Since the release of ChatGPT in November 2022, it’s been all over the headlines, and businesses are racing to capture its value. Within the technology’s first few months, McKinsey research found that generative AI (gen AI) features stand to add up to $4.4 trillion to the global economy—annually.
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The articles and reports we’ve published in this time frame examine questions such as these:
- What will the technology be good at, and how quickly?
- What types of jobs will gen AI most affect?
- Which industries stand to gain the most?
- What activities will deliver the most value for organizations?
- How do—and will—workers feel about the technology?
- What safeguards are needed to ensure responsible use of gen AI?
In this visual Explainer, we’ve compiled all the answers we have so far—in 15 McKinsey charts. We expect this space to evolve rapidly and will continue to roll out our research as that happens. To stay up to date on this topic, register for our email alerts on “artificial intelligence” here.
Gen AI finds its legs
The advanced machine learning that powers gen AI–enabled products has been decades in the making. But since ChatGPT came off the starting block in late 2022, new iterations of gen AI technology have been released several times a month. In March 2023 alone, there were six major steps forward, including new customer relationship management solutions and support for the financial services industry.
Source: What every CEO should know about generative AI
The road to human-level performance just got shorter
For most of the technical capabilities shown in this chart, gen AI will perform at a median level of human performance by the end of this decade. And its performance will compete with the top 25 percent of people completing any and all of these tasks before 2040. In some cases, that’s 40 years faster than experts previously thought.
Source: The economic potential of generative AI: The next productivity frontier
And automation of knowledge work is now in sight
Previous waves of automation technology mostly affected physical work activities, but gen AI is likely to have the biggest impact on knowledge work—especially activities involving decision making and collaboration. Professionals in fields such as education, law, technology, and the arts are likely to see parts of their jobs automated sooner than previously expected. This is because of generative AI’s ability to predict patterns in natural language and use it dynamically.
Source: The economic potential of generative AI: The next productivity frontier
Apps keep proliferating to address specific use cases
Gen AI tools can already create most types of written, image, video, audio, and coded content. And businesses are developing applications to address use cases across all these areas. In the near future, we expect applications that target specific industries and functions will provide more value than those that are more general.
Source: Exploring opportunities in the generative AI value chain
Some industries will gain more than others
Gen AI’s precise impact will depend on a variety of factors, such as the mix and importance of different business functions, as well as the scale of an industry’s revenue. Nearly all industries will see the most significant gains from deployment of the technology in their marketing and sales functions. But high tech and banking will see even more impact via gen AI’s potential to accelerate software development.
Source: The economic potential of generative AI: The next productivity frontier
01 — 02
So understanding the use cases that will deliver the most value to your industry is key
Our report, The economic potential of generative AI: The next productivity frontier, contains spotlight sections detailing how to identify the use cases with the highest value potential in the banking, life sciences, and retail and consumer-packaged-goods industries. These provide a good framework for assessing your own industry.
Source: The economic potential of generative AI: The next productivity frontier
Despite gen AI’s commercial promise, most organizations aren’t using it yet
When we asked marketing and sales leaders how much they thought their organization should be using gen AI or machine learning for commercial activities, 90 percent thought it should be at least “often.” That’s hardly surprising, given that marketing and sales is the area with the most potential for impact, as we saw earlier. But 60 percent said their organizations rarely or never do this.
Source: AI-powered marketing and sales reach new heights with generative AI
Marketing and sales leaders are most enthusiastic about three use cases
Our research found that marketing and sales leaders anticipated at least moderate impact from each gen AI use case we suggested. They were most enthusiastic about lead identification, marketing optimization, and personalized outreach.
Source: AI-powered marketing and sales reach new heights with generative AI
Software engineering, the other big value driver for many industries, could get much more efficient
When we had 40 of McKinsey’s own developers test generative AI–based tools, we found impressive speed gains for many common developer tasks. Documenting code functionality for maintainability (which considers how easily code can be improved) can be completed in half the time, writing new code in nearly half the time, and optimizing existing code (called code refactoring) in nearly two-thirds the time.
Source: Unleashing developer productivity with generative AI
And gen AI assistance could make for happier developers
Our research found that equipping developers with the tools they need to be their most productive also significantly improved their experience, which in turn could help companies retain their best talent. Developers using generative AI–based tools were more than twice as likely to report overall happiness, fulfillment, and a state of flow. They attributed this to the tools’ ability to automate grunt work that kept them from more satisfying tasks and to put information at their fingertips faster than a search for solutions across different online platforms.
Source: Unleashing developer productivity with generative AI
Momentum among workers for using gen AI tools is building
A new McKinsey survey shows that the vast majority of workers—in a variety of industries and geographic locations—have tried generative AI tools at least once, whether in or outside work. That’s pretty rapid adoption less than one year in. One surprising result is that baby boomers report using gen AI tools for work more than millennials.
Source: The state of AI in 2023: Generative AI’s breakout year
Respondents across regions, industries, and seniority levels say they are already using generative AI tools.
Reported exposure to generative AI tools, % of respondents
Select demographic
By industryBy industryBy industry
Regularly use for work
Regularly use for work and outside of work
Regularly use outside of work
Have tried at least once
No exposure
Don’t know
Image description:
An interactive bar graph allows the viewer to dig down with some subtlety through respondent’s exposure to generative AI (gen AI) tools by industry, seniority, gender, and age. By industry, respondents working in consumer goods and retail, business legal and professional services, and energy and materials, were more likely to say that they had no exposure to gen AI tools. By location, respondents in developing markets were most likely to say that they had no exposure to gen AI tools. By job title, midlevel managers were most likely to say they regularly use gen AI tools inside and outside of work. By age, adults born 1981 to 1996 were most likely to say that they regularly use gen AI tools inside and outside of work. By gender, men were most likely to say that they regularly use gen AI tools inside and outside of work.
Source: McKinsey Global Survey on AI, 1,684 participants at all levels of the organization, April 11–21, 2023.
End of image description.
Technology, media, andtelecomHealthcare, pharma,and medical productsFinancial servicesEnergy and materialsConsumer goods/retailBusiness, legal, andprofessional servicesAdvanced industries557777668866141411111616111188161610101919161613131212151518181717171747474141404050504141444437371515212126261919141415159955224433447733
Note: Figures may not sum to 100%, because of rounding. In Asia–Pacific, n = 164; in Europe, n = 515; in North America, n = 392; in Greater China (includes Hong Kong and Taiwan), n = 337; and in developing markets (includes India, Latin America, and Middle East and North Africa), n = 276. For advanced industries (includes automotive and assembly, aerospace and defense, advanced electronics, and semiconductors), n = 96; for business, legal, and professional services, n = 215; for consumer goods and retail, n = 128; for energy and materials, n = 96; for financial services, n = 248; for healthcare, pharma, and medical products, n = 130; and for technology, media, and telecom, n = 244. For C-suite respondents, n = 541; for senior managers, n = 437; and for middle managers, n = 339. For respondents born in 1964 or earlier, n = 143; for respondents born between 1965 and 1980, n = 268; and for respondents born between 1981 and 1996, n = 80. Age details were not available for all respondents. For respondents identifying as men, n = 1,025; for respondents identifying as women, n = 156. The survey sample also included respondents who identified as “nonbinary” or “other” but not a large enough number to be statistically meaningful.
Source: McKinsey Global Survey on AI, 1,684 participants at all levels of the organization, April 11–21, 2023
McKinsey & Company
But organizations still need more gen AI–literate employees
As organizations begin to set gen AI goals, they’re also developing the need for more gen AI–literate workers. As generative and other applied AI tools begin delivering value to early adopters, the gap between supply and demand for skilled workers remains wide. To stay on top of the talent market, organizations should develop excellent talent management capabilities, delivering rewarding working experiences to the gen AI–literate workers they hire and hope to retain.
Source: McKinsey Technology Trends Outlook 2023
Organizations should proceed with caution
The possibilities of gen AI are thrilling to many. But like any new technology, gen AI doesn’t come without potential risks. For one thing, gen AI has been known to produce content that’s biased, factually wrong, or illegally scraped from a copyrighted source. Before adopting gen AI tools wholesale, organizations should reckon with the reputational and legal risks to which they may become exposed. One way to mitigate the risk? Keep a human in the loop; that is, make sure a real human checks any gen AI output before it’s published or used.
Source: The state of AI in 2023: Generative AI’s breakout year
Gen AI could ultimately boost global GDP
McKinsey has found that gen AI could substantially increase labor productivity across the economy. To reap the benefits of this productivity boost, however, workers whose jobs are affected will need to shift to other work activities that allow them to at least match their 2022 productivity levels. If workers are supported in learning new skills and, in some cases, changing occupations, stronger global GDP growth could translate to a more sustainable, inclusive world.
Source: The economic potential of generative AI: The next productivity frontier
Gen AI represents just a small piece of the value potential from AI
Gen AI is a big step forward, but traditional advanced analytics and machine learning continue to account for the lion’s share of task optimization, and they continue to find new applications in a wide variety of sectors. Organizations undergoing digital and AI transformations would do well to keep an eye on gen AI, but not to the exclusion of other AI tools. Just because they’re not making headlines doesn’t mean they can’t be put to work to deliver increased productivity—and, ultimately, value.
Source: The economic potential of generative AI: The next productivity frontier
