Machine learning is being adopted heavily across industries, the latest data reveal that nearly 50% of small and large businesses have adopted machine learning worldwide.
When we dug deeper into the subject, we found that over two-thirds of the businesses in healthcare, manufacturing, and finances have adopted ML.
We have compiled some insightful data in this post to help you understand the current state of machine learning and how it will shape other industries in the coming time.
Machine Learning Statistics: Top Picks
- 48% of businesses globally use machine learning.
- The global machine learning adoption rate is growing at a rate of 42% since 2018.
- The market size of machine learning is forecasted to be valued at $158.80 billion by the end of 2023.
- There will be a 22% increase in employment of machine learning engineers every year from 2023 to 2030.
- Almost 92% of the leading businesses stated that they have invested in Machine learning and AI.
- 57% of companies and businesses use machine learning to improve consumer experience.
- 49% of the companies use Machine learning and AI in marketing and Sales.
- 34% of the companies in the United States have adopted machine learning, while 42% are exploring ML and planning to adapt it.
- 80% of the companies report that investing in Machine learning increased their revenue.
- 12.5% of the time of an employee working in the Machine learning field is invested in data collection.
Machine Learning Market Statistics
- The machine learning market size is projected to reach $158.80 billion in 2023.
According to a Statista report, the machine-learning market was valued at $72.17 billion in 2022.
It is expected to reach $528.10 billion by the end of 2030, growing at a CAGR of 18.73% between the forecast period of 2023 and 2030.
Here are further details about the Machine Learning market size and its predictions:
|Year||Machine Learning Market Size||Percentage Change in Market Size|
- The largest Machine learning market size is recorded in the United States.
The value of the machine learning market in the United States is projected to be $56.75 billion in 2023.
China is the second largest market and is followed by Germany and the United Kingdom.
|Country||Machine Learning Market Size 2022||Machine Learning Market Size 2023|
|United States||$28.29 billion||$56.75 billion|
|China||$7.38 billion||$19.36 billion|
|Germany||$3.21 billion||$6.73 billion|
|United Kingdom||$3.25 billion||$6.41 billion|
|Japan||$2.72 billion||$6.39 billion|
|France||$2.04 billion||$4.34 billion|
|Canada||$1.97 billion||$3.92 billion|
|Australia||$1.49 billion||$3.25 billion|
|Italy||$1.35 billion||$3.07 billion|
|South Korea||$1.27 billion||$3.05 billion|
- The Manufacturing Industry holds the largest share of the Machine learning market.
The Industry owns 18.88% of the share. The second largest market of the Machine learning industry is the Finance industry, with a market share of 15.42%.
The healthcare and Transportation industry follows it.
Here are further details about different industries’ machine learning market share.
|Industry||Market share as of 2022|
|Business & legal services||9.86%|
|Media & Entertainment||5.19%|
Machine Learning Industry
- Newsle led the global Machine learning industry with the highest market share of technologies.
It owned 88.71% of the share of machine learning technologies worldwide. TensorFlow and Torch followed it.
This machine learning software helps the machines to artificially learn and improve the functions based on experience without being programmed to do so.
Here are further details about the market share of the leading technologies worldwide.
|Machine learning technology||Market share|
- Machine learning and artificial intelligence industry advancements are expected to increase the GDP by 14%.
The budgets for machine learning projects are expected to increase by 25%, with highest growth in sectors like IT, banking, and manufacturing.
Further, in a study conducted by McKinsey, 50% of respondents reported that they had adopted Artificial intelligence and machine learning in at least one business function.
Investments In Machine Learning
- Open AI was the most funded Machine Learning platform in 2022.
Open AI, the parent company of Dall-E and ChatGPT received almost $1.01 billion in funding.
Scale AI, was the second most funded Machine Learning platform, received just $602.86 million in funding.
Here are further details about the funding raised by the top Machine Learning Platforms worldwide.
|Machine Learning Platform||Funding Received|
|Scale AI||$602.86 million|
|Inflection AI||$225 million|
|Weights & Biases||$200 million|
|Hugging Face||$160 million|
|AI21 Labs||$119 million|
Did You Know? OpenAI spends $700,000 every day to run ChatGPT.
- Almost 92% of the leading businesses have invested in Machine learning and AI.
The businesses notably invested in machine learning aspects like speech and pattern recognition, regression analysis, standard deviation observations, robotics, etc.
Source: Business Wire.
AI Fundings Worldwide
- The global corporate investments in artificial intelligence reached almost $92 billion in 2022.
It is a slight decrease of $1.6 billion compared to the previous year. In 2021, the investments made in total corporate AI were recorded to be $93.5 billion.
Here are further details about the global total corporate investments in artificial intelligence.
Machine learning use cases
- 57% of the companies use machine learning to improve consumer experience.
50% of businesses use ML and AI for generating customer insights and intelligence. The other most used cases of machine learning and artificial intelligence are building brand awareness, reducing customer churn, increasing customer loyalty, etc.
Here are further details about the use cases of machine learning.
|Use cases||Percentage of companies using it|
|Improving consumer experience||57%|
|Generating consumer insights and intelligence||50%|
|Increasing long-term consumer engagement.||44%|
|Interacting with customers||48%|
|Building brand awareness||31%|
|Increasing customer loyalty||40%|
|Acquiring new consumers||34%|
|Reducing consumer Churn||22%|
Role of Machine Learning in Voice Assistants
- 56.4% of mobile users use AI-powered voice assistants.
Whether you want to set a reminder, a timer, or search a query on Google search, you might have asked SIRI, Google Assistant, or Alexa to do it for you.
That’s how machine learning has transformed the use of voice assistants.
- Over half of the adults use voice search and voice assistants daily for their day-to-day tasks.
This 50% of the population primarily includes GenZ, followed by Millennials. They usually prefer voice search over typing.
- Google Assistant and Apple’s Siri are the most popular voice assistants worldwide.
Each holding a 36% share in the voice assistant market.
Microsoft Cortana owns 19% share, and Amazon’s Alexa has a 25% share.
The remaining 1% belongs to other voice assistants.
Machine Learning In Business
- 73% of business leaders believe that machine learning will improve their productivity.
Some business executives believe it can double their productivity and increase the accuracy of the work done by employees.
This is because machine learning applications help reduce the time required to work, assisting employees to achieve their targets.
- 15% of the organizations are advanced ML users.
These organizations include a list of tech companies from Silicon Valley and other parts of the globe. Companies like Open AI, Microsoft, Google, Apple, etc., use advanced ML to create products and robots.
- 45% of consumers prefer chatbots as the primary mode of communication.
When speaking about consumer services, chatbots have become the most preferred mode. Hence, most of the top business websites use Chatbots to help the consumers with the services they seek.
- 44% of organizations and businesses stated that they fear losing to startups if they are too slow in implementing AI.
Startups widely use AI and machine learning in almost every sector.
Hence, it is valid for big enterprises and organizations to have their fear in case they can’t adapt to new trends.
- Over two-thirds of the consumers are willing to submit their data to AI to improve their experience with business.
Personalized experiences have become the need of the consumers. Hence, 70% of the consumers expected personalized experiences from the brands in exchange for their personal data and preferences.
Machine Learning In Marketing
- 49% of organizations use machine learning and AI in marketing and sales.
All industries, from production to distribution, apply machine learning to identify sales prospects. At the same time, 48% of organizations use it to gain insights into their prospects and consumers.
Source: Harvard Business Review.
- Nearly one-third of the organizations using machine learning in sales and marketing have observed increased revenue.
According to the survey conducted by Harvard, 31% of the respondents reported that they noticed a rise in their company’s market share and revenue after implementing AI and ML for sales and advertising.
- Nearly one in ten sales and marketing employees fear losing jobs due to machine learning.
Over 7% of the respondents reported fear of job loss due to the increased usage of AI and automation in the field.
Source: Harvard Business Review.
- Half of the leading performance agencies implemented their saved time in strategic activities.
Implementation of ML and AI has led to a reduction in the amount of time required for a task. Hence, over 50% of the leading organizations have started implementing more than 30% of their time in strategic activities.
At the same time, two-thirds of the marketing leaders reported that their team can now focus more on strategic marketing activities.
Source: Think with Google.
- Over half of organizations use machine learning in content personalization.
Over 56.5% of the organizations reported using Artificial intelligence and machine learning to personalize their sales and marketing content. At the same time, only 14.8% of the organizations reported using AI and ML to provide the next best offer.
Here are further details about the most common uses of machine learning for marketing.
|Uses of AI and ML for marketing||Percentage of organization|
|Predictive analytics for customer insights||56.5%|
|Optimizing marketing content||33.9%|
|Conversational AI for customer service||25.2%|
|Next best offer||14.8%|
Source: CMO Survey.
- 61% of the marketers reported that artificial intelligence is one of the most critical aspects of their data strategy.
They added that AI has helped them level up their data strategies.
- Marketing leaders are 200% more likely to increase their investments in technologies and automation for marketing activities.
Machine Learning In Healthcare
- Machine learning does a great job in healthcare and sometimes performs better than human specialists.
A recent meta-analysis found that deep learning models had a sensitivity rate of 87.0% and a specificity rate of 92.5%. At the same time, the sensitivity rate of humans was 86.4%, and their specificity rate was 90.5%.
- Japan plans to have 75% of the elderly care performed by AI.
Though these solutions are effective, they are not widespread, and very few countries and healthcare centers have adopted them.
However, healthcare companies and pharmaceutical companies have already taken a step to make AI widely available.
- The global AI healthcare market was valued at $11.06 in 2021.
It is expected to reach $187.95 billion by 2030.
Here are further details about AI in the healthcare market size recorded over the past years.
|Year||AI in the healthcare market size|
Source: Precedence Research, Statista
- 70% of drug discovery costs can be cut with the application of AI and ML.
Source: Insider Intelligence
- ML can help achieve up to 95% accuracy in predicting COVID-19-related physiological deterioration.
It also can predict deaths 20 days in advance with the help of an ML-based solution.
Machine Learning Adoption Statistics
- The global machine learning adoption rate is expected to be around 42% CAGR between 2018 and 2024.
65% of businesses believe technology will help them analyze the data to make better decisions.
Hence, the latest statistics from the Statista survey show that 44.87% of small businesses and large enterprises have adopted machine learning.
- Almost half of the businesses have deployed ML in multiple areas.
According to research by Refinitiv, 46% of the companies have already deployed ML in their core business, and 44% have deployed ML in their pockets.
10% of the respondents are experimenting with the infrastructure and people and are looking forward to investing in it.
- 77% of businesses are either using or exploring AI globally.
According to IBM, 35% of businesses have started using AI. At the same time, 42% of the companies stated that they are exploring AI and looking forward to adopting it soon.
The global adoption rate of AI
- 34% of the companies have adopted AI and ML in the United States.
Further, 42% of the companies are exploring AI and ML and are looking forward to adopting it.
On the other hand, 58% of the companies have adopted AI and ML in China, and 30% of the companies are still exploring it.
Here are further details about the countries that have adopted AI or are exploring AI in their businesses.
|Country||Share of businesses that have adopted AI||Share of businesses exploring AI|
|United Arab Emirates||38%||40%|
- North America leads in ML adoption, followed by Asia and Europe.
The survey conducted by Refinitiv also highlighted that 80% of businesses and companies in North America have adopted Machine learning. At the same time, 37% and 29% of the companies and businesses in Asia and Europe have adopted Machine Learning in their business.
Machine Learning Employment Statistics
- 82% of companies and businesses need employees with machine learning skills.
Machine learning skills have become a basic necessity when applying for a job. This is especially true in tech, marketing and sales, finance, and retail industries.
So make sure to value your resume with machine-learning skills and increase your chances of getting selected.
- The employment of machine learning engineers is projected to grow at a rate of 22% between 2020 and 2030.
This simply answers why most of the students are choosing machine learning as their major in engineering.
- Convinced by the importance of machine learning and Artificial intelligence, companies have expanded their budget for recruiting ML employees.
At the same time, companies are now allocating their budget to
- Retraining and upskilling existing employees: 68% of the companies.
- Identifying and recruiting skilled talents from other companies and organizations: 58% of respondents
- Recruiting from universities: 49% of the companies.
- Organizations have started to focus on recruiting hard-skill employees as they struggle to find specialists with adequate AI and ML knowledge.
Here are further details of the skills most businesses and companies look forward to in their employees.
- Coding programming and software development: 35% of companies.
- Data visualization and analytics: 33% of companies
- An understanding of security, governance, and ethics: 34% of companies.
- Advanced degree in a closely related field to AI and ML: 27% of companies.
- 37% of European businesses consider problem-solving the most critical skill an employee must have.
As soft skills are needed in tech roles, over one-third of respondents to IBM’s survey address the skill gap.
They further added that these crucial soft skills are missing in 23% of applicants.
- Data Scientist is an in-demand job role that employers find hard to fill.
Even though several applicants apply for the data scientist role, many of them either lack the experience or the required knowledge for the job role.
- 40% of tech employees and job seekers consider the knowledge of all programming languages and software engineering to be a critical skill for an ML or tech workforce.
- In AI and Machine learning, 97 million new jobs will be created across 26 countries.
This is due to the increase in Machine learning and AI applications worldwide. So, with the increasing need for AI, job titles are expected to increase.
Source: World Economic Forum.
- The employment of machine learning engineers is expected to grow by 22% between the years 2020 and 2030.
Source: US Bureau of Labor Statistics.
Bonus: Read our article on Employee Engagement Statistics to know the real scenario of the employees at workspace .
Machine Learning Benefits
- 80% of the people reported that using machine learning rarely decreases the business’s expenses, but it definitely increases the revenue.
In a survey conducted by McKinsey, it came to the limelight that adaptation of machine learning does not always lead to cost-cutting, but it surely leads to an increase in the revenue.
While this isn’t surprising, it may be indigestible to the business that adopted it for both factors.
- 45% of the royal economic gains by 2030 will be due to Machine learning and AI.
AI-driven product enhancements stimulate consumer demand, and businesses and companies widely use AI products. This usage of ML and AI is expected to increase a large amount over the upcoming years, considering the increased accuracy and productivity rates.
Here are further details about the estimated economic growth of different regions by 2030.
|Region||Estimated economic growth|
|Asia, Oceania, and Africa||5.6%|
Source: PwC Research.
- 85% of executives believe machine learning and automation will give their company a competitive advantage.
Similarly, 74% of company leaders believe their business or organization could perform better and meet goals if they invest in machine learning and automation.
However, only 50% of the organizations have incentives that can be invested in Machine learning and automation.
- Companies that adopted AI in at least one business function witnessed an average increase of 66% in revenue within those functions.
The highest increase in revenue was witnessed in sales and marketing (79%). It was followed by strategy and corporate finance, which witnessed an average increase of 73%.
Machine Learning Challenges
- 12.5% of the employee’s time is lost in data collection.
This sums up to 5 hours a week in a 40-hour workweek.
Source: Data Dilemma Report.
- The top challenge faced by 56% of the companies while implementing AL and ML is staff skills.
At the same time, 42% of the companies face challenges of facing unknown problems, and 26% are unable to find the starting point for implementing technology.
- The top challenge in machine learning is poor-quality data.
According to the Refinitiv survey, 43% of the respondents consider poor data quality as the top challenge.
The other top challenges faced by the companies are
- Lack of data availability: 38% of the respondents
- Finding data science talents: 33% of the respondents.
- 43% of businesses face challenges in scaling up when adopting AI and machine learning.
Algorithmia survey further stated that 41% of companies face issues in versioning and reproducing machine learning models.
At the same time, 34% of the respondents stated that fetching organizational alignment and senior buy-in for ML initiatives is one of the top challenges.
- Only half of the companies with extensive experience in machine learning check for data privacy implications.
O’Reilly’s report brings into the limelight that only 53% of the companies check for data privacy implications in their machine learning project. The report further states that this number drops to 43% when all companies are included.
- 56% of respondents stated that they experience issues with audibility and security requirements when deploying artificial intelligence and machine learning.
Here are further details about the challenges that are faced by companies while deploying machine learning.
|Challenges faced in||Percentage of companies|
|IT governance, security, and audit-ability requirements||56%|
|Monitoring model performance||37%|
|Frequent updates are required to maintain model quality, performance||36%|
|Managing, and allocating ML-related infrastructure costs||35%|
|Duplication of efforts across the organization||36%|
|Getting organizational alignment and senior buy-in||30%|
|Programming language and framework support||49%|
|Versioning and reproducibility in models||27%|
Latest Developments in Machine Learning
- Tesla vehicles had driven 35 million autonomous miles under the Full Self-Driving (FSD) Beta as of July 2022.
- Google’s lung cancer detection application outperformed human radiologists with an average of 8 years of experience in 2019.
- The machine learning model predicted the mortality rates of COVID-19 patients with 92% accuracy in 2020.
- The error rate of the speech recognition system was less than 5% as of 2019.
Source: Teks Mobile
- The translation errors of Google Translate were reduced by 60% after the translation algorithm was changed to GNMT, an algorithm powered by machine learning.
- Machine learning can predict the highs and lows of the stock market with 62% accuracy.
- 40% of the annual value created by analytics is from deep learning techniques.
- Deep voice, an AI and ML-powered voice cloning tool, required just 3.7 seconds to clone a voice.
Read Other Trending Stats on DemandSage:
51% of the companies stated that they use machine learning and claim to be early adopters of AI. The rest, 49%, are exploring ML and planning to use it.
Machine learning is a basic skill required for more than 45,000 jobs in the United States listed on LinkedIn. Further, machine learning is expected to grow in demand, making it a promising career in 2025 and beyond.
Low codes and No code machine learning is expected to become popular by the end of 2023. Without extensive knowledge or technical expertise in coding, individuals can develop and implement machine learning models.
The Future of Jobs Report 2023 states that the demand for AI and machine learning specialists is expected to grow by 40% by the end of the year. This will generate 1 million job opportunities as AI and machine learning usage continues to improve.
It is worth it to learn machine learning in 2023. Machine learning has been one of the most in-demand fields in recent years, and it is expected to grow further in the future. 2023 is the best time to make a transition and get into the field.
Machine learning or AI cannot replace ML engineers. Machine learning algorithms need to be made, run, maintained, and improved by someone. Hence, no AI or ML model can replace ML engineers.