Artificial intelligence (AI) has always been a source of fascination for us, especially when it comes to how things can go wrong. From sci-fi thrillers like The Terminator and I, Robot to regular old science fiction exploration films like A.I. Artificial Intelligence and Bicentennial Man, popular media makes AI and the ethics and dangers surrounding it a common topic of study. Fast-forward to the present day, and what was once thought of as unrealistic movie scenarios are now playing out in the real world. Spam detection in your inbox, Tesla’s self-driving cars, Amazon Alexa, and even your Netflix movie recommendations are all real-world applications of AI—and more specifically, machine learning. What is machine learning and just how prevalent is it in our lives? Read on to find out.

Table of Contents

What Is Machine Learning?

Machine learning (ML) is a type of artificial intelligence that involves the development and use of computer systems that can learn about and make predictions based on datasets without the need to follow explicit human instructions. Generally, machine learning models use algorithms and statistical models to process historical data as an input, then use trends in this data to predict new output values. ML techniques are designed to make these predicted outcomes more accurate without the help of programming specifically tailored to produce a solution. Said plainly: Machine learning platforms and software applications analyze data and draw conclusions from patterns it detects in that data. Many common applications of machine learning technology are found in business, where it is used to improve performance in:

business analytics
risk management
performance analysis
reducing operating costs
assessing market opportunities

While business analytics and data-driven sports decisions may grab the bulk of the attention, applications for machine learning abound, and the market is rapidly expanding. For instance, some are seeking to adopt machine learning for meaningful tasks such as detecting breast cancer, or for financial gain by predicting stock market highs and lows. No matter the use case, machine learning adoption has grown by leaps and bounds in the past decade. In fact, you probably interact with machine learning applications on a daily basis without even realizing it. Machine learning is used in:

email filters to sort out spam
internet search engines to understand user intent and result relevancy
banking apps to detect unusual or fraudulent activity in your bank accounts or credit cards
word processing software using natural language processing (NLP) to predict what words you’ll write next

Given a vast global machine learning market, it’s nearly impossible to not interact with some application of artificial intelligence in our daily lives. But to get an idea of just how large this technological revolution this is, check out these top machine learning statistics and trends. Related:

Machine Learning Statistics: Market Size + Market Trends

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1. Machine Learning Market Size

The global machine learning market size is an estimated $21.17 billion in 2022 and is expected to reach $209.91 billion by 2029. (Source: Fortune Business Insights)¹

2. Market Share

The IT and Telecommunication industries comprise 21.5% of the market share by end user in 2022. (Source: Fortune Business Insights)¹

3. Growth Rate

The compound annual growth rate of the machine learning market is forecast to be 38.8% between 2022 and 2029. (Source: Fortune Business Insights)¹

4. Driving Forces for Machine Learning Growth

The increased adoption and advancement of technology and automation—including cloud-based platforms, networking systems, integrated memory systems, and AI-inbuilt processors—allow complex problems to be solved quickly and is the main driver of projected machine learning market growth. (Source: Market Research Future)² In particular, increasing utilization in the healthcare sector is driving the growth of machine learning applications. Within healthcare, one of the most important applications is the “detecting and diagnosing illnesses and conditions that are sometimes difficult to identify.” (Source: Fortune Business Insights)¹

5. Key Players Driving Machine Learning

The key companies in the machine learning sphere include Amazon, Microsoft, Oracle, Intel, IBM, Hewlett Packard Enterprise, SAP, SAS Institute, FICO, and BigML. (Source: Fortune Business Insights)¹ Related: Best Brokerage Account Sign-Up Bonuses, Promotions and Deals

Machine Learning Statistics: Employment Prospects

excited young woman celebrating on laptop

6. Jobs Using Machine Learning

There were 33,500 computer and information research scientist jobs in 2021. That number is expected to grow to 40,600 by 2031. (Source: Bureau of Labor Statistics)³

7. Employment Prospects From 2022 Through 2032

Computer and information research scientist jobs are expected to grow by 21% between now and 2031, while computer operations jobs are expected to grow by 15%. That’s extremely rapid compared to the 5% expected total growth for all other occupations during the same timeframe. Furthermore, 3,300 openings on average are projected each year for computer and information research scientists due to the need to replace workers who change fields or exit the labor force. (Source: Bureau of Labor Statistics)³

8. Average Compensation of Jobs Using Machine Learning Skills

The median annual compensation for computer and information research scientists was $131,490 in 2021, with the lowest 10% earning less than $74,210 and the highest 10% earning more than $208,000. (Source: Bureau of Labor Statistics)³

9. Leading Business Use Cases of Machine Learning

Reducing costs is the leading business use case of machine learning at 38%, followed closely by generating customer insights/intelligence (37%), improving customer experience (34%), and internal processing automation (30%). (Source: Algorithmia)⁴

10. Number of Data Scientists at Companies Often at 10 or Less, But Still Growing Rapidly

The number of data scientist roles at companies ballooned between 2018 and 2020. Currently 89% of companies had at least one data scientist in 2020, up from 76% in 2018. In 2018, 9% of companies reported employing 11-50 data scientists, and that number jumped to 20% by 2020. Meanwhile, the number of companies with 51 and 100 data scientists grew from a 2% share to 5%; the number with 101 to 1,000 grew from a 5% share to 11%; and 3% of companies reported more than 1,000 data scientists in 2020, “indicating small but significant growth” from 2% in 2018. (Sources: Algorithmia)⁴ If U.S. projections are any indication, expect the number of data scientist jobs in the world to keep growing. America has 113,300 data scientist jobs in 2021; that’s projected to grow by 40,500 roles by 2031. (Source: Bureau of Labor Statistics)⁵

11. Machine Learning Operationalization Is Fledgling But Maturing

In 2020, just more than 22% of companies have had machine learning models in production for one to two years. While that sounds small, that’s a huge leap over the prior year’s 13%, “demonstrating a fairly significant migration toward productionization even if it is still early days for most companies.” On top of that, another 20% said they planned on having models in production by 2021. (Source: Algorithmia)⁴

12. Main Challenges People Face When Developing ML Capabilities

“The main challenges people face when developing ML capabilities are scale, version control, model reproducibility, and aligning stakeholders.” (Source: Algorithmia)⁴

13. Average Time It Takes to Deploy a Machine Learning Model and Machine Learning Algorithm

The most common timeframe to deploy a machine learning model is eight to 30 days (28%), followed by 22% taking 31-90 days, 13% taking 91-365 days, 14% taking 0-7 days, and 5% taking 1+ years. The remaining 18% are unsure of their typical timeline. (Source: Algorithmia)⁴

14. Budgets for Machine Learning Programs Are Growing Most Often By 25%

While ML budgets vary according to industry and stage of maturity, overall, budgets for machine learning are growing regardless of company size. From 2018 to 2019, 43% of companies surveyed increased their budget by 1%-25%, 21% increased their budget by 26%-50%, 4% increased by 51%-75%, and 3% increased by more than 75%. Of the remainder, only 2% decreased their ML budgets, while 27% kept them level. (Source: Algorithmia)⁴

15. Fastest-Growing Industries for Developing and Deploying Machine Learning Models Are Banking, Manufacturing, And IT

In the banking and financial services sector, 40% of companies surveyed increased their ML budgets by 40% while a further 27% increased by 26%-50%. In manufacturing, 43% of companies surveyed increased their ML budgets by 1%-25% while a further 29% increased by 26%-50%. Finally, 47% of information technology companies increased their ML budgets by 1%-25% while a further 20% increased by 26%-50%. (Source: Algorithmia)⁴

16. Microsoft Power BI Emerging as One of the New Top Skills for Data Analysis Roles

While Microsoft Power BI has become one of the top skills needed for data analysis jobs, those who already have data analysis skills might be able to quickly adapt. That’s because there’s an 83% similarity between data analysis and Microsoft Power BI. (Source: LinkedIn)⁶ Related:

Machine Learning Statistics: Applications

engaged woman examining information on laptop

17. Applications in Healthcare

Medical use cases of machine learning algorithms and deep learning are on the rise. The major growth areas for ML applications in the healthcare field are identifying diseases, diagnosis, and medical imaging (Source: Fortune Business Insights)¹

18. Applications in Media

Machine learning and deep learning are responsible for 80% of the movies, shows, and documentaries watched on Netflix. (Source: ResearchGate)⁷

19. Applications in Social Media

Facebook uses an artificial intelligence-powered face recognition model to achieve greater than 97% accuracy in face verification. (Source: Facebook)⁸

20. Applications in Clean Energy

Through the application of AI and ML, machine learning companies Google and DeepMind provide turbine output prediction software that, as of 2019, improved the “value” of wind energy production versus a production baseline of turbines without such software. The software has continued to show strong results, to the point where French utility Engie SA agreed in 2022 to use the technology in its European wind farms. (Source: The Verge, Bloomberg)^9,10 Related:

Machine Learning Statistics: Deep Learning

Deep learning is a subset of machine learning and utilizes algorithms that analyze data with a logical structure similar to that of the human brain. “The design of such an ANN is inspired by the biological neural network of the human brain, leading to a process of learning that’s far more capable than that of standard machine learning models.” (Source: Levity)¹¹ One growing application of deep learning techniques is voice assistant technology such as Siri, Google Assistant, and Alexa.

21. Deep Learning U.S. Market Size

The U.S. deep learning software market size is estimated to explode from $80 million in 2017 to $930 million by 2025. (Source: Statista)¹²

22. Deep Learning Global Market Size

The global deep learning software market size is expected to enjoy a compound annual growth rate of 39.2% between 2020 and 2027, reaching $44.3 billion by the end of the forecast period.(Source: ReportLinker)¹³

23. Use of Deep Learning Voice Assistant Technology

In 2019, approximately 3.5 billion people were using voice-activated search and assistants globally, which equates to a little less than half the world population. It’s predicted that the number will increase to 8 billion by 2023. (Source: Juniper Research)¹⁴

24. Global Natural Language Processing Market

Between 2021 and 2026, it’s estimated that the value of the global natural language processing market will grow at a compound annual rate of 26.84% and reach $48.46 billion. (Source: Mordor Intelligence)¹⁵ Related:

Machine Learning Statistics: Milestones

Machine learning has drastically advanced AI capabilities in recent years. What is artificial intelligence capable of so far?

25. Healthcare

In 2019, Google’s lung cancer detection application was able to outperform human radiologists with an average of eight years’ experience. (Source: VB)¹⁶

26. COVID-19

Machine learning was able to predict the mortality of COVID-19 patients with 92% accuracy in 2020. (Source: Nature)¹⁷

27. Stock Market

In 2017, machine learning was able to provide a useful stock-trading signal by reading biotechnology companies’ annual reports. “In this model, we are seeing 62% accuracy for predicting the under-performing company based on the sample 10-K text.” (Source: Microsoft)¹⁸ Related: 20 Best Investing Research & Stock Analysis Websites

28. Speech Recognition

The error rate of speech recognition systems was less than 5% as of 2019. (Source: Teks Mobile)¹⁹

29. AI Writing

In its first year in 2016, the Washington Post AI writing program (called Heliograf) produced 850 stories about topics ranging from high school football games to political races. (Source: WNIP)²⁰

30. Autonomous Vehicles

As of July 2022, Tesla vehicles had driven 35 million autonomous miles under the Full Self-Driving (FSD) Beta. (Source: Tesla)²¹

Machine Learning Statistics + Trends in 2021 and 2022 Sources:

Fortune Business Insights (https://www.fortunebusinessinsights.com/machine-learning-market-102226)
Market Research Future (https://www.marketresearchfuture.com/reports/machine-learning-market-2494)
Bureau of Labor Statistics (https://www.bls.gov/ooh/computer-and-information-technology/computer-and-information-research-scientists.htm)
Algorithmia (https://info.algorithmia.com/hubfs/2019/Whitepapers/The-State-of-Enterprise-ML-2020/Algorithmia_2020_State_of_Enterprise_ML.pdf?hsLang=en-us)
Bureau of Labor Statistics (https://www.bls.gov/ooh/math/data-scientists.htm)
Linkedin (https://linkedin.github.io/future-of-skills/)
ResearchGate (https://www.researchgate.net/publication/318671349_Deep_Learning_Based_Recommender_System_A_Survey_and_New_Perspectives)
Facebook (https://research.facebook.com/publications/deepface-closing-the-gap-to-human-level-performance-in-face-verification/)
The Verge (https://www.theverge.com/2019/2/26/18241632/google-deepmind-wind-farm-ai-machine-learning-green-energy-efficiency)
Bloomberg (https://www.bloomberg.com/news/articles/2022-06-01/google-and-france-s-engie-team-up-to-accelerate-wind-power)
Levity (https://levity.ai/blog/difference-machine-learning-deep-learning)
Statista )
ReportLinker )
Juniper Research (https://www.juniperresearch.com/press/digital-voice-assistants-in-use-to-8-million-2023)
Mordor Intelligence (https://www.mordorintelligence.com/industry-reports/natural-language-processing-market)
VB (https://venturebeat.com/ai/googles-lung-cancer-detection-ai-outperforms-6-human-radiologists/)
Nature (https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&ved=2ahUKEwjW0saRy8L6AhXBOn0KHdE1DV8QFnoECAcQAQ&url=https%3A%2F%2Fwww.nature.com%2Farticles%2Fs41598-020-75767-2.pdf%3Forigin%3Dppub&usg=AOvVaw0ZtNrOp9ta1UJvGq04P1gJ)
Microsoft (https://devblogs.microsoft.com/cse/2017/12/04/predicting-stock-performance-deep-learning/)
Teks Mobile )
WNIP (https://whatsnewinpublishing.com/the-washington-posts-robot-reporter-has-published-850-articles-in-the-past-year/)
Tesla (https://tesla-cdn.thron.com/static/EIUQEC_2022_Q2_Quarterly_Update_Deck_J8VLIK.pdf)
Simplilearn (https://www.simplilearn.com/data-mining-vs-machine-learning-article)
Built In (https://builtin.com/artificial-intelligence/ai-replacing-jobs-creating-jobs)
HBR (https://hbr.org/2018/04/if-your-data-is-bad-your-machine-learning-tools-are-useless)

Related:

30 Machine Learning Statistics + Trends in 2023