Many view machine learning as synonymous with artificial intelligence. In reality, machine learning is but a subset of AI, making the latter perform tasks faster and more intelligently by providing it with learning capabilities. These benefits make machine learning a key component of AI, a fact that will be affirmed by the latest machine learning statistics.
As its growing importance warrants further investigation, we have compiled the most relevant and recent machine learning statistics around. This information will deepen your idea of what the technology does, what it can do, and how it actually helps companies. With this data on machine learning and business intelligence, you should have a better appreciation of the technology and even use the statistics we provided to decide whether or not adoption is in order.
The machine learning market size has been steadily growing. The most prominent segment of this market is the deep learning software category, which is expected to reach almost $1 billion by the year 2025. Also, current machine learning market research has revealed that the market for AI-powered hardware and assistants are also expected to experience robust growth. Check out the following statistics to find out what’s cooking in the machine learning market.
$28.5 billion – The total funding allocated to machine learning worldwide during the first quarter of 2019. 
$80 million – The estimated size of the US deep learning software market by 2025. 
$18 billion – The estimated value of the AI assistant market in the next five years. 
$935 million – The estimated value of the US deep learning software market in 2025. 
42% – The estimated compound annual growth rate of the US deep learning market in 2025. 
$120 billion – The estimated global sales of AI-powered hardware by the end of 2025. 
$13 trillion – The potential global economic that AI could deliver by 2030. 
14x – The rate of increase in the number of AI startups since the year 2000. 
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AI Funding Worldwide 2019 (In billions of US Dollars)
Machine Learning Tops AI Dollars
AI Funding Worldwide 2019 (In billions of US Dollars) Machine learning applications: 28
Machine learning applications
AI Funding Worldwide 2019 (In billions of US Dollars) Machine learning platforms: 14
Machine learning platforms
AI Funding Worldwide 2019 (In billions of US Dollars) Smart robots: 7
AI Funding Worldwide 2019 (In billions of US Dollars) Computer vision platforms: 7
Computer vision platforms
AI Funding Worldwide 2019 (In billions of US Dollars) Natural language processing: 7
Natural language processing
AI Funding Worldwide 2019 (In billions of US Dollars) Recommendation engines: 4
AI Funding Worldwide 2019 (In billions of US Dollars) Virtual assistants: 3
AI Funding Worldwide 2019 (In billions of US Dollars) Speech recognition : 2
AI Funding Worldwide 2019 (In billions of US Dollars) Gesture control: 1
AI Funding Worldwide 2019 (In billions of US Dollars) Video recognition : .7
2. Machine Learning in Voice Assistants
ML has a subset called “deep learning.” This technology is built around machine learning practice and is responsible for the creation of the platforms behind voice assistants, which include Siri, Echo and Google Assistant. Voice assistants have risen in popularity among consumers, following the explosion of mobile technology. Take a look at the following voice assistant statistics that prove this development.
97% of mobile users use AI-powered voice assistants. 
51% of consumers use voice assistants in the car. 
6% of consumers use voice assistants in public. 
20% of consumers say they have never used a voice assistant. 
12% of Android users use voice assistants in public. 
50% of all search activities will be powered by voice by the year 2020. 
$13 billion – The estimated value of the global natural language processing market by the end of 2020. 
3. Machine Learning Adoption
The adoption of ML by enterprises has reached new heights as highlighted in a recent machine learning report. Adoption has been happening at break-neck speed as companies attempt to leverage the technology to get ahead of the competition. The financial sector is one of the most prominent adopters of the technology, which now leverages the power of AI software. These tools use ML to find, analyze and gain insights from data. Factors that drive the development include machine learning capabilities like risk management, performance analysis, and reporting and automation. Below are statistics on ML adoption.
The increase in ML adoption is seen to drive the cloud computing market’s growth. 
1/3 of IT leaders are planning to use ML for business analytics. 
25% of IT leaders plan to use ML for security purposes 
16% of IT leaders want to use ML in sales and marketing. 
Capsule networks are seen to replace neural networks. 
Machine Learning Adoption By Region 2019
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4. Machine Learning in Business
Businesses around the world have embraced ML, with the majority of them claiming to be early adopters. The technology has been driving innovations in enterprises, allowing them to make smart processes using AI with learning capabilities. Among these are business intelligence solutions. All you have to do is take a look at current business intelligence statistics, and you’ll get a pretty clear picture of this development. However, the deployment of ML comes with challenges, which include lack of access to data and shortage of skilled individuals to address machine learning problems. Read the statistics below and find out more about the current state of machine learning and businesses.
Companies are seen to offer virtual agents to consumers. 
10% – The estimated improvement in productivity from AI use. 
49% of companies are exploring or planning to use ML. 
51% of organizations claim to be early adopters of ML. 
40% – The estimated productivity improvement from AI use. 
15% of organizations are already advanced ML users. 
20% of C-level executives now use machine learning as a core part of their operations. 
65% of companies who are planning to adopt machine learning say the technology helps businesses in decision-making. 
74% of companies say ML will transform jobs and industries. 
58% of businesses using machine learning say they ran models in production. 
Leading Business Intelligence Software
SAP BusinessObjects Lumira. This product from SAP allows businesses to easily visualize their complex business data securely. Learn about other features such as its self-service data access module and data transformation capabilities here in our SAP BusinessObjects Lumira review.
Tableau. This popular software solution allows users to perform data analytics and visualizations easily without the need for advanced tech skills. Learn about its intuitive dashboard and comprehensive features in our in-depth Tableau review.
SAP Crystal Reports. This SAP product allows users to translate their static reports into dynamic and interactive shareable media files. Learn how its advanced visualization capabilities can help you gain more insights from your data here in our detailed SAP Crystals Reports review.
Microsoft Power BI. In this Microsoft product, users can gather, analyze, and share insights drawn from complex data. Learn how you can collect and manipulate data easily for business intelligence with this product in our Microsoft Power BI review here.
Hotjar. Track and analyze user behavior on your web touchpoints using Hotjar’s powerful features. Read more about how it can help you transform raw web data and user feedbacks into powerful and actionable business insights here in our dedicated Hotjar review article.
5. Machine Learning Use Cases
Machine learning has found applications in a variety of business environments. The technology basically gives AI the ability to train a machine to learn. Machine learning’s origins can be traced back to the notion of enabling computers to learn without having to program them to perform tasks. Today, developers continue to find new uses for ML as you’ll find out in the statistics below.
780 million miles of driving data have been collected by Tesla. 
1 million worth of driving data is collected by Tesla every hour. 
NLP is expected to get more applications in customer service. 
80% of companies plan to adopt AI for customer service by 2020. 
10% to 20% – The estimated improvement in accuracy in supply chain management from AI use. 
20% of AI-aware businesses use one or more technologies in a core business process. 
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Machine Learning Applications That Drive Adoption 2019
Machine Learning Applications That Drive Adoption 2019 Risk management: 82
Machine Learning Applications That Drive Adoption 2019 Performance analysis and reporting: 74
Performance analysis and reporting
Machine Learning Applications That Drive Adoption 2019 Trading and investing idea generation (alpha generation): 63
Trading and investing idea generation (alpha generation)
Machine Learning Applications That Drive Adoption 2019 Automation : 61
6. Machine Learning in Marketing
As marketing has become more labor- and resource-intensive process, marketers’ use of ML does not come as a surprise. Machine learning has reshaped marketing by improving the accuracy of lead scoring, creating dynamic pricing models and speeding up customer churn prediction, among many others. This is not to mention the benefits that tools for business intelligence offer marketers. To help you find out more about the use of machine learning in marketing, we have gathered the most recent statistics on the topic.
61% of marketers say AI is the most critical aspect of their data strategy. 
87% of companies who use AI plan to use them in sales forecasting and email marketing. 
2000 – The estimated number of Amazon Go stores in the US by 2021. 
49% of consumers are willing to purchase more frequently when AI is present. 
$1 billion – The amount Netflix saved from the use of machine learning algorithm. 
15 minutes – Amazon’s ship time after it started using machine learning. 
7. Machine Learning Milestones
Here comes the most interesting part. What is machine learning capable of so far? Today, ML has imbued artificial intelligence with more capabilities. For example, adding machine learning features to computers that can already process and analyze complex data sets is further improved to become business intelligence platforms. However, the technology is still in infancy that science fiction scenarios are still further down the road. Take a look at the following stats and see how machine learning has improved since its inception.
850+ – The number of stories written by Washington Post AI writer Heliograf, during the 2016 US presidential election and the Rio Olympics. 
Machine learning, NLP and deep learning are the three most in-demand skills on Monster.com 
55% to 85% – The increase in the accuracy of Google Translate’s algorithm after using machine learning. 
Below 10,000 – The number of people who have the needed skills to address serious AI problems. 
95% – The accuracy of machine learning in predicting a patients’ death. 
62% – The accuracy of machine learning in predicting stock market highs and lows. 
3/4 of all elderly care services in Japan will be delivered by AI robots in 2025. 
5% The error rate of speech recognition systems. 
40% of the annual value created by analytics is made up of deep learning techniques. 
89% – The level of accuracy of Google’s Deep Learning program in detecting breast cancer. 
Machine learning has been found to be better at lip-reading than humans. 
50% – The difference between human and AI-generated audio. 
Machine learning has indeed reshaped the way businesses run their affairs–for starters that is. This relatively new technology has yet to achieve its full potential. Companies have read the writing on the wall, leading to the increasing adoption of the technology.
The market for machine learning is also on a steady growth path as applications increase by the day. You can confirm this by reading relevant AI statistics currently available
Many enterprises turn to ML to outdo the competition, with a majority of them claiming to have adopted the technology as early as now. The technology is currently being employed in marketing with more potential uses in the pipeline.
The best thing about ML, however, is the innovations that it has managed to create, among which are AI writing, lip-reading and even driving data collection and analysis. But machine learning is not without its problems, the most pressing of which is the lack of skilled personnel to address machine learning problems.
Senior writer at FinancesOnline who writes about a wide range of SaaS and B2B products, including trends and issues on e-commerce, accounting and customer service software. She’s also covered a wide range of topics in business, science, and technology for websites in the U.S., Australia and Singapore, keeping tabs on edge tech like 3D printed health monitoring tattoos and SpaceX’s exploration plans.
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