Machine Learning Statistics 2022 Facts, Trends and Adoption You Must Need To Know

Barry Elad

Updated · Sep 22, 2022

Machine Learning Statistics 2022 Facts, Trends and Adoption You Must Need To Know

Machine learning statistics: Technology is introducing new concepts every year to improve the overall business processes over the universe. But technology is not only useful in corporations but also in households. Machine learning, metaverse, augmented reality, and virtual reality is all technical terms introduced recently in the market. And they are proving the best possible solutions in every aspect of the world.

What is Machine Learning?

Machine learning is a computerized term that is focused on the term “learn”. In the other sense, it is a part of augmented reality which allows machines to behave just like human beings. Machine learning is more flexible than other technical terms and it has the ability to fully automate processes. A great example of Alexa or Siri who are taking over the world using machine learning techniques is just amazing. You simply a question to them, or just tell them to perform any task on your electronic devices. They read you daily news, write a message or email, call someone on the phone or just entertain you, all of these features of course hands-free!

Advantages Of Machine Learning?

  • Machine learning can automate each and every process available in the corporate world or in households
  • They can listen like humans and talk like human beings
  • Machine learning provides intellectual learning which allows the business processes to run on an error-free basis.
  • Increases productivity
  • Improves digitalization
  • Provides distance e-learning
  • With the help of machine learning, remote work is possible
  • Supports technological developments
  • It has the ability to replace manual work

Disadvantages Of Machine Learning

  • Machine learning is an expensive technology
  • Requires continuous improvements otherwise, it counts as an old technology
  • Machine learning includes difficult calculations, algorithms, and much more, which is required by humans to build upon
  • This technology can be addictive, and it can destroy the intelligence of human beings
  • Focuses only on high-end technology

Which Software is Using Machine Learning?

Hotjar: Hotjar uses AI and machine learning to understand ad track the user’s behavior in the online world. It has powerful features to convert raw data into usable data.

SAP Crystal: In the case of SAP crystal, it can transform the static report into a dynamic one.

Tableau: Tableau has the ability to analyze visualizations and data without any further need for advanced and costly technology. The reports are independent.

Microsoft Power BI: In the combination with AI and machine learning complex data can be analyzed, gathered, and shared.

SAP Business: In SAP business it is easy to perform visualization steps by analyzing the complex business data.

General Machine Learning Statistics

  • Machine learning is increasing its presence in AI funding over the global


  • Augmented intelligence is increasing its voiced-based operations


  • Machine learning is increasing its participation in business processes

top machine learning use cases

  • According to the market data forecast for 2020, in the year 2019, the global revenue for physical AI businesses such as chips reduced to 12%.
  • McKinsey states that the expected growth of AI technology by the year 2030 will rise to $13 trillion.
  • It is expected to have a value of $87.68 billion in the hardware of the AI market at a CAGR of 37.60% from the year 2019 to 2026.
  • Fortune business insights studies from the year 2020 that, the global market of machine learning will be valued at $117.19 billion at a CAGR of 39.2% by the year 2027.
  • $44.3 billion results in the global deep learning market at a CAGR of 39.2% by the year 2027.
  • Statista estimated that US deep learning market software will rise to $80 million by the year 2025.
  • Various companies globally in the first quarter of 2019 funded a total of $28.5 billion.
  • From the year 2019 to 2023, Technavio estimates that the estimated market size will rise to $75.54 billion including the global AI industry.

Voice-Supported Technology In Machine Learning

Alexa and Siri are the top examples of voice-controlled machine learning. The importance of voice-supported technology improved as it became available on handy electronic devices such as mobile phones.

Machine Learning Statistics -Voice Assistant Usage

  • According to the eMarketer, around 128 million people in the United States of America uses voice assistant.
  • On the other hand, in the year 2019, the number of users in the US was only 115.2 million.
  • In those two pandemic years, more people chose voice assistants resulting in 20% of 2019 of the population, whereas in the year 2020 the number rose to 25%.
  • The global number of voice-based assistants rose to 7% in pandemic years
  • According to Voicebot. at, in 2019, 80.5% of users who are under 30 prefer mobile phones, whereas only the number results in 60.5% of people in the old age group.
  • On average people from the age group between 30 to 44 and 45 to 60, use voice assistants on smartphones 74.7% and 68.8% respectively.
  • By the year 2023, around 8 billion people are predicted to use voice assistants.
  • Motor Intelligence in 2020 predicted the value of the global natural language processing market will be $42.04 by 2026.

Machine Learning Adoption

As the competition in the corporative world increased, it gave an advantage to technology-driven companies to adopt machine learning. Machine learning is also increasing its capabilities to provide maximum benefits to users around the world.


  • McKinsey in the year 2020 stated that 50% of the companies around the world have already shifted their technology to AI for at least one business process.
  • In the year 2019, 1 out of three 3 big IT corporations was planning for adopting machine learning for business analytics.
  • Machine learning will fulfill the purpose of security in many companies resulting in at least 25%.
  • Machine learning also supports sales and marketing therefore 16% of IT players will use the technology.
  • Mckinsey studied in 2020 and 80% of the industries reported that they could increase their revenue with machine learning, but it didn’t aid in cost-effective business activities.
  • Machine learning and AI adopted will help to increase the GDP by 14% by the year 2030.
  • Machine learning poses great challenges for technology adoption in terms of scaling resulting in 43% and versioning of machine learning models up to 41%.

Adoption of Machine Learning in Business

IT industries as well as other business processes are adopting machine learning as they are easing day-to-day activities. Today, there’s not a single field where this technology is not involved. Be it healthcare, construction, education, finance, hospitality, or engineering and the list is unending. The following statistics provide knowledge about what percentage of ML is being involved in the globe today.

  • Remote workers such as virtual agents are being employed in companies to perform their duties as stated by Dataversity, 2019.
  • Customers around the world of which 62% have agreed to submit the backend data to improve the functionality of business processes.
  • Around to Forbes in the year 2020, LinkedIn published available jobs in machine learning as required skills resulting in 44,000 jobs and 98,000 required jobs over all the world.
  • Till today, 91.5% of business processes are increasing their investments in AI.
  • Around 75% of the projects in AI are now led by C- level executives.
  • Globally 15% of the companies have maximum included machine learning users.
  • The overall productivity increases, while AI is involved in the processes, resulting in a 40% increase.
  • According to McKinsey, 51% of companies adopted AI technology in the early phase only.
  • Till today, 49% of the companies are still acknowledging the technology.
  • The projected improvement in productivity after involving AI is increased up to 54%.

Achievements By Machine Learning

Machine learning is a topmost advanced technology currently base for many businesses easing all their processes. The following analysis has been developed by many institutes.

  • 95% time machine learning is accurately predicting the patient’s death states Bloomberg.
  • According to Indeed 2020, machine learning was the second most in-demand skill needed in the list of vacancies.
  • Goggle’s deep learning program with an accuracy of 89% detecting breast cancer.
  • According to Forbes 2019, it takes 3.7 seconds to clone a voice for AI-powered voice cloning
  • Teks mobile states that, by the year 2025, Japan will deliver 3.4 of elderly care services using AI robots
  • Google claims that there’s a 60% decrease in errors happening while translating using Google translate.
  • The rate of error is reduced to 5% in terms of speech recognition systems
  • Microsoft states that AI with an accuracy of 62% recognizes highs and lows in the stock market.

Machine Learning in Marketing

  • As machine learning is not cent percent perfect, a study by Smart brief in the year 2020 showed that, 41% of the customers around the world prefer human being to solve their issues rather than an AI.
  • Netflix was able to save up to $1 billion by applying machine learning algorithms in its marketing activities.
  • Amazon uses AI and machine learning at their designated fulfillment center which means, people around the world are being delivered 10 million products in a day.
  • According to Vox 2019, around 3,000 Amazon go stores will be open focusing on AI and machine learning in the US.
  • A number of total 87% of companies that use AI are focusing their activities mainly on email marketing and sales forecasting.

Machine Learning Actual Cases

Till today, many companies have invested in machine learning along with AI. As in the current market, competition has increased, therefore it automatically becomes impossible to just operate from old way methods and stand in the competition.

  • According to Deloitte in the year 2019, Frontrunners were able to grab 47% of the sales and marketing and 32% were reduced from the operational costs
  • Businesswire states that only 14.6% of the companies have involved AI possibilities on a larger basis.
  • Customer service in NLP will be equipped with more advanced applications to ease the process.
  • As of 2019, Tesla managed to land on 1.88 billion using the advantaged technology.

Machine Learning Use Case Frequency


The company’s most important department is customer service. It provides assistance to customers and resolves their questions. Machine learning and AI have been too focused on customer service. This report shows data from a time when everyone was locked in their homes by a pandemic. This shows that machine learning can be used to enable remote work for all employees around the world.

Machine learning is an important component of remote work. Employers can connect with employees’ desktops via various apps. There are many remote job opportunities on LinkedIn today. Employees can now work remotely on LinkedIn since the pandemic. This means that 35% of the 5 days they work are in the office, and 2/3 are at home.


Looking at the numbers, it can be said that by the year 2025 each and every company around the world will be equipped with AI and machine learning.  Every sector is being benefited from this technology. The number of people using AI even in households will reach to sky soon in the coming years. As of today, AI is in the initial step and people are still planning to accommodate it with business processes.


To conclude, the world will be soon powerful enough to work with the automated machines designed by humans. The numbers 33%, and 40% will soon reach 100% easing all the processes around the globe. Machine learning technology will be more advanced in the future and change the face of everything.

Barry Elad
Barry Elad

Barry is a lover of everything technology. Figuring out how the software works and creating content to shed more light on the value it offers users is his favorite pastime. When not evaluating apps or programs, he's busy trying out new healthy recipes, doing yoga, meditating, or taking nature walks with his little one.

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