23 Incredible Machine Learning Statistics [2022]

Research Summary. Machine learning and artificial intelligence are transforming the world of technology both in the private world and in business. Here are the key statistics on machine learning in business:

• 76% of companies plan to adopt machine learning or grow their existing usage in 2021.

• 73% of business leaders believe machine learning will double productivity for their employees.

• The CAGR of the global machine learning industry is about 42%.

• 56.4% of mobile users use AI-powered voice assistants.

• 61% of marketers say machine learning and AI are the number one priority in their data strategies.

For further analysis, we broke down the data in the following ways:
Adoption | Benefits | Trends and Projections | Customer Experience

Machine Learning Industry Statistics

• As of 2018, the U.S. machine learning market is worth $100 million.

• The global CAGR of the machine learning industry is about 42%, and its value is anticipated to reach almost $9 billion in 2022.

• 91.5% of firms are continually investing in AI.

Machine Learning Adoption Statistics

• In 2021, 76% of surveyed companies prioritize AI and machine learning over other IT initiatives.

  64% said that this was one of their top priorities in 2020. Many survey respondents pointed to the COVID-19 pandemic as one of the motivating factors for changing priorities.

  43% of these companies also said that AI and machine learning initiatives are more important than they initially thought, and about 25% said they wished they’d made them a priority earlier.

• 50% of companies use AI in at least one business function.

  Most use AI for product or service development (45%) or service operations (43%).

  Other uses include:

  • Marketing and sales

  • Risk

  • Manufacturing

  • Human resources

  • Supply-chain management

  • Strategy and corporate finance

• Improving customer experience and automating processes are the top two uses of AI and machine learning.

  Generating customer insights and intelligence, increasing long-term customer engagement, and generating financial insights are the three next most popular uses.

• Europe holds 44.87% of the global machine learning market share.

  North America follows closely, however, with 44.05% of the market share. Asia-Pacific, Africa, and South America share the remaining 11.08%.

Machine Learning Benefits

• Companies report that AI and machine learning have reduced their sales call times by 60-70%.

  These surveyed companies have also seen machine learning create 40-60% cost reductions and an increase of over 50% leads and appointments.

  In addition, these systems also save human salespeople time and energy, allowing them to create even more revenue for the company.

• 74% of surveyed company leaders believe their organizations could better meet their goals if they invested more in machine learning and automation.

  More specifically, 85% of executives believe that machine learning and automation will give their companies a competitive advantage.

  However, as far as making these beliefs a reality, only 50% of organizations have incentives to invest in machine learning and automation.

• Companies that adopted AI in at least one business function saw an average revenue increase of 66% within those functions.

  Marketing and sales saw the highest average increase at 79%, followed by strategy and corporate finance that saw a 73% average increase.

• 89% of surveyed company leaders believe AI will increase their employees’ performance and productivity by 2035.

  However, only 55% of their employees share this belief.

  More specifically, 73% of those business leaders believe that productivity will double by 2035 thanks to AI, and only 39% of employees also believe this.

Machine Learning Trends and Projections

• The U.S. machine learning market is projected to grow from $100 million in 2018 to $935 million in 2025.

• The demand for employees with AI and machine learning skills is growing at a CAGR of 71% from 2020 to 2025.

• AI-based startups received $18.5 billion in venture capital in 2019, and by 2023, total investments in this sector are expected to reach $100 billion.

• The number of smartphone owners who use AI-powered voice assistants daily increased by 23% from 2018 to 2020.

Machine Learning and Customer Experience

• An estimated 85% of customers’ relationships with companies are handled without talking to a human.

  AI can save sales representatives and customers time and energy by answering their questions and solving their problems quickly and efficiently, and companies are starting to take advantage of this by implementing more and more of this technology.

• 43% of millennials would pay for a hybrid bot customer service system.

  28% of business executives say the same.

  These respondents would prefer a hybrid over a system with only humans, as 35% of consumers, millennial or otherwise, are concerned about losing a human touch via shifting totally to AI. This is why so many are willing to pay for a hybrid system that gives them the benefits of both AI and humans.

• 50% of AI users don’t know that they’re interacting with AI.

  84% of survey respondents have interacted with AI, but only 34% of them believe they’ve interacted with AI.

  Another 34% say they’ve never interacted with AI, and 32% say they aren’t sure if they have or not.

Machine Learning FAQ

1. What is machine learning used for?

   Machine learning is used for a variety of everyday digital tasks. These include:

   • Email filters

   • Search engines

   • Personalized ads

   • Voice recognition

   • Voice assistants

   • Banking alert software

   Machine learning is simply an algorithm that allows programs to take in new information and produce a result based on that information. For example, in an email filter, if the machine learning system doesn’t recognize the sender and sees some suspicious links in it, it will automatically move it to the spam folder.

   Machine learning can also be used for more advanced artificial intelligence applications such as autonomous cars or robots.

2. What is the difference between AI and machine learning?

   The difference between AI and machine learning is that machine learning is a subset of AI. AI is the broad concept of a technological machine that simulates human intelligence, while machine learning is a type of AI where a machine learns from past data on its own.

   The result of these differences is that machine learning systems are designed to perform one data-based task on their own, and AI systems are designed to perform various tasks on their own.

   Examples of AI include voice assistants, online chatbots, and online gaming. Examples of machine learning include Google’s search algorithms, suggested posts on Facebook, and email filters.

3. Will machine learning replace statistics?

   No, machine learning will not replace statistics. Statisticians focus on taking a large amount of data and separating it into smaller sets of data, also known as “statistics.” Their goal is to figure out how to collect and translate data into usable information.

   The goal of machine learning, on the other hand, is to create predictions based on past data. These predictions include things like whether or not a bank transaction is legitimate or whether or not a customer is going to want to see a specific ad.

   While machine learning and statistics both use data, they use it for different things, which means one will not replace the other.

4. What are examples of machine learning?

   There are several examples of machine learning, as it’s become interwoven with our day-to-day lives. Some of the most well-known examples include:

   • Image Recognition. If you’ve ever wondered how Facebook so accurately knows when someone tags you in a photo, the answer is machine learning. Over time, the software learned to identify your face through other images you were tagged in, as well as your profile photos.

   • Speech Recognition. The only reason Amazon Alexa or Apple Siri is able to understand you so well is through machine learning. With the use of talk-to-text programming, the system can better understand language over time.

   • Medical Diagnosis. Many bots are able to learn various symptom patterns and use image recognition to diagnose illnesses.

   • Extraction. Machine learning can be used to extract structured information from unstructured data. This can be useful for massive companies that need to extract useful information from their customers.

5. What programming languages are used for machine learning?

   There are at least five programming languages commonly used in machine learning. Each has its advantages and disadvantages, so it’s hard to claim any as being superior over the others. Here are the top five:

   • Python. This is the most widely used programming language for machine learning. It has extensive libraries of information to work with, high code readability, and flexibility but can fall short in more complex scenarios.

   • R. This programming language is also common for machine learning, as its open-sourced nature makes it highly cost-effective. It’s also easy to learn, but with the downside of slow speed and potentially poor security.

   • Java and JavaScript. Mostly popular among those who already used Java prior to getting into machine learning, this programming language is good for starting new projects from scratch and has great third-party access. However, java can be slow and requires a lot of memory space.

   • Julia. This programming language is fairly new and has exceptionally high performance. However, it’s not as common and takes up a lot of memory.

   • LISP. By far the most efficient and flexible machine learning language for solving specific scenarios, LISP also comes with poor learnability and readability.

6. Is machine learning hard?

   Machine learning can be hard, depending on your skillset. First and foremost, programming can be difficult to learn no matter what type of code you’re going to work with, and machine learning adds another layer of complexity on top of that.

   Further, even for those who understand programming languages, creativity, experimentation, and tenacity are required. That makes it an unideal starting point for beginners.

   However, you shouldn’t be dissuaded from trying. The best thing you can do to improve your coding skills is to consistently practice machine learning techniques.

7. Why is machine learning the future?

   Machine learning is the future because it will affect everything from everyday conveniences to the job market. Already, machine learning is being used to enhance products like Amazon Alexa or the YouTube algorithm. These systems will only become more advanced over time, leaning toward more convenient and personalized experiences.

   What’s more, machine learning will have a profound effect on the future job market. Current investments in AI stand to automate thousands of jobs over the coming years, which will completely change the job market. Jobs easily replaced by AI will become sparse, while highly educated and tech jobs will be abundant.

   And those are just a few reasons why machine learning will have such a profound impact on our country’s future.

Conclusion

The terms “artificial intelligence” and “machine learning” are becoming increasingly commonplace in both personal and business technological use.

Businesses know this, and 50% are already using AI in at least one business function, and 78% have made AI and machine learning their top IT priority in 2021. However, about 25% are playing catchup and wish they had prioritized it earlier.

Companies have credited AI and machine learning with shorter sales call times (by 60-70%), lower costs (by 40-60%), and increased revenues (by an average of 66%) in the areas where they’re using these technologies.

Even though companies are using these proficiently, 50% of consumers who have interacted with AI know they’ve done so. Many customers are using AI and machine learning, though, as 85% of their interactions with a company are completed without speaking to a human.

This growth in machine learning and AI is also reflected financially, as the U.S.’s machine learning market is anticipated to grow from $100 million to $935 million between 2018 and 2025.

References

1. Teks Mobile. “Machine Learning in 2019: Tracing the Artificial Intelligence Growth Path.” Accessed on November 19, 2021.

2. Business Wire. “NewVantage Partners Releases 2020 Big Data and AI Executive Survey.” Accessed on November 19, 2021.

3. Forbes. “76% of Enterprises Prioritize AI Machine Learning in 2021 IT Budgets.” Accessed on November 19, 2021.

4. McKinsey Company. “The State of AI in 2020.” Accessed on November 29, 2021.

5. Market Research Future. “Global Machine Learning Market.” Accessed on November 19, 2021.

6. Harvard Business Review. “Why Salespeople Need to Develop ‘Machine Intelligence.’” Accessed on November 19, 2021.

7. Think with Google. “Take the Lead: New Research Shows Top Marketers Use Machine Learning to Drive Growth.” Accessed on November 19, 2021.

8. Forbes. “Executives Say Artificial Intelligence Will Boost Productivity, But Their Employees Aren’t So Sure.” Accessed on November 19, 2021.

9. Forbes. “AI’s Effect on Productivity Now and in the Future.” Accessed on November 19, 2021.

10. Voicebot.AI. “Voice Assistant Use on Smartphones Rise, Siri Maintains Top Spot for Total Users in the U.S.” Accessed on November 19, 2021.

11. PwC. “Bot.Me: A Revolutionary Partnership.” Accessed on November 19, 2021.

12. Forbes. “Half of People Who Encounter Artificial Intelligence Don’t Even Realize It.” Accessed on November 19, 2021.

13. The Royal Society. “What is Machine Learning?” Accessed on November 19, 2021.

14. Java T Point. “Difference Between Artificial Intelligence and Machine Learning.” Accessed on November 19, 2021.

15. Silicon Valley Data Science. “Machine Learning vs. Statistics.” Accessed on November 19, 2021.

Author

Abby McCain

Abby is a writer who is passionate about the power of story. Whether it’s communicating complicated topics in a clear way or helping readers connect with another person or place from the comfort of their couch. Abby attended Oral Roberts University in Tulsa, Oklahoma, where she earned a degree in writing with concentrations in journalism and business.