Exploring the adoption and impacts of generative AI technology in the US: A portrait of the AI target audience

The article’s cover illustrates the title "Exploring the adoption and impacts of generative AI technology in the US: A portrait of the AI target audience" and it shows a person interacting with artificial intelligence. A young man is holding a laptop while a generative AI, depicted as an android, responds to him from the opposite side.

Part 1: Generative AI in the context of the last few years (2020–2023)

The term ”artificial intelligence” (AI) has been around for a long time. The concept was first proposed at a series of academic workshops held at Dartmouth College in the 1950s and led by John McCarthy. It was soon conceptualized by Alan Mathison Turing. Turing, a British mathematician, suggested that the human brain and machines have similarities. He believed that the newborn’s cortex is like an ”unorganized machine” that develops into an advanced one through training.

The photograph features John McCarthy, a professor of computer science in the artificial intelligence lab at Stanford in 1974. He is sitting at a computer, partially turned towards it.
John McCarthy, image source: https://static.independent.co.uk/s3fs-public/thumbnails/image/2011/10/31/20/48-John-McCarthy-AP.jpg?width=1200

In the 1950s, Turing proposed a test to determine if a machine has truly become intelligent. The test involved an examiner interacting with both a machine and a human and if the examiner cannot distinguish between the two, then the experiment is considered successful.

The image depicts the Turing test: a female operator evaluates the answers from both AI and humans to the same questions, attempting to determine which responses are from the machine and which are from the human. The impossibility of accurately identifying the AI would be the result of creating a truly advanced artificial intelligence.
Turing’s test visualization

The reliability of the test was later questioned by John Soerle, who argued that the right answers could only demonstrate the manipulation of symbols, not actual thinking processes. However, with the invention of ChatGPT, a natural language processing chatbot, in early 2022, the question of Turing’s test has emerged again. While we cannot yet determine that machines can think, they have certainly begun to impact our daily lives.

In this article, we will explore the significant global shift brought about by generative AI and its adoption among US citizens. Our objective is to address the question of who are the ones affected by these changes and what are the predictions for the proliferation of AI?

Digital transformation and the evolution of AI

Yet, AI is a broad term and before we discuss its current implementation, let’s outline several milestones in its evolution.

In the 1950s, AI technology was created to solve problems like recognizing symbols, understanding natural language, playing simple games, controlling robots, and predicting the future. Computer scientists such as those named above created the field of artificial intelligence, which remains a largely open and topically broad field of research with an emphasis on developing computer-based methods that can successfully simulate human thought processes.

In the 1970s-80s, AI technology began to extend further into the application of natural language processing, computer vision, robotics, and expert systems. It was during this time that AI technology was first used to power industrial robotics and automation, as well as space exploration.

In recent decades, AI technology has seen advances in deep learning, a subfield of machine learning that allows computer algorithms to recognize patterns and learn for themselves, providing more accurate predictions and providing real-time analysis. This development has enabled the automation of complex industrial processes, as well as more accurate and complex analytics and decision-making.

AI’s increasing sophistication is now enabling a new era of digital transformation. Companies today are using AI to analyze customer conversations and understand customer sentiment, as well as to optimize decision-making processes and create personalization at scale. AI is also being used to automate mundane and time-consuming tasks such as customer service, regulatory compliance, as well as IT and security operations.

Therefore, the current definition of AI can be summarized as computer programming with the ability to learn from data and adjust its behavior accordingly. While it can be argued that we still have a long way to go before labeling it as human-like, it is currently more accurately characterized as an assisting tool in accomplishing intricate tasks.

The rise of generative AI (GPT)

As we delve into the topic at hand — generative AI — we find ourselves approaching the present day. Generative AI is a sophisticated technology in the realm of artificial intelligence that has the capability to generate content such as text, images, audio, and more. This is achieved through training on a rich dataset that it can make use of to produce these outputs.

This remarkable technology has gained significant attention and popularity, particularly starting in 2022. However, it is important to note that the groundwork for generative AI was laid as early as 2010. By examining its evolution, as traced by Gartner, we can observe how generative AI has progressed from initial applications such as trained models for language translation to the more recent development of conversational interactions with AI models using natural language.

ALT: The image shows a timeline depicting the development of Generative AI: 2010: Near-perfect translation of natural language 2014: Mastering the meaning of words 2017-2022: Large language foundation models 2022: Conversational large language foundation models

What contributed to the sudden hype around generative AI? There have been several prerequisites to the rise of the attention:

Technical breakthrough

Technological advancements have facilitated the scaling up of artificial intelligence and machine learning (ML) algorithms, resulting in the development of more potent generative AI forms with enhanced learning speed and accuracy. Notably, OpenAI has built its products on the Transformer technology, an innovation by Google introduced in 2017. This technology uniquely operates on sequential data, such as text, freeing AI from the linear reading process. Instead, Transformer allows AI to focus on any segment of the dataset at any moment, enabling it to grasp context, grammar rules, and sentence meaning when armed with ample data. Consequently, this has elevated AI’s potential in language translation, text generation, and various other applications, leading to groundbreaking developments.

Accessibility and human fascination

When ChatGPT was about to launch, anyone could join the waiting list to interact with the AI demo. This created a lot of publicity before the official launch, creating a lot of excitement for people who were eager to experience it. The global shift towards online platforms during the pandemic also contributed to its rapid adoption, with millions of users trying ChatGPT within the first few months. However, we’ll delve deeper to understand if this technology is truly as widely accessible as it appears to be, despite the social worries it has already brought.

Сurrent social influence of generative AI

Speaking of recent representative events that highlighted the issue of chatbot implementation, several instances can be mentioned.

One that garnered significant attention in the US was the Writers Guild of America (WGA) strike, which commenced in May 2023. The primary motivation behind this strike was the disparity in wages within the movie production industry. The screenwriters advocated for enhancements in minimum wages, improved terms concerning streaming features, safeguarding of writers’ rooms, guaranteed work durations, increased weekly pay, the equitable treatment of pension and health contributions for team members, and regulations pertaining to the utilization of generative AI.

The photo features three protesters holding signs from SAG AFTRA and the Writers Guild of America. The man holds a sign from the writers’ guild that reads "On strike." The two women hold a sign from SAG AFTRA that reads "AI is not Art."
Screenwriters strike: https://www.bloomberg.com/opinion/articles/2023-08-04/wga-strike-hollywood-dirty-secrets-laid-bare-on-social-media

During the negotiations in May, their request aimed to establish regulations for AI usage in projects falling under the Minimum Basic Agreement (MBA). Specifically, their proposal stated that “AI can’t write or rewrite literary material; can’t be used as source material; and MBA-covered material can’t be used to train AI.” This brings up a big concern about artificial intelligence replacing humans at workplaces and regulations that need to be taken.

Regarding the education sector, the use of AI, particularly in the form of generative AI like GPT, has raised significant concerns regarding the authenticity and quality of student work, including instances of thesis writing. However, policies addressing this issue are still in the process of being developed. Nevertheless, AI has gained substantial traction in the field of education and is being considered as a promising area for future development.

Previously, automation in knowledge work was considered to have the lowest potential. However, the emergence of generative AI has significantly changed this perception. In the world’s education and professional training sector alone, the potential for technical automation has skyrocketed from 15% to 54%. This shift can be attributed to the fundamental functionality of generative AI, which operates with natural language and is capable of revolutionizing cognitive tasks.

The image shows a screenshot of a Business Insider news article with the headline "A college student got a top grade for an essay written with the help of ChatGPT, report says." The photograph in the article depicts Cardiff University in Wales.
Source: https://www.businessinsider.com/chatgpt-openai-college-student-highest-mark-essay-ai-2023-4

When considering the overall changes in the labor market, it is challenging to determine whether AI will lead to job reduction or simply streamline cognitive tasks as we said. According to a McKinsey survey, the adoption of generative AI is predicted to increase across various industries in the US by 2030. However, the survey suggests that only a few industries, such as STEM professionals, education and workforce training, creatives and art management, business and legal professionals, managers, and community services, are likely to experience a significant acceleration in automation adoption from generative AI, estimated at over 9%. Consequently, the impact of AI in other sectors seems to be less pronounced, making it uncertain if employees’ concerns about job cuts and replacements will become a pressing issue in the near future.

An essential question that arises is whether equal access to technology is a reality, considering the noticeable disparities in the adoption of AI among different sectors. It prompts us to consider who currently benefits from the use of generative artificial intelligence.

Part 2: Sociological aspects of generative AI adoption among US citizens

Gender aspect: addressing the gap in the United States tech industry

Addressing the gender gap in the United States tech industry is an important aspect to consider. When examining the statistics from 2023, it becomes evident that there is a significant disparity in the familiarity with generative AI between genders. Only 7% of females possess in-depth knowledge about this technology, compared to 19% of males. The difference is also apparent among those who have no understanding of this recent breakthrough in artificial intelligence.

The graph illustrates the disparity in knowledge of ChatGPT between two genders, categorized as "a lot," "a little," and "nothing at all." The percentages for women are 7% for "a lot," 29% for "a little," and 64% for "nothing at all." The percentages for men are 19% for "a lot," 38% for "a little," and 43% for "nothing at all."
Source: https://www.statista.com/statistics/1369155/knowledge-of-chatgpt-by-gender-in-us/

This gender-technology correlation extends beyond the interest in ChatGPT and encompasses the representation of women in STEM fields. In terms of gender equality across various sectors, the United States ranks 43rd in the World Economic Forum report, managing to close 74.8% of its total gender gap with a loss of 2.1 points, compared to the previous year. However, concerning the tech industry, the statistics show a ”not applicable” status. Additionally, women make up only 28% of the workforce in STEM fields.

The graph depicts the gender distribution in various sectors as percentages. The breakdown is as follows: education - 40% women, consumer services - 38% women, government and public sector - 35% women, professional services - 31% women, financial services - 27% women, technology information and media - 25% women, manufacturing - 22% women, and overall - 30% women.
Gender representation for AI talent, source: https://www3.weforum.org/docs/WEF_GGGR_2023.pdf?_gl=1*1f2cssi*_up*MQ..&gclid=CjwKCAjwjOunBhB4EiwA94JWsMy2qreWxnZQCwm9gCOXt4YUZJmkPYtn5eeXeXDmwcaIBi4zPp0dhRoC164QAvD_BwE

This trend in the United States reflects the global data, which also highlights disparities in the number of AI professionals and their seniority levels in occupations. The underlying cause can be attributed to the unique aspects of female socialization, resulting in limited employment opportunities and reduced exposure to emerging technologies.

Generation equality in the adoption of artificial intelligence

Different studies on the adoption of the latest technologies among different generations in the workplace present varying levels of representation.

According to data from Statista, there is a slight difference in AI adoption rates among Gen Z, Gen X, and Millennials in the workplace, with percentages standing at 29%, 28%, and 27% respectively.

Graphic depicting AI adoption rates among different generations in the workplace, with percentages of 29% for Gen Z, 28% for Gen X, and 27% for Millennials.

Conversely, the Pew Research Center reports slightly higher percentage differences by using different age ranges. Among US adults who are familiar with ChatGPT, 18% of individuals aged 18 to 29 stated they have used it for work tasks, compared to 13% among those aged 30 to 49, 8% for the 50 to 64 age group, and 4% for individuals aged 65 and above. These findings challenge the notion that Millennials have the least interest in technology, as indicated by Statista. Thus, Pew’s statistics suggest that younger generations tend to generate text more frequently using ChatGPT compared to individuals aged 50 and above.

Chart showing the usage of ChatGPT among adults, with the highest adoption rate among individuals aged 18-29. In this age group, 30% use ChatGPT frequently. For comparison, the adoption rates are 22% for ages 30-49, 14% for ages 50-64, and 8% for ages 65+. A similar difference exists among generations for those who rarely or never use ChatGPT.
Adults who have generated texts using ChatGPT
A bar chart illustrating the distribution of ChatGPT usage among different age groups for three purposes: entertainment, learning, and work-related tasks. The data is distributed as follows for each age group: - 18-29 years old: 31% for entertainment, 25% for learning, and 18% for work-related tasks. - 30-49 years old: 24% for entertainment, 17% for learning, and 13% for work-related tasks. - 50-64 years old: 12% for entertainment, 10% for learning, and 8% for work-related tasks. - 65+ years old: 4% for entertainment, 5% for learning, and 4% for work-related tasks.
Adults who have used ChatGPT for entertainment, learning new, and work tasks

An interesting observation is that younger generations, particularly those aged 18 to 29, primarily utilize ChatGPT for entertainment purposes, with 31% using it for entertainment and 25% for educational purposes.

Previous observations of 2022 by Wiley Edge have shown that, despite the high interest among Gen Z (with 47% expressing a desire to work in the tech industry), US employers struggle to hire young talent. This highlights the younger generations’ active consumption of technology but their limited role as producers, despite growing attention given to the hiring challenges in the industry.

Academic adoption of AI

Both Pew Research Center and Statista studies show a notable disparity in the in-depth understanding of ChatGPT between respondents with college and postgraduate education compared to those with a high school education or lower. However, both groups exhibit a similar level of general familiarity with the technology.

Chart illustrating the discrepancy in familiarity with Generative AI based on educational attainment, with a higher level of education correlating with a deeper understanding of the technology. The percentage distribution is as follows: among those with extensive knowledge of the technology, over 20% have a postgraduate degree, approximately 18% have a college degree, and less than 10% have a high school education or less. Conversely, among those who are simply familiar with the technology, the percentage is roughly the same across all education groups. Furthermore, among those with a little knowledge of the technology, the percentage again tilts in favor of those with a postgraduate degree.

Statista suggests that this could be attributed to media coverage, but it is also possible that the accessibility of generative AI technology has contributed to this familiarity. While not every American may use it for educational purposes, the ease of access has made it more accessible for experimentation than ever before.

An interesting correlation here can be found between education and income levels. According to the Pew Research Center, those with higher income know more about ChatGPT but people with lower income tend to use it for education and work tasks more often. The correlation can be attributed to several factors.

Chart comparing the knowledge and usage of ChatGPT among US citizens based on income level. Individuals with lower incomes tend to use the chat for various tasks more frequently. However, their level of familiarity with the technology is significantly lower compared to those with moderate and high incomes.
Knowledge and usage of ChatGPT among US citizens based on their income level

First, individuals with higher incomes often have better access to resources such as education, technology, and information sources. This allows them to stay updated on the latest advancements in AI technology, including ChatGPT, and acquire deeper knowledge about its capabilities and limitations.

On the other hand, individuals with lower incomes may rely more on accessible and cost-effective solutions for their educational and work-related needs. ChatGPT, being an accessible tool, provides them with a convenient option for tasks like research, document drafting, or problem-solving.

Additionally, the use of ChatGPT for education and work tasks by individuals with lower income could be driven by its potential to enhance productivity and efficiency. It offers a time-saving alternative to more traditional methods, allowing them to accomplish tasks within limited resources.

It is important to note that these observations are generalizations and individual preferences and circumstances can vary. Nonetheless, the income disparity in knowledge and usage patterns surrounding ChatGPT can be linked to differences in access to resources and the practical benefits it offers in different socioeconomic contexts.

Possible application of generative AI in academia

Academia’s acceptance of generative AI technologies like ChatGPT varies, but examples of its embrace can be found. The Ohio State University stands out in its active utilization of ChatGPT, as outlined in a manual they published. Researchers there find value in using ChatGPT for supporting scholarly work. It can generate summaries of academic articles, aiding researchers in literature reviews and the extraction of key findings. Furthermore, it might assist in data analysis and visualization by creating natural language descriptions of graphs and charts, facilitating the interpretation and communication of research results.

According to the researchers, ChatGPT can also help to enhance academic writing. It offers suggestions for improving sentence structure, vocabulary, and grammar, particularly benefiting non-native English speakers or those seeking assistance with writing skills. Additionally, researchers can utilize ChatGPT for research design, generating research questions, hypotheses, and study designs based on user input.

At the same time, the manual also addresses the limitations of generative AI, highlighting concerns over lack of originality, potential copyright issues, and data inaccuracies. These are important considerations for maintaining a responsible and balanced use of AI within academic processes.

Given that students, particularly Generation Z and those in higher education, frequently use ChatGPT, careful regulations need to be put in place to ensure its ethical use, particularly to prevent its misuse for writing complete theses or essays.

Leading industries in the adoption of AI

Chart depicting the adoption levels of AI across various industries. The most advanced industries in terms of AI adoption are marketing and advertising at 37%, technology at 35%, consulting at 27%, teaching at 19%, accounting at 16%, and healthcare at 15%.
Source: https://www.statista.com/statistics/1361251/generative-ai-adoption-rate-at-work-by-industry-us/

Although the education sector has seen some application of generative AI technologies, it’s not a leading industry in terms of AI adoption. A Statista survey on the adoption of generative AI cites marketing, tech, consulting, teaching, accounting, and healthcare as the main implementers. The survey, however, doesn’t truly reflect the future demand for generative AI implementations, nor does it represent the overall adoption across industries, but focuses on specific, task-oriented AI applications. Consequently, the high rank of marketing, (an industry with impactful potential for generative AI) is not solely reflective of marketing professionals but an indicator of its potential value across multiple industries.

Who’ll be impacted?

Graphic presenting the estimated change in labor demand and the acceleration of automation through Generative AI by occupation in the United States from 2022 to 2030.
Vertical axis — change in labor demand, horizontal axis — increase in automation adoption driven by generative AI acceleration, source: https://www.mckinsey.com/mgi/our-research/generative-ai-and-the-future-of-work-in-america#/

The McKinsey report positions healthcare first in terms of demand for generative AI, anticipating a stable 15% adoption rate till 2023. Other industries with high potential for generative AI adoption include transportation services, property maintenance, mechanical installation and repair, community services, agriculture, and management.

Chart showing the potential impact of Generative AI on various industries with its adoption. The technology is expected to have the greatest impact on education, STEM, business and legal professions, community services, creative industries, managers, and healthcare. For instance, the projected growth rate of education with Generative AI is 54%, compared to 15% without its adoption.
Impact of generative AI on technical automation potential

The most substantial changes due to generative AI implementation are expected in the professions of education and workforce training, business and legal professionals, STEM professionals, community services, and creative arts management. As of now, AI is seen as a tool aiding these professions rather than a transformative agent.

Who’ll gain the most value?

Chart indicating the degree of impact of Generative AI on various tasks across different industries. On the vertical axis, from top to bottom, are industries ranked by estimated growth in profit with the adoption of Generative AI: High tech (4.8-9.3%, $240-480B), Banking (2.8-4.7%, $200-340B), Pharma and medical products (2.6-4.5%, $60-110B), Education (2.2-4.0%, $120-230B), Telecommunications (2.3-3.7%, $60-100B), Healthcare (1.8-3.2%), Insurance (1.8-2.8%, $50-70B). On the horizontal axis, from tasks most susceptible to qualitative changes to the least, are Marketing and Sales, Software Engineering, Customer Operations, Product and R&D, Supply Chain and Operations, Risk and Legal, Strategy and Finance, Corporate IT, Talent and Organization.

Industries most likely to gain value from AI, as indicated by another McKinsey research, are high-tech, banking, pharmaceuticals and medical products, education, and telecommunications. This ranking is based on the potential for automating business functions, with marketing and sales, along with software engineering, offering the greatest possibilities.

The retail and consumer packaged goods (CPG) industry, for instance, can automate crucial tasks such as customer service and supply chain management, resulting in improved customer experience and revenue. The banking sector can also benefit from heightened productivity and robust fraud and risk monitoring, while generative AI can expedite research and development processes in the pharmaceutical and medical industry.

In education, generative AI can streamline administrative tasks, customize learning, and augment teaching capabilities. Telecommunication companies can harness AI for improved customer experience through predictive network optimization and automated customer interactions.

However, the integration of generative AI comes with challenges such as factual content generation, security threats, stringent regulations, and data constraints. Yet, the undeniable transformative potential of generative AI across diverse sectors is clear and paves the path towards improved customer experiences, enhanced productivity, and accelerated developments.

Part 3: Target audience portrait and perspectives on AI applications

Identifying the target audience for any technology, including generative AI, is critical to understanding who is benefiting from the technology, their preferences, and their areas of use. While AI adoption has permeated through myriad sectors in the US, as evidenced by the above observations, distinct groups have showcased a more pronounced level of engagement with this technology.

Technical professionals: People working in high-tech industries such as IT, software development, data science, and researchers are usually the early adopters and heavy users of AI technologies. These professionals often utilize AI to create and implement solutions, build tools, and manage systems.

Young generations: According to the Pew Research Center, Gen Z and Millennials are more prone to use AI, especially for entertainment purposes. Their increased exposure to technology from a young age makes them comfortable in experimenting with innovations.

High-income individuals: As discussed earlier, higher-income individuals are more likely to be aware of AI technologies and have the resources to apply them in their professional and personal lives.

Educated professionals: People with higher educational qualifications tend to use AI for work tasks, utilizing its capabilities to enhance productivity and efficiency.

Inclusion of excluded groups

Despite the rise in AI adoption, specific demographics such as women, seniors, and individuals with lower income and lower education have lower representation in the field. The issue of power disparity in AI is a serious one, long realized before the advent of innovations like OpenAI. Recognizing this back in 2021, Pieter Verdegem in his ”AI for Everyone?: Critical Perspectives” suggests the implementation of radical democratization as a means to counter disparities in AI use as well as to prevent domination by tech monopolies. The concept of democratization is based on the following principles:

A visual representation of AI democratization, showing three principles: accessibility, representation, and benefit for all.

Applying these principles, several strategies can be developed to increase participation from underrepresented groups:

Education and training: Providing technical and digital literacy education can help to bridge the knowledge gap. Coding boot camps, online courses, and workshops can introduce AI technology and its potential uses.

Accessible and affordable AI tools: The development of inexpensive and user-friendly AI tools can encourage their adoption among lower-income groups. Providing free trials or free versions of software can make AI more accessible.

Promotion of diversity in the tech industry: Encouraging diversity in the tech industry can improve the representation of women and minorities in AI. This can occur through scholarship programs, mentorship, and inclusive hiring practices.

While barriers to AI adoption among underrepresented groups can be addressed in different ways:

Digital divide: Bridging the digital divide by improving internet accessibility and affordability, particularly in rural and underserved areas, can facilitate wider AI adoption.

Fear of AI: Addressing fear and misconceptions about AI through public awareness campaigns can help promote its acceptance and use.

Language barriers: Making AI tools available in multiple languages can promote their adoption among non-English speakers.

Engagement of included groups

Harnessing the interest of AI-engaged audiences

The AI-engaged audience is often eager to explore more sophisticated applications. This engagement can be harnessed in several ways:

Advanced training: Providing advanced training and resources to these audiences can help them utilize AI tools more effectively and innovatively.

Beta testing and feedback: Engaged users could be involved in beta testing for new features or updates, and that’s how it was for ChatGPT. Their feedback could provide valuable insights for future developments.

AI competitions or hackathons: Organizing AI contests or hackathons can promote innovation and creativity among engaged users.

In addition to the strategies mentioned above, the following approaches can further deepen the involvement and knowledge of the included groups:

Webinars/online forums: Hosting webinars or online discussions on the latest AI trends or case studies could drive interest among AI professionals.

Collaborations: Collaborating with tech institutions or organizations for research or development projects could deepen their involvement in the AI domain.

Building AI communities: Creating communities on social networks where AI enthusiasts can share their experiences will help build an ecosystem that promotes learning and collaboration.

In summary, fostering a culture of inclusivity, understanding, and innovation would benefit all stakeholders in the AI domain. It includes inviting underrepresented groups to the table, providing them with the necessary resources to engage in AI education and usage, and finding creative ways for involved groups to keep learning and innovating with AI technologies. This comprehensive approach will ensure AI technologies, like ChatGPT, are not only universally accessible but also equitably used for productive tasks across all demographics.

Part 4: Implications for businesses and future opportunities in the US

As the ramifications of generative AI become increasingly recognized, its market share within the US is anticipated to experience significant expansion in the ensuing year. This is set to profoundly influence businesses, particularly given that the North American region leads in the integration and everyday use of AI both in the professional realm and beyond.

The image shows a bar chart representing the distribution of AI usage for different tasks across various countries. The horizontal axis of the chart indicates the different usage categories from left to right: regularly used for work, used for work and outside of work, regularly used outside of work, have tried at least once, no exposure, and don’t know. The data is presented as follows for each country: - Asia-Pacific: 4 for regularly used for work, 18 for used for work and outside of work, 19 for regularly used outside of work, 36 for have tried at least once, 19 for no exposure, and 3 for don’t know. - Developing markets: 9 for regularly used for work, 11 for used for work and outside of work, 20 for regularly used outside of work, 34 for have tried at least once, 23 for no exposure, and 3 for don’t know. - Europe: 10 for regularly used for work, 14 for used for work and outside of work, 11 for regularly used outside of work, 45 for have tried at least once, 15 for no exposure, and 6 for don’t know. - Greater China: 9 for regularly used for work, 10 for work and outside of work, 18 for tried at least once, 46 for no exposure, and 14 for don’t know; North America - 6 for regularly used, 22 for work and outside of work, 13 for tried at least once, 38 for no exposure, and 19 for don’t know.

The value-addition potential of AI is immense, estimated to add trillions of dollars to the global economy. As such, industries ranging from banking and tech to life sciences are predicted to increase their adoption of generative AI, contributing to its market share growth.

Strategies for all businesses to harness and benefit from AI technologies

Based on the McKinsey report, it is clear that investment in generative AI can bring substantial revenue growth and value addition for businesses across varied sectors. But to truly leverage and benefit from AI technologies, businesses need to adopt specific strategies such as:

Invest in AI research: Continuous research in AI will yield more refined and customized solutions for specific business needs. An investment in the research and development of AI technologies can significantly enhance a business’s competitive advantage.

Augment work with AI: AI should not be viewed as a replacement of the workforce, but as a tool to augment employee capabilities. Automating repetitive tasks with AI would allow employees to focus on more complex and creative tasks, enhancing productivity and efficiency.

AI training: To fully leverage AI technologies, a workforce that is capable of using AI tools effectively and innovatively is required. Businesses should invest in training their employees in AI applications relevant to their roles.

As well as pay attention to automating such processes as:

  • Customer operations
  • Marketing and sales
  • Software engineering
  • Research and development

Predictions and trends for the future of AI adoption

AI adoption is likely to surge, not just in high-tech industries but across all sectors. As AI continues to evolve, businesses will increasingly harness its capabilities to automate routine tasks and enhance productivity. The trend toward remote work spurred by the recent pandemic is also expected to contribute to the increased adoption of AI tools to streamline distance collaboration and operations.

Furthermore, advancements in AI’s ability to comprehend and use natural language are expected to revolutionize knowledge work, boosting productivity while fostering an environment of innovative problem-solving.

Potential impacts on society, jobs, and the economy

If appropriately managed, the rise of generative AI will have a profound positive impact on the economy, potentially enabling labor productivity growth of up to 0.6% annually through 2040. However, this also indicates a significant shift in the labor market, with automation predicted to replace about half of the current work activities by 2045 as per McKinsey’s estimates.

This will require a substantial investment in retraining and upskilling the workforce to prepare for the increased demand for AI-assisted roles. As such, initiatives for support and training will be critical, including providing resources for learning new skills and career counseling for transition into new occupations.

Moreover, with generative AI’s potential to boost labor productivity, it can contribute substantially to economic growth and the creation of a more sustainable, inclusive world. However, this digital transformation also raises questions about accessibility and digital divide issues. Policymakers, educational institutions, and businesses alike must work towards ensuring equal access to AI technologies, mitigating the risk of deepening social inequalities.

While the era of generative AI is just beginning, it is evident that its impact on businesses, society, and economies is considerable. The ability of business leaders and policymakers to manage the exponential growth and transformation that generative AI brings will be pivotal to shaping an inclusive, productive, and sustainable future.


Our exploration of the adoption and impacts of generative AI in the US over the past few years revealed several noteworthy trends. The technological evolution of AI has led to significant shifts in its applications, transforming its role from a problem-solving tool to a robust system that simulates human thought processes. The emergence of generative AI models such as ChatGPT has sparked conversations on its capabilities, ethical implications, and societal impact. These technologies are being progressively adopted in areas such as education, professional training, creative arts, and modern industries.

Key takeaways

Gender disparity and lower representation of seniors and low-income and lower-educated individuals in AI adoption are notable trends that need to be addressed. Technically proficient professionals, younger generations, high-income individuals, and educated professionals constitute a significant share of generative AI’s current target audience.

Moreover, various sectors such as healthcare, property maintenance, community services, education, business and legal professionals, and STEM professionals are predicted to witness a surge in automation adoption from generative AI. Despite the promising prospects of generative AI, issues regarding job cuts, replacements, and digital divide are imminent and need careful foreshadowing and strategic action.


Given the shift towards generative AI adoption, businesses across various industries in the US should undertake continuous research, workforce training, and AI-augmented work practices. Investing in generative AI can potentially lead to significant growth in revenues and value addition.

For a wider adoption of AI, businesses need to focus on an inclusive strategy. This can be achieved by bridging the digital divide and addressing challenges such as fear and misconception about AI and language barrier. It is also crucial for businesses to support diversity in the tech industry through mentorship programs, scholarships, and inclusive hiring practices.

The image lists two target groups for Generative AI adoption.

At the same time, industries that are expected to see a surge in automation adoption should begin to prepare for a shift in requisite job skills. This includes upskilling or reskilling the workforce, fostering an AI-ready culture, and implementing supportive measures to navigate the transition.

Overall, the rapid development and adoption of generative AI presents both immense opportunities and significant challenges. As such, the insights offered in this analysis should serve as a guide to better understand the landscape, direct strategies for successful AI integration, and create a more equitable, productive future.

References in the order of appearance

If you got interested in machine learning, AI, and people who were and still are at the front of this revolution, we made a list of resources we thought would make a good sledge.

Alan Turing’s biography in Encyclopædia Britannica

Gartner article “Gartner experts answer the top generative AI questions for your enterprise”

WGA negotiations — status as of May 1, 2023

McKinsey Global Institute report “The economic potential of generative AI: The next productivity frontier”

McKinsey report “Generative AI and the future of work in America”

Statista research “Familiarity with ChatGPT in the United States in 2023, by gender”

World Economic Forum report “Global gender gap report, 2023”

MIT Professional Education report “The Gender Gap in STEM: Still Gaping in 2023”

Statista research “Adoption rate of generative AI adoption in the workplace in the United States 2023, by generation”

Pew Research Center report “Young adults who have heard of ChatGPT are more likely than their older counterparts to have used it”

Wiley Edge report “Diversity in tech and its role in future equality”

Statista research “ChatGPT awareness in the United States in 2023, by level of education”

The Ohio State University manual “The role of artificial Intelligence in learning and development: Understanding ChatGPT a quick reference”

Statista research “Rate of generative AI adoption in the workplace in the United States 2023, by industry”

McKinsey report “The economic potential of generative AI: The next productivity frontier”

Book “AI for everyone?: Critical perspectives”, edited by Pieter Verdegem

McKinsey report “The state of AI in 2023: Generative AI’s breakout year”

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