Artificial intelligence (AI) mobile apps registered a global consumer spend of 1.42 billion U.S. dollars in 2024. This represented an increase of 274 percent year-over-year, as consumers worldwide spent around 380 million U.S. dollars on mobile AI apps in 2023.

Between January 2023 and December 2024, the United States registered over 66 percent of global consumer spend on artificial intelligence (AI) graphic editing and generator apps. China ranked second, with a share of 6.3 percent of the total consumer spending on AI visual-generating apps. The United Kingdom followed, as users based in the region accounted for 5.6 percent of global spend on apps in the AI graphics category.

This dataset provides annual estimates developed by the U.S. Bureau of Economic Analysis on consumer spending in the State of Iowa beginning in 1998. Personal consumption expenditures (PCE) is the value of the goods and services purchased by, or on the behalf of, Iowa residents. PCE is reported in millions of current dollars. Also provided is per capita PCE which is reported in current dollars. The Census Bureau’s annual midyear (July 1) population estimates are used for per capita variables. Consumption category indicates the goods or services associated with personal consumption. All includes both goods and services. Goods include both durable goods and non durable goods. Durable goods include: motor vehicles and parts, furnishings and durable household equipment, recreational goods and vehicles, and other durable goods. Non durable goods include: food and beverages purchased for off-premises consumption, clothing and footwear, gasoline and other energy goods, and other non durable goods. Services include household consumption expenditures (for services) and final consumption expenditures of nonprofit institutions serving households (NPISHs). Household consumption expenditures include: housing and utilities, health care, transportation services, recreation services, food services and accommodations, financial services and insurance, and other services. NPISH is the gross output of nonprofit institutions less receipts from sales of goods and services by nonprofit institutions.

Envestnet®| Yodlee®'s Consumer Spending Data (Aggregate/Row) Panels consist of de-identified, near-real time (T+1) USA credit/debit/ACH transaction level data – offering a wide view of the consumer activity ecosystem. The underlying data is sourced from end users leveraging the aggregation portion of the Envestnet®| Yodlee®'s financial technology platform.

Envestnet | Yodlee Consumer Panels (Aggregate/Row) include data relating to millions of transactions, including ticket size and merchant location. The dataset includes de-identified credit/debit card and bank transactions (such as a payroll deposit, account transfer, or mortgage payment). Our coverage offers insights into areas such as consumer, TMT, energy, REITs, internet, utilities, ecommerce, MBS, CMBS, equities, credit, commodities, FX, and corporate activity. We apply rigorous data science practices to deliver key KPIs daily that are focused, relevant, and ready to put into production.

We offer free trials. Our team is available to provide support for loading, validation, sample scripts, or other services you may need to generate insights from our data.

Investors, corporate researchers, and corporates can use our data to answer some key business questions such as: - How much are consumers spending with specific merchants/brands and how is that changing over time? - Is the share of consumer spend at a specific merchant increasing or decreasing? - How are consumers reacting to new products or services launched by merchants? - For loyal customers, how is the share of spend changing over time? - What is the company’s market share in a region for similar customers? - Is the company’s loyal user base increasing or decreasing? - Is the lifetime customer value increasing or decreasing?

Use Cases Categories (Our data provides an innumerable amount of use cases, and we look forward to working with new ones): 1. Market Research: Company Analysis, Company Valuation, Competitive Intelligence, Competitor Analysis, Competitor Analytics, Competitor Insights, Customer Data Enrichment, Customer Data Insights, Customer Data Intelligence, Demand Forecasting, Ecommerce Intelligence, Employee Pay Strategy, Employment Analytics, Job Income Analysis, Job Market Pricing, Marketing, Marketing Data Enrichment, Marketing Intelligence, Marketing Strategy, Payment History Analytics, Price Analysis, Pricing Analytics, Retail, Retail Analytics, Retail Intelligence, Retail POS Data Analysis, and Salary Benchmarking

1. Investment Research: Financial Services, Hedge Funds, Investing, Mergers & Acquisitions (M&A), Stock Picking, Venture Capital (VC)

2. Consumer Analysis: Consumer Data Enrichment, Consumer Intelligence

3. Market Data: Analytics B2C Data Enrichment, Bank Data Enrichment, Behavioral Analytics, Benchmarking, Customer Insights, Customer Intelligence, Data Enhancement, Data Enrichment, Data Intelligence, Data Modeling, Ecommerce Analysis, Ecommerce Data Enrichment, Economic Analysis, Financial Data Enrichment, Financial Intelligence, Local Economic Forecasting, Location-based Analytics, Market Analysis, Market Analytics, Market Intelligence, Market Potential Analysis, Market Research, Market Share Analysis, Sales, Sales Data Enrichment, Sales Enablement, Sales Insights, Sales Intelligence, Spending Analytics, Stock Market Predictions, and Trend Analysis.

Additional Use Cases: - Use spending data to analyze sales/revenue broadly (sector-wide) or granular (company-specific). Historically, our tracked consumer spend has correlated above 85% with company-reported data from thousands of firms. Users can sort and filter by many metrics and KPIs, such as sales and transaction growth rates and online or offline transactions, as well as view customer behavior within a geographic market at a state or city level. - Reveal cohort consumer behavior to decipher long-term behavioral consumer spending shifts. Measure market share, wallet share, loyalty, consumer lifetime value, retention, demographics, and more.) - Study the effects of inflation rates via such metrics as increased total spend, ticket size, and number of transactions. - Seek out alpha-generating signals or manage your business strategically with essential, aggregated transaction and spending data analytics.

Between January 2023 and December 2024, the United States accounted for over 30 percent share of global consumer spend on artificial intelligence (AI) companion apps. India ranked second, as users based in the region generated more than 24 percent of global consumer spend on AI-supported companion apps. Brazil followed, with 12.4 percent of global spending on companion apps during the same examined period.

Consumer spending on non-digital and digital home entertainment rentals in the United States declined by approximately ** percent, dropping from **** billion U.S. dollars in 2023 to **** billion in 2024.

Between September and November 2024, global consumers spent approximately nine billion U.S. dollars on role playing and strategy apps. Additionally, global users spent over six billion U.S. dollars on arcade and action apps, while entertainment apps generated approximately 4.1 billion U.S. dollars. Social media and social networking apps generated around 1.8 billion U.S. dollars in consumer spending worldwide.

In 2023, Asia was the leading market for mobile apps, with users in the country generating approximately 90 billion U.S. dollars in app spending. North America ranked second, with users spending almost 50 billion U.S. dollars on apps during the last examined year. In 2023, global consumer spending on mobile apps amounted to 171 billion U.S. dollars, up from 167 billion U.S. dollars in 2022.

Consumer Spending data by product groups quantifies the expenditures of European consumers on certain groups of products. The ratio between disposable income, demographics and expenditures for the products and services are derived from Household Budget surveys from the National Statistical Offices. By using such representative surveys and the regional and local statistics about income data and demographics, Consumer Spending data is calculated. Consumer Spending data are available for the following product groups:

Food and non-alcoholic beverages Alcoholic beverages Tobacco Clothing Footwear Furniture and furnishings, carpets and other floor coverings Household textiles Household appliances Glassware, tableware and household utensils Tools and equipment for house and garden Routine household maintenance Medical products, appliances and equipment Consumer Electronics, photographic and IT equipment Durables for recreation and culture Toys and games, hobby, sport, garden, pets Recreational and cultural services Newspapers, books and stationery Catering Services Personal care Jewellery, clocks, watches and other personal effects

How Tariffs are Impacting the Economy

Tariffs significantly affect the economy by raising the costs of imported goods and services, which can lead to inflation and reduced consumer spending. In industries like AI-driven policy and governance agents, tariffs on hardware components such as servers, chips, and computing devices increase production costs. For businesses relying on international supply chains, these rising costs are often passed on to consumers, making AI solutions more expensive.

Additionally, tariffs disrupt global supply chains by making it harder to source materials efficiently, which can lead to delays in product development and slower adoption of new technologies. This slowdown could hinder the growth of AI applications in sectors like regulatory compliance and governance.

Furthermore, the uncertainty created by tariffs can make it difficult for businesses to plan for the future, affecting investments and long-term strategies. For AI-driven solutions, higher operational costs may impact the ability to offer cost-effective regulatory tools, reducing market accessibility.

➤ Discover how our research uncovers business opportunities @ https://market.us/report/ai-driven-policy-governance-agents-market/free-sample/

AI-Enhanced Product Trial Predictive Market Outlook

According to our latest research, the AI-Enhanced Product Trial Predictive market size reached USD 2.18 billion in 2024, demonstrating robust expansion and heightened adoption across multiple industries. The market is projected to grow at a CAGR of 21.4% from 2025 to 2033, reaching an estimated USD 15.4 billion by the end of the forecast period. This rapid growth is primarily fueled by the increased integration of artificial intelligence and predictive analytics into product trial solutions, enabling businesses to enhance customer experiences, optimize conversion rates, and drive data-driven decision-making.

Several key growth factors are propelling the AI-Enhanced Product Trial Predictive market forward. First and foremost, the surge in digital transformation initiatives has led organizations to seek advanced technologies that can personalize and optimize the product trial experience. AI-powered predictive systems enable retailers, e-commerce platforms, and consumer goods companies to tailor product recommendations and virtual try-ons, significantly improving customer engagement and satisfaction. Furthermore, the proliferation of connected devices and the Internet of Things (IoT) has resulted in a massive influx of consumer data, which AI models can analyze to predict trial outcomes, personalize experiences, and reduce return rates. This data-centric approach is increasingly seen as a competitive differentiator in crowded markets.

Another significant driver is the growing demand for seamless omnichannel experiences across industries such as retail, healthcare, and automotive. Consumers expect consistent and personalized product trial experiences whether they engage online, in-store, or through mobile applications. AI-enhanced predictive tools bridge this gap by leveraging real-time analytics and machine learning to deliver hyper-personalized recommendations and simulate product usage scenarios. This not only boosts conversion rates but also helps enterprises reduce operational costs associated with product returns and unsatisfactory trials. The ability to forecast customer behavior and preferences with high accuracy is becoming indispensable for businesses aiming to stay ahead in a rapidly evolving digital landscape.

Additionally, advancements in AI algorithms, natural language processing, and computer vision are enhancing the capabilities of product trial solutions. These technologies enable more sophisticated simulations, such as virtual fitting rooms, interactive product demos, and AI-driven consultation bots. With the ongoing improvements in data processing power and cloud infrastructure, even small and medium enterprises can now access scalable AI-enhanced product trial platforms. This democratization of technology is expanding the addressable market and fostering innovation across various sectors. Moreover, regulatory support for AI adoption and increased investment in research and development are further accelerating market growth.

From a regional perspective, North America currently leads the AI-Enhanced Product Trial Predictive market, accounting for the largest share due to the presence of major technology providers, high digital adoption rates, and strong investment in AI research. Europe follows closely, driven by robust e-commerce penetration and a focus on customer-centric innovation. The Asia Pacific region is experiencing the fastest growth, fueled by expanding retail and e-commerce sectors, rising consumer spending, and increasing adoption of AI technologies by enterprises of all sizes. As these trends continue, regional dynamics are expected to play a critical role in shaping the future landscape of this market.

Component Analysis

The Component segment of the AI-Enhanced Product Trial Predictive market is categorized into software, hardware, and services, each playing a pivotal role in the ecosystem. The software segment commands the largest share, dr

Spending on AI and Analytics in Retail Market Outlook

The global spending on AI and analytics in the retail market size is projected to grow from $7.3 billion in 2023 to $27.2 billion by 2032, registering a robust CAGR of 15.8% during the forecast period. The significant growth factor driving this market is the increasing need for retailers to leverage advanced technologies for enhancing customer experience, optimizing operations, and gaining a competitive edge.

One of the primary growth factors of this market is the increasing adoption of AI-driven customer experience management solutions. Retailers are increasingly utilizing AI and analytics to provide personalized shopping experiences, which in turn boosts customer satisfaction and loyalty. Advanced analytics enable businesses to gather and analyze vast amounts of customer data, providing insights into consumer preferences and behavior, thus allowing for the creation of tailored marketing campaigns and product recommendations.

Another critical driver is the optimization of inventory management through AI and analytics. Efficient inventory management is crucial for retail operations as it minimizes costs associated with overstocking and stockouts. AI solutions can forecast demand more accurately, helping retailers maintain optimal inventory levels. This not only reduces wastage and excess costs but also ensures that the right products are available at the right time, enhancing overall operational efficiency.

AI-powered sales and marketing strategies are also significantly contributing to the market growth. By leveraging AI and analytics, retailers can gain deeper insights into market trends, customer preferences, and sales patterns. These insights empower retailers to formulate effective marketing strategies, segment their customer base more precisely, and deliver personalized promotions that resonate with the target audience, thereby driving higher conversion rates and sales.

Retail Analytics plays a pivotal role in transforming the way retailers understand and engage with their customers. By leveraging data-driven insights, retailers can make informed decisions that enhance customer satisfaction and operational efficiency. Retail Analytics encompasses a wide range of applications, from tracking customer behavior and preferences to optimizing pricing strategies and inventory management. This technology empowers retailers to anticipate market trends, personalize marketing efforts, and ultimately drive growth in a competitive landscape. As the retail industry continues to evolve, the integration of Retail Analytics is becoming increasingly essential for businesses aiming to stay ahead of the curve and deliver exceptional value to their customers.

From a regional perspective, North America is anticipated to dominate the spending on AI and analytics in the retail market, attributed to the early adoption of advanced technologies and the strong presence of key market players. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period. The rapid digital transformation in retail sectors in countries like China and India, coupled with increasing investments in AI technologies, are major contributors to this growth. Additionally, the rising penetration of e-commerce and the growing middle-class population in these regions are driving the demand for advanced retail solutions.

Component Analysis

The AI and analytics market in retail can be segmented by components into software, hardware, and services. Software solutions are expected to hold the largest market share, driven by the increasing need for advanced analytics platforms and AI-driven applications. These software solutions enable retailers to analyze customer data, optimize supply chains, and improve decision-making processes. The integration of AI and machine learning algorithms into software platforms is further propelling their adoption.

Hardware components, although a smaller segment compared to software, play a crucial role in the implementation of AI and analytics solutions. This includes advanced sensors, IoT devices, and computing infrastructure necessary for data collection and processing. With the growing trend of smart retail environments, the demand for sophisticated hardware solutions is expected to rise. High-performance computing systems and edge devices are becoming essential for real-time data processing and analytics.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Probabilistic AI: A New Approach to Artificial Intelligence

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Between January 2014 and August 2024, China held the largest share of TikTok's lifetime consumer spending. In the 10 years since its launch, the Chinese market accounted for 46.5 percent of global TikTok consumer spending. The United States followed with consumer spending on TikTok ranking up to 21.6 percent in the examined period. Germany was the third largest market for the popular social video app, accounting for around 3.3 percent of TikTok app spending. The annual consumer spending on TikTok has increased to reach around four billion U.S. dollars in 2023.

Consumer spending on digital home entertainment in the United States amounted to over ** billion U.S. dollars in 2024. SVOD spending grew by around ** billion U.S. dollars between 2023 and 2024, reflecting the consistent and growing demand for content available on platforms using this model.

AI Powered Checkout Market Outlook

The global AI powered checkout market size was valued at approximately USD 2.5 billion in 2023 and is projected to reach USD 15.3 billion by 2032, growing at a robust CAGR of 22.3% during the forecast period. This impressive growth can be attributed to the increasing consumer demand for a seamless and efficient shopping experience, as well as the retail industry's continuous push towards technological advancements.

The growing adoption of AI powered checkout systems is driven by several key factors. First, the increasing labor costs and the need for operational efficiency have pushed retailers to adopt automated checkout solutions. These systems not only reduce the dependency on human labor but also increase the speed and accuracy of the checkout process, leading to enhanced customer satisfaction. Additionally, the COVID-19 pandemic has significantly accelerated the shift towards contactless payment solutions as consumers seek safer shopping experiences, further propelling the demand for AI powered checkout systems.

Another major growth factor is the technological advancements in artificial intelligence and machine learning. These technologies have enabled the development of sophisticated checkout systems that can accurately identify products, process transactions, and even provide personalized shopping recommendations. Retailers are increasingly investing in these advanced systems to stay competitive and meet the evolving expectations of tech-savvy consumers. Moreover, the integration of AI powered checkout systems with other retail technologies such as inventory management and customer relationship management (CRM) systems is creating a synergistic effect, driving further market growth.

The increasing penetration of e-commerce is also playing a pivotal role in the expansion of the AI powered checkout market. As more consumers shift to online shopping, e-commerce platforms are leveraging AI technologies to streamline the checkout process, reduce cart abandonment rates, and enhance the overall user experience. Furthermore, the rise of omnichannel retail strategies, which integrate online and offline shopping experiences, is creating new opportunities for AI powered checkout systems to bridge the gap between different sales channels.

The Self-checkout System is becoming an integral part of the AI powered checkout market, offering a transformative shopping experience by allowing customers to scan, bag, and pay for their purchases without the need for cashier assistance. This system is particularly appealing in the current retail environment where efficiency and speed are paramount. By reducing the need for human interaction, self-checkout systems not only enhance customer satisfaction but also significantly cut down on labor costs for retailers. The integration of AI in these systems further enhances their capabilities, enabling features such as product recognition and fraud detection. As consumers become more accustomed to technology-driven shopping experiences, the demand for self-checkout systems is expected to rise, making them a critical component of modern retail strategies.

Regionally, North America currently holds the largest share of the AI powered checkout market, followed by Europe and Asia Pacific. The dominance of North America can be attributed to the presence of major technology companies, a high level of consumer awareness, and significant investments in retail technology. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period, driven by the rapid expansion of the retail sector, increasing internet penetration, and growing consumer spending power in countries like China and India.

Component Analysis

The AI powered checkout market can be segmented by component into software, hardware, and services. The software segment is expected to dominate the market due to the critical role that AI technologies play in the functioning of automated checkout systems. Software solutions encompass a range of applications, including machine learning algorithms, computer vision, and natural language processing, which are essential for tasks such as product recognition, transaction processing, and customer interaction. The continuous advancements in AI technologies are leading to the development of more sophisticated and efficient software solutions, driving the growth of this segment.

Hardware components are also integra

The per capita consumer spending on education in Morocco was forecast to continuously increase between 2024 and 2029 by in total 20.3 U.S. dollars (+42.81 percent). After the seventh consecutive increasing year, the education-related per capita spending is estimated to reach 67.76 U.S. dollars and therefore a new peak in 2029. Consumer spending, in this case education-related spending per capita, refers to the domestic demand of private households and non-profit institutions serving households (NPISHs). Spending by corporations and the state is not included. The forecast has been adjusted for the expected impact of COVID-19.Consumer spending is the biggest component of the gross domestic product as computed on an expenditure basis in the context of national accounts. The other components in this approach are consumption expenditure of the state, gross domestic investment as well as the net exports of goods and services. Consumer spending is broken down according to the United Nations' Classification of Individual Consumption By Purpose (COICOP). The shown data adheres broadly to group tenth As not all countries and regions report data in a harmonized way, all data shown here has been processed by Statista to allow the greatest level of comparability possible. The underlying input data are usually household budget surveys conducted by government agencies that track spending of selected households over a given period.The data is shown in nominal terms which means that monetary data is valued at prices of the respective year and has not been adjusted for inflation. For future years the price level has been projected as well. The data has been converted from local currencies to US$ using the average exchange rate of the respective year. For forecast years, the exchange rate has been projected as well. The timelines therefore incorporate currency effects.Find more key insights for the per capita consumer spending on education in countries like Egypt and Tunisia.

Artificial Intelligence (AI) in Retail Market Outlook

According to our latest research, the global Artificial Intelligence (AI) in Retail market size reached USD 9.5 billion in 2024, with a robust CAGR of 23.1% projected from 2025 to 2033. This trajectory is expected to propel the market to a substantial USD 74.5 billion by 2033, reflecting the rapid adoption of AI-driven solutions across the retail sector. The primary growth factor fueling this expansion is the increasing demand for personalized shopping experiences, operational efficiency, and advanced data analytics within the retail ecosystem.

The growth of the AI in Retail market is being significantly driven by the evolving expectations of consumers for highly personalized and seamless shopping experiences. Retailers are leveraging AI-powered recommendation engines, chatbots, and virtual assistants to deliver tailored product suggestions, streamline customer service, and enhance engagement across both online and offline channels. The integration of AI into loyalty programs and targeted marketing campaigns is enabling retailers to derive actionable insights from vast troves of customer data, thereby boosting conversion rates and fostering brand loyalty. As digital transformation accelerates post-pandemic, retailers are increasingly investing in AI technologies to stay competitive and responsive to shifting consumer behaviors.

Another major growth driver is the operational efficiency and cost reduction enabled by AI-powered automation in retail. AI applications such as demand forecasting, automated inventory management, and intelligent supply chain solutions are minimizing stockouts, reducing excess inventory, and optimizing logistics. Machine learning algorithms are empowering retailers to predict trends, manage dynamic pricing, and streamline procurement processes. This not only enhances profitability but also supports sustainable business practices by reducing waste and improving resource utilization. Furthermore, AI-driven fraud detection and risk management systems are helping retailers safeguard against cyber threats, ensuring secure transactions and protecting consumer data.

The proliferation of omnichannel retailing and digital commerce is further catalyzing the adoption of AI in the retail sector. Retailers are harnessing AI to bridge the gap between physical and digital storefronts, offering unified customer experiences through integrated platforms. Computer vision technology is being deployed in stores for automated checkout, real-time inventory tracking, and customer behavior analysis. The rise of AI-powered virtual fitting rooms, augmented reality applications, and voice-activated shopping assistants is reshaping the in-store experience, driving foot traffic, and increasing sales conversion rates. With the expansion of e-commerce and the growing sophistication of AI tools, retailers are poised to unlock new revenue streams and gain a competitive edge in the market.

From a regional perspective, North America currently leads the AI in Retail market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The high adoption rate of advanced technologies, presence of major AI vendors, and strong e-commerce infrastructure contribute to North America's dominance. Meanwhile, Asia Pacific is witnessing the fastest growth, driven by rapid digitalization, rising consumer spending, and increasing investments in AI by retail giants in China, Japan, and India. Europe is also experiencing steady growth, propelled by stringent data privacy regulations and a focus on enhancing customer experience. The Middle East & Africa and Latin America are emerging markets, with growing opportunities for AI adoption as digital transformation initiatives gain momentum across the retail landscape.

Component Analysis

The AI in Retail market is segmented by component into Software, Hardware, and Services. The software segment holds the largest mar

The AI/ML in Media and Entertainment Market is poised for significant growth, with a market size of USD 19942.01 million in 2023, projected to reach USD 118356.79 million by 2033, exhibiting a CAGR of 31.9% during the forecast period (2023-2033). The increasing demand for personalized and immersive entertainment experiences, coupled with advancements in AI/ML algorithms, is driving market growth. These technologies enhance content creation, distribution, and monetization, enabling media and entertainment companies to cater to evolving consumer preferences. Key market trends include the rise of deepfake detection tools to combat misinformation, AI-powered virtual assistants for personalized content recommendations, and automation of content production and distribution. Additionally, cloud computing adoption is facilitating AI/ML deployment, reducing infrastructure costs and enabling scalability. The increasing presence of OTT platforms and streaming services is also driving demand for AI/ML in entertainment, enabling personalized content experiences and targeted advertising strategies. Regional markets are expected to witness varying growth rates, with North America and Europe dominating the market due to the presence of established technology hubs and early adoption of AI/ML in media and entertainment. Asia Pacific is expected to exhibit significant growth potential due to the rapid expansion of the entertainment industry and rising consumer spending on entertainment. Recent developments include: May 2022, Gravity R&D is a top provider of personalization technology that was founded on data science. Taboola, a leader in enabling recommendations for the open web and supporting users in finding material they may enjoy, announced it has entered into a definitive agreement to buy Gravity R&D., May 2022, The newest round of computer-optimized instances, Amazon Elastic Compute Cloud (Amazon EC2) C7g instances, are powered by Graviton3 processors made by Amazon Web Services.. Key drivers for this market are: . The increasing demand for personalized content, . The growth of streaming services; . Driver impact analysis. Potential restraints include: . The ethical implications of using AI, . Restraint impact analysis. Notable trends are: Growing number of wholesalers are adopting cloud-native software is expected to drive market growth..

See earnings predictions for hundreds of public companies, powered by Earnest AI solutions suite. Predict revenue surprises, track market share, and compare performance metrics for thousands of companies based on the anonymized aggregate credit and debit data of millions of US accounts. Vela data is sourced from a variety of US financial institutions with broad geographic and demographic representation, combined to create one of the most comprehensive and accurate views of the consumer economy. AI-powered earnings predictions available for over 450 tickers on this dataset through EarnestAI Reported Metric Predictions.