60 Notable Machine Learning Statistics: 2021/2022 Market Share & Data Analysis

Many view machine learning (ML) as synonymous with artificial intelligence. In reality, machine learning is but a subset of AI, making the latter perform tasks faster and more intelligently by providing it with learning capabilities. These benefits make machine learning a key component of AI, a fact that will be affirmed by the latest machine learning statistics.

As its growing importance warrants further investigation, we have compiled the most relevant and recent machine learning statistics around. We’ve also included data on how ML is being used in the healthcare industry, particularly in accurately predicting diseases and prioritizing care for COVID-19 patients.

This information will deepen your idea of what the technology does, what it can do, and how it actually helps businesses and society as a whole. With this data on machine learning and business intelligence, you should have a better appreciation of the technology and even use the statistics we provided to decide whether or not adoption is in order.

key machine learning statistics

1. Machine Learning Market

The machine learning market size has been steadily growing. The most prominent segment of this market is the deep learning software category, which is expected to reach almost $1 billion by the year 2025. Also, current machine learning market research has revealed that the market for  AI-powered hardware and assistants are also expected to experience robust growth. Check out the following statistics to find out what’s cooking in the machine learning market.

  • $28.5 billion – The total funding allocated to machine learning worldwide during the first quarter of 2019 (Statista, 2019).
  • $80 million – The estimated size of the US deep learning software market by 2025 (Statista, 2019).
  • $75.54 billion – The projected market size of the global artificial intelligence industry as it grows the projected market size of the global artificial intelligence industry between 2019 and 2023 at a CAGR of over 33% between 2019 and 2023 (Technavio, 2019).
  • $44.3 billion – The projected size of the global deep learning market by 2027 at a CAGR of 39.2% during the forecast period (ReportLinker, 2020).
  • $117.19 billion – The expected value of the global machine learning market by 2027 at a CAGR of 39.2% during the forecast period (Fortune Business Insights, 2020).
  • $87.68 billion – The expected value of the artificial intelligence (AI) hardware market at a CAGR of 37.60% from 2019 to 2026 (NeighborWebSJ, 2021).
  • Global revenue for AI hardware, particularly in the chip business, fell by 12% due to the impact of COVID-19 (Market Data Forecast, 2020).
  • $13 trillion – The potential global economic activity that AI could deliver by 2030 (McKinsey).
  • Global revenue for AI hardware, particularly in the chip business, fell by 12% due to the impact of COVID-19 (Market Data Forecast, 2020).
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AI Funding Worldwide 2019 (In billions of US Dollars)

Machine Learning Tops AI Dollars

AI Funding Worldwide 2019 (In billions of US Dollars)
Machine learning applications: 28

Machine learning applications

AI Funding Worldwide 2019 (In billions of US Dollars)
Machine learning platforms: 14

Machine learning platforms

AI Funding Worldwide 2019 (In billions of US Dollars)
Smart robots: 7

Smart robots

AI Funding Worldwide 2019 (In billions of US Dollars)
Computer vision platforms: 7

Computer vision platforms

AI Funding Worldwide 2019 (In billions of US Dollars)
Natural language processing: 7

Natural language processing

AI Funding Worldwide 2019 (In billions of US Dollars)
Recommendation engines: 4

Recommendation engines

AI Funding Worldwide 2019 (In billions of US Dollars)
Virtual assistants: 3

Virtual assistants

AI Funding Worldwide 2019 (In billions of US Dollars)
Speech recognition : 2

Speech recognition

AI Funding Worldwide 2019 (In billions of US Dollars)
Gesture control: 1

Gesture control

AI Funding Worldwide 2019 (In billions of US Dollars)
Video recognition : .7

Video recognition


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2. Machine Learning in Voice Assistants

ML has a subset called “deep learning.” This technology is built around machine learning practice and is responsible for the creation of the platforms behind voice assistants, which include Siri, Echo, and Google Assistant. Voice assistants have risen in popularity among consumers, following the explosion of mobile technology. Take a look at the following voice assistant statistics that prove this development.

  • Approximately 3.25 billion people use voice-activated search and assistants worldwide, almost half of the world’s population (Review42, 2021).
  • Global voice assistant usage during COVID-19 rose 7% (AUM, 2020).
  • The pandemic caused more people to use a voice assistant. People who used it several times a day rose to 25% in March-April 2020 from 20% back in December 2019-January 2020 (, 2020).
  • About 128 million people in the US use voice assistants at least monthly in 2020, up from 115.2 million in 2019 (eMarketer, 2020).
  • 80.5% of under 30 consumers use a voice assistant on smartphones compared to 60.5% of the oldest age group (, 2019).
  • 74.7% of consumers ages 30-44 use voice assistants on smartphones, while 68.8% of consumers ages 45-60% do the same (, 2019).
  • It’s predicted that 8 billion people will be using voice assistants by 2023 (Review42, 2021).
  • $42.04 billion – The estimated value of the global natural language processing market by 2026 (Mordor Intelligence, 2020).

Voice assistant use

3. Machine Learning Adoption

The adoption of ML by enterprises has reached new heights as highlighted in a recent machine learning report. Adoption has been happening at break-neck speed as companies attempt to leverage the technology to get ahead of the competition. The financial sector is one of the most prominent adopters of the technology, which now leverages the power of AI software. These tools use ML to find, analyze and gain insights from data. Factors that drive the development include machine learning capabilities like risk management, performance analysis, and reporting and automation. Below are statistics on ML adoption.

  • 50% of respondents said that their companies have adopted AI in at least one business function (McKinsey, 2020).
  • 1/3 of IT leaders are planning to use ML for business analytics (Statista, 2019).
  • 25% of IT leaders plan to use ML for security purposes (Statista, 2019).
  • 16% of IT leaders want to use ML in sales and marketing (Statista, 2019).
  • The top business functions adopting AI remain the same in 2020 as it was in 2019: product/service development, service operations, and sales and marketing (McKinsey, 2020).
  • Revenue increases from AI adoption are commonly reported, but not cost decreases. For example, 80% of people said that AI has helped increase revenue. (McKinsey, 2020).
  • Advancements in AI and machine learning have the potential to increase global GDP by 14% from now until 2030 (WSJ, 2019).
  • Among the biggest challenges to machine learning adoption include scaling up (43%), versioning of ML models (41%), and getting senior buy-in. (Statista, 2021)

Source: Statista, 2021

4. Machine Learning in Business

Businesses around the world have embraced ML, with the majority of them claiming to be early adopters. The technology has been driving innovations in enterprises, allowing them to make smart processes using AI with learning capabilities. Among these are business intelligence solutions. All you have to do is take a look at current business intelligence statistics, and you’ll get a pretty clear picture of this development. However, the deployment of ML comes with challenges, which include a lack of access to data and a shortage of skilled individuals to address machine learning problems. Read the statistics below and find out more about the current state of machine learning and businesses.

  • Companies are seen to offer virtual agents to consumers (Dataversity, 2019).
  • 54% – The estimated improvement in productivity from AI use (Oberlo, 2020).
  • 49% of companies are exploring or planning to use ML (McKinsey).
  • 51% of organizations claim to be early adopters of ML (McKinsey).
  • 40% – The estimated productivity improvement from AI use (Accenture).
  • 15% of organizations are already advanced ML users (McKinsey).
  • 75% of AI projects are now under the leadership of C-level executives (Fortune, 2020).
  • 91.5% of leading businesses have ongoing investments in AI (Businesswire, 2020).
  • In the US, there are more than 44,000 jobs on LinkedIn that list machine learning as a required skill, and over 98,000 jobs worldwide (Forbes, 2020).
  • 62% of customers are willing to submit their data to AI to improve their experience with businesses (Salesforce, 2019).

early adopters of machine learning

Leading Business Intelligence Software

  1. SAP BusinessObjects Lumira. This product from SAP allows businesses to easily visualize their complex business data securely. Learn about other features such as its self-service data access module and data transformation capabilities here in our SAP BusinessObjects Lumira review.
  2. Tableau. This popular software solution allows users to perform data analytics and visualizations easily without the need for advanced tech skills. Learn about its intuitive dashboard and comprehensive features in our in-depth Tableau review.
  3. SAP Crystal Reports. This SAP product allows users to translate their static reports into dynamic and interactive shareable media files. Learn how its advanced visualization capabilities can help you gain more insights from your data here in our detailed SAP Crystals Reports review.
  4. Microsoft Power BI. In this Microsoft product, users can gather, analyze, and share insights drawn from complex data. Learn how you can collect and manipulate data easily for business intelligence with this product in our Microsoft Power BI review here.
  5. Hotjar. Track and analyze user behavior on your web touchpoints using Hotjar’s powerful features. Read more about how it can help you transform raw web data and user feedbacks into powerful and actionable business insights here in our dedicated Hotjar review article.

5. Machine Learning Use Cases

Machine learning has found applications in a variety of business environments. The technology basically gives AI the ability to train a machine to learn. Machine learning’s origins can be traced back to the notion of enabling computers to learn without having to program them to perform tasks.  Today, developers continue to find new uses for ML as you’ll find out in the statistics below.

  • Tesla has logged an estimated 1.88 billion in autonomous miles as of October 2019 (Forbes, 2019).
  • NLP is expected to get more applications in customer service (Dataversity, 2019).
  • 80% of companies plan to adopt AI for customer service by 2020 (B2C, 2020).
  • Only 14.6% of businesses reported that they have deployed AI capabilities into widespread production (Businesswire, 2020).
  • Frontrunners in AI reported better business outcomes. For example, 47% said they were able to optimize sales and marketing, while 32% said they were able to reduce operating costs (Deloitte, 2019).
  • Below 10,000 – The number of people who have the needed skills to address serious AI problems (Accenture).
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Machine Learning Applications That Drive Adoption 2019

Machine Learning Applications That Drive Adoption 2019
Risk management: 82

Risk management

Machine Learning Applications That Drive Adoption 2019
Performance analysis and reporting: 74

Performance analysis and reporting

Machine Learning Applications That Drive Adoption 2019
Trading and investing idea generation (alpha generation): 63

Trading and investing idea generation (alpha generation)

Machine Learning Applications That Drive Adoption 2019
Automation : 61




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6. Machine Learning in Marketing

As marketing has become more labor- and resource-intensive process, marketers’ use of ML does not come as a surprise. Machine learning has reshaped marketing by improving the accuracy of lead scoring, creating dynamic pricing models, and speeding up customer churn prediction, among many others. This is not to mention the benefits that tools for business intelligence offer marketers. To help you find out more about the use of machine learning in marketing, we have gathered the most recent statistics on the topic.

  • 61% of marketers say AI is the most critical aspect of their data strategy (Max G, 2019).
  • 87% of companies who use AI plan to use them in sales forecasting and email marketing (Venture Harbour, 2020).
  • 3000 – The number of Amazon Go stores Amazon plans to open in the US by the end of 2021 (Vox, 2019).
  • 10 million products – the number of products consumers can avail via one-day shipping thanks to Amazon’s use of AI and machine learning at fulfillment centers (Feedvisor, 2019).
  • 60% of consumers had a lukewarm acceptance of an AI-powered future (Smart Brief, 2020).
  • $1 billion – The amount Netflix saved from the use of machine learning algorithms (Inside Big Data).
  • When it comes to resolving customer issues, 41% of consumers said they wanted them resolved by a human agent (Smart Brief, 2020).

Source: The CMO Survey, 2019

7. Machine Learning Milestones

Here comes the most interesting part. What is machine learning capable of so far? Today, ML has imbued artificial intelligence with more capabilities. For example, adding machine learning features to computers that can already process and analyze complex data sets is further improved to become business intelligence platforms. However, the technology is still in infancy that science fiction scenarios are still further down the road. Take a look at the following stats and see how machine learning has improved since its inception.

  • Google’s lung cancer detection AI outperforms six human radiologists (VB, 2019).
  • 850+ – The number of stories written by Washington Post AI writer Heliograf, during the 2016 US presidential election and the Rio Olympics (WNIP).
  • Artificial Intelligence is now the 2nd most in-demand job based on Indeed’s 2020 Career Guide (Indeed, 2020).
  • 60% – the reduction in translation errors of Google Translate when it changed to GNMT—a translation algorithm powered by machine learning (AIM, 2020).
  • 95% – The accuracy of machine learning in predicting a patients’ death (Bloomberg).
  • Machine learning methods used to predict the mortality of COVID-19 patients demonstrated 92% accuracy (, 2020).
  • 62% – The accuracy of machine learning in predicting stock market highs and lows (Microsoft).
  • 3/4 of all elderly care services in Japan will be delivered by AI robots in 2025 (Teks Mobile, 2018).
  • 5% The error rate of speech recognition systems (Teks Mobile, 2019).
  • 40% of the annual value created by analytics is made up of deep learning techniques (McKinsey).
  • 89% – The level of accuracy of Google’s Deep Learning program in detecting breast cancer (Health Analytics).
  • 46.8% – the accuracy of Google’s machine learning-powered lip-reading system, which topples a professional human lip-readers 12.4% accuracy (VB, 2019).
  • 3.7 seconds – the time Deep Voice (AI-powered voice cloning tool) needs to clone a voice (Forbes, 2019).

number of stories written by Washington Post AI writer Heliograf

Use Machine Learning to Your Advantage

Machine learning has indeed reshaped the way businesses run their affairs. Many enterprises turn to ML to outdo the competition, with a majority of them claiming to have adopted the technology in key segments of their operations such as sales and marketing, inventory management, customer care, and cybersecurity.

The best thing about ML, however, is the innovations that it has managed to create, among which are AI writing, lip-reading, driving data collection and analysis, and even accurately predicting diseases and severity of a patient’s condition as we’ve learned from its applications in treating COVID-19 patients. This emerging technology has greatly advanced through the decades and has even surpassed human skills based on its impressive milestones.

But machine learning is not without its problems, the most pressing of which is the lack of skilled personnel to address machine learning problems. You can confirm this by reading relevant AI statistics currently available.



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James Anthony

By James Anthony

A senior FinancesOnline writer on SaaS and B2B topics, James Anthony passion is keeping abreast of the industry’s cutting-edge practices (other than writing personal blog posts on why Firefly needs to be renewed). He has written extensively on these two subjects, being a firm believer in SaaS to PaaS migration and how this inevitable transition would impact economies of scale. With reviews and analyses spanning a breadth of topics from software to learning models, James is one of FinancesOnline’s most creative resources on and off the office.

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