More than 68% of all companies in the world use machine learning. Discover the numbers behind machine learning’s market size and growth. And how fast companies are adopting the data science technology. Read in this article. What is the machine learning market size? Machine learning market growth How many companies use machine learning? How fast are companies adopting machine learning? Machine learning adoption by region What’s the impact of machine learning? How much are companies investing in machine learning? What are the most common uses of machine learning? What business sectors use machine learning the most? How machine learning helped companies? Interesting Machine Learning Music Statistics The global machine learning market was worth $15.44 billion in 2021, and it’s expected to reach $21.17 billion in 2022. From 2020 to 2021, the machine learning market value rose by 251%. More than 68% of companies worldwide already use machine learning. With 63%, Israel has the highest machine learning adoption rate, whereas Nigeria has the lowest at 23%. By using machine learning, Amazon improved its click-to-ship rate by 225%. What is the Machine Learning Market Size? The machine learning market size was worth $15.44 billion in 2021. It’s expected to rise to $21.17 billion in 2022. (FBI) YearMachine learningglobal market size2029$209.91 billion2027$44.3 billion2022$21.17 billion2021$15.44 billion2020$4.4 billion2019$8 billion2018$6.9 billion2017$1.41 billion2016$1.03 billion(Source: GlobeNewswire, GVR, Twitter, MaM) In North America, the machine learning industry market size was $5.56 billion in 2021 and $4.05 billion in 2020. (FBI) Newsle machine learning software has the biggest global market share at 88.71%. Followed by TensorFlow at 3.38%, Torch at 2.75%, and Other at 5.16%. Machine learningsoftwareGlobalmarket shareNewsle88.71%TensorFlow3.38%Torch2.75%Other5.16%(Statista) Machine Learning Market Growth The machine learning market is predicted to rise to $209.91 in 2029 at a CAGR of 38.8%. Table of global machine learning market size growth: YearMachine learningglobal market growth2029+373.8%2027+109.2%2022+37.1%2021+251%2020-45%2019+16%2018+389.3%2017+36.9%2016/(GlobeNewswire) (GVR) (Twitter) (MaM) Calculated from market size data. How many companies use machine learning? Around 76% of companies have prioritized machine learning and AI in their IT budgets for 2021. 46% of companies think machine learning and AI are very important, with 25% saying they should’ve focused on machine learning much sooner. (Forbes) 68% of companies worldwide already use machine learning. Based on the survey of 500 CEOs worldwide in 2021. 72% of CTOs think machine learning will become prominent technology in the upcoming years. (ITProPortal) How Fast are Companies Adopting Machine Learning? Approximately 44.6% of companies were already using machine learning in 2020. At the time, another 21.1% explored the possibility of using machine deep learning. State of machine learningin companiesPercentageAlready in use17.2%Recently started using16.2%Using to generate insights11.2%Exploring the possibility21.1%Not in use20%Employers aren’t awareof a company using it14.3%(Kaggle) Machine learning adoption by country As of 2020, Israel had the highest global machine learning adoption rate at 63%. CountryMachine learningadoption rateIsrael63%Netherlands57%USA56%UK& Northern Ireland54%Germany54%Australia53%France52%China52%Taiwan51%Greece49%Poland48.5%Malaysia48.2%South Africa47.6%Portugal45.6%Canada45.4%India45%Sweden44.9%Japan44.9%Singapore44.2%Vietnam44%Mexico43.8%Brazil43.6%Spain43.4%South Korea43.3%Russia42.7%Tunisia42.6%Italy42.2%Chile42.2%Turkey41.6%Pakistan37%Peru35.4%Ukraine35%Iran34.5%Kenya33.8%Thailand33.3%Indonesia32.9%Colombia32.8%Argentina32%Philippines31%Egypt31%Morocco24%Nigeria23%(Kaggle) Countries with the highest percentage of companies exploring machine learning methods. CountryPercentageChile36%Sweden35%Malaysia32%South Korea31%Perú29%(Kaggle) (BOB) Reasons why companies want to adopt machine learning. Reasons for machinelearning adoptionPercentageRisk management82%Performance analysisand reporting74%Trading & investingidea generation63%Automation61%(Refinitiv) The Impact of Machine Learning The use of machine learning in marketing and sales can increase customer satisfaction by 10%. Around 57% of enterprises think that the biggest benefit of machine learning is to increase customer experience and support. (Medium) 75% of companies in 2018 used machine learning and AI to improve customer experience by 10%. (Forbes 2) More than 58% of enterprises use machine learning and AI to tackle marketing problems. (Forbes 2) By improving demand forecasting, machine learning can increase revenue, improve retailers’ assortment efficiency, and increase online sales due to dynamic pricing. It can also result in fewer items returned and up to 20% stock reduction. Machine learning impact%Increase in revenue2%Retailers’ assortmentefficiency50%Increase inonline sales30%Stock reduction20%(Forbes 2) That is how companies have used machine learning and AI in 2020 and 2021. Use of machine learning20202021Generating customerinsights/data37%50%Improving customerexperience34%57%Retaining customers29%31%Interactingwith customers28%48%Recommendersystems27%27%Detecting fraud27%46%Reducing customerchurn26%22%Acquiring newcustomers26%34%Increasing customerloyalty20%40%Increasing long-termcustomer engagement19%44%Buildingbrand awareness14%31%Others15%1%(Statista 2) Around 61% of office workers claim that introducing AI and machine learning improved their productivity. Areas that AI and MLcan improve workPercentageProductivity61%Decisionmaking49%Creativity38%Collaborationbetween teams37%Engagementwith customers35%(Snaplogic) How much are companies investing in machine learning? 50% of companies spent more of their budget on machine learning in 2021 compared to previous years. 20% of them plan to significantly increase the budget. (Forbes) The percentage of how much companies’ budgets for machine learning and AI have changed between 2018-2020. Companies’ budget changefor AI & machine learning2018-20192019-2020Decrease2%4%No change27%13%Increaseby 1-25%43%34%Increaseby 26-50%21%29%Increaseby 51-75%4%13%Increaseabove 75%3%7%(Statista 3) What are the most common uses of machine learning? Here are the 11 most common uses of machine learning. Real-time chatbot agents Decision support – Global clinical decision support system (CDSS) market was worth $1.2 billion in 2021 (TMR). Customer recommendation engines – Global customer recommendation engine market was worth $1.77 billion in 2020 (GVR 2). Customer churn modeling Dynamic pricing tactics – 21% of e-commerce companies in the EU and US were already using dynamic pricing in 2021, with 15% planning to introduce it, while 27% were still evaluating (Statista 4). Market research and customer segmentation – E-mail marketing (targeted advertising) can bring back 77% of the investment (Wigzo). Fraud detection – The market was worth $25.66 billion in 2021 (FBI 2). According to ReserachGate, 0.4% of transactions are fraudulent (ResearchGate). Image classifications and recognition – Global image recognition market was worth $23.82 billion in 2019 (FBI 3). Operational effectiveness Informational extraction Voice assistants – Worldwide use of voice assistants has increased by 7% during the pandemic. Daily use of voice assistants in the US rose by 5% from Q4 2019 to Q1 2022 (Voicebot) (Aum). What business sectors use machine learning the most? Here are business sectors that use machine learning the most. Copywriting Contact Centers – Company Peraton improved its first call resolution rate by 25% while reducing call volume and costs by 5%, all by adding machine learning capabilities to its contact centers (Forbes 3). E-commerce Insurance – Machine learning is mostly used in new businesses (56%) and claims (40%) under the “Property/Casualty” insurance value section and in new businesses (39%) and customer experience (26%) under the “Life/Annuity” insurance value section (Ijitee). Humanitarian Aid Consumer Goods Healthcare – With the help of AI, statistical modeling, and machine learning, St. Michael Hospital reduced the mortality rate of high-risk patients by 20% (Marsdd). Banking and Finance – Banks predict they’ll reduce their cost by 22% by 2030, saving up to $1 trillion (SPD Group). Manufacturing – Predictive maintenance is on its way to becoming the fastest growing machine learning tech, increasing from 28% usage in 2020 to 66% usage in 2025 (PWC). Credit card security Housing construction – Machine learning can help assess and reduce risks before construction starts (Digital Builder). Advertising – AI and machine learning ad creation tools can save money and time while increasing customer engagement by up to 40% and boosting conversion rates by 1.5 times (Forbes 4). (Forbes 5) Machine learning is mostly used in organizations dealing with business analytics. Here’s the table of organization segments that use machine learning computer science the most: Organizationsby segmentsPercentagesBusiness Analytics33%Security25%Sales and Marketing16%Others16%Customer Service10%(Statista 5) How machine learning helps companies? With the help of machine learning, Amazon managed to improve the click-to-ship rate by 225%. While humans took to process the package from clicking to shipping in 60-75 minutes, the Kiva, a machine learning robot in the Amazon warehouse, takes only 15 minutes. Not only that, the company managed to increase its inventory by 50%, reducing operating costs by 20%. (McKinsey) Netflix estimates that it saved $1 billion annually by improving search results using machine learning. According to Netflix, people give up after 90 seconds of unsuccessful searching, which could result in unsubscription and loss for the company. (McKinsey) Most common applications that heavily rely on machine learning: Dynamic Pricing – setting a product’s price based on market conditions in real-time. Traffic Prediction – a maps app using machine learning techniques to calculate the quickest route based on previous and current data with 97% accuracy (Google). Speech Recognition – smart assistant understanding your voice commands. More than 50% of mobile users have at least tried using speech recognition in 2020 (Comscore). Smart Manufacturing – reduce cost and time by automating monitoring, quality control, and failure prevention. In the next 5 years, predictive maintenance is expected to grow by 38%, and process automation and visualization by 34% (Eletimes). Autonomous Vehicles – self-driving vehicles that analyze the road using various sensors. In one research with one hour of driving data to feed into a machine learning algorithm, cars successfully reached their destinations 92% of the time (The Brink). Customer Segmentation – product marketing specially tailored toward an individual customer. Healthcare Advancement – apart from predicting mortality rates, machine learning can even more accurately detect breast cancer (89% accuracy) than a pathologist (76% accuracy) (Google Blog). Customer Churn Modeling – a science of predictive modeling of whether a customer might leave and how to retain them. Real Estate Price Prediction – determining the real estate price to get the best deal using metrics like zip code, population density, distance, and rating of nearby restaurants, etc. Content Recommendations – generating content list tailored to your taste on social media and streaming platforms. Customer Support Chat Bots – AI assistants that help you when you can’t speak directly to a human worker. Product Recommendations – using all the data collected from a customer to better target the ads. Plant & Crop Field Monitoring – monitoring crops and more rapidly finding potential diseases or pest damage. Intelligent Video Surveillance – detecting a moving object and identifying usual or potentially dangerous behavior. Machine learning has been up to 95% accurate at detecting what’s happening on the image/video (ResearchGate). Social Media Content Moderation – using machine learning and AI to remove potentially unwanted content, saving up to 95% of the time compared to manual content monitoring (Amazon). (IDAP Blog) Machine learning algorithms can predict stock market prices with up to 85% accuracy (one day ahead) using various techniques. (MDPI) What are The Biggest Companies That Use Machine Learning? Here are some of the biggest companies that use machine learning. CompanyMarketvalueDescription ofthe companyTesla$897 billionElectric vehicle maker using machine learning for autonomous drivingNvidia$665 billionSemiconductor design and software company providing machine learning know-how to its customersMeta$448 billionThe company that looks over Facebook,Messenger and InstagramAccenture$229 billionConsultancy and professional services firmwith a machine learning research divisionServiceNow$137 billionCloud computing software platform that uses machine learning to help businesses manage workflows CrowdStrikeHoldings$62 billionCybersecurity software that leverages machine learning to automate thedetection of online threatsPalantirTechnologies$51 billionSoftware firm that specializes in AI andmachine learning platforms so businessescan unlock insights from their dataPinterest$28 billionImage- and video-based internet searchand discovery company(Motley Fool) Top 10 companies investing in artificial intelligence technology Nvidia IBM Amazon (AWS) Microsoft C3.ai Facebook (Meta) Alphabet Intel Salesforce People.ai (Analytics Insight) Read more: VoIP Statistics: Market, Industry, Users Consumer Electronics Industry, Market Size, Trends Cord Cutting Trends Technology Addiction Statistics Average Internet Data Usage Conclusion: Machine Learning Statistics Machine learning is capable of processing large quantities of big data in a short amount of time, unlike humans. Based on statistics, machine learning is going to get more integrated into our lives: workplace, social media, retail, and apps. Hopefully, you’ve learned something interesting. Were you aware of how big machine learning (ML) was? How would you use ML methods to solve existing problems? Let us know in the comments. Sources: Twitter, FBI, FBI 2, FBI 3, GlobeNewswire, GVR, GVR 2, MaM, Statista, Statista 2, Statista 3, Statista 4, Statista 5, Forbes, Forbes 2, Forbes 3, Forbes 4, Forbes 5, ITProPortal, Kaggle, BOB, Refinitiv, Medium, TMR, Wigzo, ResearchGate, ResearchGate 2, Voicebot, Aum, Ijitee, Marsdd, SPD Group, PWC, Digital Builder, McKinsey, Google, Comscore, Eletimes, The Brink, Google Blog, Amazon, IDAP Blog, Montley Fool, Analytics Insight, Snaplogic, MDPI Peter SusicFrom a childhood fascination with sound, Peter’s passion has evolved into a relentless pursuit of the finest headphones. He’s an audio expert with over 5 years of experience in testing both audiophile and consumer-grade headphones. Quote: “After many years, I can confidently tell which headphones are good and which are terrible.” Find his honest opinion in his reviews.