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The economic potential of generative AI: The next productivity frontier
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THE ECONOMIC POTENTIAL OF GENERATIVE AI: THE NEXT PRODUCTIVITY FRONTIER
June 14, 2023 | Report
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Generative AI is poised to unleash the next wave of productivity. We take a
first look at where business value could accrue and the potential impacts on the
workforce.
Contents
* Introduction
Introduction
* Key insights
Key insights
* 1. Where business value lies
Where business value lies
* 2. Industry impacts
Industry impacts
* 3. Work and productivity implications
Work and productivity implications
* 4. Considerations for business and society
Considerations for business and society
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THE ECONOMIC POTENTIAL OF GENERATIVE AI: THE NEXT PRODUCTIVITY FRONTIER
Full Report (68 pages)
AI has permeated our lives incrementally, through everything from the tech
powering our smartphones to autonomous-driving features on cars to the tools
retailers use to surprise and delight consumers. As a result, its progress has
been almost imperceptible. Clear milestones, such as when AlphaGo, an AI-based
program developed by DeepMind, defeated a world champion Go player in 2016, were
celebrated but then quickly faded from the public’s consciousness.
Generative AI applications such as ChatGPT, GitHub Copilot, Stable Diffusion,
and others have captured the imagination of people around the world in a way
AlphaGo did not, thanks to their broad utility—almost anyone can use them to
communicate and create—and preternatural ability to have a conversation with a
user. The latest generative AI applications can perform a range of routine
tasks, such as the reorganization and classification of data. But it is their
ability to write text, compose music, and create digital art that has garnered
headlines and persuaded consumers and households to experiment on their own. As
a result, a broader set of stakeholders are grappling with generative AI’s
impact on business and society but without much context to help them make sense
of it.
Sidebar
ABOUT THE AUTHORS
This article is a collaborative effort by Michael Chui, Eric Hazan, Roger
Roberts, Alex Singla, Kate Smaje, Alex Sukharevsky, Lareina Yee, and Rodney
Zemmel, representing views from QuantumBlack, AI by McKinsey; McKinsey Digital;
the McKinsey Technology Council; the McKinsey Global Institute; and McKinsey’s
Growth, Marketing & Sales Practice.
The speed at which generative AI technology is developing isn’t making this task
any easier. ChatGPT was released in November 2022. Four months later, OpenAI
released a new large language model, or LLM, called GPT-4 with markedly improved
capabilities.1“Introducing ChatGPT,” OpenAI, November 30, 2022; “GPT-4 is
OpenAI’s most advanced system, producing safer and more useful responses,”
OpenAI, accessed June 1, 2023. Similarly, by May 2023, Anthropic’s generative
AI, Claude, was able to process 100,000 tokens of text, equal to about 75,000
words in a minute—the length of the average novel—compared with roughly 9,000
tokens when it was introduced in March 2023.2“Introducing Claude,” Anthropic
PBC, March 14, 2023; “Introducing 100K Context Windows,” Anthropic PBC, May 11,
2023. And in May 2023, Google announced several new features powered by
generative AI, including Search Generative Experience and a new LLM called PaLM
2 that will power its Bard chatbot, among other Google products.3Emma Roth, “The
nine biggest announcements from Google I/O 2023,” The Verge, May 10, 2023.
To grasp what lies ahead requires an understanding of the breakthroughs that
have enabled the rise of generative AI, which were decades in the making. For
the purposes of this report, we define generative AI as applications typically
built using foundation models. These models contain expansive artificial neural
networks inspired by the billions of neurons connected in the human brain.
Foundation models are part of what is called deep learning, a term that alludes
to the many deep layers within neural networks. Deep learning has powered many
of the recent advances in AI, but the foundation models powering generative AI
applications are a step-change evolution within deep learning. Unlike previous
deep learning models, they can process extremely large and varied sets of
unstructured data and perform more than one task.
Foundation models have enabled new capabilities and vastly improved existing
ones across a broad range of modalities, including images, video, audio, and
computer code. AI trained on these models can perform several functions; it can
classify, edit, summarize, answer questions, and draft new content, among other
tasks.
All of us are at the beginning of a journey to understand generative AI’s power,
reach, and capabilities. This research is the latest in our efforts to assess
the impact of this new era of AI. It suggests that generative AI is poised to
transform roles and boost performance across functions such as sales and
marketing, customer operations, and software development. In the process, it
could unlock trillions of dollars in value across sectors from banking to life
sciences. The following sections share our initial findings.
For the full version of this report, download the PDF.
KEY INSIGHTS
Generative AI’s impact on productivity could add trillions of dollars in value
to the global economy. Our latest research estimates that generative AI could
add the equivalent of $2.6 trillion to $4.4 trillion annually across the 63 use
cases we analyzed—by comparison, the United Kingdom’s entire GDP in 2021 was
$3.1 trillion. This would increase the impact of all artificial intelligence by
15 to 40 percent. This estimate would roughly double if we include the impact of
embedding generative AI into software that is currently used for other tasks
beyond those use cases.
About 75 percent of the value that generative AI use cases could deliver falls
across four areas: Customer operations, marketing and sales, software
engineering, and R&D. Across 16 business functions, we examined 63 use cases in
which the technology can address specific business challenges in ways that
produce one or more measurable outcomes. Examples include generative AI’s
ability to support interactions with customers, generate creative content for
marketing and sales, and draft computer code based on natural-language prompts,
among many other tasks.
MOST POPULAR INSIGHTS
McKinsey technology trends outlook 2024
What to read next: McKinsey’s 2024 annual book recommendations
The loneliest job? How top CEOs manage dilemmas and vulnerability
MOST POPULAR INSIGHTS
1. McKinsey technology trends outlook 2024
2. What to read next: McKinsey’s 2024 annual book recommendations
3. The loneliest job? How top CEOs manage dilemmas and vulnerability
4. The hard stuff: Navigating the physical realities of the energy transition
5. The state of AI in early 2024: Gen AI adoption spikes and starts to generate
value
Generative AI will have a significant impact across all industry sectors.
Banking, high tech, and life sciences are among the industries that could see
the biggest impact as a percentage of their revenues from generative AI. Across
the banking industry, for example, the technology could deliver value equal to
an additional $200 billion to $340 billion annually if the use cases were fully
implemented. In retail and consumer packaged goods, the potential impact is also
significant at $400 billion to $660 billion a year.
Generative AI has the potential to change the anatomy of work, augmenting the
capabilities of individual workers by automating some of their individual
activities. Current generative AI and other technologies have the potential to
automate work activities that absorb 60 to 70 percent of employees’ time today.
In contrast, we previously estimated that technology has the potential to
automate half of the time employees spend working.4“Harnessing automation for a
future that works,” McKinsey Global Institute, January 12, 2017. The
acceleration in the potential for technical automation is largely due to
generative AI’s increased ability to understand natural language, which is
required for work activities that account for 25 percent of total work time.
Thus, generative AI has more impact on knowledge work associated with
occupations that have higher wages and educational requirements than on other
types of work.
The pace of workforce transformation is likely to accelerate, given increases in
the potential for technical automation. Our updated adoption scenarios,
including technology development, economic feasibility, and diffusion timelines,
lead to estimates that half of today’s work activities could be automated
between 2030 and 2060, with a midpoint in 2045, or roughly a decade earlier than
in our previous estimates.
Generative AI can substantially increase labor productivity across the economy,
but that will require investments to support workers as they shift work
activities or change jobs. Generative AI could enable labor productivity growth
of 0.1 to 0.6 percent annually through 2040, depending on the rate of technology
adoption and redeployment of worker time into other activities. Combining
generative AI with all other technologies, work automation could add 0.5 to 3.4
percentage points annually to productivity growth. However, workers will need
support in learning new skills, and some will change occupations. If worker
transitions and other risks can be managed, generative AI could contribute
substantively to economic growth and support a more sustainable, inclusive
world.
The era of generative AI is just beginning. Excitement over this technology is
palpable, and early pilots are compelling. But a full realization of the
technology’s benefits will take time, and leaders in business and society still
have considerable challenges to address. These include managing the risks
inherent in generative AI, determining what new skills and capabilities the
workforce will need, and rethinking core business processes such as retraining
and developing new skills.
WHERE BUSINESS VALUE LIES
Generative AI is a step change in the evolution of artificial intelligence. As
companies rush to adapt and implement it, understanding the technology’s
potential to deliver value to the economy and society at large will help shape
critical decisions. We have used two complementary lenses to determine where
generative AI, with its current capabilities, could deliver the biggest value
and how big that value could be (Exhibit 1).
Exhibit 1
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The first lens scans use cases for generative AI that organizations could adopt.
We define a “use case” as a targeted application of generative AI to a specific
business challenge, resulting in one or more measurable outcomes. For example, a
use case in marketing is the application of generative AI to generate creative
content such as personalized emails, the measurable outcomes of which
potentially include reductions in the cost of generating such content and
increases in revenue from the enhanced effectiveness of higher-quality content
at scale. We identified 63 generative AI use cases spanning 16 business
functions that could deliver total value in the range of $2.6 trillion to $4.4
trillion in economic benefits annually when applied across industries.
That would add 15 to 40 percent to the $11 trillion to $17.7 trillion of
economic value that we now estimate nongenerative artificial intelligence and
analytics could unlock. (Our previous estimate from 2017 was that AI could
deliver $9.5 trillion to $15.4 trillion in economic value.)
Our second lens complements the first by analyzing generative AI’s potential
impact on the work activities required in some 850 occupations. We modeled
scenarios to estimate when generative AI could perform each of more than 2,100
“detailed work activities”—such as “communicating with others about operational
plans or activities”—that make up those occupations across the world economy.
This enables us to estimate how the current capabilities of generative AI could
affect labor productivity across all work currently done by the global
workforce.
Some of this impact will overlap with cost reductions in the use case analysis
described above, which we assume are the result of improved labor productivity.
Netting out this overlap, the total economic benefits of generative AI—including
the major use cases we explored and the myriad increases in productivity that
are likely to materialize when the technology is applied across knowledge
workers’ activities—amounts to $6.1 trillion to $7.9 trillion annually (Exhibit
2).
Exhibit 2
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Sidebar
HOW WE ESTIMATED THE VALUE POTENTIAL OF GENERATIVE AI USE CASES
To assess the potential value of generative AI, we updated a proprietary
McKinsey database of potential AI use cases and drew on the experience of more
than 100 experts in industries and their business functions.1”Notes from the AI
frontier: Applications and value of deep learning,” McKinsey Global Institute,
April 17, 2018.
Our updates examined use cases of generative AI—specifically, how generative AI
techniques (primarily transformer-based neural networks) can be used to solve
problems not well addressed by previous technologies.
We analyzed only use cases for which generative AI could deliver a significant
improvement in the outputs that drive key value. In particular, our estimates of
the primary value the technology could unlock do not include use cases for which
the sole benefit would be its ability to use natural language. For example,
natural-language capabilities would be the key driver of value in a customer
service use case but not in a use case optimizing a logistics network, where
value primarily arises from quantitative analysis.
We then estimated the potential annual value of these generative AI use cases if
they were adopted across the entire economy. For use cases aimed at increasing
revenue, such as some of those in sales and marketing, we estimated the
economy-wide value generative AI could deliver by increasing the productivity of
sales and marketing expenditures.
Our estimates are based on the structure of the global economy in 2022 and do
not consider the value generative AI could create if it produced entirely new
product or service categories.
While generative AI is an exciting and rapidly advancing technology, the other
applications of AI discussed in our previous report continue to account for the
majority of the overall potential value of AI. Traditional advanced-analytics
and machine learning algorithms are highly effective at performing numerical and
optimization tasks such as predictive modeling, and they continue to find new
applications in a wide range of industries. However, as generative AI continues
to develop and mature, it has the potential to open wholly new frontiers in
creativity and innovation. It has already expanded the possibilities of what AI
overall can achieve (see sidebar “How we estimated the value potential of
generative AI use cases”).
In this section, we highlight the value potential of generative AI across
business functions.
Generative AI could have an impact on most business functions; however, a few
stand out when measured by the technology’s impact as a share of functional cost
(Exhibit 3). Our analysis of 16 business functions identified just four—customer
operations, marketing and sales, software engineering, and research and
development—that could account for approximately 75 percent of the total annual
value from generative AI use cases.
Exhibit 3
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Notably, the potential value of using generative AI for several functions that
were prominent in our previous sizing of AI use cases, including manufacturing
and supply chain functions, is now much lower.5Pitchbook. This is largely
explained by the nature of generative AI use cases, which exclude most of the
numerical and optimization applications that were the main value drivers for
previous applications of AI.
In addition to the potential value generative AI can deliver in
function-specific use cases, the technology could drive value across an entire
organization by revolutionizing internal knowledge management systems.
Generative AI’s impressive command of natural-language processing can help
employees retrieve stored internal knowledge by formulating queries in the same
way they might ask a human a question and engage in continuing dialogue. This
could empower teams to quickly access relevant information, enabling them to
rapidly make better-informed decisions and develop effective strategies.
In 2012, the McKinsey Global Institute (MGI) estimated that knowledge workers
spent about a fifth of their time, or one day each work week, searching for and
gathering information. If generative AI could take on such tasks, increasing the
efficiency and effectiveness of the workers doing them, the benefits would be
huge. Such virtual expertise could rapidly “read” vast libraries of corporate
information stored in natural language and quickly scan source material in
dialogue with a human who helps fine-tune and tailor its research, a more
scalable solution than hiring a team of human experts for the task.
In other cases, generative AI can drive value by working in partnership with
workers, augmenting their work in ways that accelerate their productivity. Its
ability to rapidly digest mountains of data and draw conclusions from it enables
the technology to offer insights and options that can dramatically enhance
knowledge work. This can significantly speed up the process of developing a
product and allow employees to devote more time to higher-impact tasks.
Following are four examples of how generative AI could produce operational
benefits in a handful of use cases across the business functions that could
deliver a majority of the potential value we identified in our analysis of 63
generative AI use cases. In the first two examples, it serves as a virtual
expert, while in the following two, it lends a hand as a virtual collaborator.
CUSTOMER OPERATIONS: IMPROVING CUSTOMER AND AGENT EXPERIENCES
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Generative AI has the potential to revolutionize the entire customer operations
function, improving the customer experience and agent productivity through
digital self-service and enhancing and augmenting agent skills. The technology
has already gained traction in customer service because of its ability to
automate interactions with customers using natural language. Research found that
at one company with 5,000 customer service agents, the application of generative
AI increased issue resolution by 14 percent an hour and reduced the time spent
handling an issue by 9 percent.1Erik Brynjolfsson, Danielle Li, and Lindsey R.
Raymond, Generative AI at work, National Bureau of Economic Research working
paper number 31161, April 2023. It also reduced agent attrition and requests to
speak to a manager by 25 percent. Crucially, productivity and quality of service
improved most among less-experienced agents, while the AI assistant did not
increase—and sometimes decreased—the productivity and quality metrics of more
highly skilled agents. This is because AI assistance helped less-experienced
agents communicate using techniques similar to those of their higher-skilled
counterparts.
The following are examples of the operational improvements generative AI can
have for specific use cases:
* Customer self-service. Generative AI–fueled chatbots can give immediate and
personalized responses to complex customer inquiries regardless of the
language or location of the customer. By improving the quality and
effectiveness of interactions via automated channels, generative AI could
automate responses to a higher percentage of customer inquiries, enabling
customer care teams to take on inquiries that can only be resolved by a human
agent. Our research found that roughly half of customer contacts made by
banking, telecommunications, and utilities companies in North America are
already handled by machines, including but not exclusively AI. We estimate
that generative AI could further reduce the volume of human-serviced contacts
by up to 50 percent, depending on a company’s existing level of automation.
* Resolution during initial contact. Generative AI can instantly retrieve data
a company has on a specific customer, which can help a human customer service
representative more successfully answer questions and resolve issues during
an initial interaction.
* Reduced response time. Generative AI can cut the time a human sales
representative spends responding to a customer by providing assistance in
real time and recommending next steps.
* Increased sales. Because of its ability to rapidly process data on customers
and their browsing histories, the technology can identify product suggestions
and deals tailored to customer preferences. Additionally, generative AI can
enhance quality assurance and coaching by gathering insights from customer
conversations, determining what could be done better, and coaching agents.
We estimate that applying generative AI to customer care functions could
increase productivity at a value ranging from 30 to 45 percent of current
function costs.
Our analysis captures only the direct impact generative AI might have on the
productivity of customer operations. It does not account for potential knock-on
effects the technology may have on customer satisfaction and retention arising
from an improved experience, including better understanding of the customer’s
context that can assist human agents in providing more personalized help and
recommendations.
MARKETING AND SALES: BOOSTING PERSONALIZATION, CONTENT CREATION, AND SALES
PRODUCTIVITY
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Generative AI has taken hold rapidly in marketing and sales functions, in which
text-based communications and personalization at scale are driving forces. The
technology can create personalized messages tailored to individual customer
interests, preferences, and behaviors, as well as do tasks such as producing
first drafts of brand advertising, headlines, slogans, social media posts, and
product descriptions.
MARKETING
Introducing generative AI to marketing functions requires careful consideration.
For one thing, mathematical models trained on publicly available data without
sufficient safeguards against plagiarism, copyright violations, and branding
recognition risks infringing on intellectual property rights. A virtual try-on
application may produce biased representations of certain demographics because
of limited or biased training data. Thus, significant human oversight is
required for conceptual and strategic thinking specific to each company’s needs.
Potential operational benefits from using generative AI for marketing include
the following:
* Efficient and effective content creation. Generative AI could significantly
reduce the time required for ideation and content drafting, saving valuable
time and effort. It can also facilitate consistency across different pieces
of content, ensuring a uniform brand voice, writing style, and format. Team
members can collaborate via generative AI, which can integrate their ideas
into a single cohesive piece. This would allow teams to significantly enhance
personalization of marketing messages aimed at different customer segments,
geographies, and demographics. Mass email campaigns can be instantly
translated into as many languages as needed, with different imagery and
messaging depending on the audience. Generative AI’s ability to produce
content with varying specifications could increase customer value,
attraction, conversion, and retention over a lifetime and at a scale beyond
what is currently possible through traditional techniques.
* Enhanced use of data. Generative AI could help marketing functions overcome
the challenges of unstructured, inconsistent, and disconnected data—for
example, from different databases—by interpreting abstract data sources such
as text, image, and varying structures. It can help marketers better use data
such as territory performance, synthesized customer feedback, and customer
behavior to generate data-informed marketing strategies such as targeted
customer profiles and channel recommendations. Such tools could identify and
synthesize trends, key drivers, and market and product opportunities from
unstructured data such as social media, news, academic research, and customer
feedback.
* SEO optimization. Generative AI can help marketers achieve higher conversion
and lower cost through search engine optimization (SEO) for marketing and
sales technical components such as page titles, image tags, and URLs. It can
synthesize key SEO tokens, support specialists in SEO digital content
creation, and distribute targeted content to customers.
* Product discovery and search personalization. With generative AI, product
discovery and search can be personalized with multimodal inputs from text,
images, and speech, and a deep understanding of customer profiles. For
example, technology can leverage individual user preferences, behavior, and
purchase history to help customers discover the most relevant products and
generate personalized product descriptions. This would allow CPG, travel, and
retail companies to improve their e-commerce sales by achieving higher
website conversion rates.
We estimate that generative AI could increase the productivity of the marketing
function with a value between 5 and 15 percent of total marketing spending.
Our analysis of the potential use of generative AI in marketing doesn’t account
for knock-on effects beyond the direct impacts on productivity. Generative
AI–enabled synthesis could provide higher-quality data insights, leading to new
ideas for marketing campaigns and better-targeted customer segments. Marketing
functions could shift resources to producing higher-quality content for owned
channels, potentially reducing spending on external channels and agencies.
SALES
Generative AI could also change the way both B2B and B2C companies approach
sales. The following are two use cases for sales:
* Increase probability of sale. Generative AI could identify and prioritize
sales leads by creating comprehensive consumer profiles from structured and
unstructured data and suggesting actions to staff to improve client
engagement at every point of contact. For example, generative AI could
provide better information about client preferences, potentially improving
close rates.
* Improve lead development. Generative AI could help sales representatives
nurture leads by synthesizing relevant product sales information and customer
profiles and creating discussion scripts to facilitate customer conversation,
including up- and cross-selling talking points. It could also automate sales
follow-ups and passively nurture leads until clients are ready for direct
interaction with a human sales agent.
Our analysis suggests that implementing generative AI could increase sales
productivity by approximately 3 to 5 percent of current global sales
expenditures.
This analysis may not fully account for additional revenue that generative AI
could bring to sales functions. For instance, generative AI’s ability to
identify leads and follow-up capabilities could uncover new leads and facilitate
more effective outreach that would bring in additional revenue. Also, the time
saved by sales representatives due to generative AI’s capabilities could be
invested in higher-quality customer interactions, resulting in increased sales
success.
SOFTWARE ENGINEERING: SPEEDING DEVELOPER WORK AS A CODING ASSISTANT
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Treating computer languages as just another language opens new possibilities for
software engineering. Software engineers can use generative AI in pair
programming and to do augmented coding and train LLMs to develop applications
that generate code when given a natural-language prompt describing what that
code should do.
Software engineering is a significant function in most companies, and it
continues to grow as all large companies, not just tech titans, embed software
in a wide array of products and services. For example, much of the value of new
vehicles comes from digital features such as adaptive cruise control, parking
assistance, and IoT connectivity.
According to our analysis, the direct impact of AI on the productivity of
software engineering could range from 20 to 45 percent of current annual
spending on the function. This value would arise primarily from reducing time
spent on certain activities, such as generating initial code drafts, code
correction and refactoring, root-cause analysis, and generating new system
designs. By accelerating the coding process, generative AI could push the skill
sets and capabilities needed in software engineering toward code and
architecture design. One study found that software developers using Microsoft’s
GitHub Copilot completed tasks 56 percent faster than those not using the
tool.1Peter Cihon et al., The impact of AI on developer productivity: Evidence
from GitHub Copilot, Cornell University arXiv software engineering working
paper, arXiv:2302.06590, February 13, 2023. An internal McKinsey empirical study
of software engineering teams found those who were trained to use generative AI
tools rapidly reduced the time needed to generate and refactor code—and
engineers also reported a better work experience, citing improvements in
happiness, flow, and fulfillment.
Our analysis did not account for the increase in application quality and the
resulting boost in productivity that generative AI could bring by improving code
or enhancing IT architecture—which can improve productivity across the IT value
chain. However, the quality of IT architecture still largely depends on software
architects, rather than on initial drafts that generative AI’s current
capabilities allow it to produce.
Large technology companies are already selling generative AI for software
engineering, including GitHub Copilot, which is now integrated with OpenAI’s
GPT-4, and Replit, used by more than 20 million coders.2Michael Nuñez, “Google
and Replit join forces to challenge Microsoft in coding tools,” VentureBeat,
March 28, 2023.
PRODUCT R&D: REDUCING RESEARCH AND DESIGN TIME, IMPROVING SIMULATION AND TESTING
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Generative AI’s potential in R&D is perhaps less well recognized than its
potential in other business functions. Still, our research indicates the
technology could deliver productivity with a value ranging from 10 to 15 percent
of overall R&D costs.
For example, the life sciences and chemical industries have begun using
generative AI foundation models in their R&D for what is known as generative
design. Foundation models can generate candidate molecules, accelerating the
process of developing new drugs and materials. Entos, a biotech pharmaceutical
company, has paired generative AI with automated synthetic development tools to
design small-molecule therapeutics. But the same principles can be applied to
the design of many other products, including larger-scale physical products and
electrical circuits, among others.
While other generative design techniques have already unlocked some of the
potential to apply AI in R&D, their cost and data requirements, such as the use
of “traditional” machine learning, can limit their application. Pretrained
foundation models that underpin generative AI, or models that have been enhanced
with fine-tuning, have much broader areas of application than models optimized
for a single task. They can therefore accelerate time to market and broaden the
types of products to which generative design can be applied. For now, however,
foundation models lack the capabilities to help design products across all
industries.
In addition to the productivity gains that result from being able to quickly
produce candidate designs, generative design can also enable improvements in the
designs themselves, as in the following examples of the operational improvements
generative AI could bring:
* Enhanced design. Generative AI can help product designers reduce costs by
selecting and using materials more efficiently. It can also optimize designs
for manufacturing, which can lead to cost reductions in logistics and
production.
* Improved product testing and quality. Using generative AI in generative
design can produce a higher-quality product, resulting in increased
attractiveness and market appeal. Generative AI can help to reduce testing
time of complex systems and accelerate trial phases involving customer
testing through its ability to draft scenarios and profile testing
candidates.
We also identified a new R&D use case for nongenerative AI: deep learning
surrogates, the use of which has grown since our earlier research, can be paired
with generative AI to produce even greater benefits. To be sure, integration
will require the development of specific solutions, but the value could be
significant because deep learning surrogates have the potential to accelerate
the testing of designs proposed by generative AI.
While we have estimated the potential direct impacts of generative AI on the R&D
function, we did not attempt to estimate the technology’s potential to create
entirely novel product categories. These are the types of innovations that can
produce step changes not only in the performance of individual companies but in
economic growth overall.
INDUSTRY IMPACTS
Across the 63 use cases we analyzed, generative AI has the potential to generate
$2.6 trillion to $4.4 trillion in value across industries. Its precise impact
will depend on a variety of factors, such as the mix and importance of different
functions, as well as the scale of an industry’s revenue (Exhibit 4).
Exhibit 4
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For example, our analysis estimates generative AI could contribute roughly $310
billion in additional value for the retail industry (including auto dealerships)
by boosting performance in functions such as marketing and customer
interactions. By comparison, the bulk of potential value in high tech comes from
generative AI’s ability to increase the speed and efficiency of software
development (Exhibit 5).
Exhibit 5
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In the banking industry, generative AI has the potential to improve on
efficiencies already delivered by artificial intelligence by taking on
lower-value tasks in risk management, such as required reporting, monitoring
regulatory developments, and collecting data. In the life sciences industry,
generative AI is poised to make significant contributions to drug discovery and
development.
We share our detailed analysis of these industries below.
GENERATIVE AI SUPPORTS KEY VALUE DRIVERS IN RETAIL AND CONSUMER PACKAGED GOODS
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The technology could generate value for the retail and consumer packaged goods
(CPG) industry by increasing productivity by 1.2 to 2.0 percent of annual
revenues, or an additional $400 billion to $660 billion.1Vehicular retail is
included as part of our overall retail analysis. To streamline processes,
generative AI could automate key functions such as customer service, marketing
and sales, and inventory and supply chain management. Technology has played an
essential role in the retail and CPG industries for decades. Traditional AI and
advanced analytics solutions have helped companies manage vast pools of data
across large numbers of SKUs, expansive supply chain and warehousing networks,
and complex product categories such as consumables. In addition, the industries
are heavily customer facing, which offers opportunities for generative AI to
complement previously existing artificial intelligence. For example, generative
AI’s ability to personalize offerings could optimize marketing and sales
activities already handled by existing AI solutions. Similarly, generative AI
tools excel at data management and could support existing AI-driven pricing
tools. Applying generative AI to such activities could be a step toward
integrating applications across a full enterprise.
GENERATIVE AI AT WORK IN RETAIL AND CPG
REINVENTION OF THE CUSTOMER INTERACTION PATTERN
Consumers increasingly seek customization in everything from clothing and
cosmetics to curated shopping experiences, personalized outreach, and food—and
generative AI can improve that experience. Generative AI can aggregate market
data to test concepts, ideas, and models. Stitch Fix, which uses algorithms to
suggest style choices to its customers, has experimented with DALL·E to
visualize products based on customer preferences regarding color, fabric, and
style. Using text-to-image generation, the company’s stylists can visualize an
article of clothing based on a consumer’s preferences and then identify a
similar article among Stitch Fix’s inventory.
Retailers can create applications that give shoppers a next-generation
experience, creating a significant competitive advantage in an era when
customers expect to have a single natural-language interface help them select
products. For example, generative AI can improve the process of choosing and
ordering ingredients for a meal or preparing food—imagine a chatbot that could
pull up the most popular tips from the comments attached to a recipe. There is
also a big opportunity to enhance customer value management by delivering
personalized marketing campaigns through a chatbot. Such applications can have
human-like conversations about products in ways that can increase customer
satisfaction, traffic, and brand loyalty. Generative AI offers retailers and CPG
companies many opportunities to cross-sell and upsell, collect insights to
improve product offerings, and increase their customer base, revenue
opportunities, and overall marketing ROI.
ACCELERATING THE CREATION OF VALUE IN KEY AREAS
Generative AI tools can facilitate copy writing for marketing and sales, help
brainstorm creative marketing ideas, expedite consumer research, and accelerate
content analysis and creation. The potential improvement in writing and visuals
can increase awareness and improve sales conversion rates.
RAPID RESOLUTION AND ENHANCED INSIGHTS IN CUSTOMER CARE
The growth of e-commerce also elevates the importance of effective consumer
interactions. Retailers can combine existing AI tools with generative AI to
enhance the capabilities of chatbots, enabling them to better mimic the
interaction style of human agents—for example, by responding directly to a
customer’s query, tracking or canceling an order, offering discounts, and
upselling. Automating repetitive tasks allows human agents to devote more time
to handling complicated customer problems and obtaining contextual information.
DISRUPTIVE AND CREATIVE INNOVATION
Generative AI tools can enhance the process of developing new versions of
products by digitally creating new designs rapidly. A designer can generate
packaging designs from scratch or generate variations on an existing design.
This technology is developing rapidly and has the potential to add text-to-video
generation.
FACTORS FOR RETAIL AND CPG ORGANIZATIONS TO CONSIDER
As retail and CPG executives explore how to integrate generative AI in their
operations, they should keep in mind several factors that could affect their
ability to capture value from the technology:
* External inference. Generative AI has increased the need to understand
whether generated content is based on fact or inference, requiring a new
level of quality control.
* Adversarial attacks. Foundation models are a prime target for attack by
hackers and other bad actors, increasing the variety of potential security
vulnerabilities and privacy risks.
To address these concerns, retail and CPG companies will need to strategically
keep humans in the loop and ensure security and privacy are top considerations
for any implementation. Companies will need to institute new quality checks for
processes previously handled by humans, such as emails written by customer reps,
and perform more-detailed quality checks on AI-assisted processes such as
product design.
WHY BANKS COULD REALIZE SIGNIFICANT VALUE
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Generative AI could have a significant impact on the banking industry,
generating value from increased productivity of 2.8 to 4.7 percent of the
industry’s annual revenues, or an additional $200 billion to $340 billion. On
top of that impact, the use of generative AI tools could also enhance customer
satisfaction, improve decision making and employee experience, and decrease
risks through better monitoring of fraud and risk.
Banking, a knowledge and technology-enabled industry, has already benefited
significantly from previously existing applications of artificial intelligence
in areas such as marketing and customer operations.1“Building the AI bank of the
future,” McKinsey, May 2021. Generative AI applications could deliver additional
benefits, especially because text modalities are prevalent in areas such as
regulations and programming language, and the industry is customer facing, with
many B2C and small-business customers.2McKinsey’s Global Banking Annual Review,
December 1, 2022.
Several characteristics position the industry for the integration of generative
AI applications:
* Sustained digitization efforts along with legacy IT systems. Banks have been
investing in technology for decades, accumulating a significant amount of
technical debt along with a siloed and complex IT architecture.3Akhil Babbar,
Raghavan Janardhanan, Remy Paternoster, and Henning Soller, “Why most digital
banking transformations fail—and how to flip the odds,” McKinsey, April 11,
2023.
* Large customer-facing workforces. Banking relies on a large number of service
representatives such as call-center agents and wealth management financial
advisers.
* A stringent regulatory environment. As a heavily regulated industry, banking
has a substantial number of risk, compliance, and legal needs.
* White-collar industry. Generative AI’s impact could span the organization,
assisting all employees in writing emails, creating business presentations,
and other tasks.
GENERATIVE AI AT WORK IN BANKING
Banks have started to grasp the potential of generative AI in their front lines
and in their software activities. Early adopters are harnessing solutions such
as ChatGPT as well as industry-specific solutions, primarily for software and
knowledge applications. Three uses demonstrate its value potential to the
industry.
A VIRTUAL EXPERT TO AUGMENT EMPLOYEE PERFORMANCE
A generative AI bot trained on proprietary knowledge such as policies, research,
and customer interaction could provide always-on, deep technical support. Today,
frontline spending is dedicated mostly to validating offers and interacting with
clients, but giving frontline workers access to data as well could improve the
customer experience. The technology could also monitor industries and clients
and send alerts on semantic queries from public sources. For example, Morgan
Stanley is building an AI assistant using GPT-4, with the aim of helping tens of
thousands of wealth managers quickly find and synthesize answers from a massive
internal knowledge base.4Hugh Son, “Morgan Stanley is testing an OpenAI-powered
chatbot for its 16,000 financial advisors,” CNBC, March 14, 2023. The model
combines search and content creation so wealth managers can find and tailor
information for any client at any moment.
One European bank has leveraged generative AI to develop an environmental,
social, and governance (ESG) virtual expert by synthesizing and extracting from
long documents with unstructured information. The model answers complex
questions based on a prompt, identifying the source of each answer and
extracting information from pictures and tables.
Generative AI could reduce the significant costs associated with back-office
operations. Such customer-facing chatbots could assess user requests and select
the best service expert to address them based on characteristics such as topic,
level of difficulty, and type of customer. Through generative AI assistants,
service professionals could rapidly access all relevant information such as
product guides and policies to instantaneously address customer requests.
CODE ACCELERATION TO REDUCE TECH DEBT AND DELIVER SOFTWARE FASTER
Generative AI tools are useful for software development in four broad
categories. First, they can draft code based on context via input code or
natural language, helping developers code more quickly and with reduced friction
while enabling automatic translations and no- and low-code tools. Second, such
tools can automatically generate, prioritize, run, and review different code
tests, accelerating testing and increasing coverage and effectiveness. Third,
generative AI’s natural-language translation capabilities can optimize the
integration and migration of legacy frameworks. Last, the tools can review code
to identify defects and inefficiencies in computing. The result is more robust,
effective code.
PRODUCTION OF TAILORED CONTENT AT SCALE
Generative AI tools can draw on existing documents and data sets to
substantially streamline content generation. These tools can create personalized
marketing and sales content tailored to specific client profiles and histories
as well as a multitude of alternatives for A/B testing. In addition, generative
AI could automatically produce model documentation, identify missing
documentation, and scan relevant regulatory updates to create alerts for
relevant shifts.
FACTORS FOR BANKS TO CONSIDER
When exploring how to integrate generative AI into operations, banks can be
mindful of a number of factors:
* The level of regulation for different processes. These vary from unregulated
processes such as customer service to heavily regulated processes such as
credit risk scoring.
* Type of end user. End users vary widely in their expectations and familiarity
with generative AI—for example, employees compared with high-net-worth
clients.
* Intended level of work automation. AI agents integrated through APIs could
act nearly autonomously or as copilots, giving real-time suggestions to
agents during customer interactions.
* Data constraints. While public data such as annual reports could be made
widely available, there would need to be limits on identifiable details for
customers and other internal data.
PHARMACEUTICALS AND MEDICAL PRODUCTS COULD SEE BENEFITS ACROSS THE ENTIRE VALUE
CHAIN
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Our analysis finds that generative AI could have a significant impact on the
pharmaceutical and medical-product industries—from 2.6 to 4.5 percent of annual
revenues across the pharmaceutical and medical-product industries, or $60
billion to $110 billion annually. This big potential reflects the
resource-intensive process of discovering new drug compounds. Pharma companies
typically spend approximately 20 percent of revenues on R&D,1Research and
development in the pharmaceutical industry, Congressional Budget Office, April
2021. and the development of a new drug takes an average of ten to 15 years.
With this level of spending and timeline, improving the speed and quality of R&D
can generate substantial value. For example, lead identification—a step in the
drug discovery process in which researchers identify a molecule that would best
address the target for a potential new drug—can take several months even with
“traditional” deep learning techniques. Foundation models and generative AI can
enable organizations to complete this step in a matter of weeks.
GENERATIVE AI AT WORK IN PHARMACEUTICALS AND MEDICAL PRODUCTS
Drug discovery involves narrowing the universe of possible compounds to those
that could effectively treat specific conditions. Generative AI’s ability to
process massive amounts of data and model options can accelerate output across
several use cases:
IMPROVE AUTOMATION OF PRELIMINARY SCREENING
In the lead identification stage of drug development, scientists can use
foundation models to automate the preliminary screening of chemicals in the
search for those that will produce specific effects on drug targets. To start,
thousands of cell cultures are tested and paired with images of the
corresponding experiment. Using an off-the-shelf foundation model, researchers
can cluster similar images more precisely than they can with traditional models,
enabling them to select the most promising chemicals for further analysis during
lead optimization.
ENHANCE INDICATION FINDING
An important phase of drug discovery involves the identification and
prioritization of new indications—that is, diseases, symptoms, or circumstances
that justify the use of a specific medication or other treatment, such as a
test, procedure, or surgery. Possible indications for a given drug are based on
a patient group’s clinical history and medical records, and they are then
prioritized based on their similarities to established and evidence-backed
indications.
Researchers start by mapping the patient cohort’s clinical events and medical
histories—including potential diagnoses, prescribed medications, and performed
procedures—from real-world data. Using foundation models, researchers can
quantify clinical events, establish relationships, and measure the similarity
between the patient cohort and evidence-backed indications. The result is a
short list of indications that have a better probability of success in clinical
trials because they can be more accurately matched to appropriate patient
groups.
Pharma companies that have used this approach have reported high success rates
in clinical trials for the top five indications recommended by a foundation
model for a tested drug. This success has allowed these drugs to progress
smoothly into Phase 3 trials, significantly accelerating the drug development
process.
FACTORS FOR PHARMACEUTICALS AND MEDICAL PRODUCTS ORGANIZATIONS TO CONSIDER
Before integrating generative AI into operations, pharma executives should be
aware of some factors that could limit their ability to capture its benefits:
* The need for a human in the loop. Companies may need to implement new quality
checks on processes that shift from humans to generative AI, such as
representative-generated emails, or more detailed quality checks on
AI-assisted processes, such as drug discovery. The increasing need to verify
whether generated content is based on fact or inference elevates the need for
a new level of quality control.
* Explainability. A lack of transparency into the origins of generated content
and traceability of root data could make it difficult to update models and
scan them for potential risks; for instance, a generative AI solution for
synthesizing scientific literature may not be able to point to the specific
articles or quotes that led it to infer that a new treatment is very popular
among physicians. The technology can also “hallucinate,” or generate
responses that are obviously incorrect or inappropriate for the context.
Systems need to be designed to point to specific articles or data sources,
and then do human-in-the-loop checking.
* Privacy considerations. Generative AI’s use of clinical images and medical
records could increase the risk that protected health information will leak,
potentially violating regulations that require pharma companies to protect
patient privacy.
WORK AND PRODUCTIVITY IMPLICATIONS
Technology has been changing the anatomy of work for decades. Over the years,
machines have given human workers various “superpowers”; for instance,
industrial-age machines enabled workers to accomplish physical tasks beyond the
capabilities of their own bodies. More recently, computers have enabled
knowledge workers to perform calculations that would have taken years to do
manually.
These examples illustrate how technology can augment work through the automation
of individual activities that workers would have otherwise had to do themselves.
At a conceptual level, the application of generative AI may follow the same
pattern in the modern workplace, although as we show later in this chapter, the
types of activities that generative AI could affect, and the types of
occupations with activities that could change, will likely be different as a
result of this technology than for older technologies.
The McKinsey Global Institute began analyzing the impact of technological
automation of work activities and modeling scenarios of adoption in 2017. At
that time, we estimated that workers spent half of their time on activities that
had the potential to be automated by adapting technology that existed at that
time, or what we call technical automation potential. We also modeled a range of
potential scenarios for the pace at which these technologies could be adopted
and affect work activities throughout the global economy.
Technology adoption at scale does not occur overnight. The potential of
technological capabilities in a lab does not necessarily mean they can be
immediately integrated into a solution that automates a specific work
activity—developing such solutions takes time. Even when such a solution is
developed, it might not be economically feasible to use if its costs exceed
those of human labor. Additionally, even if economic incentives for deployment
exist, it takes time for adoption to spread across the global economy. Hence,
our adoption scenarios, which consider these factors together with the technical
automation potential, provide a sense of the pace and scale at which workers’
activities could shift over time.
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Sidebar
ABOUT THE RESEARCH
This analysis builds on the methodology we established in 2017. We began by
examining the US Bureau of Labor Statistics O*Net breakdown of about 850
occupations into roughly 2,100 detailed work activities. For each of these
activities, we scored the level of capability necessary to successfully perform
the activity against a set of 18 capabilities that have the potential for
automation.
We also surveyed experts in the automation of each of these capabilities to
estimate automation technologies’ current performance level against each of
these capabilities, as well as how the technology’s performance might advance
over time. Specifically, this year, we updated our assessments of technology’s
performance in cognitive, language, and social and emotional capabilities based
on a survey of generative AI experts.
Based on these assessments of the technical automation potential of each
detailed work activity at each point in time, we modeled potential scenarios for
the adoption of work automation around the world. First, we estimated a range of
time to implement a solution that could automate each specific detailed work
activity, once all the capability requirements were met by the state of
technology development. Second, we estimated a range of potential costs for this
technology when it is first introduced, and then declining over time, based on
historical precedents. We modeled the beginning of adoption for a specific
detailed work activity in a particular occupation in a country (for 47
countries, accounting for more than 80 percent of the global workforce) when the
cost of the automation technology reaches parity with the cost of human labor in
that occupation.
Based on a historical analysis of various technologies, we modeled a range of
adoption timelines from eight to 27 years between the beginning of adoption and
its plateau, using sigmoidal curves (S-curves). This range implicitly accounts
for the many factors that could affect the pace at which adoption occurs,
including regulation, levels of investment, and management decision making
within firms.
The modeled scenarios create a time range for the potential pace of automating
current work activities. The “earliest” scenario flexes all parameters to the
extremes of plausible assumptions, resulting in faster automation development
and adoption, and the “latest” scenario flexes all parameters in the opposite
direction. The reality is likely to fall somewhere between the two.
The analyses in this paper incorporate the potential impact of generative AI on
today’s work activities. The new capabilities of generative AI, combined with
previous technologies and integrated into corporate operations around the world,
could accelerate the potential for technical automation of individual activities
and the adoption of technologies that augment the capabilities of the workforce.
They could also have an impact on knowledge workers whose activities were not
expected to shift as a result of these technologies until later in the future
(see sidebar “About the research”).
AUTOMATION POTENTIAL HAS ACCELERATED, BUT ADOPTION TO LAG
Based on developments in generative AI, technology performance is now expected
to match median human performance and reach top-quartile human performance
earlier than previously estimated across a wide range of capabilities (Exhibit
6). For example, MGI previously identified 2027 as the earliest year when median
human performance for natural-language understanding might be achieved in
technology, but in this new analysis, the corresponding point is 2023.
Exhibit 6
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As a result of these reassessments of technology capabilities due to generative
AI, the total percentage of hours that could theoretically be automated by
integrating technologies that exist today has increased from about 50 percent to
60–70 percent. The technical potential curve is quite steep because of the
acceleration in generative AI’s natural-language capabilities.
Interestingly, the range of times between the early and late scenarios has
compressed compared with the expert assessments in 2017, reflecting a greater
confidence that higher levels of technological capabilities will arrive by
certain time periods (Exhibit 7).
Exhibit 7
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Our analysis of adoption scenarios accounts for the time required to integrate
technological capabilities into solutions that can automate individual work
activities; the cost of these technologies compared with that of human labor in
different occupations and countries around the world; and the time it has taken
for technologies to diffuse across the economy. With the acceleration in
technical automation potential that generative AI enables, our scenarios for
automation adoption have correspondingly accelerated. These scenarios encompass
a wide range of outcomes, given that the pace at which solutions will be
developed and adopted will vary based on decisions that will be made on
investments, deployment, and regulation, among other factors. But they give an
indication of the degree to which the activities that workers do each day may
shift (Exhibit 8).
Exhibit 8
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As an example of how this might play out in a specific occupation, consider
postsecondary English language and literature teachers, whose detailed work
activities include preparing tests and evaluating student work. With generative
AI’s enhanced natural-language capabilities, more of these activities could be
done by machines, perhaps initially to create a first draft that is edited by
teachers but perhaps eventually with far less human editing required. This could
free up time for these teachers to spend more time on other work activities,
such as guiding class discussions or tutoring students who need extra
assistance.
Our previously modeled adoption scenarios suggested that 50 percent of time
spent on 2016 work activities would be automated sometime between 2035 and 2070,
with a midpoint scenario around 2053. Our updated adoption scenarios, which
account for developments in generative AI, models the time spent on 2023 work
activities reaching 50 percent automation between 2030 and 2060, with a midpoint
of 2045—an acceleration of roughly a decade compared with the previous
estimate.6The comparison is not exact because the composition of work activities
between 2016 and 2023 has changed; for example, some automation has occurred
during that time period.
Adoption is also likely to be faster in developed countries, where wages are
higher and thus the economic feasibility of adopting automation occurs earlier.
Even if the potential for technology to automate a particular work activity is
high, the costs required to do so have to be compared with the cost of human
wages. In countries such as China, India, and Mexico, where wage rates are
lower, automation adoption is modeled to arrive more slowly than in higher-wage
countries (Exhibit 9).
Exhibit 9
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GENERATIVE AI’S POTENTIAL IMPACT ON KNOWLEDGE WORK
Previous generations of automation technology were particularly effective at
automating data management tasks related to collecting and processing data.
Generative AI’s natural-language capabilities increase the automation potential
of these types of activities somewhat. But its impact on more physical work
activities shifted much less, which isn’t surprising because its capabilities
are fundamentally engineered to do cognitive tasks.
As a result, generative AI is likely to have the biggest impact on knowledge
work, particularly activities involving decision making and collaboration, which
previously had the lowest potential for automation (Exhibit 10). Our estimate of
the technical potential to automate the application of expertise jumped 34
percentage points, while the potential to automate management and develop talent
increased from 16 percent in 2017 to 49 percent in 2023.
Exhibit 10
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Generative AI’s ability to understand and use natural language for a variety of
activities and tasks largely explains why automation potential has risen so
steeply. Some 40 percent of the activities that workers perform in the economy
require at least a median level of human understanding of natural language.
As a result, many of the work activities that involve communication,
supervision, documentation, and interacting with people in general have the
potential to be automated by generative AI, accelerating the transformation of
work in occupations such as education and technology, for which automation
potential was previously expected to emerge later (Exhibit 11).
Exhibit 11
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Labor economists have often noted that the deployment of automation technologies
tends to have the most impact on workers with the lowest skill levels, as
measured by educational attainment, or what is called skill biased. We find that
generative AI has the opposite pattern—it is likely to have the most incremental
impact through automating some of the activities of more-educated workers
(Exhibit 12).
Exhibit 12
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Another way to interpret this result is that generative AI will challenge the
attainment of multiyear degree credentials as an indicator of skills, and others
have advocated for taking a more skills-based approach to workforce development
in order to create more equitable, efficient workforce training and matching
systems.7A more skills-based approach to workforce development predates the
emergence of generative AI. Generative AI could still be described as
skill-biased technological change, but with a different, perhaps more granular,
description of skills that are more likely to be replaced than complemented by
the activities that machines can do.
Previous generations of automation technology often had the most impact on
occupations with wages falling in the middle of the income distribution. For
lower-wage occupations, making a case for work automation is more difficult
because the potential benefits of automation compete against a lower cost of
human labor. Additionally, some of the tasks performed in lower-wage occupations
are technically difficult to automate—for example, manipulating fabric or
picking delicate fruits. Some labor economists have observed a “hollowing out of
the middle,” and our previous models have suggested that work automation would
likely have the biggest midterm impact on lower-middle-income quintiles.
However, generative AI’s impact is likely to most transform the work of
higher-wage knowledge workers because of advances in the technical automation
potential of their activities, which were previously considered to be relatively
immune from automation (Exhibit 13).
Exhibit 13
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GENERATIVE AI COULD PROPEL HIGHER PRODUCTIVITY GROWTH
Global economic growth was slower from 2012 to 2022 than in the two preceding
decades.8Global economic prospects, World Bank, January 2023. Although the
COVID-19 pandemic was a significant factor, long-term structural
challenges—including declining birth rates and aging populations—are ongoing
obstacles to growth.
Declining employment is among those obstacles. Compound annual growth in the
total number of workers worldwide slowed from 2.5 percent in 1972–82 to just 0.8
percent in 2012–22, largely because of aging. In many large countries, the size
of the workforce is already declining.9Yaron Shamir, “Three factors contributing
to fewer people in the workforce,” Forbes, April 7, 2022. Productivity, which
measures output relative to input, or the value of goods and services produced
divided by the amount of labor, capital, and other resources required to produce
them, was the main engine of economic growth in the three decades from 1992 to
2022 (Exhibit 14). However, since then, productivity growth has slowed in tandem
with slowing employment growth, confounding economists and policy makers.10“The
U.S. productivity slowdown: an economy-wide and industry-level analysis,”
Monthly Labor Review, US Bureau of Labor Statistics, April 2021; Kweilin
Ellingrud, “Turning around the productivity slowdown,” McKinsey Global
Institute, September 13, 2022.
Exhibit 14
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The deployment of generative AI and other technologies could help accelerate
productivity growth, partially compensating for declining employment growth and
enabling overall economic growth. Based on our estimates, the automation of
individual work activities enabled by these technologies could provide the
global economy with an annual productivity boost of 0.5 to 3.4 percent from 2023
to 2040, depending on the rate of automation adoption—with generative AI
contributing 0.1 to 0.6 percentage points of that growth—but only if individuals
affected by the technology were to shift to other work activities that at least
match their 2022 productivity levels (Exhibit 15). In some cases, workers will
stay in the same occupations, but their mix of activities will shift; in others,
workers will need to shift occupations.
Exhibit 15
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CONSIDERATIONS FOR BUSINESS AND SOCIETY
History has shown that new technologies have the potential to reshape societies.
Artificial intelligence has already changed the way we live and work—for
example, it can help our phones (mostly) understand what we say, or draft
emails. Mostly, however, AI has remained behind the scenes, optimizing business
processes or making recommendations about the next product to buy. The rapid
development of generative AI is likely to significantly augment the impact of AI
overall, generating trillions of dollars of additional value each year and
transforming the nature of work.
But the technology could also deliver new and significant challenges.
Stakeholders must act—and quickly, given the pace at which generative AI could
be adopted—to prepare to address both the opportunities and the risks. Risks
have already surfaced, including concerns about the content that generative AI
systems produce: Will they infringe upon intellectual property due to
“plagiarism” in the training data used to create foundation models? Will the
answers that LLMs produce when questioned be accurate, and can they be
explained? Will the content generative AI creates be fair or biased in ways that
users do not want by, say, producing content that reflects harmful stereotypes?
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Sidebar
USING GENERATIVE AI RESPONSIBLY
Generative AI poses a variety of risks. Stakeholders will want to address these
risks from the start.
Fairness: Models may generate algorithmic bias due to imperfect training data or
decisions made by the engineers developing the models.
Intellectual property (IP): Training data and model outputs can generate
significant IP risks, including infringing on copyrighted, trademarked,
patented, or otherwise legally protected materials. Even when using a provider’s
generative AI tool, organizations will need to understand what data went into
training and how it’s used in tool outputs.
Privacy: Privacy concerns could arise if users input information that later ends
up in model outputs in a form that makes individuals identifiable. Generative AI
could also be used to create and disseminate malicious content such as
disinformation, deepfakes, and hate speech.
Security: Generative AI may be used by bad actors to accelerate the
sophistication and speed of cyberattacks. It also can be manipulated to provide
malicious outputs. For example, through a technique called prompt injection, a
third party gives a model new instructions that trick the model into delivering
an output unintended by the model producer and end user.
Explainability: Generative AI relies on neural networks with billions of
parameters, challenging our ability to explain how any given answer is produced.
Reliability: Models can produce different answers to the same prompts, impeding
the user’s ability to assess the accuracy and reliability of outputs.
Organizational impact: Generative AI may significantly affect the workforce, and
the impact on specific groups and local communities could be disproportionately
negative.
Social and environmental impact: The development and training of foundation
models may lead to detrimental social and environmental consequences, including
an increase in carbon emissions (for example, training one large language model
can emit about 315 tons of carbon dioxide).1Ananya Ganesh, Andrew McCallum, and
Emma Strubell, “Energy and policy considerations for deep learning in NLP,”
Proceedings of the 57th Annual Meeting of the Association for Computational
Linguistics, June 5, 2019.
There are economic challenges too: the scale and the scope of the workforce
transitions described in this report are considerable. In the midpoint adoption
scenario, about a quarter to a third of work activities could change in the
coming decade. The task before us is to manage the potential positives and
negatives of the technology simultaneously (see sidebar “Using generative AI
responsibly”). Here are some of the critical questions we will need to address
while balancing our enthusiasm for the potential benefits of the technology with
the new challenges it can introduce.
COMPANIES AND BUSINESS LEADERS
How can companies move quickly to capture the potential value at stake
highlighted in this report, while managing the risks that generative AI
presents?
How will the mix of occupations and skills needed across a company’s workforce
be transformed by generative AI and other artificial intelligence over the
coming years? How will a company enable these transitions in its hiring plans,
retraining programs, and other aspects of human resources?
Do companies have a role to play in ensuring the technology is not deployed in
“negative use cases” that could harm society?
How can businesses transparently share their experiences with scaling the use of
generative AI within and across industries—and also with governments and
society?
POLICY MAKERS
What will the future of work look like at the level of an economy in terms of
occupations and skills? What does this mean for workforce planning?
How can workers be supported as their activities shift over time? What
retraining programs can be put in place? What incentives are needed to support
private companies as they invest in human capital? Are there
earn-while-you-learn programs such as apprenticeships that could enable people
to retrain while continuing to support themselves and their families?
What steps can policy makers take to prevent generative AI from being used in
ways that harm society or vulnerable populations?
Can new policies be developed and existing policies amended to ensure
human-centric AI development and deployment that includes human oversight and
diverse perspectives and accounts for societal values?
INDIVIDUALS AS WORKERS, CONSUMERS, AND CITIZENS
How concerned should individuals be about the advent of generative AI? While
companies can assess how the technology will affect their bottom lines, where
can citizens turn for accurate, unbiased information about how it will affect
their lives and livelihoods?
How can individuals as workers and consumers balance the conveniences generative
AI delivers with its impact in their workplaces?
Can citizens have a voice in the decisions that will shape the deployment and
integration of generative AI into the fabric of their lives?
--------------------------------------------------------------------------------
Technological innovation can inspire equal parts awe and concern. When that
innovation seems to materialize fully formed and becomes widespread seemingly
overnight, both responses can be amplified. The arrival of generative AI in the
fall of 2022 was the most recent example of this phenomenon, due to its
unexpectedly rapid adoption as well as the ensuing scramble among companies and
consumers to deploy, integrate, and play with it.
All of us are at the beginning of a journey to understand this technology’s
power, reach, and capabilities. If the past eight months are any guide, the next
several years will take us on a roller-coaster ride featuring fast-paced
innovation and technological breakthroughs that force us to recalibrate our
understanding of AI’s impact on our work and our lives. It is important to
properly understand this phenomenon and anticipate its impact. Given the speed
of generative AI’s deployment so far, the need to accelerate digital
transformation and reskill labor forces is great.
These tools have the potential to create enormous value for the global economy
at a time when it is pondering the huge costs of adapting and mitigating climate
change. At the same time, they also have the potential to be more destabilizing
than previous generations of artificial intelligence. They are capable of that
most human of abilities, language, which is a fundamental requirement of most
work activities linked to expertise and knowledge as well as a skill that can be
used to hurt feelings, create misunderstandings, obscure truth, and incite
violence and even wars.
We hope this research has contributed to a better understanding of generative
AI’s capacity to add value to company operations and fuel economic growth and
prosperity as well as its potential to dramatically transform how we work and
our purpose in society. Companies, policy makers, consumers, and citizens can
work together to ensure that generative AI delivers on its promise to create
significant value while limiting its potential to upset lives and livelihoods.
The time to act is now.11The research, analysis, and writing in this report was
entirely done by humans.
ABOUT THE AUTHOR(S)
Michael Chui is a partner in McKinsey’s Bay Area office, where Roger Roberts is
a partner and Lareina Yee is a senior partner; Eric Hazan is a senior partner in
McKinsey’s Paris office; Alex Singla is a senior partner in the Chicago office;
Kate Smaje and Alex Sukharevsky are senior partners in the London office; and
Rodney Zemmel is a senior partner in the New York office.
The authors wish to thank Pedro Abreu, Rohit Agarwal, Steven Aronowitz, Arun
Arora, Charles Atkins, Elia Berteletti, Onno Boer, Albert Bollard, Xavier
Bosquet, Benjamin Braverman, Charles Carcenac, Sebastien Chaigne, Peter
Crispeels, Santiago Comella-Dorda, Eleonore Depardon, Kweilin Ellingrud, Thierry
Ethevenin, Dmitry Gafarov, Neel Gandhi, Eric Goldberg, Liz Grennan, Shivani
Gupta, Vinay Gupta, Dan Hababou, Bryan Hancock, Lisa Harkness, Leila Harouchi,
Jake Hart, Heiko Heimes, Jeff Jacobs, Begum Karaci Deniz, Tarun Khurana,
Malgorzata Kmicinska, Jan-Christoph Köstring, Andreas Kremer, Kathryn Kuhn,
Jessica Lamb, Maxim Lampe, John Larson, Swan Leroi, Damian Lewandowski, Richard
Li, Sonja Lindberg, Kerin Lo, Guillaume Lurenbaum, Matej Macak, Dana Maor,
Julien Mauhourat, Marco Piccitto, Carolyn Pierce, Olivier Plantefeve, Alexandre
Pons, Kathryn Rathje, Emily Reasor, Werner Rehm, Steve Reis, Kelsey Robinson,
Martin Rosendahl, Christoph Sandler, Saurab Sanghvi, Boudhayan Sen, Joanna Si,
Alok Singh, Gurneet Singh Dandona, François Soubien, Eli Stein, Stephanie Strom,
Michele Tam, Robert Tas, Maribel Tejada, Wilbur Wang, Georg Winkler, Jane Wong,
and Romain Zilahi for their contributions to this report.
For the full list of acknowledgments, see the downloadable PDF.
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