Machine learning statistics
August 13, 2024
Machine learning market & adoption rate
The global machine learning market is steadily growing: in 2022, it was valued at $19.20 billion, and owing to the increasing adoption of technological advancements, it’s expected to grow from $26.03 billion in 2023 to $225.91 billion by 2030, at a CAGR of 36.2%. (Fortune Business Insights).
In 2023, the global AI market was valued at $538.13 billion and is expected to hit 2,575.16 billion by 2032
$24.58 billion is the forecasted global explainable AI market size by 2030
The global natural language processing market is expected to grow from $29.71 billion in 2024 to $158.04 billion by 2032
59% of large companies surveyed in India, 58% in the UAE, 53% in Singapore, and 50% in China are actively using AI, which makes these countries AI adoption leaders
42% of enterprise-scale companies surveyed report using AI in their business, and an additional 40% of respondents say they are exploring AI
63% of IT specialists at large companies currently deploying AI also report that their organization is adopting generative AI
The three top external drivers of AI adoption among enterprises 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
1 in 4 companies is adopting AI because of labor or skills shortages.
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ML use cases across industries & functions
Although today's use cases for AI and machine learning are becoming more varied, process automation remains their most common application. According to IBM, 33% of respondents rely on AI to automate IT processes. As for AI adoption in specific industries, IBM identifies financial services as the leading sector in terms of AI adoption.
Marketing & sales
Adopting generative AI can increase marketing productivity by 5-15% of current global marketing spend and boost sales productivity by 3-5% of total sales spend
Copywriters using generative AI to draft content can experience a productivity boost of more than 50%
The share of sales tasks fulfilled by AI is expected to grow from 45% in 2023 to 60% by 2028
Healthcare
The global AI in healthcare market was valued at $15.1 billion in 2022 and is expected to reach $187.95 billion by 2030
North America led the AI in healthcare market in 2022 with a revenue share above 59% and is forecasted to continue doing so up to 2030
The customer acceptance rate of AI chatbots in healthcare is 27%
Banking
The global AI in banking market size was $19.90 billion in 2023 and is expected to reach $315.50 billion by 2033
The adoption of generative AI in the global banking sector could add between $200 billion and $340 billion in value annually through increased productivity
Automating middle-office tasks with ML and AI can save North American banks $70 billion by 2025
European banks that replaced statistical approaches with ML techniques experienced up to 10% increases in sales of new products and 20% declines in churn
Manufacturing
$3.2 billion was the value of the AI in manufacturing market in 2023
$20.8 billion is the anticipated AI in manufacturing market size by 2028
Industry 4.0 front-runners applying AI use cases, such as demand forecasting and heavy-transport equipment routing, experienced a two to three times increase in productivity and a 30% decrease in energy consumption
Generative AI for content generation, insights extraction, and other tasks can lead to productivity improvements of up to two times across manufacturing activities
Retail
The global AI in retail market is forecasted to grow from $9.97 billion in 2023 to $54.92 billion by 2033 at a CAGR of 18.6% during the forecast period 2024-2033
Online retail has the highest customer acceptance rate (34%) of artificial intelligence chatbots
The potential impact of generative AI on retail may range between $400 billion and $660 billion a year due to streamlining customer service, marketing and sales, and inventory and supply chain management
Internal productivity
Software code development
Customer service
Marketing
Knowledge worker effectiveness
Operations
IT
Sales & sales operations
Customer onboarding
Non-software R&D
Finance
HR
Legal
Product differentiation
Natural language interfaces
Core product performance enhancements
New products or services
Machine learning benefits
As an extension but not a replacement for human capabilities, 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. (Accenture)
As ML and AI initiatives are becoming more widespread, companies are getting more value out of their investments:
97% of companies deploying AI technologies like machine learning and generative AI have benefited from them, achieving increased productivity, improved customer service, and reduced human error
Sectors more exposed to AI are experiencing 4.8x greater labor productivity growth compared to average growth rates
Tech companies are expected to be most impacted by generative AI, adding value of up to 9% of global industry revenue
Human resources is the business function in which the largest share of respondents (50%) report cost decreases thanks to generative AI
29% of global IT professionals claim their employees are already saving time with new AI and automation software and tools
53% of small business owners report the positive impact of AI on customer experience
Investments in ML
Enticed by the potential payoffs of AI adoption, enterprises are increasing their investments in ML and AI initiatives. In this regard, Goldman Sachs predicts that AI investments will approach $200 billion globally by 2025.
By 2025, Global 2000 companies are expected to allocate over 40% of their IT spend to AI initiatives
59% of companies already exploring or deploying AI have accelerated their rollout or investments in this technology
81% of organizations are investigating or implementing computer vision technology
89.6% of Fortune 1000 CIOs surveyed reported that investment in generative AI is increasing within their company
20% of large companies are investing up to $50 million per year in generative AI
Machine learning skills demand & employment
With artificial intelligence and machine learning becoming mainstream, organizations hire new specialists and launch reskilling initiatives to fill skills gaps within their workforce.
Despite a recent decline, machine learning is the most in-demand AI skill, required by 0.7% of all job postings in the US and followed by AI, NLP, autonomous driving, and neural networks
Postings for AI specialist jobs are growing 3.5x faster than for all jobs
The most in-demand skills in AI-related jobs postings in the US include Python (152,201), computer science (133,066), SQL (93,541), data analysis (91,883), data science (85,480), Agile methodology (73,069), and software engineering (64,557)
32% of executives and 38% of IT professionals believe that organizations should begin with investing in talent and training to help beginners use AI technologies like ML effectively
The average salary of an ML engineer in the US varies from $116,416 to $140,180, with the final number impacted by experience, industry, and geographic location
The average salary of a data scientist with ML expertise in the US is $119,380 per year
$164,765 is the estimated total pay per year for a machine learning engineer in the US, with an average salary of $119,992 per year
Jobs requiring AI expertise carried an average premium of 25% in the US and 14% in Great Britain
Machine learning challenges
From workforce skills gaps to concerns about data security and machine learning algorithm accuracy, there are multiple factors that risk hindering individual ML initiatives and holding back the adoption of AI on a broader scale.
Skill shortage
72% of IT leaders mention AI skills as one of the crucial gaps that needs to be addressed urgently
Only 12% of IT professionals have significant experience working with AI and ML
Only 34% of companies surveyed are currently training or reskilling employees to work with AI
One-in-three IT leaders are struggling with finding qualified AI and ML specialists
60% of public sector IT professionals consider AI skills shortages to be the top challenge to implementing AI
Transparency & reliability
44% of surveyed organizations mentioned transparency and explainability as relevant AI adoption concerns
The number of AI incidents, such as autonomous cars causing pedestrian fatalities or facial recognition systems resulting in wrongful arrests, increased by 32.3% between 2022 and 2023
Inaccuracy is the most cited risk of generative AI, mentioned by 63% of the organizations surveyed
The concentration of pretrained AI models in the hands of 1% of AI vendors by 2025 will make responsible AI (a concept involving metrics like AI explainability, fairness, and transparency) a societal concern
Data management
Privacy & security
Privacy and data governance risks like data leaks are the leading AI concerns across the globe, selected by 42% of North American organizations and 56% of European ones
Cybersecurity and data privacy are currently US executives' top concerns when it comes to implementing generative AI, at 81% and 78% respectively
Public perception
In 2023, 52% of Americans felt more concerned than excited about AI, compared to 38% in 2022
Only 37% of respondents feel AI will improve their job
95% of workers see value in generative AI in the workplace but don’t expect their organizations to ensure positive outcomes for everyone
Environmental impact
CO2 equivalent emissions by major machine learning models during their training ranged between 502 tonnes for GPT-3 to 3.17 tonnes for Luminous Base
Our machine learning services
We guide your organization throughout your ML development and implementation journey to help maximize the benefits of this technology and overcome potential challenges.
- Use case identification
- Data mapping and quality assessment
- Existing solution audit
- Advisory about initial project setup
- Development process review
- ROI analysis
- Solution architecture design
- Tech stack selection
- Project budgeting
- MVP conceptualization
- Risk management strategy creation
- User training (demos, tutorials, etc.) and support
We develop ML solutions tailored to your business requirements or enhance your existing software with ML algorithms in line with evolving corporate goals and market or technology trends.
- ETL pipeline setup
- Data preprocessing (cleansing, annotation, transformation)
- Data protection and cybersecurity elaboration
- Selection of suitable machine learning techniques and ML algorithms
- ML and deep learning model training
- Software integrations and APIs creation
- UX/UI and data visualization setup
- Deployment to the production environment
- End-to-end testing
- Post-launch support, optimization, and upgrades
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A closing word
Companies from any sector have a high chance of reaping significant benefits by undertaking relevant AI initiatives. At the same time, machine learning implementation isn’t always a smooth process. For instance, despite the proven superiority of machine learning over statistical methods, ML models are still far from achieving absolute accuracy. Furthermore, the advances in AI and ML are being slowed by the shortage of employees with required skills.
With an experienced partner like Itransition, your organization can easily fill in-house skill gaps and build secure and reliable ML solutions fully compliant with your industry’s standards and regulations.
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