Every AI Statistic & Trend You Need To Know In 2024

The role of artificial intelligence (AI) in our daily lives is expanding rapidly. It affects everything from business operations and healthcare delivery to ethical considerations and the future of work.

The rapid introduction of AI tools has many of us wondering what AI really means and how these tools are being used.

Discussions about the "rise of AI" have dominated recent headlines. However, we've had forms of narrow AI for some time. Examples include the Google search engine, Netflix's recommendation algorithm, and voice-activated digital assistants like Apple's Siri or Amazon's Alexa.

These tools operate on narrow, task-specific algorithms.
They are tailored to specific tasks, like setting reminders, sending texts, or answering factual questions.
They can't deviate from these rules to perform new skills or tasks on their own.

Current narrow AI projects include:

Generative AI
Predictive analytics
Natural language processing (NLP) (like chatbots, sentiment analysis algorithms, and machine translation)
Computer vision tools like facial recognition and object detection
Automated robots
Anomaly detection
Text mining
Simulations and modeling
Healthcare AI (for drug discovery and diagnostics)

AI’s promise of making companies more efficient and giving them an advantage over their competitors means it’s crucial for all of us to understand AI tools and trends.

This guide is a comprehensive resource for those interested in learning about the impact of AI on humanity and the economy. It covers its innovative uses, predicts future trends that will affect everyone, and includes the most recent stats about AI.

1. Overall AI Market Value

The value of the AI market is difficult to determine because it is evolving rapidly. This is evident from the unexpected success of innovations such as ChatGPT. However, even though the numbers fluctuate, they demonstrate the incredible amount of capital that companies are devoting to AI.

Estimates of AI’s current and future market worth vary widely. For example, some researchers say:

The AI market is worth $136.55 billion, and experts anticipate it to reach $2 trillion by 2030.

However, other estimates are higher, noting that:

The global AI market was valued at approximately $454.12 billion in 2022 and is expected to reach $2.58 trillion by 2032.
This growth represents a Compound Annual Growth Rate (CAGR) of 19% from 2023 to 2032.
In 2022, the AI market in North America alone was valued at around $167.30 billion.

Other AI valuation estimates:

Some research shows that generative AI alone will be worth $1.3 trillion by 2032.
Experts expect AI software to generate global revenues of $100 billion by 2025.
A recent McKinsey report estimates that AI will boost the global economy by $2.6 trillion to $4.4 trillion annually.

Various industries utilize AI, such as retail, manufacturing, banking, construction, healthcare, and marketing. This demonstrates the extensive reach and effectiveness of AI.

* Note that these figures are subject to change as the field continues to mature and produce groundbreaking innovations. The statistics serve as a snapshot, albeit a blurry one, of a market in constant motion.

2. AI’s Contribution to the Global Economy

According to Goldman Sachs Research in 2023, breakthroughs in generative artificial intelligence could lead to a 7% increase in global GDP (almost $7 trillion) and boost productivity growth by 1.5 percentage points over a 10-year period.

IDC expects global spending on AI to exceed $301 billion by 2026.

44% of private sector companies have plans to invest in AI in 2023.

In 2022, the United States led global AI private investment with $47.4 billion.

This is approximately 3.5 times more than the next highest country, China, at $13.4 billion.
The United States had the highest number of newly funded AI companies. It had 1.9 times more than the EU and UK combined. Additionally, it had 3.4 times more than China.

2.1 Economic impact statistics

Generative AI has the potential to add an estimated $2.6 to $4.4 trillion annually to the global economy across 63 use cases.

The amount is equivalent to the entire GDP of the UK in 2021, which is $3.1 trillion.
75% of the economic value from generative AI is focused on customer operations, marketing and sales, software engineering, and R&D.

2.2 Jobs lost and created due to AI

In February 2023, ResumeBuilder.com conducted a survey on US businesses. The survey revealed that:

49% of these businesses currently use ChatGPT.
30% of the companies expressed their intention to adopt ChatGPT in the future.
48% of companies using ChatGPT admitted it had replaced workers.
25% said its use had saved them $75k+.
While 93% of current users planned to increase their use of ChatGPT.

90% of business leaders still insist that ChatGPT experience is a beneficial skill for job seekers.

In May 2023, layoffs by U.S.-based employers reached more than 80,000, with an estimated 3,900 (or 5%) attributed to AI.

McKinsey estimates that half of current work activities could be automated between 2030 and 2060, a decade earlier than previously expected.

The number of AI-related job postings across nearly all American industrial sectors increased from 1.7% in 2021 to 1.9% in 2022.

This indicates a rising demand for AI-related professional skills.
The only exception was in the sectors of agriculture, forestry, fishing, and hunting.

A company’s current use of generative AI tools like ChatGPT is an indicator of how likely they are to lay off workers. A survey of 1,000 business leaders in 2023 found that:

48% of companies have replaced an employee with ChatGPT since November 2022.
33% of respondents said ChatGPT will “definitely” lead to more worker layoffs by the end of the year.
63% were confident it would lead to layoffs over the next 5 years.
Only 9% of business leaders who are not currently using ChatGPT believe it will “definitely” lead to future layoffs.

3. Consumer Awareness of AI

Despite a general willingness to use AI tools, people still have concerns about how AI will affect them, especially when it comes to employment.

77% of consumers are concerned about AI causing job loss within the next year.

When it comes to public perception and understanding of AI tools:

76% of consumers are concerned about misinformation from AI tools like Google Bard, ChatGPT, and Bing Chat.
54% believe they can distinguish between human-written and AI-generated content.
65% of consumers say they trust businesses that use AI technology, while 14% do not.

Despite concerns about misinformation, a Forbes consumer survey found that public trust in AI is higher than one may expect:

55% are comfortable with AI analyzing their social media usage.
54% believe AI can enhance long-form written content.
53% see potential in AI improving instant messaging and chatbots.
48% approve of AI analyzing their prior purchases.
46% think AI could improve text messages, and 48% think the same for emails.
39% feel AI could improve phone calls.
33% are okay with AI analyzing their text messages.
40% see potential in AI for personalized advertising.
21% are comfortable with AI analyzing their phone conversations.
9% do not approve of businesses using AI at all.

But the longer AI tools are around, the less tension they tend to cause. For example, between March and June of 2023, KPMG found that people have become less

In March, 41% were concerned about reduced opportunities for professional development and advancement; in June, the number dropped to 26%.
In March, 30% were concerned about the potential of AI to lead to decreased creativity and innovation in humans; in June, the number dropped to 16%.

These numbers will likely continue to fluctuate based on the number and knowledge of people surveyed and how AI is incorporated into industries.

4. AI Adoption Statistics

How many people use AI? It may be more than you think, especially if you have a smartphone.

97% of mobile device users use AI-powered voice assistants like Siri.
176 million Americans use facial recognition.
68% use it to unlock devices like phones and laptops.
Even back in 2020, there were already 4.2 billion digital voice assistants in use globally.
By 2024 this number is expected to reach 8.4 billion units – which exceeds the number of people in the world.

4.1 Artificial intelligence trends in business 2024

In 2022, IBM found that 35% of companies globally were using AI in their business, and 42% were exploring it. Their survey of 7,500 businesses also found that:

44% are in the process of onboarding AI tools.
54% of organizations have realized cost savings and efficiencies from AI.
The top challenges hindering AI adoption are limited AI skills (34%), high costs (29%), lack of development tools or platforms (25%), project complexity (24%), and data complexity (24%).

Larger companies are twice as likely to have actively deployed AI in their business operations, whereas smaller companies are more inclined to explore or not pursue AI.

Overall, the uptake of AI in business has been high, especially after the release of generative AI tools.

97% of business owners believe ChatGPT will benefit their business.
64% of business owners think AI will increase productivity.

Despite the proliferation of AI throughout various sectors:

74% of organizations haven't taken steps to reduce bias in AI.
68% aren't tracking performance variations and model drift, which are essential to maintaining the reliability of AI tools.
61% haven't ensured their AI-powered decisions can be explained.

It’s worth noting that private investment in AI recently decreased for the first time in a decade. According to Stanford’s 2023 Artificial Intelligence Index Report, global private investment in AI peaked in 2021 at $125.36 billion.

In 2022, it decreased by 26.7% year-over-year, totaling $91.9 billion in 2022.
Both the number of AI-related funding events and the number of newly funded AI companies also saw a decrease in 2022.
Despite this decline, the total private investment in AI for 2022 was still 18 times greater than in 2013.

Despite the 2022 slump, the share of venture capital investment in AI companies has more than doubled from 2022 to 2023.

4.2 Personal use AI stats 2024

According to a Forbes Advisor survey of 2,000 Americans, the top AI uses are:

45%: Responding to text/email messages.
43%: Answering financial questions.
38%: Planning travel itineraries.
31%: Crafting an email.
30%: Prep for a job interview.
25%: Write a post for social media.
19%: Summarize long or complex text.

4.3 Country-specific adoption rates

China, India, Italy, Singapore, and the UAE were most likely to have deployed AI solutions.

Canada, the United Kingdom, Singapore, and South Korea were most likely to be in the process of exploring AI integration.

In China and India, around 60% of IT professionals utilize AI in their organizations. This percentage is significantly higher than in South Korea, Australia, the US, and the UK. In South Korea, only 22% of IT professionals use AI, while in Australia, the percentage is 24%. In the US, the figure stands at 25%, and in the UK, it is 26%.

The AI Readiness Report (2022) asks how prepared governments are to use AI in providing public services to citizens. The top 8 countries ready for AI, based on factors like infrastructure, governance, ethics, digital capacity, and innovation, are:

United States of America 85.72
Singapore 84.12
United Kingdom 78.54
Finland 77.59
Canada 77.39
Republic of Korea 76.76
France 75.78
Australia 75.29

When it comes to the primary business objectives of countries exploring or implementing AI:

39% of businesses in the U.S., Europe, the Middle East, and Africa cited improving operational efficiency first.
The Asia Pacific region’s top reason was to “Increase revenue from new markets, products, and /or customers.”

A 2022 survey showed that positive outlooks about the future of AI tend to vary by region.

78% of Chinese respondents reported that they believed products and services using AI have more benefits than drawbacks.
76% of respondents from Saudi Arabia and 71% from India also felt optimistic about AI overall.
Meanwhile, only 35% of sampled Americans felt that AI products and services had more benefits than drawbacks.

5. New AI Trends: Generative AI Stats in 2024

Some estimates project that generative AI will produce an estimated $2.6 to $4.4 trillion in value across various industries.

74% of business leaders rank generative AI as the emerging technology they most expect to impact their business over the next 18 months.
83% anticipate increasing their investment in generative AI by 50% or more over the subsequent 6-12 months.

Despite the proliferation of generative AI, Verge reporter James Vincent described the AI tools as: “...vast autocomplete systems, trained to predict which word follows the next in any given sentence. As such, they have no hard-coded database of ‘facts’ to draw on — just the ability to write plausible-sounding statements. This means they have a tendency to present false information as truth since whether a given sentence sounds plausible does not guarantee its factuality.”

This is an accurate description. Nevertheless,

77% of executives see generative AI as the most impactful emerging technology they will use.
The next biggest innovation is advanced robotics, which only 39% of executives believe will make a significant impact.
71% plan to implement their first generative AI solution within two years.
However, the challenges to AI adoption include:
A lack of skilled talent.
A lack of investment
Heightened risk of cybercrime.
Some researchers estimate that the cost of cybercrime worldwide could reach $13.82 trillion by 2028 (in addition to hidden costs, like those associated with the loss of customer trust, company valuation, and intellectual property).

5.1. Generative AI in Academics

The business world is enthusiastic about AI. However, there are concerns that AI tools such as ChatGPT could lead to issues in education. These issues include cheating and plagiarism.

One survey of 1,000 college students found that 51% of college students consider using generative AI tools like ChatGPT on schoolwork to be cheating.

43% of college students have used generative AI tools, but only 22% have used them to complete schoolwork.
61% of respondents believed generative AI would become the “new normal.”
60% said they had not received any instruction on how to use the tools ethically/responsibly.

Regarding K-12 education AI trends, a survey of 1,000 students and 1,000 teachers showed that:

33% of students aged 12-17 use ChatGPT for school.
47% of students aged 12-14 used it.
68% of students believed it could be used to enhance learning.
38% of teachers have allowed student use, while 10% have caught unauthorized use.
59% of teachers see educational uses for ChatGPT, compared to 24% who think it'll be used for cheating.

The same study showed that teachers were also likely to use generative AI, with 77% of teachers saying ChatGPT could help them grow professionally.

Interestingly, in a different study performed in the summer of 2023, 60% of teachers said they used ChatGPT to do their jobs.

58% of teachers said they had a favorable view of ChatGPT.

In higher education, a 2020-2021 study of 2,000 academic researchers showed that

16% of researchers are extensive users of AI in their research.
21% of researchers would read papers peer-reviewed by AI, up five percentage points from 2019.
58% of researchers are unwilling to read AI-peer-reviewed articles.

6. Industry-Specific Artificial Intelligence Statistics 2024

According to Stanford’s AI Index Report, the fields with the most AI investment are:

Healthcare ($6.1 billion).
Data management, processing, and cloud ($5.9 billion).
Fintech ($5.5 billion).

In addition to generative AI, artificial intelligence trends in businesses include the use of ML tools such as predictive analytics. This is a method used to predict future outcomes based on historical data using machine learning algorithms, such as deep learning and NLP.

Predictive analytics have been used for everything from anticipating sales trends or customer behaviors to assessing the risk of recidivism among criminal offenders in the courtroom.

The market size for predictive analytics was $10.2 billion in 2022.

It's projected to grow to $67.86 billion by 2032.
The market is expected to grow at an annual rate of 21.4% between 2023 and 2032.

The artificial intelligence stats below include the use of generative AI, predictive analytics, anomaly detection, simulations, robotics, and more.

6.1 AI stats in marketing

Using AI tools allows marketers to reclaim roughly 2.5 hours each day, which adds up to 25-26 extra workdays annually. (However, a separate survey found that marketers anticipate that generative AI could carve out an extra five hours weekly, amounting to over four weeks annually.)

In addition to saving time:

AI aids 83% of marketers in producing substantially more content.
A HubSpot report found that 90% of marketers experience a reduction in manual tasks, 80% find more enjoyment in their work, and 79% feel AI improves the creative aspect of their jobs.
90% of marketers who use generative AI believe it improves their content quality.
82% agree that AI tools are key in generating qualified leads and increasing revenue.
Close to 20% have allocated over 40% of their marketing budget to campaigns powered by AI.

41.9% of marketers said their main reason for not using generative AI yet was because of a lack of understanding, while 23.7% cited the high cost of implementation.

According to a recent Salesforce survey:
76% of marketers used generative AI to craft basic content and perform copywriting tasks.
71% used it to spur brainstorming sessions.
63% used it for market data analysis.
62% used it for image asset creation.
The priority for 31% of marketers is the quality and accuracy of generative AI, followed by trustworthiness (20%), skill requirements (19%), and job security (18%).
39% of marketers are unsure about the secure usage of generative AI.
43% are unclear on how to maximize the technology's value.
And while 54% believe that specialized training in generative AI is crucial for its effective use, 70% indicate that their organizations have not provided any such training.

6.2 AI stats in sales

The sales industry is projected to invest more than $10 billion in the year 2023.

In 2023, 71% of sales experts anticipate that AI and automation technologies will transform their sales approach.
64% of business owners think AI will improve customer relationships.

A 2023 Salesforce and YouGov survey of 1,036 full-time sales employees and 1,023 full-time service employees from companies in a variety of sectors in the United States, UK, and Australia found:

Over 50% of sales and service teams report not knowing how to maximize the value of generative AI, citing a lack of training and trust in the technology.
61% of salespeople believe generative AI will improve their customer service.
61% say it will help them sell more efficiently.
63% of service professionals believe it will speed up customer service.
Among those who have adopted generative AI, 84% of salespeople say it increases sales by enhancing customer interactions.
39% of sales professionals fear losing their jobs if they don't adopt generative AI skills.
63% of salespeople report that their company's data is not properly set up for generative AI.

Recent data from a HubSpot survey of over 1,000 global sales professionals indicates that:

68% of sales professionals predict that most of their software will have built-in AI by 2024.
More than a third of sales professionals use AI to automate menial tasks.
81% say AI helps them spend less time on manual tasks.
74% agree that AI lets them spend more time on job aspects they enjoy.
70% agree that AI will make them more productive at work.
The top three AI use cases in sales are automating manual tasks (35%), offering data-driven insights (34%), and helping write sales content or prospect outreach messages (31%).
52% of sales professionals find AI tools important in their daily roles.
18% of sales professionals use generative AI for content creation.
16% of sales professionals use AI for prospect outreach and research.
14% use AI for data analysis and reporting.
78% agree that AI helps them focus on critical aspects of their role.
76% agree that AI helps organize and share data more effectively.
73% agree that AI can provide insights from data they couldn't find otherwise.
65% say AI will help them understand their customers better.
69% agree that AI can help personalize the customer experience.
42% of sales professionals are concerned about AI replacing their jobs, and 42% are not.

6.3 AI stats for manufacturing

AI is used in manufacturing for tasks such as:

Predictive/preventive maintenance.
Process improvement.
Digital twinning.
Plant floor IoT analysis.
Customer management.
Quality improvement.
Demand and price forecasting.
Warehouse optimization.
Robotics.
Vision systems.
Supply chain and procurement.

The most common AI use cases in the manufacturing industry are:

Discovery or analysis apps.
Intelligent task or process automation.
Optimization engines.

74% of manufacturers are actively using or planning to use AI/ML to manage the complexities of supply chain planning.

An oft-cited statistic from Accenture is that the expected financial impact of AI on manufacturing by 2035 will be close to $3.8 trillion. That information is from 2017 and skews higher than other statistics suggest.

IDC has predicted that the manufacturing sector would spend over $8 billion on AI in 2023.
Globally, the AI market in manufacturing was $2.6 billion in 2022, according to Emergen Research. They predict it will grow to $102.66 billion by 2032.

The Future of Manufacturing 2023 Report is the result of a survey from the Manufacturing Leadership Council. While it is not specific about who was polled or how many responses they received, the report found that:

20% of respondents expect investments to increase by 50% to more than 100% by 2030.
40% expect plants and factories to become largely autonomous but with a significant human role.
20% expect some factories to become fully autonomous.
Only 2% expect all factories to become fully autonomous.

Manufacturers tend to feel behind the curve when it comes to AI adoption and the expertise of their current employees to onboard new technology.

51% have a moderate confidence level in internal expertise to manage AI.
8% have high confidence in internal expertise to manage AI.
43% feel they are behind competitors in AI usage.
Interestingly, only 19% of respondents gave AI tools a high level of importance when it came to their impact on their business; 27% said it would be of high importance by 2030.
This would help explain why only 29% of manufacturers responding said they have a formal corporate AI plan and strategy.

6.4 AI in IT and software engineering

AI is increasingly playing a critical role in both IT and software engineering by:

Accelerating regression testing in software development, reducing both time and errors.
Enabling self-service solutions for IT help desks by analyzing and resolving user queries automatically.
Streamlining business processes by automating manual tasks, thereby cutting operational costs.
Facilitating coding processes, even pointing toward the future capability of managing the entire software development cycle autonomously.
Increasing the efficiency of IT operations management.

30% of global IT professionals report that employees at their companies are already saving time due to AI and automation tools.

53% of IT professionals have accelerated their AI rollout in the last 24 months, a notable increase from 43% in 2021.

The most common AI use cases in the IT industry are:

Optimization engines.
Intelligent task or process automation.
Discovery or analysis apps.

TechRepublic reports that Gartner’s (paywalled) “Market Guide for AI-Augmented Software Testing Tools” predicts that by 2027, 80% of organizations will have integrated AI-augmented testing tools into their software engineering toolchain.

This represents an increase from just 10% in 2022.

The same report predicts that 50% of software developers will use ML-powered coding tools by 2027.

Today, only 5% of developers use these tools, according to Gartner.

6.5 AI in the retail industry

In the retail sector, AI is used to:

Automate customer service agents.
Provide shopping recommendations.
Smart staffing.
Optimize prices.
Give customers a convenient cashierless checkout.
Enhance supply chain management, logistics, and fleet management.

According to Mordor Intelligence, AI in retail is expected to reach $7.30 billion in 2023 and reach $29.45 billion in 2028. They also note that AI adoption in the retail sector is expected to leap from 40% to over 80% in the next three years, according to IBM.

Currently, North America accounts for the largest market share of retail AI.
Asia-Pacific is the fastest-growing region.

In 2019, IBM found that 80% of retail executives expected their companies to adopt AI-powered intelligent automation by 2027.

The global AI in retail market size was valued at $5.50 billion in 2022, is projected to grow from $7.14 billion in 2023, and explode in worth to $55.53 billion by 2030.

According to McKinsey, the annual impact of AI on retail and consumer packaged goods could range from $400 to $660 billion (though they did not set a time frame).
Meanwhile, a 2023 analysis by ReportLinker predicted that AI spending in retail would grow from $7.3 billion in 2023 to $29.45 billion by 2028.

6.6 AI stats for financial services

In banking and financial services, AI can be used for:

Fraud detection.
Customer relationship management.
Predictive analytics (such as credit risk assessment).
Regulatory compliance.
Algorithmic trading.
Document verification.
Customer onboarding.
Sentiment analysis.
Wealth management.
Operational automation.
Personal financial management.
Real-time analytics.
Market analysis.

JLL's 2023 Banking and Finance Outlook report predicted that organizations would accelerate their digitization efforts and spend an additional $31 billion worldwide by 2025 on AI.

If that’s true, they predict banking and financial services would represent the largest AI investors in 2023.
The industry is expected to spend over $10 billion in 2023.
In banking, it could add an additional $200 to $340 billion annually.

6.7 Stats on AI in healthcare

AI can be used at nearly every stage of the healthcare journey and in multiple fields of medicine. It’s currently being used for:

Diagnosis and risk assessment.
Early disease prediction.
Anomaly detection.
Treatment planning.
Hospital resource allocation.
Revenue cycle management (including automating billing, coding, and claims processing).
Patient remote monitoring.
Telemedicine.
Clinical decision support.
Public health modeling.
Mental health sentiment analysis.
Virtual therapy.
Virtual assistants (which can remind patients to take medication, provide diet and exercise guidelines, etc.).

In 2021, the global AI in healthcare market was worth approximately $11 billion.

● Some forecasts estimate it will grow to nearly $188 billion by 2030.

Fierce Health reported on a Medscape survey of doctors’ overall feelings about the use of AI despite its wide range of applications.

While 70% of surveyed U.S. doctors thought AI could help improve the accuracy of their decisions.
Similarly, 53% of physicians express interest in using AI for specific tasks like checking drug interactions and 52% for looking up treatment guidelines.
However, only 7% of U.S. physicians use AI for professional purposes.
Less than 25% will even use online booking tools to streamline their practice.
Doctors in other parts of the world are less hesitant to use AI.
49% of U.S. doctors are still hesitant to use AI.
35% of physicians in Europe said the same.
Just 30% of physicians in Latin America expressed hesitation.

A Pew survey of over 11,000 U.S. patients in December 2022 saw a similar willingness to accept the power of AI in medicine but hesitance to participate in medical encounters if doctors use AI tools:

On the positive side:

40% of Americans think that using AI would reduce healthcare provider mistakes.
65% would want AI to be used in their skin cancer screening, and 55% believe it would make diagnoses more accurate.
Among those concerned about racial and ethnic bias in healthcare, 51% think AI would improve the situation.
Men, younger adults, and those with higher educational levels are generally more open to the use of AI in healthcare (though age is not a factor when it comes to AI-assisted surgery).

However, many people have concerns about the use of AI in healthcare:

About 60% of American adults say they would be uncomfortable with their healthcare provider relying on AI for diagnosis and treatment recommendations.
Only 38% believe using AI would improve healthcare outcomes.
57% believe that using AI would worsen the patient-provider relationship.
37% are concerned that using AI could compromise the security of their health records.
75% are concerned that healthcare providers will move too quickly in implementing AI.
Interestingly, the strongest resistance to AI in healthcare seems to come in the field of mental health.
79% say they would not use an AI chatbot for mental health support, and only 20% would consider it.
46% say such chatbots should only be used by people also seeing a therapist, and 28% think they shouldn't be available at all.
Even among those aware of mental health chatbots, 71% wouldn't want to use one, and only 19% consider it a major advance.

Negativity toward the use of AI in mental healthcare is shared by psychiatrists.

Global academic research found that of 791 psychiatrists surveyed in a recent study, 83% thought it was unlikely that AI tools could provide empathetic care.
And only 3.8% felt that it would jeopardize their jobs.

The same survey paper showed that other medical professionals around the world have a more positive view of AI in healthcare.

77% of radiologists reported favorable attitudes toward AI.
Yet 89% were not concerned about job loss. That’s likely because radiology has used computer-aided detection since 1992.
Pathologists were also largely optimistic, with only 17.6% concerned about job security.
60% of neurosurgeons reported using AI for predicting outcomes.

In specific geographical areas:

90% of German physicians expect there to be a mix of AI and human intelligence in the future of healthcare.
In Saudi Arabia, 77.75% of respondents including physicians, nurses, and technologists are concerned about their future jobs.

Physicians in Europe currently spend about 50% of their time on administrative tasks.

With AI implementation, it's forecasted that physicians could spend almost 20% more time with patients.
Nurses are expected to be able to spend about 8% more time with patients due to reduced administrative and regulatory activities.

Patient care and paperwork/administrative tasks are two very different things, and hospital executives seem keen to onboard AI to help these institutions run more efficiently. This is likely, in part, due to understaffing issues that started even before the COVID pandemic.

A 2023 Health Management Academy survey of 40 global healthcare executives found that 47.5% of health system executives report currently using AI solutions for workforce management.
52.5% are currently evaluating or considering these AI solutions.
AI investments are typically focused first on back-office operations, followed by clinical operations, and then clinical care.
78.0% of executives report that their health system is currently using or evaluating AI for revenue cycle management.
Only 15% of executives report current use of AI for nursing.
However, 82.5% of executives are evaluating or considering AI solutions for nursing.
27.5% of executives report the current use of conversational AI solutions.
However, 72.5% are currently evaluating or considering conversational AI solutions.
84.5% of executives who report current use of AI solutions expect a moderate to significant increase in AI investment in the next 1-3 years.

While there are many ethical and logistical concerns that need to be addressed in a field that requires such discretion, it’s important to note that AI adoption can apply to hospital scheduling and supplies without ever affecting patients or their health records.

6.8 Life Sciences AI stats

While related to medicine and healthcare, AI plays a unique role in research in the Life Sciences. This includes:

Drug discovery and development.
Medical device design.
Genomics and proteomics.
Personalized medicine.
Optimizing clinical trials.
Biomedical engineering.
Regulatory compliance.

Third-party investments in AI-enabled drug discovery have more than doubled each year for five years, reaching over $5.2 billion by the end of 2021.

Biotech companies adopting an AI-first approach have shown impressive results.

By March 2022, a BCG report noted that there were over 150 small-molecule drugs in the discovery phase and more than 15 in clinical trials.
The rate of expansion for this AI-fueled pipeline is almost 40% annually.

Drug creation company AbSci used AI to create new antibodies digitally, potentially cutting the time for drug development from six years to 18-24 months.

● The company can test 3 million designs a week, speeding up drug discovery and lowering costs.

Insilico Medicine has received the FDA's first Orphan Drug Designation for a medication designed using AI.

The drug, called INS018_055, is intended to treat idiopathic pulmonary fibrosis (IPF), a lung disease.
The AI identified a new biological target for the disease and generated molecules to act on that target.

Clinical trials are a critical, expensive, and time-consuming part of drug development.

According to the Association of Clinical Research Professionals, only about 10% of drugs that enter the clinical trial stage receive approval from the U.S. Food and Drug Administration (FDA).
One of the challenges in clinical trials is recruiting and retaining suitable participants. Incorrectly enrolling participants who are later found to be ineligible is wasteful and can even be harmful.
Research from 2020 found that 23% of clinical trials fail to meet their planned recruitment timelines.
One example of an AI tool making a difference is DQuest, which can help exclude 60% to 80% of trials for which a patient is not eligible.

A Deloitte study in 2021 of 150 biopharma leaders found that 38% had already adopted AI solutions.

While the number may seem low, that's partly due to the cost and complexity of onboarding the kind of AI tools that are able to process such large datasets.

A review of 1,725 studies of clinical trials between 2016 and March 2022 found that 1,573 (91.19%) of them involved AI in some way.

* There are, of course, many other industries being affected by AI, including Human Resources, Construction, Architecture, Facilities Management, Public Services (such as smart cities), Governance, etc.

7. AI Startups and Funding

Investments into startups using or creating artificial intelligence innovations rose to record highs of $113.3 billion in 2021. However, it fell to $60 billion in 2022 amid a credit crunch in the aftermath of inflation skyrocketing to record highs.

With renewed attention to this revolutionary technology, there has been a significant uptick in investments in 2023. In the year's first three months, $13.5 billion was invested in various AI innovations.

Stanford’s AI Index Report revealed the largest private AI investments in 2022:

$2.5 billion in funding for Chinese electric vehicle manufacturer GAC Aion New Energy Automobile.
A $1.5 billion Series E funding round for U.S. Defense company Anduril Industries, which builds tools for military agencies and border surveillance.
$1.2 billion investment in German business data consulting company Celonis.

7.1 Notable startups

In 2023, over 25% of venture capital invested in American startups has been directed towards AI-related companies, more than doubling from an average of 12% over the previous five years.

Typically, OpenAI, the company that makes ChatGPT, is listed among notable AI startups. However, it has received significant investment since its founding in 2015 and has formed partnerships with companies like Microsoft, making it a relatively mature company.

While the definition of startup is fluid, the following AI companies are considered among the most promising in the field:

Abnormal Security specializes in email security and uses machine learning algorithms to detect and prevent cyber threats. The company has raised $284 million over four rounds of funding since 2019.
Anthropic has raised a staggering $1.6 billion over six rounds of funding since 2021. The company focuses on researching and developing safe, transparent, and understandable AI technologies.
Causaly specializes in cause-and-effect AI to analyze complex relationships in scientific texts, primarily for research and development. It has raised $93.4 million over five rounds of funding since 2018.
Inflection AI has already raised $1.5 billion over just two rounds of funding since 2022. Inflection AI is a public benefit corporation focused on creating personal and empathetic conversational AIs.
Landing AI has raised $57 million over four rounds of funding since 2019. Founded by machine learning expert Andrew Ng, the company creates custom artificial intelligence solutions designed for industrial applications, offering tools and services from data annotation and model training to computer vision and predictive maintenance.
Observe.AI provides an AI platform for call centers to improve agent performance and customer experiences. The company has raised $214 million over six rounds of funding since 2017.
People.ai has raised $200 million over nine rounds of funding since 2016 and has two other organizations in that time. It provides an AI platform for sales, marketing, and customer services to capture team activity and customer engagement.
Shield AI specializes in developing autonomous systems for the government, including defense. It has raised $573.1 million over 11 rounds of funding since 2016 and has acquired two other organizations.
Signifyd specializes in fraud protection for e-commerce businesses. It has raised $390 million over six rounds of funding since 2012.
Synthesia raised $156.6 million over five rounds of funding since 2017. It's known for its AI-driven video synthesis technology that allows users to create videos without cameras, microphones, or editing software by generating realistic digital avatars and synchronizing them with user-provided text or audio scripts.
Tempus has acquired three companies and raised $1.3 billion over 13 rounds of funding since 2017. It specializes in AI for healthcare, specifically oncology, and other specialized medicines. According to their website, 50% of all U.S. academic medical centers and 50% of all U.S. oncologists are connected to Tempus.
Unlearn utilizes AI to create digital twins of patients for more efficient clinical trials. It has raised $84.9 million over six rounds of funding since 2017.
Viz.ai has raised $291.6 million over eight rounds of funding since 2016. It specializes in healthcare AI, focusing on interpreting medical imaging.

8. AI Ethical Challenges

AI tools can lead to ethical quandaries and violations if humans forget their limits and rely too much on them. When humans train these on our texts and information about human behaviors, for example, they process this data much faster than any human could. As a result:

They seem intelligent - sometimes more so than us - because they can "learn" and evolve based on new information we give them.
We can make them more powerful by giving them authority over our decision-making processes.

But they lack:

Consciousness.
Compassion.
Empathy.
Intuition.
Creativity.
Morality/ethics.
Imagination

Perhaps most importantly, they also lack intrinsic motivation. They can't have desire or ambition. So, the only thing guiding their behavior are the rules we program into them.

It's important to know that these tools have no objectives of their own. If they do harm, they do so because a human gave them the power to do so. (Precisely who would be "at fault" for such harm, whether it's a D on a term paper or incorrect medical diagnosis, is still unclear.)

Some of the most pressing ethical questions about AI are:

How can we guarantee that AI tools respect human values, dignity, rights, and autonomy?
What measures can we take to avoid bias, discrimination, error, harm, or misuse of AI?
How can we encourage fairness, accountability, transparency, and explainability in AI applications?
How do we ensure that AI systems handle sensitive data in a secure manner?
Are users aware that AI is processing their data or making decisions that affect them?
What ethical obligations do we have to workers who may be replaced by AI?
How do we address the high energy consumption of training large-scale AI models?
What ethical constraints are necessary for AI research that could be used for harmful purposes, such as autonomous weapons?
Could the benefits of AI go disproportionately to already-wealthy individuals or corporations, exacerbating social divides?
How do we ensure that AI technologies are available and usable for people with disabilities?
Who gets to set and enforce the ethical standards for AI that may be used globally?
Who owns the AI-generated content and how do we regulate the intellectual
What are the ethical implications of using AI to enhance human capabilities?
What is the impact of AI on human psychology, especially if it can mimic human behaviors?
How do we ensure the long-term safety of humanity with the advancement of AI, especially with concerns about superintelligent AI?

8.1 Ethics of AI emerging technologies

Current and AI projects that raise ethical questions include:

Emotion recognition: Some AI programs claim to be able to read human emotions through facial recognition or voice analysis, which raises questions about privacy and ethical considerations.
Deepfake technology: Deepfakes are becoming increasingly sophisticated, allowing for the creation of realistic but entirely fake videos and audio recordings. This technology has broad implications for misinformation.
AI in predictive policing: Some police forces are using AI to predict where and when crimes might occur, which raises ethical questions about data accuracy and racial or economic profiling.
AI-generated art and music: While intriguing, the notion that a machine could replace human creativity in fields like art and music is unsettling to some.
Virtual influencers: AI-generated social media "personalities" that can interact with real people, generate their own content, and even have "relationships" with other AI or real individuals.
Immortality projects: Some ventures aim to use AI to achieve biological immortality, either by halting aging or by "uploading" minds into computers, effectively bypassing biological death.
Surveillance state: In some countries, AI and machine learning are being used to monitor citizens on an unprecedented scale, often without explicit consent.
AI-driven social engineering: Projects that aim to use AI to manipulate public opinion or even individual behavior, potentially undermining democracy or individual free will.

9. Fun AI Facts and Stats

U.S.-China AI collaborations grew 4x since 2010 and are 2.5x more than the next closest pair, UK-China

Formerly, most new machine learning models were produced by academics. But not anymore. In 2022, academia produced only 3, and industry produced 32.

AI is now being used to improve itself. Nvidia and Google have built large language models (LLMs) that suggest self-improvement.

The percentage of new computer science PhDs from U.S. universities specializing in AI increased to 19.1% in 2021, up from 14.9% in 2020 and 10.2% in 2010.

Despite this, new faculty hires in North American computer science, computer engineering, and information fields dropped to 710 in 2021 from 733 in 2012. Tenure-track hires peaked at 422 in 2019 and fell to 324 in 2021.
In 2021, 78.7% of U.S. AI PhDs were male.

AI-related legislation is growing globally. The number of AI bills passed increased from 1 in 2016 to 37 in 2022.

Mentions of AI in global legislative records have increased 6.5x since 2016.
In the U.S., 10% of federal AI bills were passed in 2022, up from 2% in 2021.
35% of state-level AI bills were also passed last year.

There were 110 AI-related legal cases in the U.S. in 2022.

31% of Americans report being excited about AI for its potential to improve life and society.

Concerns include job loss (19%), surveillance and privacy issues (16%), and reduced human connection (12%).

25 companies are operating fully autonomous vehicles.

There were 130 recorded crashes involving these vehicles in 2022.

44% of U.S. adults think the widespread use of driverless cars is bad for society, while 26% think it's good

About 63% wouldn't want to ride in an autonomous vehicle, while 37% would.
Men, adults under 50, and college graduates are more open to it.
45% of Americans wouldn't feel comfortable sharing the road with widespread driverless vehicles.

In 2022, a little over 2% of US job postings required an AI skillset of some kind.

The following companies have banned employees from using generative AI tools like ChatGPT (typically due to privacy concerns over IP or client data):

Apple
JPMorgan Chase
Deutsche Bank
Verizon
Northrop Grumman
Samsung
Amazon
Accenture

ChatGPT is also banned in the following countries due to either privacy or misinformation concerns:

Russia
China
North Korea
Cuba
Iran
Syria
Italy

10. Future Prospects and AI Growth Statistics

The World Economic Forum’s 2023 Future of Jobs Report predicts that:

23% of jobs are expected to change in the next five years.
AI is expected to result in job churn, with 50% of organizations expecting AI to create job growth and 25% expecting it to result in job losses.
Employment in roles like data analysts, big data specialists, and AI machine learning specialists is expected to grow by 30% by 2027.
The fastest declining roles are in clerical or secretarial roles, including bank tellers, cashiers, and data entry clerks.
75% of surveyed companies are expected to adopt AI.
Yet, expectations for further automation have been revised down to 42% of tasks by 2027, compared to 2020 estimates of 47% by 2025.
Regarding skill gaps, 60% of workers will require training before 2027, but only 50% currently have access to adequate training opportunities.
44% of an individual worker’s skills will need to be updated.

Some data and analytics leaders are looking at these statistics and thinking, “Not so fast.” More specifically, they are grappling with a gap between high expectations from business leaders for revenue growth through AI and ML initiatives and the reality of being understaffed and under-resourced. One recent report showed that:

95% of U.S. chief data officers and chief data analytics officers said that their company leadership expects AI and ML investments to lead to revenue growth.
33% expect these investments to result in a double-digit percentage increase in revenue.
Only 19% believe they have the necessary resources to meet these expectations.
29.4% report a "meaningful shortage" in staff, funding, and technological resources needed for revenue growth through AI and ML.
87% highlighted that a shortage in tech skills, particularly in data science roles, is a major hindrance to their organization's ability to innovate in AI and ML.

Generative AI seems to be setting the tone for talk about the near future of AI. An April 2023 McKinsey survey found that 1/3 of organizations are regularly using generative AI for at least one function.

55% of respondents say their organizations have adopted AI tools.
40% expect to invest more in AI due to generative AI.
The most commonly reported business functions using generative AI are:
Marketing and Sales
Product and Service Development
Service Operations.
These areas, along with software engineering, have the potential to deliver about 75% of the total annual value from generative AI use cases.
75% of all respondents expect a significant or disruptive change in their industry due to generative AI within the next three years.
The industries most likely to be impacted are Technology and Financial Services.
Only 21% have policies governing the use of generative AI technologies.
The main risks of adopting AI were inaccuracy, cybersecurity, and regulatory compliance.
Prompt engineering is a rapidly growing field; 7% of companies using AI have made hires in this area.
Nearly 40% of organizations expect to reskill more than 20% of their workforce to work with AI.
8% of organizations expect a workforce decrease of over 20% due to AI.

11. Summary of the State of AI

Investment patterns in AI startups reveal a compelling story of peaks and valleys. Record-high investment poured into this space in 2021, only to fall the following year.

What does this fluctuation tell us? Does AI's luster fade for investors during challenging economic times, such as during a credit crisis or spikes in inflation?

Contrast this erratic financial backing with the high hopes company leaders have vested in AI technology.

A large number of U.S. chief data officers anticipate AI will contribute to significant revenue growth, yet a mere fraction are confident they possess the essential resources to meet these expectations.

Is this incongruence indicative of a broader disconnect between the optimism about AI’s capabilities and the logistical hurdles that come with its deployment?
This gap needs resolution to prevent potential over-investment and ensuing disillusionment.

While businesses wrestle with resource limitations, ethical considerations in AI are far from settled.

Despite the technology's pervasive reach, less than a quarter of organizations have established policies to govern their ethical employment.
This shortfall in ethical governance poses a societal risk that may neutralize AI's promised benefits.

The implications for the labor force add another layer of complexity. On the one hand, AI promises job growth in sectors like data analytics; on the other, it forecasts the decline of clerical roles.

With more than half of the workforce requiring retraining and only half having access to suitable training programs, how can the labor market maintain pace with the rapid shifts initiated by AI?

Generative AI, a burgeoning frontier, has already been adopted by a third of organizations for at least one business function. However, a surprisingly low number have developed policies to govern its ethical use or mitigate its associated risks, such as data inaccuracies or cyber threats.

As we ponder the future trajectory of AI, these questions become a clarion call for stakeholders across sectors to engage in a more nuanced examination of AI's impact. In a field as dynamic as AI, the window for proactive action is narrow and closing fast.

Humanity has lofty plans for AI, but the role it will truly play in our world in the near future is anyone’s guess.

12. References and Further Reading

Accenture, https://www.accenture.com/us-en/insights/artificial-intelligence-summary-index
https://www.accenture.com/t20170620T055506__w__/us-en/_acnmedia/Accenture/next-gen-5/insight-ai-industry-growth/pdf/Accenture-AI-Industry-Growth-Full-Report.pdf?la=en
BCG, https://www.bcg.com/capabilities/artificial-intelligence
Grand View Research, https://www.grandviewresearch.com/industry-analysis/artificial-intelligence-ai-market
McKinsey, https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-economic-potential-of-generative-ai-the-next-productivity-frontier#key-insights
https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai-in-2023-generative-ais-breakout-year
Statista, https://www.statista.com/statistics/1365145/artificial-intelligence-market-size/
https://www.statista.com/forecasts/1280009/cost-cybercrime-worldwide
Omdia, https://omdia.tech.informa.com/topic-pages/artificial-intelligence
Precedence Research, https://www.precedenceresearch.com/artificial-intelligence-market
https://www.precedenceresearch.com/predictive-analytics-market
Bloomberg, https://www.bloomberg.com/company/press/generative-ai-to-become-a-1-3-trillion-market-by-2032-research-finds/
Forbes, https://www.forbes.com/advisor/business/artificial-intelligence-consumer-sentiment/
https://www.forbes.com/advisor/business/ai-statistics/#sources_section
Goldman Sachs, https://www.goldmansachs.com/intelligence/pages/generative-ai-could-raise-global-gdp-by-7-percent.html
Dataiku, https://content.dataiku.com/idc-infobrief-2023
NextGov, https://www.nextgov.com/emerging-tech/2022/11/ai-data-analytics-star-biggest-planned-investments-2023/379495/
Stanford AI Index, https://aiindex.stanford.edu/wp-content/uploads/2023/04/HAI_AI-Index-Report-2023_CHAPTER_4.pdf
ResumeBuilder, https://www.resumebuilder.com/1-in-4-companies-have-already-replaced-workers-with-chatgpt
Challenger, Gray, & Christmas, Inc., https://omscgcinc.wpenginepowered.com/wp-content/uploads/2023/06/The-Challenger-Report-May23.pdf
Oxford Insights, https://www.oxfordinsights.com/government-ai-readiness-index-2022
KPMG, https://advisory-marketing.us.kpmg.com/speed/genai2023.html
https://advisory.kpmg.us/content/dam/advisory/en/pdfs/2023/generative-ai-survey.pdf
IBM, https://www.ibm.com/downloads/cas/GVAGA3JP
The Verge, https://www.theverge.com/2023/1/5/23540291/chatgpt-ai-writing-tool-banned-writing-academic-icml-paper
Deloitte, https://www2.deloitte.com/us/en/pages/finance/articles/cfo-insights-seven-hidden-costs-cyberattack.html
Best Colleges, https://www.bestcolleges.com/research/college-students-ai-tools-survey/
Impact Research, https://8ce82b94a8c4fdc3ea6d-b1d233e3bc3cb10858bea65ff05e18f2.ssl.cf2.rackcdn.com/ae/84/133976234126a2ad139411c1e770/impact-research-teachers-and-students-tech-poll-summary-memo.pdf
EdWeek, https://www.edweek.org/technology/more-teachers-are-embracing-chatgpt-students-not-so-much/2023/07
Elsevier, https://beta.elsevier.com/connect/research-futures-2022?trial=true
Hubspot, https://blog.hubspot.com/sales/predictive-sales-analytics
https://blog.hubspot.com/marketing/state-of-ai-report
https://blog.hubspot.com/sales/state-of-ai-sales
Berkeley, https://cmr.berkeley.edu/2021/12/how-to-analyze-data-to-predict-customer-behavior-accounting-for-post-pandemic-effects/
Science, https://www.science.org/doi/10.1126/sciadv.aao5580
HackerNoon, https://hackernoon.com/decoding-the-future-50-ai-statistics-highlighting-marketings-transformation-in-2023
Salesforce, https://www.salesforce.com/news/stories/generative-ai-statistics
https://www.salesforce.com/news/stories/sales-service-research-generative-ai/
Adobe, https://blog.adobe.com/en/publish/2023/06/22/consumers-marketers-see-role-responsible-generative-ai-in-customer-experiences
Influencer Marketing Hub, https://influencermarketinghub.com/ai-marketing-benchmark-report
Plex, https://www.plex.com/sites/default/files/2022-05/10292_7th_State-of-Smart-Manufacturing_Supply-Chain.pdf
Emergence Research, https://www.emergenresearch.com/industry-report/artificial-intelligence-in-manufacturing-market
Manufacturing Leadership Council, https://www.manufacturingleadershipcouncil.com/wp-content/uploads/2023/06/The-Future-Of-AI-In-Manufacturing-MLC-2023.pdf
Softengi, https://softengi.com/blog/ai-in-it-how-artificial-intelligence-will-transform-the-it-industry/
UNSECO, https://www.unesco.org/en/artificial-intelligence/recommendation-ethics/cases
Council of Europe, https://www.coe.int/en/web/bioethics/common-ethical-challenges-in-ai
TechRepublic, https://www.techrepublic.com/article/artificial-intelligence-machine-learning-software-engineering
https://www.techrepublic.com/article/ai-ml-application-expectations-too-high-say-chief-data-officers/
Mordor Intelligence, https://www.mordorintelligence.com/industry-reports/artificial-intelligence-in-retail-market
Fortune Business Insights, https://www.fortunebusinessinsights.com/artificial-intelligence-ai-in-retail-market-101968
Shop Association, http://shopassociation.org/retailers-ai-spending-expected-to-quadruple-by-2028
JLL, https://www.us.jll.com/en/trends-and-insights/research/banking-and-finance-outlook
Association of Clinical Researcher Professionals, https://acrpnet.org/2023/06/forward-thinking-for-the-integration-of-ai-into-clinical-trials/
Proclinical, https://www.proclinical.com/blogs/2023-4/top-20-artificial-intelligence-life-sciences
BCG, https://www.bcg.com/publications/2022/adopting-ai-in-pharmaceutical-discovery
AbSci, https://investors.absci.com/news-releases/news-release-details/absci-first-create-and-validate-de-novo-antibodies-zero-shot
Gen News, https://www.genengnews.com/news/insilico-gains-fdas-first-orphan-drug-designation-for-ai-candidate/
International Journal of Environmental Research and Public Health, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9602501/
IBM, https://www.ibm.com/blog/optimizing-clinical-trial-site-performance-a-focus-on-three-ai-capabilities/
World Economic Forum, https://www.weforum.org/press/2023/04/future-of-jobs-report-2023-up-to-a-quarter-of-jobs-expected-to-change-in-next-five-years/
HR Brew, https://www.hr-brew.com/stories/2023/05/11/these-companies-have-banned-chatgpt-in-the-office
Digital Trends, https://www.digitaltrends.com/computing/these-countries-chatgpt-banned/
AI & Society, https://link.springer.com/article/10.1007/s00146-022-01576-y
Department of Homeland Security, https://www.dhs.gov/sites/default/files/publications/increasing_threats_of_deepfake_identities_0.pdf
Los Angeles Times, https://www.latimes.com/california/story/2022-07-04/researchers-use-ai-to-predict-crime-biased-policing
Wired, https://www.wired.com/story/picture-limitless-creativity-ai-image-generators/
New Scientist, https://www.newscientist.com/article/mg25433900-800-the-rise-of-computer-generated-artificially-intelligent-influencers/
Future of Humanity Institute, https://www.fhi.ox.ac.uk/brain-emulation-roadmap-report.pdf

Dr. Jessica BaronPhD, History and Philosophy of Science

Jessica Baron is a multi-faceted consultant, writer, and researcher, renowned for her expertise in technology, future tech, digital innovation and ethical leadership. She has written for publications like Forbes, HuffPost, Aeon, and TechCrunch, alongside numerous other industry publications.