Machine Learning Statistics, Trends for 2023

Today, we’re going to explore some of the most valuable Machine Learning statistics for 2023.

Let’s get started.

Machine learning stats

Table 1: Machine Learning Statistics

Statistic	Percentage/Accuracy
Netflix savings due to machine learning algorithm	$1 billion
Accuracy of machine learning in predicting stock market highs and lows	62%
Reduction in Google Translate errors when changed to GNMT	60%
Accuracy of machine learning in predicting mortality of COVID-19 patients	92%
Accuracy of Google’s AI machine learning algorithm in predicting patient death	95%
Mobile users using AI-powered voice assistants	97%
Google’s lung cancer detection AI outperforms six human radiologists	–
Google’s Deep Learning ML program accuracy in detecting breast cancer	89%
AI could prevent cyber attacks and security threats	86%
Percentage of elderly care services in Japan delivered by AI by 2025	75%
Millennials who would pay a premium for a hybrid human-bot customer service channel	43%

Note: “-” indicates that a percentage/accuracy value is not applicable for the given statistic.

How much did Netflix save due to its machine learning algorithm?

Netflix saved $1 billion due to its machine learning algorithm, which combined personalization and content recommendations.

What is the accuracy of machine learning in predicting stock market highs and lows?

Machine learning has an accuracy of 62% in predicting stock market highs and lows.

How much was Google Translate errors reduced with machine learning?

Google Translate errors were reduced by 60% with the implementation of GNMT, a translation algorithm powered by machine learning.

What was the accuracy of machine learning in predicting the mortality of COVID-19 patients?

Machine learning demonstrated a 92% accuracy in predicting the mortality of COVID-19 patients.

What is the accuracy of Google’s AI machine learning algorithm in predicting a patient’s death?

Google’s AI machine learning algorithm has an accuracy of 95% in predicting a patient’s death.

What percentage of mobile users are using AI-powered voice assistants?

97% of mobile users are using AI-powered voice assistants.

How many human radiologists are outperformed by Google’s lung cancer detection AI?

Google’s lung cancer detection AI outperformed six human radiologists.

What is the accuracy of Google’s Deep Learning ML program in detecting breast cancer?

Google’s Deep Learning ML program has an 89% accuracy in detecting breast cancer.

How much cyber attacks and security threats could AI prevent?

AI could prevent 86% of cyber attacks and security threats.

What percentage of all elderly care services in Japan will be delivered by AI by 2025?

By 2025, 3/4 of all elderly care services in Japan will be delivered by AI.

What percentage of millennials would pay a premium for a hybrid human-bot customer service channel?

43% of millennials would pay a premium for a hybrid human-bot customer service channel.

Machine Learning Adoption Statistics 2023

Table 1: Machine Learning Market and Adoption Statistics

Category	Data
Global Machine Learning Market Value in 2021	$15.44 billion
Projected Global Machine Learning Platforms Market Size by 2028	$31.36 billion
Projected AI Market Value in 2023	$500 billion
Projected AI Market Value in 2030	$1,597.1 billion
Forecasted Global Explainable AI Market Size by 2030	$21 billion
Projected Global Natural Language Processing Market Size by 2029	$161.81 billion
North America’s Share of Global AI Market in 2022	43%
Percentage of Companies Using AI in Business	35%
Percentage of Companies Exploring AI	42%
Percentage of Respondents Considering AI and ML Projects of High Priority	49%
Percentage of Respondents Claiming AI and ML Initiatives as Top Priority in 2021	28%
Percentage of Organizations Planning to Implement AI in the Next 3 Years	46%
Percentage of Enterprises Implementing Some Form of AI by 2025	Nearly 100%
Percentage of Companies Adopting AI Due to Labor or Skills Shortages	25%

Note: CAGR refers to Compound Annual Growth Rate.

1. Experts forecast the global machine learning adoption rate to be around 42% CAGR between 2018 and 2024.

2. 91.5% of leading businesses have ongoing investments in ML and AI.
3. Advancements in machine learning and artificial intelligence may increase the global GDP by 14% by 2030.

4. 44% of respondents said they deployed ML in pockets.

5. Using machine learning rarely decreases business expenses, but it surely does increase your revenue, as reported by 80% of people in a survey by McKinsey.
6. 25% of top IT leaders believe machine learning programs will help curb security risks in their respective companies.

7. 33% of top IT leaders adopt machine learning mainly to improve their business analytics processes.

Q: What is the current value of the global machine learning market, and what is its projected growth?

A: The global machine learning market was valued at $15.44 billion in 2021. It is expected to grow from $21.17 billion in 2022 to $209.91 billion by 2029, at a CAGR of 38.8%.

Q: What is the projected size of the global machine learning platforms market by 2028?

A: The projected size of the global machine learning platforms market is $31.36 billion by 2028.

Q: What is the projected value of the AI market in 2023 and 2030?

A: The AI market is expected to reach the $500 billion mark in 2023 and $1,597.1 billion in 2030, with a registered CAGR of 38.1% from 2022 to 2030.

Q: What is the projected size of the global explainable AI market by 2030?

A: The forecasted size of the global explainable AI market is $21 billion by 2030.

Q: How much is the global natural language processing market expected to grow by 2029?

A: The global natural language processing market is expected to grow from $26.42 billion in 2022 to $161.81 billion by 2029.

Q: Which region accounted for the largest share of the global AI market in 2022?

A: North America accounted for 43% of the global AI market in 2022.

Q: How many companies report using AI in their business?

A: 35% of companies report using AI in their business.

Q: What percentage of organizations are planning to implement AI in the next three years?

A: 46% of organizations are planning to implement AI in the next three years.

Q: What are the top drivers of AI adoption?

A: The top drivers of AI adoption are the technology’s increasing accessibility, the need to reduce costs and automate key processes, and the increasing implementation of AI into standard off-the-shelf business applications.

Q: Why are 1 in 4 companies adopting AI?

A: 1 in 4 companies is adopting AI because of labor or skills shortages.

Q: Are larger or smaller companies more likely to have actively deployed AI?

A: Larger companies are twice as likely to have actively deployed AI, while smaller companies are more likely to be exploring or not pursuing AI at all.

Machine Learning Application Areas Statistics

Machine Learning Application Areas	Statistics
Least critical use of machine learning	Oryx
Number of stories written by The Washington Post AI during the 2016 US Presidential campaign and the Rio Olympics	>850
Highest usage of machine learning	Business analytics (33%)
Second highest usage of machine learning	Security (25%)
Third highest usage of machine learning	Marketing (16%)
Percentage of executives employing at least one machine learning model in production	58%
Percentage of executives planning to use machine learning for sales forecasting and email marketing	87%
Fastest-growing sector for smart speakers worldwide	Smart speakers
Countries leading the way in the growth of smart speakers	US, China, Korea
Accuracy of machine learning compared to humans in lip-reading	Much more accurate

What is the least critical application area for machine learning?

According to Oryx, machine learning is considered the least critical application area.

How many stories did The Washington Post AI write during the 2016 US Presidential campaign and Rio Olympics?

The Washington Post AI wrote more than 850 stories during the 2016 US Presidential campaign and the Rio Olympics.

Which application area has the highest usage of machine learning?

Business analytics has the highest usage of machine learning, followed by Security and then Marketing.

What percentage of executives with machine learning employ at least one model in production?

58% of executives with machine learning employ at least one machine learning model in production.

What percentage of executives plan to use machine learning in their sales department?

87% of executives plan to use machine learning within their sales department for sales forecasting and email marketing.

What is the fastest-growing sector in the world for smart speakers?

Smart speakers are fast becoming the world’s fastest-growing sector, with the US, China, and Korea leading the way.

Is machine learning more accurate than humans at lip-reading?

Yes, machine learning has been found to be much more accurate than humans at lip-reading.

Machine Learning Use Cases 2023

8. Cost reduction is the number one reason why companies use machine learning.

9. According to C-level Executives and Data Scientists, the top use of ML is Risk Management (82%).
10. 14% of the organizations surveyed took 0 to 7 days to deploy a single machine learning model.
11. 51% of respondents from a survey by O’Reilly say they use internal data analysts and machine learning engineers to build their ML models.

12. An ML algorithm can detect if a child has epilepsy with 73% accuracy.
13. A machine learning algorithm can detect lung cancer with 94% accuracy.
14. A deep learning model can predict under-performing businesses with an accuracy of 62%.
15. A machine learning algorithm can read lips with 95% accuracy.
16. Machine learning facts show that human editors can only tell the difference between AI and human authors 50% of the time.
17. After Nissan started using Google’s automated bidding products, its conversion rates increased by 67%.
18. The accuracy of predicting a patient’s death by Google AI is 95%.
19. Amazon uses a machine-learning algorithm to automate picking items and packing them in logistic regression.
20. Netflix saved over $1 billion because of its machine learning system.

21. Facebook has an AI-powered face recognition model with 97% accuracy.
22. Tesla recorded over 4 billion autonomous miles as of Jan 2021.

Machine learning skills demand & employment stats

Table 1: Demand and Supply of AI-Related Skills in Enterprises Worldwide

Skill	Percentage of Enterprises Needing Skill
Machine Learning	82%
Coding, programming, and software development	35%
Governance, security, and ethics	34%
Data visualization and analytics	33%
Advanced degree in a field closely related to AI/ML	27%

Table 2: Skills and Attributes Needed for AI Teams

Skill/Attribute	Percentage of Companies Looking for Skill/Attribute
Advanced degree in a field closely related to AI/ML	27%
Governance, security, and ethics	34%
Data visualization and analytics	33%
Coding, programming, and software development	35%

Table 3: Critical Soft Skills for AI Roles

Soft Skill	Percentage of Tech Recruiters Struggling to Find Applicants with Skill
Problem-solving	23%

Q: What is the demand for machine learning skills in the workforce?

A: According to Statista, 82% of organizations require machine learning skills.

Q: What percentage of enterprises state the supply of machine learning skills is adequate?

A: Only 12% of enterprises believe that the supply of machine learning skills is at an adequate level.

Q: How does the shortage of AI skills impact digital innovation and economic growth?

A: The shortage of AI skills can hold back digital innovation and economic growth, according to the IBM Global AI Adoption Index in 2022.

Q: How are companies addressing the shortage of AI skills?

A: Companies are retraining and upskilling existing employees, identifying and recruiting skilled talent from other companies and organizations, and recruiting from universities.

Q: What are the hard skills that organizations are looking for in their AI teams?

A: Organizations are looking for coding, programming, and software development skills, an understanding of governance, security and ethics, data visualization and analytics skills, and an advanced degree in a field closely related to AI/ML.

Q: What are the soft skills that are needed for these tech roles?

A: Problem-solving is considered the most critical soft skill, according to IBM’s survey Addressing the AI Skills Gap in Europe, and 23% of tech recruiters struggle with finding applicants with this aptitude.

Machine Learning Market Statistics 2023

23. The ML market is forecasted to grow from $1 billion in 2016 to $9 billion in 2022, at a CAGR of 44%.
24. The US deep learning and machine learning market will be $80 billion by 2025.
25. COVID-19 is to blame for the production decrease of 12% in the ML chip-making business.

Investments in ML

Table 1: Investments in Machine Learning (ML) and Artificial Intelligence (AI)

Country/Region	Total Spending on AI (by 2025)	CAGR (2021-2025)
United States	$120 billion	26.0%

Table 2: Organizations’ Investment in AI and ML

Statistics	Percentage
Organizations investigating or implementing computer vision technology	81%
Top businesses with ongoing investment in AI and ML	91%
Top businesses increasing their investment in AI and ML	91.7%

Table 3: Industry-wise Investment in AI

Industry	Expected CAGR of AI Spending	Percentage of AI Spending in 2025
Professional Services	>30%	N/A
Media	>30%	N/A
Securities and Investment Services	>30%	N/A
Retail	N/A	14%
Banking	N/A	14%

What is the expected growth in spending on artificial intelligence in the United States?

According to IDC, spending on artificial intelligence will grow up to $120 billion by 2025 in the United States, representing a compound annual growth rate of 26.0% in 2021-2025.

Are companies investing more in AI and machine learning?

Yes, according to Statista, 59% of respondents stated that accelerating investments in AI and machine learning is a part of a strategy for becoming future-proof to changing customer demand and operational challenges. Additionally, 91% of top businesses report having an ongoing investment in AI and ML and 91.7% said they are increasing their investments, as reported by NewVantage Partners.

Which industries are expected to see the fastest growth in AI spending in the United States?

Professional services, media, and securities and investment services are expected to see the fastest growth in AI spending in the United States, with CAGRs greater than 30%, according to IDC.

What percentage of organizations are investigating or implementing computer vision technology?

Insight and IDG report that 81% of organizations are investigating or implementing computer vision technology.

What percentage of AI spending in the United States is expected to be represented by retail and banking in 2025?

Retail and banking are expected to represent nearly 28% of all AI spending in the United States in 2025, according to IDC.

Careers in Machine Learning 2023

26. There are less than 10,000 individuals who have the necessary skill to tackle serious AI problems.
27. On LinkedIn, data scientists rose by over 650% between 2012 and 2021.
28. Only 4.5% of data researchers or data scientists in the USA work exclusively as ML engineers.

Machine learning benefits

Table 1: Benefits of Machine Learning and Intelligent Automation

Benefit	Source
Time savings for employees	30% of global IT professionals (IBM)
Average cost reduction expected by organizations adopting intelligent automation in the next three years	31% (Deloitte)
Faster response to customers, competitors, regulators, and partners	50% faster for AI-powered enterprises by 2024 (Oracle)
Increase in labor productivity due to AI and machine learning	Up to 37% by 2025 (ITRE study)
ML and AI initiatives exceeding profitability expectations	42% of companies (Accenture)
Returns on data and AI investments	92.1% of companies (NewVantage Partners)
Earnings before interest and taxes attributable to machine learning-based AI	At least 5% for 27% of respondents in the McKinsey Global Survey on AI report
Global GDP increase due to ML and AI accelerated development and take-up	Up to 14% in 2030, or up to $15.7 trillion to the global economy (PwC)
Economic gains resulting from AI-driven product enhancement stimulating consumer demand	45% of total economic gains by 2030 (PwC)
Regions seeing the biggest economic gains with AI	China and North America, with GDP enhancements of 26.1% and 14.5%, respectively (PwC)

Q: What are the benefits of machine learning?

A: Machine learning enables companies to automate complex processes, improve the quality, effectiveness and creativity of employee decisions with rich analytics and pattern prediction capabilities, and uncover gaps and opportunities in the market to introduce new products and services, hyper personalize customer experience, and much more.

Q: Are companies seeing value from their investments in AI and automation?

A: Yes, companies are seeing value from their investments in AI and automation. For instance, 30% of global IT professionals claim their employees are already saving time with new AI and automation software and tools. Additionally, 42% of companies stated that the profitability of their ML and AI initiatives exceeded their expectations, while only 1% said it didn’t meet expectations.

Q: How does AI impact the global economy?

A: AI will have a significant impact on the global economy, with the potential to increase the global GDP up to 14% higher in 2030, according to PwC research. The economic gains will come from AI-driven product enhancement, stimulating consumer demand. All regions of the global economy will benefit from ML and AI, with China and North America seeing the biggest economic gains with AI enhancing GDP by 26.1% and 14.5% accordingly.

Q: What is the expected cost reduction by organizations adopting intelligent automation in the next three years?

A: According to Deloitte, 31% is an average cost reduction expected by organizations adopting intelligent automation in the next three years.

Q: How fast will AI-powered enterprises respond to customers, competitors, regulators, and partners compared to their peers?

A: AI-powered enterprises will respond 50% faster to customers, competitors, regulators, and partners than their peers by 2024, according to Oracle.

Q: How much will AI and machine learning contribute to the labor productivity increase by 2025?

A: AI and machine learning will contribute to the labor productivity increase up to 37% by 2025, according to the study requested by the Committee on Industry, Research and Energy (ITRE).

Q: What percentage of companies state they are achieving returns on their data and AI investments?

A: 92.1% of companies state they are achieving returns on their data and AI investments, according to NewVantage Partners.

Q: What is the percentage of earnings before interest and taxes attributable to machine learning-based AI?

A: According to the latest McKinsey Global Survey on AI report, 27% of the respondents report at least 5% of earnings before interest and taxes attributable to machine learning-based AI.

Machine Learning in Marketing

Table 1: Machine Learning in Marketing and Sales

Metric	Percentage of Organizations
Organizations using ML and AI in marketing and sales processes	49%
Organizations who apply ML and AI to identify sales prospects	49%
Organizations who apply ML and AI to gain insight into prospects and customers	48%
Respondents who agree that ML and AI in marketing and sales will be critical for future competition	67%
Respondents who have increased revenue and market share through ML and AI in sales and marketing	31%
Marketing leaders who say their teams can focus more on strategic marketing activities thanks to automation and machine learning	66%
Marketers who feel enthusiastic about deploying automation and machine learning	78%
Leading performance agencies who shifted more than 30% of their time to strategic activities with machine learning	>50%
Respondents who fear job loss among sales and marketing teams from the use of AI and automation	7%

29. 16% of respondents from a Refinitiv survey said they believe ML would improve their sales and marketing campaigns.
30. 61% of marketers claim that AI is the most vital part of their data strategy.
31. 87% of AI adopters say they use or consider using it for sales forecasting or improving their email marketing campaigns.
32. Marketing and sales are the most profitable departments to incorporate machine learning systems.

Machine learning for Retail

Table 1: Machine Learning and AI in Retail

Area	Potential
Personalized Design and Production	High
Anticipating Customer Demand	High
Inventory and Delivery Management	High

Table 2: Market Forecast for AI in Retail

Year	Forecasted Market Size (in billions USD)	CAGR
2022	$8.41	N/A
2032	$45.74	18.45%

Table 3: AI Spending by Industry in the US

Industry	AI Spending
Retail	Largest

Table 4: AI Chatbots in Ecommerce

Type of Retail	Acceptance Rate for AI Chatbots	Predicted Ecommerce Transactions via Chatbots (in billions USD) by 2023
Online Retail	Highest	$112

Q: What are the areas with the biggest machine learning and AI potential in retail?

A: The three areas with the biggest machine learning and AI potential in retail are personalized design and production, anticipating customer demand, and inventory and delivery management.

Q: What is the forecast for the global AI in the retail market?

A: The global AI in the retail market is forecasted to grow from $8.41 billion in 2022 to $45.74 billion by 2032 at a CAGR of 18.45% during the forecast period 2023-2032.

Q: What industry will remain the largest AI spending industry in the US in 2021-2025?

A: Retail will remain the largest AI spending industry in the US in 2021-2025.

Q: Which type of retail has the highest acceptance rate of artificial intelligence chatbots?

A: Online retail has the highest acceptance rate of artificial intelligence chatbots.

Q: What is the predicted amount of ecommerce transactions via chatbots by 2023?

A: Ecommerce transactions via chatbots are predicted to amount to $112 billion by 2023.

Machine Learning in Business 2023

Table 1: Machine Learning in Business Statistics

Statistic	Value
Estimated improvement in business productivity by using AI	54%
Percentage of organizations that are advanced ML users	15%
Total amount raised for machine learning companies	$3.1B
Number of companies that have invested in machine learning	4,400
Percentage of businesses planning to adopt AI as a customer service solution	80%
Percentage of end-users that prefer chatbots for customer service inquiries	45%
Percentage of organizations using AI that report reduced business costs	44%
Expected increase in investment in AI in the coming years	300%
Percentage of customers willing to submit their data to AI for better experiences	62%
Percentage of organizations that fear losing out to startups without AI	44%
Tasks executives are using AI to cut out	Paperwork (82%), Scheduling (79%), Timesheets (78%)

• 33% of IT leaders said they use machine learning for business analytics.

33. Businesses leveraging machine learning algorithms can increase business productivity by 54%.
34. C-level executives are usually responsible for overseeing 75% of AI projects in their respective firms.
35. Customer service is synonymous with artificial intelligence. That’s why over 80% of businesses will use it in this department.
36. 15% of all manufacturing businesses say they are willing to use AI in widespread production.
37. There were over 4400 funding rounds for machine learning businesses in 2021, raising $73.7B from all the rounds.

38. 75% of businesses that use AI and machine learning increased customer satisfaction by around 10%.

What are the benefits of using Machine Learning in business?

Using machine learning in business can save time, reduce costs, and drive innovation. Here are some statistics to support its benefits:

• The estimated improvement in business productivity by using AI is 54%.
• 44% of organizations using AI report reduced business costs.
• Executives are using AI to cut out repetitive tasks such as paperwork (82%), scheduling (79%), and timesheets (78%).
• 80% of businesses plan to adopt AI as a customer service solution.

How common is the use of Machine Learning in business?

Machine learning is becoming increasingly common in business processes. Here are some statistics to support its growing popularity:

• 15% of organizations are advanced ML users.
• $3.1B has been raised for machine learning companies with the investments of more than 4400 companies.
• 62% of customers are willing to submit their data to AI for a better business and user experience.
• Investment in AI will increase more than 300% in the coming years.

What are the risks of not implementing Machine Learning in business?

Not implementing machine learning in business can result in a loss of competitive edge. Here are some statistics to support the risks of not adopting AI:

• 44% of organizations fear they’ll lose out to startups if they’re too slow to implement AI.

Machine learning for manufacturing

Table 1: Market Size and Growth for AI in Manufacturing

Year	Market Size (in billions of USD)	Growth Rate (CAGR)
2022	2.3	N/A
2027	16.3	45.91%

Table 2: AI and ML in Manufacturing Adoption and Expectations

Metric	Percentage
Companies that believe AI will be the key growth and innovation driver	93%
Companies that believe ML and AI will have a tangible effect on manufacturing in two to five years	83%
Manufacturing companies whose ML and AI projects meet their expectations	9%

Table 3: AI in Manufacturing Market Size in the US

Year	Market Size (in millions of USD)	Growth Rate (CAGR)
2021	543.42	N/A
2022	788.82	N/A
2027	5,245.50	45.91%

Table 4: Areas with the Biggest Potential for ML and AI in Manufacturing

Area
Auto-correction of manufacturing processes
Supply chain and production optimization
On-demand production

Q: What is the potential of Machine Learning (ML) and Artificial Intelligence (AI) in manufacturing?

A: The three areas with the biggest ML and AI potential in manufacturing are auto-correction of manufacturing processes, supply chain and production optimization, and on-demand production.

Q: What is the market size of AI in manufacturing?

A: The value of the AI in manufacturing market size is $2.3 billion in 2022 and is anticipated to grow to $16.3 billion by 2027.

Q: What is the US market size for AI in manufacturing?

A: The US AI in manufacturing market size was estimated at USD 543.42 million in 2021 and is expected to reach USD 788.82 million in 2022. It is projected to grow at a CAGR of 45.91% to reach USD 5,245.50 million by 2027.

Q: What do companies believe about AI and ML in manufacturing?

A: 93% of companies believe AI will be the key growth and innovation driver, according to Deloitte’s survey on AI adoption in manufacturing. Additionally, 83% of companies consider that ML and AI will have a tangible effect on manufacturing in two to five years.

Q: Are companies achieving expected benefits from ML and AI projects in manufacturing?

A: No, only 9% of manufacturing companies state ML and AI projects meet their expectations in terms of achieved benefits, budget and time invested, etc.

Machine Learning Methods for Sales Teams 2023

Table 1: Machine Learning for Sales Teams Statistics

Metric	Percentage/Value
Companies using AI for sales with increased leads	>50%
Companies using AI for sales with reduced call time	60-70%
Companies using AI for sales with cost reductions	40-60%
Increase in shopping frequency with AI present	49%
Increase in spending with AI present	34%
Companies worldwide using AI in at least one sales process	30%
B2B companies with call-time reductions	up to 70%
B2B companies with increased leads and appointments	50%
Average sales increase due to chatbots	67%
Sales teams using AI in their daily tasks	25%
AI-led businesses able to optimize sales and marketing	47%
AI-led businesses able to reduce operating costs	32%

39. Businesses that use AI for sales can increase their leads and appointments by over 50%.

Why are more sales departments turning to machine learning?

An increasing number of sales departments are using machine learning (ML) to help move customers more quickly through the sales funnel, resulting in better conversions.

What benefits can companies expect from using AI in sales?

Companies using AI for sales can expect to see an increase in leads by more than 50%, reduced call time by 60-70%, and cost reductions of 40-60%.

How does AI impact consumer behavior?

When AI is present, 49% of consumers are willing to shop more frequently, while 34% will spend more money.

How many companies worldwide will be using AI in their sales processes?

30% of companies worldwide will be using AI in at least one of their sales processes.

What benefits have B2B companies realized from using AI in sales?

B2B companies using AI in sales have seen call-time reductions of up to 70% and a 50% increase in leads and appointments.

How have chatbots impacted sales?

Business leaders report that chatbots have increased sales by 67% on average.

What percentage of sales teams are using AI in their daily tasks?

One in four sales teams is currently using AI in their daily tasks.

What benefits have AI-led businesses reported?

47% of AI-led businesses said they could optimize sales and marketing, while 32% said they were able to reduce operating costs.

Machine Learning in Voice Assistants 2023

Table: Voice Assistant Usage Statistics

Statistic	Data
Global number of people using voice-activated search and assistants	Approximately 3.25 billion people worldwide (Review42, 2021)
Global voice assistant usage increase during COVID-19	7% rise (AUM, 2020)
Increase in frequent usage during COVID-19	25% of people used voice assistants several times a day in March-April 2020, up from 20% in Dec 2019-Jan 2020 (Voicebot.ai, 2020)
Number of monthly voice assistant users in the US	128 million in 2020, up from 115.2 million in 2019 (eMarketer, 2020)
Age group comparison of smartphone voice assistant users	80.5% of under 30 consumers use a voice assistant, compared to 60.5% of the oldest age group. (Voicebot.ai, 2019)
Age group comparison of smartphone voice assistant users	74.7% of consumers ages 30-44 use voice assistants on smartphones, while 68.8% of consumers ages 45-60% do the same. (Voicebot.ai, 2019)
Projected number of global voice assistant users by 2023	8 billion (Review42, 2021)
Estimated value of global natural language processing market by 2026	$42.04 billion (Mordor Intelligence, 2020)

Q: What is machine learning in voice assistants?

A: Machine learning, particularly its subset called “deep learning,” is responsible for the creation of platforms behind voice assistants such as Siri, Echo, and Google Assistant.

Q: How popular are voice assistants worldwide?

A: Approximately 3.25 billion people worldwide use voice-activated search and assistants, which is almost half of the world’s population.

Q: How has the COVID-19 pandemic affected voice assistant usage?

A: Global voice assistant usage during the pandemic rose by 7%. The pandemic caused more people to use a voice assistant, with 25% of people using it several times a day in March-April 2020, up from 20% in December 2019-January 2020.

Q: How many people in the US use voice assistants?

A: Around 128 million people in the US use voice assistants at least once a month in 2020, which is up from 115.2 million in 2019.

Q: Who uses voice assistants the most?

A: Consumers under the age of 30 use voice assistants on smartphones the most, with 80.5% using them compared to 60.5% of the oldest age group. However, 74.7% of consumers aged 30-44 use voice assistants on smartphones, while 68.8% of consumers aged 45-60 do the same.

Q: How many people are predicted to use voice assistants by 2023?

It is predicted that 8 billion people will be using voice assistants by 2023.

Q: What is the estimated value of the global natural language processing market by 2026?

A: The estimated value of the global natural language processing market by 2026 is $42.04 billion.

40. By 2026, the value of the global natural language processing market will be around $42.04 billion.
41. 50% of people around the world use voice assistants.
42. During the COVID-19 pandemic voice, AI usage increased by 7%.
43. In 2022, using voice assistance multiple times a day went up by 5% in the past six months.
44. 80.5% of consumers under 30 use a voice assistant on their smartphones versus 60.5% of those over 30.

Machine learning for banking

Table 1: Machine Learning and AI in Banking

Metric	Value
Estimated Global Market for AI in BFSI in 2022	$3.23 billion
Projected Global Market for AI in BFSI by 2028	$15.32 billion
CAGR of AI Market in BFSI during 2022-2028	29.6%
Percentage of Banks Implementing AI Strategies	75% (for banks with over $100 billion in assets)
Percentage of Financial-Services Respondents Embedded with AI Capability	60%
Estimated Savings from Automating Middle-Office Tasks with ML and AI by 2025	$70 billion (for North American Banks)
Forecasted AI Platform Revenues within Insurance by 2024	$3.4 billion (23% growth between 2019 and 2024)
Percentage of Respondents Considering AI and ML Technology in Stock Market Workflows	76%

Note: BFSI refers to the Banking, Financial Services, and Insurance industry.

Q: What is the impact of AI and machine learning on the banking industry?

A: According to McKinsey, AI and its related technologies will have a seismic impact on all aspects of the insurance industry, from distribution to underwriting and pricing to claims. The biggest potential for ML and AI in the sector will be observed in areas such as personalized financial planning, fraud detection and anti-money laundering, and process automation.

Q: What is the estimated global market for AI in BFSI?

A: The estimated global market for AI in BFSI is $3.23 billion in 2022, and it is projected to grow to $15.32 billion by 2028, growing at a CAGR of 29.6% during the forecast period 2022-2028.

Q: Are banks aware of the potential benefits of AI and machine learning?

A: Yes, 80% of banks are highly aware of the potential benefits presented by AI and machine learning. In fact, 75% of respondents at banks with over $100 billion in assets are currently implementing AI strategies, according to Business Insider.

Q: How many financial-services sector respondents have already embedded at least one AI capability?

A: According to McKinsey, 60% of financial-services sector respondents state they have already embedded at least one AI capability.

Q: How much money can North American banks save by automating middle-office tasks with ML and AI?

A: North American banks can save $70 billion by 2025 by automating middle-office tasks with ML and AI, according to Insider Intelligence.

Q: What is the forecast for AI platform revenues within insurance?

A: AI platform revenues within insurance are forecasted to grow by 23% to $3.4 billion between 2019 and 2024, according to GlobalData.

Q: What is the biggest roadblock for banks in executing their ML and AI strategies?

A: The biggest roadblock keeping banks from executing their ML and AI strategies is the “black box” dilemma, according to Deloitte.

Q: Are there any plans to apply AI and ML technology in stock market workflows?

A: Yes, 76% of respondents of Statista’s report consider applying AI and ML technology in stock market workflows.

General Machine Learning Statistics 2023

45. 59% of professionals say Tensorflow is their favorite ML platform.

46. 38% of jobs in the US can be automated by 2030.

47. Employment in the research and computer science space is forecasted to grow by 15% from 2019 to 2029.
48. Of the newly filed patent applications in the healthcare industry, 40% have an ML or AI aspect.

Machine learning for healthcare

Table 1: AI in Healthcare Market Statistics

Market Statistic	Value
Global AI in healthcare market value in 2021	$11.06 billion
Global AI in healthcare market value forecast for 2030	$187.95 billion
Segment generating the most revenue in 2021	Clinical trials (24.2%)
Region leading the AI in healthcare market in 2021	North America
Estimated cost reduction in drug discovery with AI	70%
Accuracy achievable in predicting Covid-19-related physiological deterioration and death	Up to 95% up to 20 days in advance
Increase in AI market in 2020 compared to 2019	1.5 times
Percentage of healthcare organizations with an AI strategy or planning to implement one	98%

Q: What are the three areas with the biggest AI potential in healthcare?

A: The three areas with the biggest AI potential in healthcare are ML-based medical diagnosis, early identification of potential pandemics and tracking incidence of the disease, and imaging diagnostics such as radiology and pathology.

Q: What is the value of the global AI in the healthcare market?

A: The global AI in the healthcare market was valued at $11.06 billion in 2021 and is expected to reach $187.95 billion by 2030.

Q: Which segment dominates the global AI in the healthcare market?

A: The clinical trials segment dominates the global AI in the healthcare market and brought over 24.2% of revenue in 2021.

Q: Which region leads the AI in the healthcare market?

A: North America led the AI in the healthcare market in 2021 and is forecasted to continue doing so up to 2030.

Q: How much can be cut from drug discovery costs with AI and ML?

A: Up to 70% of drug discovery costs can be cut with the application of AI and ML.

Q: What is the accuracy level achieved with an ML-based solution in predicting Covid-19-related physiological deterioration and death?

A: Up to 95% accuracy in predicting Covid-19-related physiological deterioration and death up to 20 days in advance can be achieved with an ML-based solution.

Q: How did the AI market grow in 2020 compared to 2019?

A: The AI market grew 1.5 times in 2020 compared to 2019 due to the COVID-19 pandemic.

Q: What percentage of healthcare organizations already have an AI strategy or are planning to implement one?

A: 98% of healthcare organizations either already have an AI strategy or are planning to implement one.

Overall, AI and ML have the potential to revolutionize healthcare by improving diagnosis accuracy, reducing costs, and enabling early disease identification. As healthcare organizations continue to invest in AI, we can expect significant advancements in healthcare in the coming years.

MACHINE LEARNING AND CUSTOMER EXPERIENCE

Table 1: Customer Experience and Machine Learning

Data	Percentage
Customers’ relationships with companies handled without talking to a human	85%
Percentage of millennials willing to pay for a hybrid bot customer service system	43%
Percentage of business executives willing to pay for a hybrid bot customer service system	28%
Percentage of consumers concerned about losing human touch via total shift to AI	35%
Percentage of AI users who don’t know they’re interacting with AI	50%
Percentage of survey respondents who believe they’ve interacted with AI	34%
Percentage of survey respondents who have never interacted with AI	34%
Percentage of survey respondents who aren’t sure if they’ve interacted with AI	32%

Q: How much of customers’ relationships with companies are handled without talking to a human?

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

Q: How can AI save time and energy for sales representatives and customers?

A: AI can save time and energy for sales representatives and customers by answering their questions and solving their problems quickly and efficiently.

Q: What percentage of millennials would pay for a hybrid bot customer service system?

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

Q: How many business executives say they would pay for a hybrid bot customer service system?

A: 28% of business executives say they would pay for a hybrid bot customer service system.

Q: Why do consumers prefer a hybrid system over a system with only humans?

A: Consumers prefer a hybrid system over a system with only humans because they are concerned about losing a human touch via shifting totally to AI. This is why many are willing to pay for a hybrid system that gives them the benefits of both AI and humans.

Q: How many AI users don’t know that they’re interacting with AI?

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

Q: How many survey respondents have interacted with AI?

A: 84% of survey respondents have interacted with AI.

Q: How many survey respondents believe they’ve interacted with AI?

A: Only 34% of survey respondents believe they’ve interacted with AI.

Q: How many survey respondents say they’ve never interacted with AI?

A: Another 34% say they’ve never interacted with AI.

Q: How many survey respondents aren’t sure if they’ve interacted with AI or not?

A: 32% of survey respondents say they aren’t sure if they have interacted with AI or not.

Machine learning statistics rank cost reduction as the number one reason why businesses make use of this technology.

Machine learning challenges

What are some of the main challenges facing machine learning?

According to IDC research, the main challenges facing machine learning include the lack of skilled personnel, the cost of AI solutions, data management issues, and problems with the ML algorithm explainability and selection. Additionally, the inability to scale AI and ML projects up is a major challenge that is hindering AI from reaching its potential.

What are some specific struggles AI adopters face?

IBM Global AI Adoption Index 2022 research shows that a majority of organizations haven’t taken key steps to ensure their AI is trustworthy and responsible. 85% of IT professionals agree that consumers are more likely to choose a company that’s transparent about how its AI models are built, managed and used. However, the main struggles facing AI adopters include issues with security and auditability requirements, difficulties with integration, the lack of skills and experience, and change management issues.

What are some examples of the limitations of machine learning?

Despite the potential benefits of machine learning, there are also limitations to its effectiveness. For example, research shows that some AI systems used to scan for the signs of breast cancer are less accurate than the analysis of a single radiologist. Similarly, while some ML models seem effective for event-level prediction of crimes, other studies prove the opposite.

Additionally, many consumers believe that chatbots cannot understand their needs as well as humans. Some health-tech firms providing AI healthcare solutions also struggle to keep patient data 100% confidential.

How many AI and ML PoCs reach production deployments?

According to IDC research, only one-tenth of AI and ML PoCs reach production deployments, indicating that there are significant challenges facing organizations looking to implement these technologies effectively.

What percentage of consumers believe that transparency is important when it comes to AI models?

According to the IBM Global AI Adoption Index 2022 research, 85% of IT professionals agree that consumers are more likely to choose a company that’s transparent about how its AI models are built, managed and used.

MACHINE LEARNING TRENDS AND PROJECTIONS

Table 1: Machine Learning Trends and Projections

Year	Country	Market Size (in USD)	CAGR
2018	United States	$100 million	N/A
2025	United States	$935 million	N/A
2020-2025	United States	N/A	71%

Table 2: Investment in AI-based Startups

Year	Total Investment (in USD)
2019	$18.5 billion
2023 (Projected)	$100 billion

Table 3: Use of AI-powered Voice Assistants

Year	Percentage Increase in Daily Users
2018-2020	23%

Q: What is the projected growth of the machine learning market in the U.S.?

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

Q: What is the rate of growth for demand of employees with AI and machine learning skills?

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

Q: How much venture capital did AI-based startups receive in 2019?

A: AI-based startups received $18.5 billion in venture capital in 2019.

Q: What is the projected total investment in AI-based startups by 2023?

A: By 2023, total investments in AI-based startups are expected to reach $100 billion.

Q: How much has the number of smartphone owners using AI-powered voice assistants daily increased by?

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

Machine Learning Milestones stats

Milestones	Statistics
Lung cancer detection AI	Outperformed 6 human radiologists in Google’s study (VB, 2019)
Washington Post AI writer Heliograf	Wrote 850+ stories during the 2016 US presidential election and the Rio Olympics (WNIP)
Artificial Intelligence Job Ranking	Ranked as the 2nd most in-demand job in Indeed’s 2020 Career Guide (Indeed, 2020)
Translation error reduction	Google Translate reduced errors by 60% when using GNMT – a machine learning algorithm (AIM, 2020)
Prediction of patient death	Machine learning accurately predicted death in 95% of patients (Bloomberg)
Prediction of COVID-19 mortality	Machine learning predicted COVID-19 patient mortality with 92% accuracy (Nature.com, 2020)
Stock market prediction	Machine learning predicted stock market highs and lows with 62% accuracy (Microsoft)
Elderly care services by AI robots	3/4 of elderly care services in Japan will be delivered by AI robots in 2025 (Teks Mobile, 2018)
Speech recognition error rate	Speech recognition systems had an error rate of 5% (Teks Mobile, 2019)
Deep learning value creation	40% of the annual value created by analytics was due to deep learning techniques (McKinsey)
Breast cancer detection	Google’s Deep Learning program detected breast cancer with 89% accuracy (Health Analytics)
Lip-reading accuracy	Google’s machine learning-powered lip-reading system had an accuracy of 46.8%, toppling a professional human lip-reader with 12.4% accuracy (VB, 2019)
AI-powered voice cloning	Deep Voice (AI-powered voice cloning tool) needs only 3.7 seconds to clone a voice (Forbes, 2019)

What is the current capability of machine learning?

Machine learning has given artificial intelligence more capabilities, such as improving business intelligence platforms.

How has machine learning improved since its inception?

Machine learning has achieved several milestones, such as Google’s AI detecting lung cancer better than human radiologists, 60% fewer translation errors in Google Translate, 95% accuracy in predicting patients’ deaths, and 89% accuracy in detecting breast cancer.

How is machine learning being used in the stock market?

Machine learning methods are used to predict stock market highs and lows with 62% accuracy.

What is the error rate of speech recognition systems?

The error rate of speech recognition systems is 5%.

What is the role of machine learning in elderly care services?

In Japan, 3/4 of all elderly care services will be delivered by AI robots in 2025.

How fast can Deep Voice clone a voice?

Deep Voice, an AI-powered voice cloning tool, needs only 3.7 seconds to clone a voice.

What is the annual value of deep learning techniques?

Deep learning techniques make up 40% of the annual value created by analytics.

How accurate is Google’s machine learning-powered lip-reading system?

Google’s machine learning-powered lip-reading system has an accuracy of 46.8%, which is higher than a professional human lip-reader’s 12.4% accuracy.

What is the demand for artificial intelligence jobs?

Artificial Intelligence is now the 2nd most in-demand job based on Indeed’s 2020 Career Guide.

How accurate is machine learning in predicting COVID-19 patients’ mortality?

Machine learning demonstrated 92% accuracy in predicting the mortality of COVID-19 patients.

Machine Learning stats FAQs

Grow Your Business With Machine Learning

Having read the above machine learning statistics, you’ll better understand what machine learning is and how it can impact your life and business.

In a nutshell, ML’s main aim is to empower people using it to make calculated predictions based on input data.

And to do that effectively make use of data in your business, you’ll need the best business intelligence tools:

• Zoho Analytics allows you to upload and connect to various data sources in different formats. This tool has a drag-and-drop interface, which you can use to combine reports and make interactive graphs and charts.
• Big Eval is a bit more user-friendly than Zoho Analytics, plus it allows you to perform in-depth quality checks by testing algorithms, case execution, and case organization. This tool has a free demo where you’ll be walked through all the features and have your questions answered by an expert.
• Yellowfin has a free 30-day trial plan and a mobile app – unlike the other two. It can help you automatically analyze data and share information to workflows for reuse in storyboards, stories, and dashboards.

Businesses and individuals in various disciplines worldwide are starting to embrace machine learning. Don’t be left behind. Get started today!