“Unlocking the Future: A Global Deep Dive into Machine Learning Statistics 2024”

Introduction

Machine Learning Statistics: Machine learning (ML) is an emerging discipline that is transforming the operations of businesses and sectors in 2024. This approach leverages data and algorithms to train computers for decision-making or forecasting, enhancing processes and developing more intelligent systems. From tailoring customer experiences to improving healthcare assessments and automating processes, ML is spearheading advancements worldwide. Statistics highlight the growing influence of ML, as numerous enterprises embrace it for revenue enhancement, efficiency improvements, and competitive edges.

The international market is rapidly increasing, bolstered by progress in artificial intelligence, data analytics, and computational capacity. As machine learning becomes increasingly woven into everyday activities, comprehending its trends and statistics allows both businesses and individuals to navigate the transformations it engenders and fully realize its capabilities.

Editor’s Choice

• According to Machine Learning Statistics, the global market for Machine Learning is projected to attain USD 79.29 billion in size by 2024.
• As of 2024, approximately 48% of global enterprises have incorporated machine learning into their functions.
• Meanwhile, around 80% of businesses report that investing in machine learning has resulted in higher revenue.
• The manufacturing sector claims the largest portion of the machine learning market at 18.88%, closely followed by the finance domain at 15.42%.
• The United States is expected to emerge as the foremost market in the industry, with a projected market size of USD 21.14 billion in 2024.
• OpenAI leads the funding race in the machine learning sector, having secured over USD 11 billion in investments.
• Roughly 57% of firms leverage machine learning to enhance customer experiences.
• Conversely, nearly 34% of businesses in the United States have adopted machine learning, while 42% are investigating its potential application.
• The global AI sector was valued at USD 538.13 billion in 2023 and is projected to expand to USD 2,575.16 billion by 2032.
• The worldwide Natural Language Processing (NLP) market is anticipated to grow from USD 29.71 billion in 2024 to USD 158.04 billion by 2032.

General Machine Learning Statistics

• The manufacturing industry constitutes approximately 18.88% of the overall machine learning market, leading among various sectors.
• About 46% of organizations claim they utilize machine learning for fraud detection.
• Moreover, only 22% of enterprises deploy machine learning to help mitigate customer churn.
• 73% of individuals prefer chatbots over human agents for straightforward question-and-answer assistance.
• The industry is predicted to surpass USD 500 billion by the conclusion of 2030, marking a 551% rise from 2024 and exceeding its 2020 value by 1,005%.
• Machine Learning Statistics indicate that the data processing segment represented the most significant share by application, accounting for roughly 39.2% by 2024.
• Additionally, in solution provision, segments together represented a share of 54.2%.
• For example, within vertical markets, the BFSI sector contributes the largest share at 38.3%.
• Marketing and sales are identified as the most lucrative applications of machine learning.
• Machine learning can enhance customer satisfaction by approximately 10%.
• Organizations primarily harness machine learning to curtail expenses and save costs, comprising 38% of the market.
• Nissan increased its conversion rates by 67% with a machine learning model.

Machine Learning Market Statistics

(Source: statista.com)

• Machine Learning Statistics indicate that the global Machine Learning market’s size is projected to reach USD 79.29 billion by 2024, up from USD 50.86 billion in 2023.
• The machine learning market is forecasted to grow at a compound annual growth rate (CAGR) of 36.08% from 2024 to 2030, reaching an estimated value of USD 503.40 billion by 2030.
• Moreover, the anticipated ML market sizes for the upcoming years include: 2025 (USD 113.10 billion), 2026 (USD 159.80 billion), 2027 (USD 221.70 billion), 2028 (USD 298.70 billion), and 2029 (USD 394 billion).

The following table illustrates the changes in Machine Learning market size:

Year	Growth Rate
2023	-20.61%
2024	55.92%
2025	42.65%
2026	41.27%
2027	38.73%
2028	34.74%
2029	31.91%
2030	27.76%

Machine Learning Revenue Statistics by Country

• The Machine Learning market is anticipated to grow to USD 21.14 billion in 2024.
• It is expected to rise at an annual growth rate of 36.07% from 2024 to 2030, reaching USD 134.20 billion by 2030.

According to Machine Learning Statistics, the analyses of the markets in the other top four countries for 2024 are presented in the table below:

Country	Market Size (USD)	CAGR (from 2024 to 2030)
China	15.15 billion	36.07%
Japan	3.52 billion	36.06%
Germany	3.39 billion	36.08%
India	2.81 billion	36.11%

• Additionally, across nations, machine learning market assessments are reported as follows: United Kingdom (USD 2.56 billion), France (USD 2.31 billion), Canada (USD 1.78 billion), Australia (USD 1.41 billion), Italy (USD 1.67 billion), and South Korea (USD 1.39 billion).

By Region

(Source: aiprm.com)

• Machine Learning Data for 2024 indicates that the continent’s machine learning sector will surpass 29 billion, representing a 20% increase over others.

Region	Revenue (USD)	CAGR (2024 to 2029)
Asia	29.07 billion	36.08%
Americas	27.08 billion	36.07%
Europe	20.03 billion	36.08%
Africa	1.49 billion	36.10%
Australia & Oceania	1.61 billion	36.14%
Caribbean	94.54 million	36.08%

Machine Learning Statistics by Industry

(Reference: aiprm.com)

• The manufacturing sector retains the most significant portion in machine learning at 18.88%.
• The finance sector follows closely, holding a 15.42% percentage in the marketplace.
• Additionally, other sectors’ ML market shares include healthcare (12.23%), transportation (10.63%), security (10.10%), business and legal services (9.86%), energy (5.58%), media and entertainment (5.19%), retail (4.67%), semiconductor (1.6%), and others (5.83%).

By Company’s Usage

(Reference: founderjar.com)

• Machine Learning Data In 2024 indicates that approximately 33% of firms utilized machine learning for enhanced business analytics.
• In addition, one in four IT executives (25%) believe that machine learning can mitigate security threats within their organizations.
• Conversely, usage distributions include sales and marketing (16%), customer service (10%), and others (16%).

Machine Learning Investment Statistics by Platforms

• Machine Learning Data also indicated that OpenAI, the developer of ChatGPT, stands as the foremost machine learning platform, acquiring around USD 11.3 billion in funding by 2024.
• ScaleAI secured USD 602.6 million, which is 45% higher than its competitors, rendering it the second-largest funded entity globally.
• Adept and Cohere AI each received investments exceeding USD 400 million, with Adept garnering USD 415 million and Cohere AI following closely at USD 414.9 million.

Further funding statistics from other platforms in the Machine Learning domain are illustrated in the table below:

Platforms	ML Investment (USD )
Any scale	259 million
Inflection AI	225 million
Weights and Biases	205 million
Hugging Face	160.2 million
OctoML	131.9 million
A121Labs	118.5 million

Machine Learning Usage Statistics

(Source: aiprm.com)

• A recent study reveals that 57% of organizations employ machine learning to enhance customer experience.
• This ranks as the primary motive, outpacing others like generating consumer insights (50%), which was the only other objective selected by half of the participants.
• According to Machine Learning Data, roughly 48% of businesses utilized machine learning for customer interactions, while just over 46% used it for fraud detection.
• In 2024, around 44% of firms concentrated on long-lasting engagement, and 40% emphasized enhancing customer loyalty.
• Additionally, only 22% of businesses applied machine learning to decrease customer attrition and 27% for recommendations.

Machine Learning Companies Adoption Statistics

• By 2024, one in three enterprises (33%) report that automating IT processes is their primary reason for embracing AI, particularly through machine learning.
• Identifying security threats was acknowledged by 26% of firms, while 25% highlighted automation and AI governance.

Throughout the same period, the adoption proportions of other companies in Machine Learning are illustrated in the table below:

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Companies	ML Adoption Share
Business analytics or intelligence	24%
Automation processing, understanding, and flow of documents	24%
Automation of customer or employee self-service answers and actions	23%
Automation of business processes	22%
Digital Labor	22%
Fraud detection	22%
Search and knowledge discovery	21%
Human resources and talent acquisition	19%
Financial planning and analysis	18%
Supply chain insights	18%

Duration Needed To Implement A Single Machine Learning Model

• Machine Learning Data in 2024 also indicates that 14% of firms launched a single machine learning model within a mere 0 to 7 days.
• 18% of individuals must be informed about the time it requires to execute machine learning.

ML Implementation Duration	Corporate Distribution
8 to 30 days	28%
1 to 3 months	22%
3 months to 1 year	13%
Over one year	5%

Machine Learning Utilization Data

(Source: founderjar.com)

• Machine Learning Data also reveals that enterprises primarily utilize machine learning for cost savings and expense reduction, which comprises 38% of the market in 2024.
• Other significant motivations include acquiring customer insights/intelligence (37%), enhancing customer experience (34%), automating internal processes (30%), and customer retention (29%).
• The three least prioritized reasons are boosting conversion rates (17%), screening assets and content (14%), and fostering brand awareness (14%).

Machine Learning Data in Marketing

• As of 2024, nearly 87% of businesses leveraging ML claim it is used for sales predictions in email campaigns.
• AI is regarded as the cornerstone of data strategies by 61% of marketing professionals.
• Approximately 56.5% of marketers employ AI and ML to customize content and enhance customer interactions.
• 16% mentioned that machine learning could bolster their sales and marketing initiatives.
• ML assists in predicting customer churn, accurately scoring leads, and creating adaptive pricing frameworks.

In Commerce

• According to Machine Learning Data, the implementation of machine learning algorithms aids businesses in elevating productivity by 54%.
• 45% of consumers now favor utilizing chatbots for their customer service inquiries.
• About 15% of producers are prepared to embrace ML for extensive production.
• LinkedIn lists over 44,000 jobs in the US and 98,000 jobs globally that require machine learning expertise.
• Around 75% of companies employing AI and ML observed a 10% increase in customer satisfaction.

AI/ML Influence on Business Supply Chains – Regional Statistics

(Source: statista.com)

• The Asia Pacific region is anticipated to experience the most significant transformation in supply chains utilizing AI and ML from 2023 to 2025, making up 48% of the overall share.
• Moreover, the shares of other regions are noted as North America (45%), Western (35%), and Total (44%).

Influence of AI and ML Utilization on Retail Performance Data

(Source: statista.com)

• The profit increase from 2023 to 2024 was 8.1% for those presently using AI/ML, compared to 3.1% for those not currently utilizing AI/ML.
• Similarly, sales growth within the same timeframe was 14.2% for those currently using AI/ML and 6.9% for those not using AI/ML.

Factors Influencing Demand for Machine Learning

• The introduction of advanced models like OpenAI’s GPT-4o in May 2024, which can process and generate text, images, and audio, has broadened ML applications across numerous fields.
• As of 2024, around 48% of businesses worldwide have incorporated machine learning into their operations, capitalizing on its potential to improve efficiency and decision-making.
• Databricks launched DBRX, an open-source large language model, in March 2024.
• Google DeepMind revealed plans to launch Gemini 2.0 Flash in December 2024.
• The goal is to enhance AI functionality in autonomous agents and further stimulate ML demand.
• 57% of enterprises apply machine learning to elevate customer experiences, underscoring its significance in improving client interactions.

Prospective Aspects of Machine Learning Data

• By 2030, the applications of ML in healthcare, including drug discovery, diagnostics, and personalized treatment, are projected to reach USD 20 billion.
• During the same period, over 70% of organizations globally are expected to utilize machine learning in their operations, with sectors like retail, finance, and manufacturing leading the way.
• Generative AI models are forecasted to represent 10% of all global data by 2025, up from less than 1% in 2021.
• The global edge AI market, valued at USD 1.3 billion in 2023, is anticipated to expand to USD 8 billion by 2028. This sector allows ML models to analyze data locally instead of depending on centralized cloud servers.
• The autonomous vehicle industry, heavily reliant on ML, is projected to reach USD 733.86 billion by 2030.
• NLP, a fundamental aspect of ML, is expected to surge from USD 29.71 billion in 2024 to USD 158.04 billion by 2032, revolutionizing customer support, language translation, and sentiment analysis.
• By 2025, at minimum 60% of businesses adopting AI will integrate explainable AI (XAI) to promote transparency and trust.

Summary

Machine learning is revolutionizing sectors on a global scale, enhancing tasks to be quicker, more intelligent, and efficient. As it evolves, enterprises, healthcare, education, and manufacturing continue to experience its advantages in refining processes and addressing intricate challenges.

Nonetheless, responsible utilization and tackling ethical issues are imperative as this technology progresses. With ongoing innovation, machine learning holds the promise of a future replete with opportunities for improved decision-making, increased productivity, and innovative solutions to challenges. Its potential remains vast, shaping the world in extraordinary ways.