Machine learning is being adopted heavily across industries. The latest data reveal that nearly 50% of small and large businesses have adopted machine learning worldwide.
When we investigated the subject further, we found that over two-thirds of businesses in healthcare, manufacturing, and finance have adopted ML.
Let’s explore details about the machine learning market worldwide, in the United States, investments received by the market, its adoption in different industries, and additional machine learning Statistics in this article.
Machine Learning Statistics 2024: Top Picks
- The machine learning market is estimated to grow by 36.08% between 2024 and 2030.
- The market is estimated to be valued at $79.29 billion in 2024 and is projected to reach $503.40 billion by 2030.
- 48% of businesses globally use machine learning as of 2024.
- Open AI is the most funded machine learning platform, with over 11 billion in investments received.
- Almost 92% of the leading businesses stated that they have invested in Machine learning and AI.
- 57% of companies and businesses use machine learning to improve consumer experience.
- 34% of the companies in the United States have adopted machine learning, while 42% are exploring ML and planning to adapt it.
- 80% of the companies report that investing in Machine learning increased their revenue.
Machine Learning Market Statistics
- The machine learning market worldwide is estimated to be valued at $79.29 billion in 2024.
The market is anticipated to grow at a CAGR of 36.08% between 2024 and 2030, resulting in a market volume of $503.40 billion by 2030.
The United States is anticipated to be the largest market in the industry, with a market size of $21.14 billion in 2024.
Source: Statista
- The Manufacturing Industry holds the largest share of the Machine learning market.
The Industry owns 18.88% of the share. The second largest market in the machine learning industry is the finance industry, with a market share of 15.42%.
The healthcare and Transportation industry follows it.
Here are further details about different industries’ machine learning market share.
| Industry | Market Share As Of 2022 |
|---|---|
| Manufacturing | 18.88% |
| Finance | 15.42% |
| Healthcare | 12.23% |
| Transportation | 10.63% |
| Security | 10.10% |
| Business & legal services | 9.86% |
| Others | 5.83% |
| Energy | 5.58% |
| Media & Entertainment | 5.19% |
| Retail | 4.67% |
| Semiconductor | 1.61% |
Source: Statista.
Machine Learning Industry
- Asia Pacific is expected to witness the biggest change from utilizing AI and ML technologies in the supply chain from 2023 to 2025.
Meanwhile, North America is expected to witness a 45% change in the business supply chain industry due to the adoption of machine learning.
Further, Western Europe is anticipated to witness a 35% change in the supply chain with ML adoption.
Here is a table displaying the effect of ML and AI on the supply chain adoption industry by region between 2023 and 2025.
| Region | Effect On the Supply Chain Industry |
|---|---|
| Asia/Pacific | 48% |
| Western Europe | 35% |
| North America | 45% |
| Total | 44% |
Source: Statista
Newsle led the global Machine learning industry with the highest market share of technologies.
It owned 88.71% of the share of machine learning technologies worldwide. TensorFlow and Torch followed it.
This machine learning software helps machines to artificially learn and improve their functions based on experience without being programmed to do so.
Here are further details about the market share of the leading technologies worldwide.
| Machine Learning Technology | Market Share |
|---|---|
| Newsle | 88.71% |
| TensorFlow | 3.38% |
| Torch | 2.75% |
| Other | 5.16% |
Source: Statista.
- Machine learning and artificial intelligence industry advancements are expected to increase the GDP by 14%.
The budgets for machine learning projects are expected to increase by 25%, with the highest growth in sectors like IT, banking, and manufacturing.
Further, in a study conducted by McKinsey, 50% of respondents reported that they had adopted Artificial intelligence and machine learning in at least one business function.
Source: Founderjar.
Investments In Machine Learning
Did You Know? OpenAI spends $700,000 every day to run ChatGPT.
- Almost 92% of the leading businesses have invested in Machine learning and AI.
The businesses notably invested in machine learning aspects like speech and pattern recognition, regression analysis, standard deviation observations, robotics, etc.
Source: Business Wire.
AI Fundings Worldwide
- Open AI is the most funded machine learning platform worldwide, valued at over 11 billion.
Scale AI is the second most funded machine learning platform, receiving over $602 million in funding.
Other most funded machine learning startups worldwide are Adept, Cohere.ai, and Anyscale.
The following table displays the most funded machine-learning platforms worldwide:
| Machine Learning Platform | Funding Received |
|---|---|
| OpenAI | 11,300.1 million |
| Scale AI | 602.6 million |
| Adept | 415 million |
| Cohere.ai | 414.9 million |
| Anyscale | 259 million |
| Inflection AI | 225 million |
| Weights & Biases | 200 million |
| Hugging Face | 160.2 million |
| OctoML | 131.9 million |
| AI21 Labs | 118.5 million |
Source: Statista
- The global corporate investments in artificial intelligence reached almost $92 billion in 2022.
It is a slight decrease of $1.6 billion compared to the previous year. In 2021, the investments made in total corporate AI were recorded to be $93.5 billion.
Here are further details about the global total corporate investments in artificial intelligence.
| Year | Investment Made |
|---|---|
| 2022 | $91.9 billion |
| 2021 | $93.5 billion |
| 2020 | $67.85 billion |
| 2019 | $48.85 billion |
| 2018 | $43.81 billion |
| 2017 | $44.08 billion |
| 2016 | $17.7 billion |
| 2015 | $12.75 billion |
Source: Statista.
Machine Learning Use Cases
- 57% of the companies use machine learning to improve consumer experience.
50% of businesses use ML and AI to generate customer insights and intelligence. The other most used cases of machine learning and artificial intelligence are building brand awareness, reducing customer churn, increasing customer loyalty, etc.
Here are further details about the use cases of machine learning.
| Use Cases | Percentage Of Companies Using It |
|---|---|
| Improving consumer experience | 57% |
| Generating consumer insights and intelligence | 50% |
| Increasing long-term consumer engagement. | 44% |
| Detecting fraud | 46% |
| Interacting with customers | 48% |
| Recommender system | 27% |
| Building brand awareness | 31% |
| Retaining consumers | 31% |
| Increasing customer loyalty | 40% |
| Acquiring new consumers | 34% |
| Reducing consumer Churn | 22% |
Role Of Machine Learning In Voice Assistants
- 56.4% of mobile users use AI-powered voice assistants.
Whether you want to set a reminder, a timer, or search a query on Google search, you might have asked SIRI, Google Assistant, or Alexa to do it for you.
That’s how machine learning has transformed the use of voice assistants.
- Over half of the adults use voice search and voice assistants daily for their day-to-day tasks.
This 50% of the population primarily includes GenZ, followed by Millennials. They usually prefer voice search over typing.
Machine Learning In Business
- 73% of business leaders believe that machine learning will improve their productivity.
Some business executives believe it can double their productivity and increase the accuracy of the work done by employees.
This is because machine learning applications help reduce the time required to work, assisting employees to achieve their targets.
Source: Zippia.
- 15% of the organizations are advanced ML users.
These organizations include a list of tech companies from Silicon Valley and other parts of the globe. Companies like Open AI, Microsoft, Google, Apple, etc., use advanced ML to create products and robots.
Source: G2.
- 45% of consumers prefer chatbots as the primary mode of communication.
When speaking about consumer services, chatbots have become the most preferred mode. Hence, most of the top business websites use Chatbots to help the consumers with the services they seek.
Source: G2.
- Over two-thirds of the consumers are willing to submit their data to AI to improve their experience with business.
Personalized experiences have become the need of the consumers. Hence, 70% of consumers expect personalized experiences from brands in exchange for their personal data and preferences.
Source: Salesforce.
Machine Learning In Marketing
- 49% of organizations use machine learning and AI in marketing and sales.
All industries, from production to distribution, apply machine learning to identify sales prospects. At the same time, 48% of organizations use it to gain insights into their prospects and consumers.
Source: Harvard Business Review.
- Nearly one-third of the organizations using machine learning in sales and marketing have observed increased revenue.
According to the survey conducted by Harvard, 31% of the respondents reported that they noticed a rise in their company’s market share and revenue after implementing AI and ML for sales and advertising.
Machine Learning In Healthcare
- Japan plans to have 75% of the elderly care performed by AI.
Though these solutions are effective, they are not widespread, and very few countries and healthcare centers have adopted them.
However, healthcare companies and pharmaceutical companies have already taken a step to make AI widely available.
Source: nix.
- 70% of drug discovery costs can be cut with the application of AI and ML.
Source: Insider Intelligence
- ML can help achieve up to 95% accuracy in predicting COVID-19-related physiological deterioration.
It also can predict deaths 20 days in advance with the help of an ML-based solution.
Source: Nature
Machine Learning Adoption Statistics
- Almost half of the businesses have deployed ML in multiple areas.
According to research by Refinitiv, 46% of the companies have already deployed ML in their core business, and 44% have deployed ML in their pockets.
10% of the respondents are experimenting with the infrastructure and people and are looking forward to investing in it.
- 77% of businesses are either using or exploring AI globally.
According to IBM, 35% of businesses have started using AI. At the same time, 42% of the companies stated that they are exploring AI and looking forward to adopting it soon.
Machine Learning Employment Statistics
- 82% of companies and businesses need employees with machine learning skills.
Machine learning skills have become a basic necessity when applying for a job. This is especially true in tech, marketing and sales, finance, and retail industries.
So make sure to value your resume with machine-learning skills and increase your chances of getting selected.
Source: Zippia.
- Convinced by the importance of machine learning and Artificial intelligence, companies have expanded their budget for recruiting ML employees.
At the same time, companies are now allocating their budget to
- Retraining and upskilling existing employees: 68% of the companies.
- Identifying and recruiting skilled talents from other companies and organizations: 58% of respondents
- Recruiting from universities: 49% of the companies.
Source: SnapLogic.
- 37% of European businesses consider problem-solving the most critical skill an employee must have.
As soft skills are needed in tech roles, over one-third of respondents to IBM’s survey address the skill gap.
They further added that these crucial soft skills are missing in 23% of applicants.
Source: IBM.
Bonus: Read our article on Employee Engagement Statistics to know the real scenario of the employees at workspace.
Machine Learning Benefits
- 80% of the people reported that using machine learning rarely decreases the business’s expenses, but it definitely increases the revenue.
In a survey conducted by McKinsey, it came to the limelight that adaptation of machine learning does not always lead to cost-cutting, but it surely leads to an increase in revenue.
While this isn’t surprising, it may be indigestible to the business that adopted it for both factors.
- 45% of the royal economic gains by 2030 will be due to Machine learning and AI.
AI-driven product enhancements stimulate consumer demand, and businesses and companies widely use AI products. This usage of ML and AI is expected to increase a large amount over the upcoming years, considering the increased accuracy and productivity rates.
Here are further details about the estimated economic growth of different regions by 2030.
| Region | Estimated Economic Growth |
|---|---|
| Southern Europe | 11.5% |
| North America | 14.5% |
| China | 26.1% |
| Developed | 10.4% |
| Northern Europe | 9.9% |
| Asia, Oceania, and Africa | 5.6% |
| Latin America | 5.4% |
| Others | 16.6% |
Source: PwC Research.
- 85% of executives believe machine learning and automation will give their company a competitive advantage.
Similarly, 74% of company leaders believe their business or organization could perform better and meet goals if they invest in machine learning and automation.
However, only 50% of the organizations have incentives that can be invested in Machine learning and automation.
Machine Learning Challenges
- 43% of businesses face challenges in scaling up when adopting AI and machine learning.
Algorithmia survey further stated that 41% of companies face issues in versioning and reproducing machine learning models.
At the same time, 34% of the respondents stated that fetching organizational alignment and senior buy-in for ML initiatives is one of the top challenges.
- Only half of the companies with extensive experience in machine learning check for data privacy implications.
O’Reilly’s report brings into the limelight that only 53% of the companies check for data privacy implications in their machine learning project. The report further states that this number drops to 43% when all companies are included.
Latest Developments In Machine Learning
- Google’s lung cancer detection application outperformed human radiologists with an average of 8 years of experience in 2019.
Source: VB.
- The machine learning model predicted the mortality rates of COVID-19 patients with 92% accuracy in 2020.
Source: Nature.
- The error rate of the speech recognition system was less than 5% as of 2019.
Source: Teks Mobile
- The translation errors of Google Translate were reduced by 60% after the translation algorithm was changed to GNMT, an algorithm powered by machine learning.
Source: AIM
- Machine learning can predict the highs and lows of the stock market with 62% accuracy.
Source: Microsoft.
- 40% of the annual value created by analytics is from deep learning techniques.
Source: McKinsey
