Published Date : 11 May 2023
The global artificial intelligence (AI) market value is expected at USD 164.99 billion in 2023 and is projected to touch around USD 1,591.03 billion by 2030, growing at a CAGR of 38.1% during the forecast period from 2022 to 2030.
The field of computer science known as artificial intelligence (AI), also referred to as machine intelligence, focuses on creating and managing tools that can make decisions and conduct transactions on behalf of people. Artificial intelligence applications in quantum computers and supercomputers are two examples of the intelligence criteria against which AI algorithms are now being tested. In the upcoming years, such developments in AI technology will likely help the sector grow.
The expansion of the worldwide artificial intelligence market is being driven by an increase in demand for intelligent systems to boost productivity and efficiency. Faster speech recognition and natural language processing are just two examples of how technological developments in the AI sector are helping the market for AI grow. Artificial intelligence (AI) solutions' high installation costs hampered the market's expansion. On the other hand, throughout the forecast period, it is anticipated that rising digital dependency and industry 4.0 trends will present lucrative prospects for advancing the AI market.
Regional Snapshot:
In 2021, the Artificial Intelligence Market will generate more than 40% of revenue from North America. The market domination can be attributed to supportive government initiatives encouraging AI adoption across several industries. For instance, the U.S. President unveiled the American AI Initiative as the nation's strategy to foster leadership in artificial intelligence in February 2019. Federal authorities developed criteria for creating and practically applying AI-based systems in several industrial sectors to build public confidence in them.
During the forecast period, the APAC AI market will likely expand at a CAGR of 8.6%. Artificial intelligence spending has dramatically expanded, which has been the main driver of market expansion.
Artificial Intelligence (AI) Market Report Scope:
| Report Coverage | Details |
| Market Revenue in 2023 | USD 164.99 Billion |
| Projected Forecast Revenue in 2030 | USD 1,591.03 Billion |
| Growth Rate from 2022 to 2030 | CAGR of 38.1% |
| Largest Market | North America |
| Base Year | 2022 |
| Forecast Period | 2022 to 2030 |
| Regions Covered | North America, Europe, Asia-Pacific, Latin America, and Middle East & Africa |
Market Dynamics:
Market Drivers:
Reducing fraud and harmful assaults is a critical factor in market expansion
According to the cybersecurity sector, the theft of sensitive organizational data and personal information has increased in recent years. Businesses want to use AI technologies to battle these risks better. Artificial intelligence technologies speed up response times by identifying dangers and patterns. Additionally, it aids in fending off threats like Distributed Denial of Service (DDoS) assaults.
For instance, a security flaw at the Indian food technology business Zomato led to the loss of 17 million consumers' data. Organizations need to safeguard user and internal data from these online security dangers. Big data is used in cyber security analytics to give a predictive analysis of the manner and timing of cyber attacks. Network performance may be examined by comparing data samples using predictive analytics algorithms.
The main issue preventing market expansion is the need for more AI expertise
Most businesses need more resources and knowledge to embrace AI properly. Deep learning and machine learning applications need access to big data sets, specialized infrastructure, and computing power. A skilled AI team committed to the task is required. Since there is intense rivalry among leading IT businesses to hire the best people, artificial intelligence specialists are difficult to come by.
Before using AI/ML algorithms at scale, businesses must consider the best use cases for their implementation. This necessitates substantial investments, making AI challenging and out of reach for most organizations. More expertise is needed to speed up the development of powerful artificial intelligence technology. So, this is one of the main issues that can prevent the market from expanding during the forecast period.
Complicated regulatory restrictions are preventing the use of AI
Legal, technical, and business requirements, along with government prohibitions, are typically the subject of compliance laws in industrial settings, impacting operations. Depending on the industry and market, compliance standards can cover a wide range of topics, including worker safety, environmental effects, public health and safety, and product safety. However, they can also specifically include controls for automation systems. Regulatory requirements, which usually include thorough validation and verification of modifications to industrial processes, may be incompatible with AI-based automation goals, which prioritize rapid process adaption through closed-loop input.
Industry challenges for AI training
The popularity of "deep learning" has been a significant focus of recent AI hype. Most of these findings are based on problems with supervised learning, where deep neural networks are formed with labelled training data. Even though gathering enough labelled training data to train machine learning models adequately can be challenging in any field, it can be particularly challenging in industrial settings since the most interesting "black swans" occurrences, such as part or product failures, are less common. As a result, the machine learning system's overall construction cost increases along with the complexity of the training process.
Challenges in data collection and storage
Industrial AI systems usually rely on data collected by sensors that try to digitally mirror the virtual environment, as opposed to "born digital" data obtained via online interaction logs, for example. Unfortunately, this method can produce datasets that are already naturally noisy. Potentially vast amounts of sensor data exist, and collecting and preserving this data for analysis is challenging. Furthermore, simulation is frequently used due to the high cost of generating training data under various conditions. High-fidelity simulations, commonly called "digital twins," can be beneficial but challenging to create, keep up with, and employ because of their high computing costs.
Recent Developments:
Major Key Players:
Market Segmentation:
By Offering
By Technology
By Deployment
By Organization Size
By Business Function
By End-Use
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