks, such as data collection, data generation, annotation, and validation, to build high-quality training sets for the most challenging master models, including the Large Language Model (LLM)., September 2023: Cogito Tech, a player in data labeling for AI development, launched an appeal to AI vendors globally by introducing a “Nutrition Facts” style model for an AI training dataset known as DataSum. The company has been actively encouraging a more Ethical approach to AI, ML, and employment practices., June 2023: Sama, a provider of data annotation solutions that power AI models, launched Platform 2.0, a new computer vision platform designed to reduce the risk of ML algorithm failure in AI training models., May 2023: Appen Limited, a player in AI lifecycle data, announced a partnership with Reka AI, an emerging AI company making its way from stealth. This partnership aims to combine Appen's data services with Reka's proprietary multimodal language models., March 2022: Appen Limited invested in Mindtech, a synthetic data company focusing on the development of training data for AI computer vision models. This investment is part of Appen's strategy to invest capital in product-led businesses generating new and emerging sources of training data for supporting the AI lifecycle.. Key drivers for this market are: Rapid Adoption of AI Technologies for Training Datasets to Aid Market Growth. Potential restraints include: Lack of Skilled AI Professionals and Data Privacy Concerns to Hinder Market Expansion. Notable trends are: Rising Usage of Synthetic Data for Enhancing Authentication to Propel Market Growth.

During a 2022 survey conducted in the United States, it was found that 18 percent of respondents thought that artificial intelligence will lead to there being many fewer jobs. By contrast, 25 percent of respondents aged between 30 and 44 years stated that AI will create many more jobs.

Artificial intelligence

Artificial intelligence (AI) is the ability of a computer or machine to mimic the competencies of the human mind, learning from previous experiences to understand and respond to language, decisions, and problems. Particularly, a large amount of data is often used to train AI into developing algorithms and skills. The AI ecosystem consists of machine learning (ML), robotics, artificial neural networks, and natural language processing (NLP). Nowadays, tech and telecom, financial services, healthcare, and pharmaceutical industries are prominent for AI adoption in companies.

AI companies and startups

More and more companies and startups are engaging in the artificial intelligence market, which is forecast to grow rapidly in the coming years. Examples of big tech firms are IBM, Microsoft, Baidu, and Tencent, with the last owning the highest number of AI and ML patent families, amounting to over nine thousand. Moreover, driven by the excitement for this new technology and by the large investments in it, the number of startups involved in the industry around the world has grown in recent years. For instance, in the United States, the New York company UiPath was the top-funded AI startup.

The Artificial Intelligence Software market is valued at USD 80.26 Billion in 2022 and will be USD 612.36 Billion by 2030 with a CAGR of 29.06% during the forecast period. Factors Affecting the Artificial Intelligence Software Market

Increasing demand for IoT solutions boosts Artificial Intelligence Software market growth:

Software that uses artificial intelligence is increasingly being used in industries from healthcare to defense, as it is one of the most effective ways to eliminate the need for human labor. To compete in the artificial intelligence software market, other well-known companies are also releasing new AI software at the same time. For instance, Amazon announced the release of a new artificial intelligence tool called “Create with Alexa '' in November 2022 to produce stories for children. This brand-new artificial intelligence program creates briefings that include animation, music, and pictures.

Increasing demand for AI in the healthcare sector

The health industry's application of artificial intelligence software improved the standard of living for workers. In the coming years, the market for artificial intelligence software will probably be driven by rising demand from the healthcare industry. Technology advancements also offer profitable chances for market expansion.

Restraint for Artificial Intelligence Software Market

The difficulty associated adoption of AI tools can hamper market growth:

The market's limiting factors include the absence of Al talent in emerging nations, difficulties with the all-at-once adoption of Al tools, and the black box effect. To combat these factors and end the "black box effect," businesses have improved their solutions with more moral and explicable Al models. The black box effect causes the Al algorithms to occasionally provide results that are difficult to verify. These algorithms' results could be biased in a subtle way that is hard to detect. Therefore, the results are not adequately explained. As a result, consumers frequently embrace Al tools without feeling secure or trusted

Impact of the COVID-19 Pandemic on the Artificial Intelligence Software Market:

The pandemic crisis altered the way businesses functioned and made them more complex. Businesses moved their business operations to the cloud to adapt pt this development., machine learning, and other cutting-edge technologies saw a spike in use as a result. One of the first industries to use this technology, which increased the precision and effectiveness of diagnoses, treatments, and predictions, was the healthcare industry. For instance, according to the study report from January 2022, researchers at Indiana University and the Regenstrief Institute discovered that machine learning (ML) models could aid in public health decision-making during the pandemic. What is Artificial Intelligence Software?

A mainframe program called artificial intelligence (Al) software imitates human behavior by gaining knowledge from various insights and data patterns. Artificial intelligence platforms, chatbots, deep learning software, and machine learning software are a few examples of different types of Al software. Additional features of Al software include voice and speech recognition, machine learning, and virtual assistants. To automate company procedures and organize data for better data insights, various sorts of enterprises utilize artificial intelligence software which is machine learning incorporated. In addition, the market for Al software is anticipated to increase exponentially in the future due to rising technological advancements.

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Artificial Intelligence (AI) Infrastructure Market Size 2024-2028

The artificial intelligence (ai) infrastructure market size is forecast to increase by USD 22.07 billion at a CAGR of 20.6% between 2023 and 2028.

The market is experiencing significant growth, driven by the emerging application of machine learning (ML) in various industries. The increasing availability of cloud-based AI applications is also fueling market expansion. However, privacy concerns associated with AI deployment pose a challenge to market growth. As ML algorithms collect and process vast amounts of data, ensuring data security and privacy becomes crucial. Despite these challenges, the market is expected to continue its growth trajectory, driven by advancements in AI technologies and their increasing adoption across sectors. The implementation of robust data security measures and regulatory frameworks will be essential to address privacy concerns and foster market growth.

What will be the Size of the Artificial Intelligence (AI) Infrastructure Market During the Forecast Period?

Request Free SampleThe market encompasses the hardware and software solutions required to build, train, deploy, and scale AI models. Key market drivers include the increasing demand for machine learning workloads, data processing for various applications such as image recognition and natural language processing, and the need for computational power and networking capabilities to handle large data sets. The market is characterized by continuous improvement and competitive advantage through the use of GPUs and TPUs for AI algorithms, as well as cloud computing solutions offering high-bandwidth and scalability. Security is a critical consideration, with data handling and storage solutions implementing robust encryption and access control measures.AI infrastructure is utilized across diverse industries, including healthcare and finance, to drive innovation and precision medicine, and to enhance operational efficiency and productivity. Data processing frameworks play a pivotal role in facilitating the deployment and scaling of AI models, enabling organizations to maintain flexibility and adapt to evolving business needs.

How is this Artificial Intelligence (AI) Infrastructure Industry segmented and which is the largest segment?

The artificial intelligence (ai) infrastructure industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments. TypeProcessorStorageMemoryGeographyNorth AmericaUSEuropeGermanyUKAPACChinaJapanSouth AmericaMiddle East and Africa

By Type Insights

The processor segment is estimated to witness significant growth during the forecast period.

The market is experiencing significant growth due to the increasing adoption of AI and machine learning (ML) technologies across various industries. The market encompasses hardware, software, machine learning workloads, data processing, model training, deployment, scalability, flexibility, security, and computational power. Hardware solutions include GPUs and TPUs, while software solutions consist of data processing frameworks, image recognition, natural language processing, and AI algorithms. Industries such as healthcare, finance, and precision medicine are leveraging AI for decision-making, autonomous systems, and real-time data processing. AI infrastructure requires high computational demands, and cloud computing provides scalable storage solutions and cost-efficiency. Networking solutions offer high-bandwidth and low-latency for data transfer, ensuring data residency and data security.Data architecture includes databases, data warehouses, data lakes, in-memory databases, and caching mechanisms. Data preparation and resource utilization are crucial for model inference, data reconciliation, data classification, data visualization, and model validation. AI model production and data preprocessing are essential for continuous improvement and competitive advantage. AI accelerators, AI workflows, and data ingestion further enhance the capabilities of AI infrastructure. The market's growth is driven by the increasing need for cost-efficiency, integration, and modular systems.

Get a glance at the Artificial Intelligence (AI) Infrastructure Industry report of share of various segments Request Free Sample

The Processor segment was valued at USD 3.76 billion in 2018 and showed a gradual increase during the forecast period.

Regional Analysis

North America is estimated to contribute 49% to the growth of the global market during the forecast period.

Technavio’s analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

For more insights on the market share of various regions, Req

As of December 2022, Baidu was the largest owner of active machine learning and artificial intelligence (AI) patent families worldwide, with ****** active patent families owned. In 2022, the company had claimed the leading position from Tencent now ranked second with ****** active patent families owned. IBM ranked fifth with just under ***** active patent families. The statistic is based on data provided by PatentSight.

The Big Data Technology Market size was valued at USD 349.40 USD Billion in 2023 and is projected to reach USD 918.16 USD Billion by 2032, exhibiting a CAGR of 14.8 % during the forecast period. Big data is larger, more complex data sets, especially from new data sources. These data sets are so voluminous that traditional data processing software just can’t manage them. But these massive volumes of data can be used to address business problems that wouldn’t have been able to tackle before. Big data technology is defined as software-utility. This technology is primarily designed to analyze, process and extract information from a large data set and a huge set of extremely complex structures. This is very difficult for traditional data processing software to deal with. Among the larger concepts of rage in technology, big data technologies are widely associated with many other technologies such as deep learning, machine learning, artificial intelligence (AI), and Internet of Things (IoT) that are massively augmented. In combination with these technologies, big data technologies are focused on analyzing and handling large amounts of real-time data and batch-related data. Recent developments include: February 2024: - SQream, a GPU data analytics platform, partnered with Dataiku, an AI and machine learning platform, to deliver a comprehensive solution for efficiently generating big data analytics and business insights by handling complex data., October 2023: - MultiversX (ELGD), a blockchain infrastructure firm, formed a partnership with Google Cloud to enhance Web3’s presence by integrating big data analytics and artificial intelligence tools. The collaboration aims to offer new possibilities for developers and startups., May 2023: - Vpon Big Data Group partnered with VIOOH, a digital out-of-home advertising (DOOH) supply-side platform, to display the unique advertising content generated by Vpon’s AI visual content generator "InVnity" with VIOOH's digital outdoor advertising inventories. This partnership pioneers the future of outdoor advertising by using AI and big data solutions., May 2023: - Salesforce launched the next generation of Tableau for users to automate data analysis and generate actionable insights., March 2023: - SAP SE, a German multinational software company, entered a partnership with AI companies, including Databricks, Collibra NV, and DataRobot, Inc., to introduce the next generation of data management portfolio., November 2022: - Thai Oil and Retail Corporation PTT Oil and Retail Business Public Company implemented the Cloudera Data Platform to deliver insights and enhance customer engagement. The implementation offered a unified and personalized experience across 1,900 gas stations and 3,000 retail branches., November 2022: - IBM launched new software for enterprises to break down data and analytics silos that helped users make data-driven decisions. The software helps to streamline how users access and discover analytics and planning tools from multiple vendors in a single dashboard view., September 2022: - ActionIQ, a global leader in CX solutions, and Teradata, a leading software company, entered a strategic partnership and integrated AIQ’s new HybridCompute Technology with Teradata VantageCloud analytics and data platform.. Key drivers for this market are: Increasing Adoption of AI, ML, and Data Analytics to Boost Market Growth . Potential restraints include: Rising Concerns on Information Security and Privacy to Hinder Market Growth. Notable trends are: Rising Adoption of Big Data and Business Analytics among End-use Industries.

Dataset Overview

The "AI and ML Job Listings USA" dataset provides a comprehensive collection of job postings in the field of Artificial Intelligence (AI) and Machine Learning (ML) across the United States. The dataset includes job listings from 2022 to 2024, capturing the evolving landscape of AI/ML job opportunities. This dataset is valuable for researchers, job seekers, and data scientists interested in understanding trends, demands, and opportunities in the AI/ML job market.

Data Science Applications

This dataset can be utilized for various data science applications, including: - Trend Analysis: Identifying trends in job titles, locations, and required skills over time. - Demand Forecasting: Predicting future demand for AI/ML roles based on historical data. - Skills Gap Analysis: Analyzing the skills and experience levels in demand versus the available workforce. - Geospatial Analysis: Mapping job opportunities across different regions in the USA. - Salary Prediction: Developing models to predict salaries based on job descriptions and other attributes. Some job descriptions include salary information, which can be identified by exploring the 'description' column for mentions of compensation, pay, or salary-related terms.

Column Descriptors

1. title: The job title (e.g., AI/ML Engineer).
2. location: The location of the job (e.g., New York, NY).
3. publishedAt: The date the job was published (e.g., 2024-05-29).
4. companyName: The name of the company offering the job (e.g., Wesper).
5. description: A detailed description of the job (e.g., responsibilities, qualifications, and sometimes salary information).
6. applicationsCount: The number of applications received (e.g., Over 200 applicants).
7. contractType: The type of contract (e.g., Full-time).
8. experienceLevel: The level of experience required (e.g., Mid-Senior level).
9. workType: The type of work (e.g., Engineering and Information Technology).
10. sector: The industry sector of the job (e.g., Internet Publishing).

Ethically Mined Data

This dataset has been ethically mined using an API, ensuring no private information has been revealed. Sensitive data, such as the recruiter name, has been removed to protect privacy and comply with ethical standards.

Acknowledgments

• LinkedIn: For providing the platform where these job listings were originally posted.
• DALL·E 3: For generating the thumbnail image used for this dataset.

This dataset provides a rich resource for analyzing and understanding the AI and ML job market in the USA, offering insights into job trends, requirements, and opportunities in this rapidly growing field.

In 2022, the global total corporate investment in artificial intelligence (AI) reached almost 92 billion U.S. dollars, a slight decrease from the previous year. In 2018, the yearly investment in AI saw a slight downturn, but that was only temporary. Private investments account for a bulk of total AI corporate investment. AI investment has increased more than sixfold since 2016, a staggering growth in any market. It is a testament to the importance of the development of AI around the world.

What is Artificial Intelligence (AI)?

Artificial intelligence, once the subject of people’s imaginations and the main plot of science fiction movies for decades, is no longer a piece of fiction, but rather commonplace in people’s daily lives whether they realize it or not. AI refers to the ability of a computer or machine to imitate the capacities of the human brain, which often learns from previous experiences to understand and respond to language, decisions, and problems. These AI capabilities, such as computer vision and conversational interfaces, have become embedded throughout various industries’ standard business processes.

AI investment and startups

The global AI market, valued at 142.3 billion U.S. dollars as of 2023, continues to grow driven by the influx of investments it receives. This is a rapidly growing market, looking to expand from billions to trillions of U.S. dollars in market size in the coming years. From 2020 to 2022, investment in startups globally, and in particular AI startups, increased by five billion U.S. dollars, nearly double its previous investments, with much of it coming from private capital from U.S. companies. The most recent top-funded AI businesses are all machine learning and chatbot companies, focusing on human interface with machines.

Analysis of ‘Offer and infrastructure as well as use, time and costs of company training in companies by company size’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/https-www-datenportal-bmbf-de-portal-2-7-23 on 16 January 2022.

--- Dataset description provided by original source is as follows ---

Table 2.7.23: Offer and infrastructure as well as use, time and costs of company training in companies by company size

--- Original source retains full ownership of the source dataset ---

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Cloud Artificial Intelligence (AI) Market Size 2024-2028

The cloud artificial intelligence (ai) market size is forecast to increase by USD 12.61 billion, at a CAGR of 24.1% between 2023 and 2028.

The market is experiencing significant growth, driven by the emergence of technologically advanced devices and the increasing adoption of 5G and mobile penetration. These advancements enable faster and more efficient data processing, leading to increased demand for cloud-based AI solutions. However, the market also faces challenges from open-source platforms, which offer free alternatives to proprietary AI offerings. Companies must navigate this competitive landscape by focusing on providing value-added services and maintaining a strong competitive edge through innovation and differentiation. To capitalize on market opportunities, organizations should explore applications in sectors such as healthcare, finance, and manufacturing, where AI can drive operational efficiency, enhance customer experiences, and generate new revenue streams. Effective strategic planning and a strong focus on data security will be crucial for businesses seeking to succeed in this dynamic and evolving market.

What will be the Size of the Cloud Artificial Intelligence (AI) Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2018-2022 and forecasts 2024-2028 - in the full report.
Request Free SampleThe market continues to evolve, driven by advancements in machine learning (ML), computer vision, and natural language processing. Bias mitigation and responsible AI are increasingly prioritized, with knowledge graphs and explainable AI (XAI) playing crucial roles in ensuring transparency and trust. Agile development and AI ethics are integral to creating ethical and unbiased AI systems. ML models are being applied across various sectors, from fraud detection and sales forecasting to speech recognition and image recognition. Data security and privacy remain paramount, with cloud computing and edge computing solutions offering secure alternatives. Deep learning (DL) and reinforcement learning are advancing rapidly, enabling more sophisticated AI applications. Semantic reasoning and predictive analytics are transforming decision making, while AI-powered chatbots and virtual assistants enhance customer service. Data labeling and model training are essential components of AI development, with API integration streamlining deployment and model training. Risk management and predictive analytics are critical for businesses seeking to mitigate potential threats and optimize operations. The ongoing unfolding of market activities reveals a dynamic landscape, with AI regulations and governance emerging as key considerations. Sentiment analysis and text analytics offer valuable insights into customer behavior and preferences. In the ever-evolving AI ecosystem, continuous innovation and adaptation are essential. The integration of various AI technologies and applications will shape the future of business and society.

How is this Cloud Artificial Intelligence (AI) Industry segmented?

The cloud artificial intelligence (ai) industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments. ComponentSoftwareServicesGeographyNorth AmericaUSEuropeGermanyUKAPACChinaJapanRest of World (ROW)

By Component Insights

The software segment is estimated to witness significant growth during the forecast period.Artificial Intelligence (AI) software development is a significant area of innovation in the business world, with applications ranging from automating operations to personalizing service delivery and generating insights. AI technologies, such as machine learning (ML), deep learning (DL), computer vision, speech recognition, and natural language processing, are transforming industries. Responsible AI practices, including bias mitigation and explainable AI (XAI), are crucial for building trust and ensuring fairness in AI systems. Agile development methodologies facilitate the integration of AI capabilities into existing software. Data security and privacy are paramount in AI implementations. Cloud computing and edge computing provide flexible solutions for storing and processing sensitive data. AI regulations, such as those related to data privacy and security, are shaping the market. AI ethics are also a critical consideration, with transparency and accountability essential for building trust in AI systems. AI is revolutionizing various industries, from healthcare to finance and marketing. In healthcare, AI is used for predictive analytics, sales forecasting, and fraud detection, improving patient outcomes and operational efficiency. In finance, AI is used for risk management

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Artificial Intelligence In Marketing Size 2024-2028

The artificial intelligence in marketing size is forecast to increase by USD 41.02 billion, at a CAGR of 30.9% between 2023 and 2028.

The Artificial Intelligence (AI) market in marketing is experiencing significant growth, driven by the increasing adoption of cloud-based applications and services. This shift towards cloud solutions enables businesses to leverage AI technologies more efficiently and cost-effectively, enhancing their marketing capabilities. Furthermore, the ongoing digitalization and expanding internet penetration are fueling the demand for AI solutions in marketing, as companies seek to engage with customers more effectively in the digital space. However, the market's growth is not without challenges. The lack of skilled professionals poses a significant obstacle to wider AI adoption in marketing.
As AI applications become more complex, the need for specialized expertise in areas such as machine learning, data analytics, and programming grows. Companies must invest in upskilling their workforce or partner with external experts to overcome this challenge and fully capitalize on the opportunities presented by AI in marketing.

What will be the Size of the Artificial Intelligence In Marketing during the forecast period?

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Artificial intelligence (AI) continues to reshape marketing landscapes, with dynamic market activities unfolding across various sectors. Machine learning models optimize digital marketing strategies, enabling predictive analytics for marketing ROI and customer engagement. Brands build stronger connections through AI-powered personalization and sentiment analysis. Data privacy regulations necessitate transparency and accountability, influencing marketing technology stacks and Data Security measures. A/B testing and conversion rate optimization are enhanced through AI-driven insights, while marketing automation workflows streamline customer relationship management. Marketing analytics software and dashboards provide data-driven insights, enabling marketing budget allocation and multi-channel marketing strategies. Behavioral targeting and customer journey mapping are refined through AI, enhancing marketing attribution models and email marketing automation.

Virtual assistants and chatbots facilitate seamless customer experiences, while marketing automation platforms optimize search engine optimization, pay-per-click advertising, and social media advertising. Natural language processing and AI marketing consultants aid content marketing strategies, ensuring algorithmic bias and ethical AI considerations remain at the forefront. Marketing dynamics remain in a constant state of evolution, with AI-driven innovations continuing to transform the industry. Data Governance, marketing attribution models, and programmatic advertising are among the many areas where AI is making an impact. The ongoing integration of AI into marketing technologies and strategies ensures a continuously adaptive and effective marketing landscape.

How is this Artificial Intelligence Ining Industry segmented?

The artificial intelligence ining industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Deployment

  On-premises
  Cloud


Application

  Social Media Advertising
  Search Engine Marketing/ Search Advertising
  Virtual Assistant
  Content Curation
  Sales & Marketing Automation
  Analytics Platform
  Others


Technology

  Machine Learning
  Natural Language Processing
  Computer Vision
  Others


Geography

  North America

    US
    Canada


  Europe

    Germany
    UK


  APAC

    China
    Japan
    Australia
    India


  South America

    Brazil
    Argentina


  Middle East and Africa

    UAE


  Rest of World (ROW)

By Deployment Insights

The on-premises segment is estimated to witness significant growth during the forecast period.

Artificial Intelligence (AI) is revolutionizing marketing, with machine learning models at its core. Brands are building stronger connections with consumers through AI-driven personalization and predictive analytics. A/B testing and marketing analytics software enable data-driven insights, while conversion rate optimization and marketing automation workflows streamline campaigns. Data privacy regulations ensure transparency and accountability, shaping marketing strategies. Behavioral targeting and sentiment analysis provide deeper customer understanding, enhancing customer engagement. Predictive analytics and marketing ROI are key performance indicators, driving marketing budget allo

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AI Market In Media And Entertainment Industry Size 2024-2028

The ai market in media and entertainment industry size is forecast to increase by USD 30.73 billion, at a CAGR of 26.4% between 2023 and 2028.

The AI market in the media and entertainment industry is witnessing significant growth, driven by the increasing utilization of multimodal AI to enhance consumer experiences. This technology allows AI systems to process and analyze various forms of data, including text, images, and speech, enabling more personalized and engaging content. Another key trend is the adoption of blockchain technology to securely store and share data for AI model training. This ensures data privacy and security, addressing a major concern for media and entertainment companies.
However, the reliance on external sources of data for training AI models poses a challenge. Ensuring data accuracy, ownership, and ethical usage is crucial to mitigate potential risks and maintain consumer trust. Companies in this industry must navigate these dynamics to effectively capitalize on the opportunities presented by AI and provide innovative, personalized experiences for their audiences.

What will be the Size of the AI Market In Media And Entertainment Industry during the forecast period?

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The AI market in media and entertainment continues to evolve, with dynamic applications across various sectors. In game development, AI training datasets enhance player experiences through realistic non-playable characters and intelligent enemy behavior. Recommendation engines personalize content for streaming services, while cybersecurity measures protect against potential threats. AI-powered video editing streamlines production workflows, enabling real-time rendering and automated dubbing. Deep learning algorithms enable sentiment analysis, allowing content distributors to tailor recommendations based on viewer preferences. Machine learning models optimize programmatic advertising, ensuring targeted delivery to specific audiences. Data analytics and licensing agreements facilitate revenue generation in animation studios, while bias detection ensures ethical AI usage.

Interactive advertising engages viewers through object detection and metadata tagging, enhancing user experience. Project management software streamlines workflows, from pre-production to post-production. Natural language processing and CGI rendering bring AI-powered content creation tools to life, while cloud rendering and monetization strategies enable scalability and profitability. AI ethics, explainable AI, and facial recognition are crucial considerations in this rapidly evolving landscape. Virtual production and AI-powered post-production workflows revolutionize television production, while social media platforms leverage AI for content moderation and personalized content delivery. Big data processing and model interpretability enable more efficient and effective AI implementation. In the ever-changing media and entertainment industry, AI continues to unfold new patterns and applications, driving innovation and growth.

How is this AI In Media And Entertainment Industry Industry segmented?

The ai in media and entertainment industry industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Technology

  Machine learning
  Computer vision
  Speech recognition


End-user

  Media companies
  Gaming industry
  Advertising agencies
  Film production houses


Offering

  Software
  Services


Application

  Media
  Entertainment


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  Middle East and Africa

    Egypt
    KSA
    Oman
    UAE


  APAC

    China
    India
    Japan


  South America

    Argentina
    Brazil


  Rest of World (ROW)

By Technology Insights

The machine learning segment is estimated to witness significant growth during the forecast period.

The media and entertainment industry has been significantly transformed by the integration of artificial intelligence (AI) technologies. Machine learning (ML), in particular, has been instrumental in enhancing video data management and analytics. For instance, Wasabi Technologies' latest object storage solutions employ AI and ML capabilities for automated tagging and metadata indexing of video content. These advancements enable seamless storage of video content in S3-compatible object storage systems, improving content accessibility and searchability. AI is also revolutionizing game development with the use of deep learning algorithms for creating more

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US Deep Learning Market Size 2024-2028

The US deep learning market size is forecast to increase by USD 3.55 billion at a CAGR of 27.17% between 2023 and 2028. The market is experiencing significant growth due to several key drivers. Firstly, the increasing demand for industry-specific solutions is fueling market expansion. Additionally, the high data requirements for deep learning applications are leading to increased data generation and collection. Cloud analytics is another significant trend, as companies seek to leverage cloud computing for cost savings and scalability. However, challenges persist, including the escalating cyberattack rate and the need for strong customer data security. Education institutes are also investing in deep learning research and development to prepare the workforce for the future. Overall, the market is poised for continued growth, driven by these factors and the potential for innovation and advancement in various sectors.

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Deep learning, a subset of artificial intelligence (AI), is a machine learning technique that uses neural networks to model and solve complex problems. This technology is gaining significant traction in various industries across the US, driven by the availability of large datasets and advancements in cloud-based technology. One of the primary areas where deep learning is making a mark is in data centers. Deep learning algorithms are being used to analyze vast amounts of data, enabling businesses to gain valuable insights and make informed decisions. Cloud-based technology is facilitating the deployment of deep learning models at scale, making it an attractive solution for businesses looking to leverage their data.

Furthermore, the market is rapidly evolving, driven by innovations in cloud-based technology, neural networks, and big-data analytics. The integration of machine vision technology and image and visual recognition has driven advancements in industries such as self driving vehicles, digital marketing, and virtual assistance. Companies are leveraging generative adversarial networks (GANs) for cutting-edge news accumulation and content generation. Additionally, machine vision is transforming sectors like retail and manufacturing by enhancing automation and human behavior analysis. With the use of human brain cells generated information, researchers are pushing the boundaries of artificial intelligence. The growing importance of photos and visual data in decision-making further accelerates the market, highlighting the potential of deep learning technologies.

Market Segmentation

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Application

  Image recognition
  Voice recognition
  Video surveillance and diagnostics
  Data mining


Type

  Software
  Services
  Hardware


End-user

  Security
  Automotive
  Healthcare
  Retail and commerce
  Others


Geography

  US

By Application Insights

The Image recognition segment is estimated to witness significant growth during the forecast period. Deep learning, a subset of artificial intelligence (AI), is revolutionizing various industries in the US through its ability to analyze and interpret complex data. One of its key applications is image recognition, which utilizes neural networks and graphics processing units (GPUs) to identify objects or patterns within images and videos. This technology is increasingly being adopted in data centers and cloud-based solutions for applications such as visual search, product recommendations, and inventory management. In the automotive sector, image recognition is integral to advanced driver assistance systems (ADAS) and autonomous vehicles, enabling the identification of pedestrians, other vehicles, road signs, and lane markings.

Additionally, image recognition is essential for cybersecurity applications, industrial automation, Internet of Things (IoT) devices, and robots, enhancing their functionality and efficiency. Image recognition is transforming industries by providing accurate and real-time insights from visual data, ultimately improving user experience and productivity.

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The Image recognition segment was valued at USD 265.10 billion in 2017 and showed a gradual increase during the forecast period.

Our market researchers analyzed the data with 2023 as the base year, along with the key drivers, trends, and challenges. A holistic analysis of drivers will help companies refine their marketing strategies to gain a competitive advantage.

Market Driver

Industry-specific solutions is the key driver of the market. Deep learning has become a pivotal technology in addressing classification tasks across numerous industrie

The Artificial Intelligence (AI) in Sports market is experiencing explosive growth, projected to reach $5.93 billion in 2025 and maintain a Compound Annual Growth Rate (CAGR) exceeding 28.69% from 2025 to 2033. This rapid expansion is fueled by several key drivers. Firstly, the increasing availability of high-volume, high-velocity data from various sources, including wearable sensors, video analytics, and social media, provides rich fodder for AI algorithms. Secondly, advancements in AI technologies, particularly in machine learning and deep learning, enable more accurate predictions, personalized fan experiences, and optimized player performance analysis. Thirdly, the growing adoption of cloud-based solutions facilitates scalability, accessibility, and cost-effectiveness for sports organizations of all sizes. Finally, the competitive landscape is pushing teams and leagues to leverage AI for a decisive edge, driving further market expansion. The market segmentation reveals significant opportunities across applications. Player analysis, encompassing injury prediction, performance optimization, and talent scouting, represents a substantial portion of the market. Fan engagement, leveraging AI-powered personalization and interactive experiences, is another rapidly growing segment. Data interpretation and analysis tools are crucial for extracting valuable insights from the vast amounts of data generated in modern sports. While on-premises deployment continues to be relevant, cloud-based solutions are gaining significant traction due to their flexibility and scalability. Key players like SAS Institute, Opta Sports, Sportsradar, Catapult Group, IBM, SAP, and Salesforce (Tableau) are actively shaping the market through innovative solutions and strategic partnerships. Geographical growth will likely see strong performance in North America and Europe initially, followed by increasing adoption in the Asia-Pacific region as technological infrastructure and digital adoption mature. The continued refinement of AI algorithms, coupled with increasing investment in sports technology, positions the AI in sports market for sustained and substantial future growth. Recent developments include: May 2023 - Sportradar announces to Invests in AI Technology for Sports Betting Industry, as By replacing human data collectors with digital systems, the company is able to provide deeper insights into sports and create new value-creating products for its clients, October 2022 - Epoxy.ai announced the launch of Audience Cloud, the gaming market's first sports media and betting-specific insights platform. This solution is designed to utilize purpose-built AI in order to provide an ongoing view of sports fan behavior, enabling enhanced sports gaming and media analysis., January 2022 - The University of the Pacific unveiled a new partnership with data integration and visualization pioneer SMT, as well as a new concentration aimed at preparing students for employment in the field of sports analytics. Through this program, students will gain the skills and knowledge necessary to succeed in the dynamic and ever-changing world of sports data analysis.. Key drivers for this market are: Growing Demand for Real Time Data Analytics, Demand for Predictive Insights To Enhance Fan Engagement; Increasing Engagement of Fans in Sports. Potential restraints include: Growing Demand for Real Time Data Analytics, Demand for Predictive Insights To Enhance Fan Engagement; Increasing Engagement of Fans in Sports. Notable trends are: Increasing Engagement of Fans in Sports is Expected to Drive the Market.

During a 2022 survey conducted in the United States, it was found that 26 percent of respondents thought that artificial intelligence will not make their lives either easier or harder. Moreover, 31 percent of respondents aged between 30 and 44 years stated that AI will make their lives much easier.

Artificial intelligence

Artificial intelligence (AI) is the ability of a computer or machine to mimic the competencies of the human mind, learning from previous experiences to understand and respond to language, decisions, and problems. Particularly, a large amount of data is often used to train AI into developing algorithms and skills. The AI ecosystem consists of machine learning (ML), robotics, artificial neural networks, and natural language processing (NLP). Nowadays, tech and telecom, financial services, healthcare, and pharmaceutical industries are prominent for AI adoption in companies.

AI companies and startups

More and more companies and startups are engaging in the artificial intelligence market, which is forecast to grow rapidly in the coming years. Examples of big tech firms are IBM, Microsoft, Baidu, and Tencent, with the last owning the highest number of AI and ML patent families, amounting to over nine thousand. Moreover, driven by the excitement for this new technology and by the large investments in it, the number of startups involved in the industry around the world has grown in recent years. For instance, in the United States, the New York company UiPath was the top-funded AI startup.

The European AI in defense market, valued at approximately €1.5 billion in 2025, is projected to experience robust growth, driven by escalating geopolitical tensions and the increasing need for advanced military capabilities. A Compound Annual Growth Rate (CAGR) of 8% from 2025 to 2033 indicates a significant expansion, reaching an estimated market size of over €3 billion by 2033. Key drivers include the adoption of AI-powered systems for enhanced situational awareness, improved decision-making, and autonomous weapon systems. The market is segmented across hardware, software, and platforms (land, air, and naval), with applications spanning cybersecurity, battlefield healthcare, and warfare platform optimization. Leading companies like BAE Systems, Lockheed Martin, and Thales are heavily investing in R&D, fostering innovation and competition. The UK, Germany, and France are expected to be the largest national markets within Europe, due to their robust defense budgets and advanced technological infrastructure. However, challenges remain, including data privacy concerns, ethical implications of autonomous weapons, and the need for robust cybersecurity measures to protect AI systems from adversarial attacks. The substantial growth is fueled by several trends. Governments across Europe are prioritizing the modernization of their defense forces, incorporating AI technologies to gain a competitive advantage. The increasing availability of large datasets suitable for AI training, coupled with advancements in deep learning and machine learning algorithms, are further accelerating market expansion. Furthermore, the rising demand for AI-powered solutions for predictive maintenance, logistics optimization, and intelligence gathering is significantly impacting market growth. Despite these positive factors, restraints include the high cost of AI development and implementation, along with concerns about potential job displacement due to automation in the defense sector. Nevertheless, the long-term outlook for the European AI in defense market remains overwhelmingly positive, with significant opportunities for growth and innovation. Recent developments include: Jul 2022: The Defense Ministry of France announced that it had authorized the go-ahead for the final phase of new artificial intelligence and big data processing capability, which is being developed by the company Athea, a joint venture between Thales and Atos. The main aim of such a project will be to provide France with secure and sovereign artificial intelligence and big data platforms that can analyze massive data generated by military equipment as well as other sensors., May 2022: Palantir Technologies Inc., a data analytics company, announced that it successfully won a USD 12.5 million contract with the Defense Ministry of the United Kingdom. Plantir Technologies Inc., under the contract, will be providing support for its Foundry platform, which will enable the users to cut down on their costs by work automation and reduction in the time required to process the data.. Notable trends are: Increasing Investments In Artificial Intelligence Will Drive The Market During The Forecast Period.

The global Big Data Analytics in Defense market is experiencing robust growth, projected to maintain a Compound Annual Growth Rate (CAGR) exceeding 13% from 2025 to 2033. This expansion is fueled by several key factors. The increasing reliance on advanced technologies for enhanced situational awareness and improved decision-making within military operations is a primary driver. The need to analyze vast quantities of data from diverse sources, including sensor networks, satellite imagery, and social media, is pushing the adoption of sophisticated big data analytics solutions. Furthermore, the growing demand for predictive intelligence and improved cybersecurity within defense organizations is further accelerating market growth. Technological advancements in artificial intelligence (AI), machine learning (ML), and cloud computing are continuously enhancing the capabilities of big data analytics platforms, making them more efficient and effective. Segmentation reveals a strong demand across all platforms (Army, Navy, Air Force), with hardware, software, and services all contributing significantly to the overall market value. While the market faces some restraints, such as data security concerns and the high cost of implementation, these are being mitigated by ongoing innovation and government investment in defense modernization initiatives. The North American market currently holds a substantial share, driven by significant defense spending and the presence of major technology players. However, the Asia-Pacific region is poised for rapid expansion due to increasing military modernization efforts in countries like China and India. The competitive landscape is dominated by established defense contractors and technology giants, indicating a robust ecosystem fostering further innovation and market penetration. The market's trajectory suggests continued high growth over the forecast period, driven by the increasing strategic importance of big data analytics in national security and defense operations. The market's future is characterized by a strong focus on developing AI-powered analytics solutions for real-time threat detection, predictive maintenance of defense equipment, and optimized resource allocation. Furthermore, the integration of big data analytics with other emerging technologies, such as the Internet of Things (IoT) and blockchain, will further expand its capabilities and applications. The increasing emphasis on cybersecurity and data privacy is likely to drive demand for robust and secure data analytics solutions. Collaborative partnerships between defense organizations and technology providers are crucial for developing and deploying effective big data analytics solutions. Government initiatives to encourage innovation and investment in the defense technology sector will play a significant role in shaping the market's future trajectory. The continued growth in defense budgets globally will further support the market's expansion, making it a highly attractive investment opportunity for both established players and emerging technology companies. Recent developments include: September 2022: The United States Air Force signed a contract worth USD 1.25 million with ZeroEyto procure an AI gun detection solution for the service's unmanned aerial vehicles (UAVs) at the Dover Air Force Base, Delaware. ZeroEyes' technology will enable drones to detect handheld weapons for base protection., July 2022: The Indian Ministry of Defense launched 75 newly developed artificial intelligence (AI) products and technologies during the first-ever 'AI in Defense symposium and exhibition in New Delhi. These include autonomous systems, AI platform automation, command, control, communication, computer (C4), blockchain-based automation, intelligence, surveillance & reconnaissance (ISR), intelligent monitoring systems, cyber security, and others.. Notable trends are: Software Segment Will Showcase Remarkable Growth During the Forecast Period.

The Artificial Intelligence (AI) in Sports market is experiencing explosive growth, projected to reach $5.93 billion in 2025 and maintain a Compound Annual Growth Rate (CAGR) exceeding 28.69% from 2025 to 2033. This surge is driven by several key factors. Firstly, the increasing availability and affordability of advanced AI technologies, including machine learning and deep learning algorithms, are lowering the barrier to entry for sports teams, leagues, and organizations. Secondly, the demand for enhanced performance analysis is fueling adoption. AI-powered tools provide granular insights into player performance, enabling data-driven strategies for training, recruitment, and injury prevention. This extends beyond player analysis to encompass fan engagement, with personalized experiences and predictive analytics driving improved marketing and revenue generation. Finally, the growing volume of sports data from various sources, including wearable sensors, video analysis, and social media, provides the fuel for sophisticated AI models to extract valuable information previously unavailable. The market is segmented by application (player analysis, fan engagement, data interpretation & analysis, and other applications) and deployment (on-premises and cloud), reflecting the diverse ways AI is integrated into the sports ecosystem. Major players like SAS Institute, Salesforce (Tableau), Catapult Group International, Trumedia Networks, IBM, Sportsradar, Opta Sports, and SAP are actively shaping this dynamic market. The rapid expansion is further fueled by the increasing sophistication of AI algorithms and their ability to handle complex data sets. While the on-premises deployment model still holds relevance, the cloud-based solutions are gaining significant traction owing to their scalability, cost-effectiveness, and accessibility. The North American market currently holds a significant share, driven by early adoption and technological advancements, but the Asia-Pacific region is expected to experience the fastest growth in the coming years, driven by increasing digitalization and a burgeoning sports industry. While some restraints, such as data privacy concerns and the need for skilled professionals to manage and interpret AI-generated insights exist, these are being addressed through regulatory frameworks and upskilling initiatives, ensuring continued market expansion. The long-term outlook remains overwhelmingly positive, signaling a future where AI becomes integral to every facet of the sports industry. This comprehensive report delves into the burgeoning market of Artificial Intelligence (AI) in Sports, projecting a significant expansion from $XXX million in 2025 to $XXX million by 2033. Analyzing the historical period (2019-2024), base year (2025), and forecast period (2025-2033), this report offers invaluable insights for stakeholders across the sports ecosystem. It explores the key applications of AI, including player analysis, fan engagement, and data interpretation, and examines the impact of various deployment models (on-premises and cloud) across different segments. Note: Replace "XXX" with actual market value figures from your research. Recent developments include: May 2023 - Sportradar announces to Invests in AI Technology for Sports Betting Industry, as By replacing human data collectors with digital systems, the company is able to provide deeper insights into sports and create new value-creating products for its clients, October 2022 - Epoxy.ai announced the launch of Audience Cloud, the gaming market's first sports media and betting-specific insights platform. This solution is designed to utilize purpose-built AI in order to provide an ongoing view of sports fan behavior, enabling enhanced sports gaming and media analysis., January 2022 - The University of the Pacific unveiled a new partnership with data integration and visualization pioneer SMT, as well as a new concentration aimed at preparing students for employment in the field of sports analytics. Through this program, students will gain the skills and knowledge necessary to succeed in the dynamic and ever-changing world of sports data analysis.. Key drivers for this market are: Growing Demand for Real Time Data Analytics, Demand for Predictive Insights To Enhance Fan Engagement; Increasing Engagement of Fans in Sports. Potential restraints include: High Initial Investments Required to Implement Complete Solutions. Notable trends are: Increasing Engagement of Fans in Sports is Expected to Drive the Market.

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Artificial Intelligence (AI) Market In Retail Sector Size 2024-2028

The artificial intelligence (ai) market in retail sector size is forecast to increase by USD 42.22 billion, at a CAGR of 42% between 2023 and 2028.

The Artificial Intelligence (AI) market in retail is experiencing significant growth, fueled by escalating investments and research and development in AI startups. This trend is driven by the increasing adoption of AI technologies in various retail applications, particularly in e-commerce, where AI is being used for personalized product recommendations, fraud detection, and customer service. However, the deployment of AI in retail comes with challenges. One of the most pressing issues is privacy concerns. Retailers must address these challenges by implementing robust data security measures and transparent communication with customers regarding the collection and use of their data. Effective management of these challenges will enable retailers to capitalize on the vast opportunities presented by AI, enhancing customer experiences, improving operational efficiency, and driving innovation in the retail sector.

What will be the Size of the Artificial Intelligence (AI) Market In Retail Sector during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2018-2022 and forecasts 2024-2028 - in the full report.
Request Free SampleThe retail sector continues to witness the integration of artificial intelligence (AI) technologies, revolutionizing various aspects of business operations. From promotion optimization to customer service automation, AI applications span across numerous retail functions. Image recognition and machine learning algorithms enhance operational efficiency by automating tasks such as inventory management and data mining. Sales forecasting and demand prediction are further advanced through AI-powered recommendations and real-time analytics. Facial recognition and customer segmentation enable personalized shopping experiences, while virtual assistants and recommendation systems streamline the customer journey. AI's role extends to supply chain management, cost reduction, and targeted advertising through retail analytics and predictive analytics. Moreover, AI's integration into omni-channel retail enhances conversion rates, customer satisfaction, and loyalty programs. Automated checkout and process automation contribute to efficiency gains, while deep learning and marketing automation optimize pricing and UX. Data security and decision support systems ensure data-driven insights for business intelligence and sentiment analysis. Fraud detection and predictive modeling further strengthen retail operations, with smart shelves and business intelligence systems providing valuable insights for retailers. AI's continuous evolution in the retail sector is transforming the industry, offering endless opportunities for innovation and growth.

How is this Artificial Intelligence (AI) In Retail Sector Industry segmented?

The artificial intelligence (ai) in retail sector industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments. ApplicationSales and marketingIn-storePPPLogistics managementGeographyNorth AmericaUSCanadaEuropeUKAPACChinaJapanRest of World (ROW)

By Application Insights

The sales and marketing segment is estimated to witness significant growth during the forecast period.In the retail sector, artificial intelligence (AI) is revolutionizing sales and marketing functions. Customer Relationship Management (CRM) strategies are enhanced through AI, allowing businesses to understand customer interaction histories and tailor sales efforts accordingly. Operational efficiency is a priority, with AI-based chatbots and virtual assistants driving customer engagement and freeing up human resources. Machine learning algorithms, image recognition, and predictive analytics are key technologies, powering personalized shopping experiences, targeted advertising, and real-time inventory management. Cloud computing enables seamless data access for AI applications, from demand forecasting to sentiment analysis and fraud detection. AI-powered recommendation systems and supply chain management optimize sales conversion and reduce costs. Businesses are embracing omni-channel retail, integrating AI into various touchpoints, from mobile commerce to in-store analytics. Deep learning and computer vision technologies further enhance the customer experience, with applications in price optimization, shelf optimization, and predictive modeling. Data security and decision support systems are essential considerations, ensuring customer satisfaction and maintaining business intelligence. AI's role in retail is expanding, with applicati

Artificial intelligence (AI) is a technology that enables products to be combined with new features and create innovative customer experiences . A lot of businesses have embraced various AI tools to offer customer care interactions. Research gaps arise from an unclear picture of how customers' experience with online shopping will be affected by the experience and usage of AI tools. This study aims to predict satisfied online shoppers based on their usage experience with AI tools, by leveraging data mining methods and machine learning techniques. Data was collected from India, China, and Canada in 2021 and 2022 by distributing online survey to online shoppers with exposure to AI tools. Five machine learning algorithms; decision tree, random forest, naïve bayes, gradient boosted tree and multilayer perceptron neural network techniques were applied and compared to predict satisfied shoppers using. Overall, all the models showed a prediction accuracy of more than 86.5% f-score value and random forest outperformed with 91.5% f-score value. The findings demonstrated that the online retail business can identify satisfied customers with 91.5% accuracy using machine learning. Business can derive such data-driven actionable knowledge from integrating machine learning into their operations, resulting in a more satisfied customer base and a more efficient and competitive business model.