The market for artificial intelligence grew beyond 184 billion U.S. dollars in 2024, a considerable jump of nearly 50 billion compared to 2023. This staggering growth is expected to continue with the market racing past 826 billion U.S. dollars in 2030. AI demands data Data management remains the most difficult task of AI-related infrastructure. This challenge takes many forms for AI companies. Some require more specific data, while others have difficulty maintaining and organizing the data their enterprise already possesses. Large international bodies like the EU, the US, and China all have limitations on how much data can be stored outside their borders. Together these bodies pose significant challenges to data-hungry AI companies. AI could boost productivity growth Both in productivity and labor changes, the U.S. is likely to be heavily impacted by the adoption of AI. This impact need not be purely negative. Labor rotation, if handled correctly, can swiftly move workers to more productive and value-added industries rather than simple manual labor ones. In turn, these industry shifts will lead to a more productive economy. Indeed, AI could boost U.S. labor productivity growth over a 10-year period. This, of course, depends on a variety of factors, such as how powerful the next generation of AI is, the difficulty of tasks it will be able to perform, and the number of workers displaced.

Graph Analytics Market size was valued at USD 77.1 Million in 2023 and is projected to reach USD 637.1 Million by 2030, growing at a CAGR of 35.1% during the forecast period 2024-2030.

Global Graph Analytics Market Drivers
The market drivers for the Graph Analytics Market can be influenced by various factors. These may include:

Growing Need for Data Analysis: In order to extract insightful information from the massive amounts of data generated by social media, IoT devices, and corporate transactions, there is a growing need for sophisticated analytics tools like graph analytics.

Growing Uptake of Big Data Tools: Graph analytics solutions are becoming more and more popular due to the spread of big data platforms and technology. Businesses are using these technologies to improve the efficiency of their analysis of intricately linked datasets.

Developments in AI and ML: The capabilities of graph analytics solutions are being improved by advances in machine learning and artificial intelligence. These technologies make it possible for recommendation systems, anomaly detection, and forecasts based on graph data to be more accurate.

Increasing Recognition of the Advantages of Graph Databases: Businesses are realizing the advantages of graph databases for handling and evaluating highly related data. Consequently, there’s been a sharp increase in the use of graph analytics tools to leverage the potential of graph databases for diverse applications.

The use of advanced analytics solutions, such as graph analytics, for fraud detection, cybersecurity, and risk management is becoming more and more important as a result of the increase in cyberthreats and fraudulent activity.

Demand for Personalized suggestions: Companies in a variety of sectors are using graph analytics to provide their clients with suggestions that are tailored specifically to them. Personalized recommendations increase consumer engagement and loyalty on social networking, e-commerce, and entertainment platforms.

Analysis of Networks and Social Media is Necessary: In order to comprehend relationships, influence patterns, and community structures, networks and social media data must be analyzed using graph analytics. The capacity to do this is very helpful for security agencies, sociologists, and marketers.

Government programs and Regulations: The need for graph analytics solutions is being driven by regulations pertaining to data security and privacy as well as government programs aimed at encouraging the adoption of data analytics. These tools are being purchased by organizations in order to guarantee compliance and reduce risks.

Emergence of Industry-specific Use Cases: Graph analytics is finding applications in a number of areas, such as healthcare, finance, retail, and transportation. These use cases include supply chain management, customer attrition prediction, and financial fraud detection in addition to patient care optimization.

Technological Developments in Graph Analytics Tools: As graph analytics tools, algorithms, and platforms continue to evolve, their capabilities and performance are being enhanced. Adoption is being fueled by this technological advancement across a variety of industries and use cases.

In 2021, the market for artificial intelligence (AI) in marketing was estimated at 15.84 billion U.S. dollars. The source projected that the value would increase to more than 107.5 billion by 2028.

What is AI and who uses it?

Artificial intelligence (AI) has become one of the most impactful digital innovations of the past few decades. The term refers to the ability of a computer or machine to mimic the competencies of the human mind, with the current ecosystem consisting of machine learning, robotics, artificial neural networks, and natural language processing. All of these features and algorithms are highly versatile and adaptable to the specific requirements of the user, explaining why they have become embedded into many different industries, ranging from telecommunications and financial services to healthcare and pharma. Overall, the global artificial intelligence market was valued at around 327 billion U.S. dollars in 2021.

AI at the marketing wheel

AI is deeply embedded into the digital marketing landscape, and based on the latest reports, more than 80 percent of industry experts integrate some form of AI technology into their online marketing activities. This vast adaptation of artificial intelligence for marketing purposes is no surprise considering that its benefits include task automation, campaign personalization, and data analysis, to name but a few. When asked about marketers' main application areas of AI in a recent survey, roughly 50 percent of respondents from the U.S., Canada, the UK, and India mentioned ad targeting. Other popular activities they trusted AI with included personalizing content, optimizing e-mail send times, and calculating conversion probability.

According to Cognitive Market Research, the global Graph Analytics market size will be USD 2522 million in 2024 and will expand at a compound annual growth rate (CAGR) of 34.0% from 2024 to 2031. Market Dynamics of Graph Analytics Market

Key Drivers for Graph Analytics Market

Increasing Recognition of the Advantages of Graph Databases- One of the main reasons for the Graph Analytics market is the increasing recognition of the advantages of graph databases. Unlike traditional relational databases, graph databases excel at handling complex relationships and interconnected data, making them ideal for use cases such as fraud detection, recommendation engines, and social network analysis. Businesses are leveraging these capabilities to uncover insights and patterns that were previously difficult to detect. The rise of big data and the need for real-time analytics are further driving the adoption of graph databases, as they offer enhanced performance and scalability for large-scale data sets. Additionally, advancements in artificial intelligence and machine learning are amplifying the value of graph databases, enabling more sophisticated data modeling and predictive analytics.
Growing Uptake of Big Data Tools to Drive the Graph Analytics Market's Expansion in the Years Ahead.

Key Restraints for Graph Analytics Market

Limited Awareness and Understanding pose a serious threat to the Graph Analytics industry.
The market also faces significant difficulties related to data security and privacy.

Introduction of the Graph Analytics Market

The Graph Analytics Market is rapidly expanding, driven by the growing need for advanced data analysis techniques in various sectors. Graph analytics leverages graph structures to represent and analyze relationships and dependencies, providing deeper insights than traditional data analysis methods. Key factors propelling this market include the rise of big data, the increasing adoption of artificial intelligence and machine learning, and the demand for real-time data processing. Industries such as finance, healthcare, telecommunications, and retail are major contributors, utilizing graph analytics for fraud detection, personalized recommendations, network optimization, and more. Leading vendors are continually innovating to offer scalable, efficient solutions, incorporating advanced features like graph databases and visualization tools.

According to Cognitive Market Research, the global Neural Network market size will be USD 15214.20 million in 2024. It will expand at a compound annual growth rate (CAGR) of 27.20% from 2024 to 2031.

North America held the major market share for more than 40% of the global revenue with a market size of USD 6085.68 million in 2024 and will grow at a compound annual growth rate (CAGR) of 25.4% from 2024 to 2031.
Europe accounted for a market share of over 30% of the global revenue with a market size of USD 4564.26 million.
Asia Pacific held a market share of around 23% of the global revenue with a market size of USD 3499.27 million in 2024 and will grow at a compound annual growth rate (CAGR) of 29.2% from 2024 to 2031.
Latin America had a market share of more than 5% of the global revenue with a market size of USD 760.71 million in 2024 and will grow at a compound annual growth rate (CAGR) of 26.6% from 2024 to 2031.
Middle East and Africa had a market share of around 2% of the global revenue and was estimated at a market size of USD 304.28 million in 2024 and will grow at a compound annual growth rate (CAGR) of 26.9% from 2024 to 2031.
The Software category is the fastest growing segment of the Neural Network industry

Market Dynamics of Neural Network Market

Key Drivers for Neural Network Market

Rising Investments in AI Research and Development to Boost Market Growth

Rising investments in AI research and development are significantly driving the neural network market by accelerating advancements in technology and expanding applications. Increased funding from both public and private sectors fuels innovation, enabling the development of more sophisticated and efficient neural network models. This investment supports breakthroughs in areas such as deep learning, natural language processing, and computer vision. Enhanced research efforts lead to improved algorithms, reduced training times, and greater accuracy in neural networks. Additionally, increased R&D funding helps address current limitations, such as interpretability and scalability, further boosting market growth. As more resources are allocated to AI research, the capabilities and adoption of neural networks continue to expand, driving the overall market forward. For instance, Google AI has introduced GraphWorld, a tool designed to enhance performance benchmarking for graph neural networks (GNNs). This tool enables AI engineers and researchers to evaluate new GNN architectures using larger graph datasets, facilitating innovative approaches to testing and designing GNN architectures.

Growing Interest in Artificial Intelligence to Drive Market Growth

The growing interest in artificial intelligence (AI) is driving the neural network market as organizations across various sectors recognize the transformative potential of AI technologies. Neural networks, a core component of AI, offer powerful solutions for complex data analysis, pattern recognition, and decision-making. The increasing demand for AI-driven innovations in fields such as healthcare, finance, and autonomous systems fuels the need for advanced neural network applications. As businesses and governments invest in AI to gain competitive advantages, enhance efficiency, and create personalized experiences, the adoption of neural networks rises. This heightened focus on AI encourages continuous development and refinement of neural network technologies, contributing to market growth and expanding their applications in solving real-world challenges.

Restraint Factor for the Neural Network Market

High Computational Costs, will Limit Market Growth

High computational costs are a significant restraint on the neural network market due to the substantial resources required for training and deploying complex models. Neural networks, especially deep learning models, demand powerful hardware such as GPUs and TPUs, which incurs high expenses. The energy consumption associated with running these models also adds to operational costs. For many organizations, particularly startups and small enterprises, these costs can be prohibitive, limiting their ability to invest in advanced neural network technologies. Additionally, the need for specialized infrastructure and maintenance further escalates expenses. As a result, high computational costs can hinder the widespread adoption and development of neural networks, impacting the overall growth of the market.

Impact of ...

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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.

The global big data market is forecasted to grow to 103 billion U.S. dollars by 2027, more than double its expected market size in 2018. With a share of 45 percent, the software segment would become the large big data market segment by 2027.

What is Big data?

Big data is a term that refers to the kind of data sets that are too large or too complex for traditional data processing applications. It is defined as having one or some of the following characteristics: high volume, high velocity or high variety. Fast-growing mobile data traffic, cloud computing traffic, as well as the rapid development of technologies such as artificial intelligence (AI) and the Internet of Things (IoT) all contribute to the increasing volume and complexity of data sets.

Big data analytics

Advanced analytics tools, such as predictive analytics and data mining, help to extract value from the data and generate new business insights. The global big data and business analytics market was valued at 169 billion U.S. dollars in 2018 and is expected to grow to 274 billion U.S. dollars in 2022. As of November 2018, 45 percent of professionals in the market research industry reportedly used big data analytics as a research method.

As of March 3, 2025, Apple was the leading tech company by market capitalization globally at 3.62 trillion U.S. dollars. Nvidia ranked second at just over three trillion U.S. dollars, a figure that tumbled in January 2025 following the dramatic release and success of DeepSeek's AI model. The Chinese-made AI model jumped to the top of the Apple Store in late January, stunning investors, and sinking multiple tech stocks. Apple leads the pack Since its foundation in a Californian garage in 1976, Apple has expanded massively, becoming one of the most valuable companies in the world. The company started its origins in the PC industry with the Macintosh, but soon entered other segments of the consumer electronics market. Today, the iPhone is the most popular Apple product, although Mac, iPad, wearables, and services also contribute to its high revenues. Aiming at innovation, Apple invests every year in research and development, spanning a wide array of technologies from AI through to extended reality. Nvidia's immense growth With a focus that began with origins in gaming, Nvidia's business strategy has been transformed by demand from data centers that sit at the heart of the AI boom. The company's chips have been favored to support in the training and running of a range of large language models, most notably in the development of OpenAI's ChatGPT.

The tech industry had a rough start to 2024. Technology companies worldwide saw a significant reduction in their workforce in the first quarter of 2024, with over 57 thousand employees being laid off. By the second quarter, layoffs impacted more than 43 thousand tech employees. In the final quarter of the year around 12 thousand employees were laid off. Layoffs impacting all global tech giants Layoffs in the global market escalated dramatically in the first quarter of 2023, when the sector saw a staggering record high of 167.6 thousand employees losing their jobs. Major tech giants such as Google, Microsoft, Meta, and IBM all contributed to this figure during this quarter. Amazon, in particular, conducted the most rounds of layoffs with the highest number of employees laid off among global tech giants. Industries most affected include the consumer, hardware, food, and healthcare sectors. Notable companies that have laid off a significant number of staff include Flink, Booking.com, Uber, PayPal, LinkedIn, and Peloton, among others. Overhiring led the trend, but will AI keep it going? Layoffs in the technology sector started following an overhiring spree during the COVID-19 pandemic. Initially, companies expanded their workforce to meet increased demand for digital services during lockdowns. However, as lockdowns ended, economic uncertainties persisted and companies reevaluated their strategies, layoffs became inevitable, resulting in a record number of 263 thousand laid off employees in the global tech sector by trhe end of 2022. Moreover, it is still unclear how advancements in artificial intelligence (AI) will impact layoff trends in the tech sector. AI-driven automation can replace manual tasks leading to workforce redundancies. Whether through chatbots handling customer inquiries or predictive algorithms optimizing supply chains, the pursuit of efficiency and cost savings may result in more tech industry layoffs in the future.

In 2023, the number of data compromises in the United States stood at 3,205 cases. Meanwhile, over 353 million individuals were affected in the same year by data compromises, including data breaches, leakage, and exposure. While these are three different events, they have one thing in common. As a result of all three incidents, the sensitive data is accessed by an unauthorized threat actor. Industries most vulnerable to data breaches Some industry sectors usually see more significant cases of private data violations than others. This is determined by the type and volume of the personal information organizations of these sectors store. In 2022, healthcare, financial services, and manufacturing were the three industry sectors that recorded most data breaches. The number of healthcare data breaches in the United States has gradually increased within the past few years. In the financial sector, data compromises increased almost twice between 2020 and 2022, while manufacturing saw an increase of more than three times in data compromise incidents. Largest data exposures worldwide In 2020, an adult streaming website, CAM4, experienced a leakage of nearly 11 billion records. This, by far, is the most extensive reported data leakage. This case, though, is unique because cyber security researchers found the vulnerability before the cyber criminals. The second-largest data breach is the Yahoo data breach, dating back to 2013. The company first reported about one billion exposed records, then later, in 2017, came up with an updated number of leaked records, which was three billion. In March 2018, the third biggest data breach happened, involving India’s national identification database Aadhaar. As a result of this incident, over 1.1 billion records were exposed.