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1、The economic potential of generative AI June 2023The economic potential of generative AI The next productivity frontierAuthorsMichael ChuiEric HazanRoger RobertsAlex SinglaKate SmajeAlex SukharevskyLareina YeeRodney ZemmeliiThe economic potential of generative AI:The next productivity frontierConten
2、tsKey insights3Chapter 1:Generative AI as a technology catalyst 4Glossary 6Chapter 2:Generative AI use cases across functions and industries8Spotlight:Retail and consumer packaged goods 27Spotlight:Banking 28Spotlight:Pharmaceuticals and medical products 30Chapter 3:The generative AI future of work:
3、Impacts on work activities,economic growth,and productivity 32Chapter 4:Considerations for businesses and society 48Appendix 531The economic potential of generative AI:The next productivity frontier2The economic potential of generative AI:The next productivity frontier1.Generative AIs impact on prod
4、uctivity 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 analyzedby comparison,the United Kingdoms entire GDP in 2021 was$3.1 trillion.This woul
5、d 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.2.About 75 percent of the value that generative AI use cases cou
6、ld 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 generat
7、ive AIs 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.3.Generative AI will have a significant impact across all industry sectors.Banking,high tech,and life sciences are
8、 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 con
9、sumer packaged goods,the potential impact is also significant at$400 billion to$660 billion a year.4.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 techn
10、ologies 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.1 The acceleration in the potential for technical automation is large
11、ly due to generative AIs 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
12、other types of work.5.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 todays work activit
13、ies could be automated between 2030 and 2060,with a midpoint in 2045,or roughly a decade earlier than in our previous estimates.6.Generative AI can substantially increase labor productivity across the economy,but that will require investments to support workers as they shift work activities or chang
14、e 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 0.2 to 3.3 percenta
15、ge 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.7.Th
16、e era of generative AI is just beginning.Excitement over this technology is palpable,and early pilots are compelling.But a full realization of the technologys benefits will take time,and leaders in business and society still have considerable challenges to address.These include managing the risks in
17、herent 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.Key insights3The economic potential of generative AI:The next productivity frontierGenerative AI as a technology catalystTo
18、 grasp what lies ahead requires an understanding of the breakthroughs that have enabled the rise of generative AI,which were decades in the making.ChatGPT,GitHub Copilot,Stable Diffusion,and other generative AI tools that have captured current public attention are the result of significant levels of
19、 investment in recent years that have helped advance machine learning and deep learning.This investment undergirds the AI applications embedded in many of the products and services we use every day.But because AI has permeated our lives incrementallythrough everything from the tech powering our smar
20、tphones to autonomous-driving features on cars to the tools retailers use to surprise and delight consumersits progress was 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 quickl
21、y faded from the publics consciousness.ChatGPT and its competitors have captured the imagination of people around the world in a way AlphaGo did not,thanks to their broad utilityalmost anyone can use them to communicate and createand preternatural ability to have a conversation with a user.The lates
22、t 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
23、,a broader set of stakeholders are grappling with generative AIs impact on business and society but without much context to help them make sense of it.14The economic potential of generative AI:The next productivity frontierHow did we get here?Gradually,then all of a sudden For the purposes of this r
24、eport,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
25、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 unstr
26、uctured 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,answ
27、er questions,and draft new content,among other tasks.Continued innovation will also bring new challenges.For example,the computational power required to train generative AI with hundreds of billions of parameters threatens to become a bottleneck in development.2 Further,theres a significant movespea
28、rheaded by the open-source community and spreading to the leaders of generative AI companies themselvesto make AI more responsible,which could increase its costs.Nonetheless,funding for generative AI,though still a fraction of total investments in artificial intelligence,is significant and growing r
29、apidlyreaching a total of$12 billion in the first five months of 2023 alone.Venture capital and other private external investments in generative AI increased by an average compound growth rate of 74 percent annually from 2017 to 2022.During the same period,investments in artificial intelligence over
30、all rose annually by 29 percent,albeit from a higher base.The rush to throw money at all things generative AI reflects how quickly its capabilities have developed.ChatGPT was released in November 2022.Four months later,OpenAI released a new large language model,or LLM,called GPT-4 with markedly impr
31、oved capabilities.3 Similarly,by May 2023,Anthropics generative AI,Claude,was able to process 100,000 tokens of text,equal to about 75,000 words in a minutethe length of the average novelcompared with roughly 9,000 tokens when it was introduced in March 2023.4 And in May 2023,Google announced severa
32、l 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.5From a geographic perspective,external private investment in generative AI,mostly from tech giants and venture capital firms,is lar
33、gely concentrated in North America,reflecting the continents current domination of the overall AI investment landscape.Generative AIrelated companies based in the United States raised about$8 billion from 2020 to 2022,accounting for 75 percent of total investments in such companies during that perio
34、d.6 Generative AI has stunned and excited the world with its potential for reshaping how knowledge work gets done in industries and business functions across the entire economy.Across functions such as sales and marketing,customer operations,and software development,it is poised to transform roles a
35、nd boost performance.In the process,it could unlock trillions of dollars in value across sectors from banking to life sciences.We have used two overlapping lenses in this report to understand the potential for generative AI to create value for companies and alter the workforce.The following sections
36、 share our initial findings.5The economic potential of generative AI:The next productivity frontierGlossaryApplication programming interface(API)is a way to programmatically access(usually external)models,data sets,or other pieces of software.Artificial intelligence(AI)is the ability of software to
37、perform tasks that traditionally require human intelligence.Artificial neural networks(ANNs)are composed of interconnected layers of software-based calculators known as“neurons.”These networks can absorb vast amounts of input data and process that data through multiple layers that extract and learn
38、the datas features.Deep learning is a subset of machine learning that uses deep neural networks,which are layers of connected“neurons”whose connections have parameters or weights that can be trained.It is especially effective at learning from unstructured data such as images,text,and audio.Early and
39、 late scenarios are the extreme scenarios of our work-automation model.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 l
40、ikely to fall somewhere between the two.Fine-tuning is the process of adapting a pretrained foundation model to perform better in a specific task.This entails a relatively short period of training on a labeled data set,which is much smaller than the data set the model was initially trained on.This a
41、dditional training allows the model to learn and adapt to the nuances,terminology,and specific patterns found in the smaller data set.Foundation models(FM)are deep learning models trained on vast quantities of unstructured,unlabeled data that can be used for a wide range of tasks out of the box or a
42、dapted to specific tasks through fine-tuning.Examples of these models are GPT-4,PaLM,DALLE 2,and Stable Diffusion.Generative AI is AI that is typically built using foundation models and has capabilities that earlier AI did not have,such as the ability to generate content.Foundation models can also b
43、e used for nongenerative purposes(for example,classifying user sentiment as negative or positive based on call transcripts)while offering significant improvement over earlier models.For simplicity,when we refer to generative AI in this article,we include all foundation model use cases.Graphics proce
44、ssing units(GPUs)are computer chips that were originally developed for producing computer graphics(such as for video games)and are also useful for deep learning applications.In contrast,traditional machine learning and other analyses usually run on central processing units(CPUs),normally referred to
45、 as a computers“processor.”Large language models(LLMs)make up a class of foundation models that can process massive amounts of unstructured text and learn the relationships between words or portions of words,known as tokens.This enables LLMs to generate natural-language text,performing tasks such as
46、 summarization or knowledge extraction.GPT-4(which underlies ChatGPT)and LaMDA(the model behind Bard)are examples of LLMs.6The economic potential of generative AI:The next productivity frontierMachine learning(ML)is a subset of AI in which a model gains capabilities after it is trained on,or shown,m
47、any example data points.Machine learning algorithms detect patterns and learn how to make predictions and recommendations by processing data and experiences,rather than by receiving explicit programming instruction.The algorithms also adapt and can become more effective in response to new data and e
48、xperiences.Modality is a high-level data category such as numbers,text,images,video,and audio.Productivity from labor is the ratio of GDP to total hours worked in the economy.Labor productivity growth comes from increases in the amount of capital available to each worker,the education and experience
49、 of the workforce,and improvements in technology.Prompt engineering refers to the process of designing,refining,and optimizing input prompts to guide a generative AI model toward producing desired(that is,accurate)outputs.Self-attention,sometimes called intra-attention,is a mechanism that aims to mi
50、mic cognitive attention,relating different positions of a single sequence to compute a representation of the sequence.Structured data are tabular data(for example,organized in tables,databases,or spreadsheets)that can be used to train some machine learning models effectively.Transformers are a relat
51、ively new neural network architecture that relies on self-attention mechanisms to transform a sequence of inputs into a sequence of outputs while focusing its attention on important parts of the context around the inputs.Transformers do not rely on convolutions or recurrent neural networks.Technical
52、 automation potential refers to the share of the worktime that could be automated.We assessed the technical potential for automation across the global economy through an analysis of the component activities of each occupation.We used databases published by institutions including the World Bank and t
53、he US Bureau of Labor Statistics to break down about 850 occupations into approximately 2,100 activities,and we determined the performance capabilities needed for each activity based on how humans currently perform them.Use cases are targeted applications to a specific business challenge that produc
54、es one or more measurable outcomes.For example,in marketing,generative AI could be used to generate creative content such as personalized emails.Unstructured data lack a consistent format or structure(for example,text,images,and audio files)and typically require more advanced techniques to extract i
55、nsights.7The economic potential of generative AI:The next productivity frontierGenerative AI is a step change in the evolution of artificial intelligence.As companies rush to adapt and implement it,understanding the technologys potential to deliver value to the economy and society at large will help
56、 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).Generative AI use cases across functions and industries 28The economic potential of generative AI:
57、The next productivity frontierThe 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 appli
58、cation 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 generat
59、ive 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$11.0 trillion to$17.7 trillion of economic value that we now estimate nongen
60、erative 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 AIs potential impact on the work activities required in some 850 occupa
61、tions.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 capabil
62、ities of generative AI could affect labor productivity across all work currently done by the global workforce.Exhibit 1The potential impact of generative AI can be evaluated through two lenses.McKinsey&CompanyLens 1Total economic potential of 60-plus organizational use cases11For quantitative analys
63、is,revenue impacts were recast as productivity increases on the corresponding spend in order to maintain comparability with cost impacts and not to assume additional growth in any particular market.Revenue impacts of use cases1Cost impacts of use casesLens 2Labor productivity potential across 2,100
64、detailed work activities performed by global workforce 9The economic potential of generative AI:The next productivity frontierSome 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
65、overlap,the total economic benefits of generative AIincluding 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 activitiesamounts to$6.1 trillion to$7.9 trillion annually(Exhibit 2).Exhibit
66、2Generative AI could create additional value potential above what could be unlocked by other AI and analytics.McKinsey&CompanyAIs potential impact on the global economy,$trillion1Updated use case estimates from Notes from the AI frontier:Applications and value of deep learning,”McKinsey Global Insti
67、tute,April 17,2018.Advanced analytics,traditional machinelearning,and deeplearning1New generativeAI use casesTotal usecase-drivenpotentialAll worker productivityenabled by generativeAI,including in usecasesTotal AIeconomicpotential11.017.713.622.117.125.62.64.46.17.91540%incremental economic impact3
68、570%incremental economic impact10The economic potential of generative AI:The next productivity frontierWhile 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
69、 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 h
70、as the potential to open wholly new frontiers in creativity and innovation.It has already expanded the possibilities of what AI overall can achieve(please see Box 1,“How we estimated the value potential of generative AI use cases”).In this chapter,we highlight the value potential of generative AI ac
71、ross two dimensions:business function and modality.Box 11“Notes from the AI frontier:Applications and value of deep learning,”McKinsey Global Institute,April 17,2018.How we estimated the value potential of generative AI use casesTo assess the potential value of generative AI,we updated a proprietary
72、 McKinsey database of potential AI use cases and drew on the experience of more than 100 experts in industries and their business functions.1 Our updates examined use cases of generative AIspecifically,how generative AI techniques(primarily transformer-based neural networks)can be used to solve prob
73、lems 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 ben
74、efit 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 ann
75、ual 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 expen
76、ditures.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.11The economic potential of generative AI:The next productivity frontierValue potential by functionWhile g
77、enerative AI could have an impact on most business functions,a few stand out when measured by the technologys impact as a share of functional cost(Exhibit 3).Our analysis of 16 business functions identified just fourcustomer operations,marketing and sales,software engineering,and research and develo
78、pmentthat could account for approximately 75 percent of the total annual value from generative AI use cases.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 mu
79、ch lower.7 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.Exhibit 3Web Exhibit of Using generative AI in just a few functions could drive most of the
80、 technologys impact across potential corporate use cases.McKinsey&CompanyNote:Impact is averaged.Excluding software engineering.Source:Comparative Industry Service(CIS),IHS Markit;Oxford Economics;McKinsey Corporate and Business Functions database;McKinsey Manufacturing and Supply Chain 360;McKinsey
81、 Sales Navigator;Ignite,a McKinsey database;McKinsey analysis Impact as a percentage of functional spend,%Impact,$billionMarketingSalesPricingCustomer operationsCorporate IT1Product and R&D1Software engineering(for corporate IT)Software engineering(for product development)Supply chainProcurement man
82、agementManufacturingLegalRisk and complianceStrategyFinanceTalent and organization(incl HR)00200300400500Represent 75%of total annual impact of generative AI12The economic potential of generative AI:The next productivity frontierGenerative AI as a virtual expertIn addition to the potentia
83、l 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 AIs impressive command of natural-language processing can help employees retrieve stored internal knowle
84、dge 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 Institut
85、e(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
86、 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.Following are examples of how generat
87、ive AI could produce operational benefits as a virtual expert in a handful of use cases.In addition to the potential value generative AI can deliver in specific use cases,the technology could drive value across an entire organization by revolutionizing internal knowledge management systems.13The eco
88、nomic potential of generative AI:The next productivity frontierHow customer operations could be transformedCustomer self-service interactionsCustomer interacts with a humanlike chatbot that delivers immediate,personalized responses to complex inquiries,ensuring a consistent brand voice regardless of
89、 customer language or location.Customeragent interactionsHuman agent uses AI-developed call scripts and receives real-time assistance and suggestions for responses during phone conversations,instantly accessing relevant customer data for tailored and real-time information delivery.Agent self-improve
90、mentAgent receives a summarization of the conversation in a few succinct points to create a record of customer complaints and actions taken.Agent uses automated,personalized insights generated by AI,including tailored follow-up messages or personalized coaching suggestions.14The economic potential o
91、f generative AI:The next productivity frontierCustomer operationsGenerative 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
92、 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 t
93、ime spent handling an issue by 9 percent.8 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 increaseand sometimes decreasedthe productivity and qua
94、lity 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
95、 self-service.Generative AIfueled 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
96、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,i
97、ncluding 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 companys existing level of automation.Resolution during initial contact.Generative AI can instantly retrieve data a company has on a specific c
98、ustomer,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 an
99、d 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 gathe
100、ring 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
101、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 customers context that can assist hum
102、an agents in providing more personalized help and recommendations.15The economic potential of generative AI:The next productivity frontierHow marketing and sales could be transformedStrategizationSales and marketing professionals efficiently gather market trends and customer information from unstruc
103、tured data sources(for example,social media,news,research,product information,and customer feedback)and draft effective marketing and sales communications.AwarenessCustomers see campaigns tailored to their segment,language,and demographic.ConsiderationCustomers can access comprehensive information,c
104、omparisons,and dynamic recommendations,such as personal“try ons.”16The economic potential of generative AI:The next productivity frontierMarketing and salesGenerative AI has taken hold rapidly in marketing and sales functions,in which text-based communications and personalization at scale are drivin
105、g 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.However,introducing generative AI to mark
106、eting 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
107、 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 companys needs.ConversionVirtual sales representatives enabled by generative AI emulate humanlike qualitiessu
108、ch as empathy,personalized communication,and natural language processingto build trust and rapport with customers.RetentionCustomers are more likely to be retained with customized messages and rewards,and they can interact with AI-powered customer-support chatbots that manage the relationship proact
109、ively,with fewer escalations to human agents.17The economic potential of generative AI:The next productivity frontierPotential operational benefits from using generative AI for marketing include the following:Efficient and effective content creation.Generative AI could significantly reduce the time
110、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 cohesi
111、ve 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 aud
112、ience.Generative AIs 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 functi
113、ons overcome the challenges of unstructured,inconsistent,and disconnected datafor example,from different databasesby 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 cust
114、omer 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
115、 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
116、 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 deep understanding of customer profiles.For example,technology can leverage i
117、ndividual 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 ecommerce sales by achieving higher website conversion rates.We estimate tha
118、t 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 doesnt account for knock-on effects beyond the direct impacts on productivity.Generative AIenabl
119、ed 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.
120、18The economic potential of generative AI:The next productivity frontierGenerative 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 compreh
121、ensive 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.Generat
122、ive 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 nurtu
123、re 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 tha
124、t generative AI could bring to sales functions.For instance,generative AIs 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 AIs cap
125、abilities could be invested in higher-quality customer interactions,resulting in increased sales success.Generative AI as a virtual collaboratorIn other cases,generative AI can drive value by working in partnership with workers,augmenting their work in ways that accelerate their productivity.Its abi
126、lity 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.G
127、enerative AI could increase sales productivity by 3 to 5 percent of current global sales expenditures.19The economic potential of generative AI:The next productivity frontierHow software engineering could be transformedInception and planningSoftware engineers and product managers use generative AI t
128、o assist in analyzing,cleaning,and labeling large volumes of data,such as user feedback,market trends,and existing system logs.System designEngineers use generative AI to create multiple IT architecture designs and iterate on the potential configurations,accelerating system design,and allowing faste
129、r time to market.CodingEngineers are assisted by AI tools that can code,reducing development time by assisting with drafts,rapidly finding prompts,and serving as an easily navigable knowledge base.TestingEngineers employ algorithms that can enhance functional and performance testing to ensure qualit
130、y and can generate test cases and test data automatically.20The economic potential of generative AI:The next productivity frontierSoftware engineeringTreating computer languages as just another language opens new possibilities for software engineering.Software engineers can use generative AI in pair
131、 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 ti
132、tans,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
133、 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 t
134、he 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 Microsofts GitHub Copilot completed tasks 56 percent faster than those not using the tool.9 An internal McKinse
135、y 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 codeand engineers also reported a better work experience,citing improvements in happiness,flow,and fulfillment.Our analysis did not account
136、 for the increase in application quality and the resulting boost in productivity that generative AI could bring by improving code or enhancing IT architecturewhich can improve productivity across the IT value chain.However,the quality of IT architecture still largely depends on software architects,r
137、ather than on initial drafts that generative AIs 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 OpenAIs GPT-4,and Replit,used by more than 20 million coders.10Mainten
138、anceEngineers use AI insights on system logs,user feedback,and performance data to help diagnose issues,suggest fixes,and predict other high-priority areas of improvement.21The economic potential of generative AI:The next productivity frontierHow product R&D could be transformedEarly research analys
139、isResearchers use generative AI to enhance market reporting,ideation,and product or solution drafting.Virtual designResearchers use generative AI to generate prompt-based drafts and designs,allowing them to iterate quickly with more design options.Virtual simulationsResearchers accelerate and optimi
140、ze the virtual simulation phase if combined with new deep learning generative design techniques.Physical test planningResearchers optimize test cases for more efficient testing,reducing the time required for physical build and testing.Product R&DGenerative AIs potential in R&D is perhaps less well r
141、ecognized 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 th
142、eir 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 therape
143、utics.But the same principles can be applied to the design of many other products,including larger-scale physical products and electrical circuits,among others.22The economic potential of generative AI:The next productivity frontierWhile other generative design techniques have already unlocked some
144、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 mod
145、els 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 re
146、sult 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
147、 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
148、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
149、of which has grown since our earlier research,can be paired with generative AI to produce even greater benefits(see Box 2,“Deep learning surrogates”).To be sure,integration will require the development of specific solutions,but the value could be significant because deep learning surrogates have the
150、 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 technologys potential to create entirely novel product categories.These are the types of innovations
151、that can produce step changes not only in the performance of individual companies but in economic growth overall.Box 2Deep learning surrogatesProduct design in industries producing physical products often involves physics-based virtual simulations such as computational fluid dynamics(CFD)and finite
152、element analysis(FEA).Although they are faster than actual physical testing,these techniques can be time-and resource-intensive,especially for designing complex partsrunning CFD simulations on graphics processing units can take hours.And these techniques are even more complex and compute-intensive w
153、hen they involve simulations coupled across multiple disciplines(for example,physical stress and temperature distribution),which is sometimes called multiphysics.Deep learning applications are now revolutionizing the virtual testing phase of the R&D process by using deep learning models to emulate(m
154、ulti)physics-based simulations at higher speeds and lower costs.Instead of taking hours to run physics-based models,these deep learning surrogates can produce the results of simulations in just a few seconds,allowing researchers to test many more designs and enabling faster decision making on produc
155、ts and designs.23The economic potential of generative AI:The next productivity frontierValue potential by modalityTechnology has revolutionized the way we conduct business,and text-based AI is on the frontier of this change.Indeed,text-based data is plentiful,accessible,and easily processed and anal
156、yzed at large scale by LLMs,which has prompted a strong emphasis on them in the initial stages of generative AI development.The current investment landscape in generative AI is also heavily focused on text-based applications such as chatbots,virtual assistants,and language translation.However,we est
157、imate that almost one-fifth of the value that generative AI can unlock across our use cases would take advantage of multimodal capabilities beyond text to text.While most of generative AIs initial traction has been in text-based use cases,recent advances in generative AI have also led to breakthroug
158、hs in image generation,as OpenAIs DALLE and Stable Diffusion have so amply illustrated,and much progress is being made in audio,including voice and music,and video.These capabilities have obvious applications in marketing for generating advertising materials and other marketing content,and these tec
159、hnologies are already being applied in media industries,including game design.Indeed,some of these examples challenge existing business models around talent,monetization,and intellectual property.11The multimodal capabilities of generative AI could also be used effectively in R&D.Generative AI syste
160、ms could create first drafts of circuit designs,architectural drawings,structural engineering designs,and thermal designs based on prompts that describe requirements for a product.Achieving this will require training foundation models in these domains(think of LLMs trained on“design languages”).Once
161、 trained,such foundation models could increase productivity on a similar magnitude to software development.Value potential by industryAcross 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
162、 on a variety of factors,such as the mix and importance of different functions,as well as the scale of an industrys revenue(Exhibit 4).Across 63 use cases,generative AI has the potential to generate$2.6 trillion to$4.4 trillion in value across industries.24The economic potential of generative AI:The
163、 next productivity frontierExhibit 4Generative AI use cases will have diferent impacts on business functions across industries.McKinsey&CompanyAdministrative and professional servicesAdvanced electronics and semiconductorsAdvanced manufacturing3AgricultureBankingBasic materialsChemicalConstructionCo
164、nsumer packaged goodsEducationEnergyHealthcareHigh techInsuranceMedia and entertainmentPharmaceuticals and medical productsPublic and social sectorReal estateRetail4TelecommunicationsTravel,transport,and logisticsTotal,$billion000340900230024046
165、050706039060100180300Total,%ofindustryrevenue0.91.41.32.31.42.40.61.02.84.70.7 1.20.81.30.71.21.42.32.24.01.0 1.61.83.24.89.31.8 2.81.5 2.62.64.50.50.91.01.71.21.92.33.71.22.0Generative AI productivity impact by business functions Marketing and salesCustomer operationsProduct a
166、nd R&DSoftware engineeringSupply chain and operationsRisk and legalStrategy and fnanceCorporate IT2Talent and organization2,6004,400Note:Figures may not sum to 100%,because of rounding.1Excludes implementation costs(eg,training,licenses).2Excluding software engineering.3Includes aerospace,defense,an
167、d auto manufacturing.4Including auto retail.Source:Comparative Industry Service(CIS),IHS Markit;Oxford Economics;McKinsey Corporate and Business Functions database;McKinsey Manufacturing and Supply Chain 360;McKinsey Sales Navigator;Ignite,a McKinsey database;McKinsey analysis 7601,200340470 230420
168、5801,200 280530 180260 120260 4050 6090 Low impactHigh impact25The economic potential of generative AI:The next productivity frontierFor example,our analysis estimates generative AI could contribute roughly$310 billion in additional value for the retail industry(including auto dealerships)by boostin
169、g performance in functions such as marketing and customer interactions.By comparison,the bulk of potential value in high tech comes from generative AIs ability to increase the speed and efficiency of software development(Exhibit 5).In the banking industry,generative AI has the potential to improve o
170、n 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 an
171、d development.We share our detailed analysis of these industries in the following industry spotlights.Exhibit 5Generative AI could deliver signifcant value when deployed in some use cases across a selection of top industries.McKinsey&CompanySelected examples of key use cases for main functional valu
172、e drivers(nonexhaustive)Value potential,as%of operating profts1Product R&D,software engineeringCustomer operationsMarketing and salesOther functionsRetail and consumer packaged goods2400660(12%)2744Consumer researchAccelerate consumer research by testing scenarios,and enhance customer targeting by c
173、reating“synthetic customers”to practice withAugmented realityassisted customer support Rapidly inform the workforce in real time about the status of products and consumer preferences Assist copy writing for marketing content creationAccelerate writing of copy for marketing content and advertising sc
174、riptsProcurement suppliers process enhancementDraft playbooks for negotiating with suppliersPharma and medical products60110(35%)1525Research and drug discoveryAccelerate the selection of proteins and molecules best suited as candidates for new drug formulationCustomer documentation generationDraft
175、medication instructions and risk notices for drug resaleGenerate content for commercial representativesPrepare scripts for interactions with physiciansContract generationDraft legal documents incorporating specifc regulatory requirementsBanking200340(35%)915Legacy code conversion Optimize migration
176、of legacy frameworks with natural-language translation capabilitiesCustomer emergency interactive voice response(IVR)Partially automate,accelerate,and enhance resolution rate of customer emergencies through generative AIenhanced IVR interactions(eg,for credit card losses)Custom retail banking ofers
177、Push personalized marketing and sales content tailored for each client of the bank based on profle and history(eg,personalized nudges),and generate alternatives for A/B testingRisk model documentation Create model documentation,and scan for missing documentation and relevant regulatory updates Total
178、 value potential per industry,$billion(%of industry revenue)Value potential of function for the industry LowHighOperating proft based on average proftability of selected industries in the 202022 period.2Includes auto retail.26The economic potential of generative AI:The next productivity frontierSpot
179、light:Retail and CPGGenerative AI could change the game for retail and consumer packaged goods companies1 Vehicular retail is included as part of our overall retail analysis.The technology could generate value for the retail and consumer packaged goods(CPG)industry by increasing productivity by 1.2
180、to 2.0 percent of annual revenues,or an additional$400 billion to$660 bil-lion.1 To streamline processes,generative AI could automate key functions such as customer service,marketing and sales,and inventory and supply chain manage-ment.Technology has played an essen-tial role in the retail and CPG i
181、ndus-tries 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 catego-ries such as consumables.In addition,the industries are heavily customer faci
182、ng,which offers opportu-nities for generative AI to complement previously existing artificial intelli-gence.For example,generative AIs ability to personalize offerings could optimize marketing and sales activities already handled by existing AI solutions.Similarly,generative AI tools excel at data m
183、anagement and could support existing AI-driven pricing tools.Applying gener-ative AI to such activities could be a step toward integrating applications across a full enterprise.Generative AI is already at work in some retail and CPG companies:Reinvention of the customer interaction patternConsumers
184、increasingly seek customiza-tion in everything from clothing and cos-metics to curated shopping experiences,personalized outreach,and foodand generative AI can improve that expe-rience.Generative AI can aggregate market data to test concepts,ideas,and models.Stitch Fix,which uses algorithms to sugge
185、st style choices to its custom-ers,has experimented with DALLE to visualize products based on customer preferences regarding color,fabric,and style.Using text-to-image generation,the companys stylists can visualize an article of clothing based on a consumers preferences and then identify a similar a
186、rticle among Stitch Fixs inventory.Retailers can create applications that give shoppers a next-generation experi-ence,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 impro
187、ve the process of choosing and ordering ingre-dients for a meal or preparing foodimagine 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 throu
188、gh 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 prod-uct offerings,and in
189、crease their cus-tomer 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 accel-erate content analysis and
190、 creation.The potential improvement in writing and visuals can increase awareness and improve sales conversion rates.Rapid resolution and enhanced insights in customer careThe growth of e-commerce also elevates the importance of effective consumer interactions.Retailers can combine existing AI tools
191、 with generative AI to enhance the capabilities of chatbots,enabling them to better mimic the interaction style of human agentsfor example,by responding directly to a customers query,tracking or cancel-ing an order,offering discounts,and upselling.Automating repetitive tasks allows human agents to d
192、evote more time to handling complicated customer problems and obtaining contextual infor-mation.Disruptive and creative innovationGenerative AI tools can enhance the process of developing new versions of products by digitally creating new designs rapidly.A designer can generate packaging designs fro
193、m scratch or gen-erate variations on an existing design.This technology is developing rapidly and has the potential to add text-to-video generation.Additional factors to considerAs retail and CPG executives explore how to integrate generative AI in their operations,they should keep in mind several f
194、actors that could affect their ability to capture value from the technol-ogy.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
195、 for attack by hackers and other bad actors,increasing the vari-ety of potential security vulnerabilities and privacy risks.To address these concerns,retail and CPG companies will need to strate-gically keep humans in the loop and ensure security and privacy are top considerations for any implementa
196、tion.Companies will need to institute new quality checks for processes previous-ly handled by humans,such as emails written by customer reps,and per-form more-detailed quality checks on AI-assisted processes such as product design.27The economic potential of generative AI:The next productivity front
197、ierSpotlight:Banking1“Building the AI bank of the future,”McKinsey,May 2021.2 McKinseys Global Banking Annual Review,December 1,2022.3 Akhil Babbar,Raghavan Janardhanan,Remy Paternoster,and Henning Soller,“Why most digital banking transformations failand how to flip the odds,”McKinsey,April 11,2023.
198、4 Hugh Son,“Morgan Stanley is testing an OpenAI-powered chatbot for its 16,000 financial advisors,”CNBC,March 14,2023.Banks could realize substantial value from generative AI Generative AI could have a significant impact on the banking industry,gener-ating value from increased productivity of 2.8 to
199、 4.7 percent of the industrys 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 satis-faction,improve decision making and employee experience,and decrease risks through better monitoring of fraud and risk.Bank
200、ing,a knowledge and technolo-gy-enabled industry,has already bene-fited significantly from previously exist-ing applications of artificial intelligence in areas such as marketing and custom-er operations.1 Generative AI applica-tions could deliver additional benefits,especially because text modaliti
201、es are prevalent in areas such as regulations and programming language,and the industry is customer facing,with many B2C and small-business customers.2 Several characteristics position the industry for the integration of genera-tive AI applications:Sustained digitization efforts along with legacy IT
202、 systems.Banks have been investing in technology for decades,accumulating a significant amount of technical debt along with a siloed and complex IT architecture.3 Large customer-facing workforces.Banking relies on a large number of service representatives such as call-center agents and wealth manage
203、ment 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 AIs impact could span the organization,assisting all employees in writing emails,creating business presentat
204、ions,and other tasks.On the moveBanks have started to grasp the poten-tial 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 use
205、s demonstrate its value potential to the industry:A virtual expert to augment employee performanceA generative AI bot trained on pro-prietary knowledge such as policies,research,and customer interaction could provide always-on,deep techni-cal support.Today,frontline spending is dedicated mostly to v
206、alidating offers and interacting with clients,but giv-ing 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 assis
207、tant 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.4 The model combines search and content creation so wealth managers can find and tailor information for any client at any moment.One European bank
208、has leveraged gen-erative AI to develop an environmental,social,and governance(ESG)virtual expert by synthesizing and extracting from long documents with unstruc-tured information.The model answers complex questions based on a prompt,identifying the source of each answer and extracting information f
209、rom pic-tures and tables.Generative AI could reduce the signifi-cant 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 custome
210、r.Through generative AI assistants,service professionals could rapidly access all relevant infor-mation such as product guides and policies to instantaneously address customer requests.Code acceleration to reduce tech debt and deliver software fasterGenerative AI tools are useful for soft-ware devel
211、opment in four broad cate-gories.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,an
212、d review different code tests,accelerating testing and increasing coverage and effectiveness.Third,generative AIs natural-language translation capabilities can optimize the integration and migration of legacy frameworks.Last,the tools can review code to identify defects and inefficien-cies in comput
213、ing.The result is more robust,effective code.28The economic potential of generative AI:The next productivity frontierProduction of tailored content at scaleGenerative AI tools can draw on existing documents and data sets to substan-tially streamline content generation.These tools can create personal
214、ized 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 docu-mentation,and scan relevant regulatory updates to create alerts f
215、or relevant shifts.Factors for banks to considerWhen exploring how to integrate gen-erative 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 suc
216、h as credit risk scoring.Type of end user.End users vary widely in their expectations and familiarity with generative AIfor 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 r
217、eal-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.A generative AI bot trained on proprietary knowledge such a
218、s policies,research,and customer interaction could provide always-on,deep technical support.29The economic potential of generative AI:The next productivity frontierSpotlight:Pharmaceuticals and medical productsGenerative AI deployment could unlock potential value equal to 2.6 to 4.5 percent of annua
219、l revenues across the pharmaceutical and medical-product industries1 Research and development in the pharmaceutical industry,Congressional Budget Office,April 2021.Our analysis finds that generative AI could have a significant impact on the pharmaceutical and medical-product industriesfrom$60 billio
220、n to$110 bil-lion annually.This big potential reflects the resource-intensive process of dis-covering new drug compounds.Pharma companies typically spend approximate-ly 20 percent of revenues on R&D,1 and the development of a new drug takes an average of ten to 15 years.With this level of spending a
221、nd time-line,improving the speed and quality of R&D can generate substantial value.For example,lead identificationa step in the drug discovery process in which researchers identify a molecule that would best address the target for a potential new drugcan take several months even with“traditional”dee
222、p learning techniques.Foundation models and generative AI can enable organiza-tions to complete this step in a matter of weeks.Generative AI use cases aligned to industry needsDrug discovery involves narrowing the universe of possible compounds to those that could effectively treat specific con-diti
223、ons.Generative AIs ability to process massive amounts of data and model options can accelerate output across several use cases:Improve automation of preliminary screeningIn the lead identification stage of drug development,scientists can use founda-tion models to automate the preliminary screening o
224、f 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 experi-ment.Using an off-the-shelf foundation model,researchers can cluster similar images more precisely than they can wi
225、th traditional models,enabling them to select the most promising chemicals for further analysis during lead optimization.Enhance indication findingAn important phase of drug discovery involves the identification and prioriti-zation of new indicationsthat is,dis-eases,symptoms,or circumstances that j
226、ustify the use of a specific medication or other treatment,such as a test,pro-cedure,or surgery.Possible indications for a given drug are based on a patient groups clinical history and medical records,and they are then prioritized based on their similarities to established and evidence-backed indica
227、tions.Researchers start by mapping the patient cohorts clinical events and medical historiesincluding potential diagnoses,prescribed medications,and performed proceduresfrom real-world data.Using foundation models,researchers can quantify clinical events,establish relationships,and measure the simil
228、arity 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 reporte
229、d high success rates in clinical trials for the top five indi-cations 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 pro-cess.Additional factors to considerBefore integr
230、ating 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
231、-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 gener
232、ated 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
233、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
234、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.30The economic potential of generative AI:The next productivity frontierIn this chapter,we have
235、estimated the organizational value generative AI could deliver through use cases across industries and business functions,but the technologys potential is much greater.As it is embedded into tools used by every knowledge worker,its additional impact may be more diffuse but no less valuable than that
236、 associated with these use cases.Companies need to find ways to maximize the value created by the generative AI they deploy while also taking care to monitor and manage its impact on their workforces and society at large.31The economic potential of generative AI:The next productivity frontierTechnol
237、ogy 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 wor
238、kers 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 t
239、he 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 Gl
240、obal 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 The generative AI future of work:Impacts on work
241、 activities,economic growth,and productivity 332The economic potential of generative AI:The next productivity frontieradapting 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 c
242、ould 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 activitydeve
243、loping 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 adoptio
244、n 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.Large-scale shifts in the mix of work activities and occupations are not unprecedented.Consider the work of a farmer today
245、 compared with what a farmer did just a few short years ago.Many farmers now access market information on mobile phones to determine when and where to sell their crops or download sophisticated modeling of weather patterns.From a more macro perspective,agricultural employment in China went from an 8
246、2 percent share of all workers in 1962 to 13 percent in 2013.Labor markets are also dynamic:millions of people leave their jobs every month in the United States.12 But this does not minimize the challenges faced by individual workers whose lives are upended by these shifts,or the organizational or s
247、ocietal challenges of ensuring that workers have the skills to take on the work that will be in demand and that their incomes are sufficient to grow their standards of living.Also,demographics have made such shifts in activities a necessity from a macroeconomic perspective.An economic growth gap has
248、 opened as a result of the slowing growth of the worlds workforce.In some major countries,workforces have shrunk because populations are aging.Labor productivity will have to accelerate to achieve economic growth and enhance prosperity.The analyses in this paper incorporate the potential impact of g
249、enerative AI on todays 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 th
250、e 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 Box 3,“About the research”).Labor productivity will have to accelerate to achieve economic growth and enhanc
251、e prosperity.33The economic potential of generative AI:The next productivity frontierBox 3About the researchThis 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
252、 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(exhibit).We also surveyed experts in the automation of each of these capabilities to estimate automation t
253、echnologies current performance level against each of these capabilities,as well as how the technologys performance might advance over time.Specifically,this year,we updated our assessments of technologys performance in cognitive,language,and social and emotional capabilities based on a survey of ge
254、nerative 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
255、 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 beginni
256、ng 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 an
257、alysis 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 regulati
258、on,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 a
259、nd adoption,and the“latest”scenario flexes all parameters in the opposite direction.The reality is likely to fall somewhere between the two.ExhibitOur analysis assesses the potential for technical automation across some 2,100 activities and 18 capabilities.McKinsey&CompanySource:McKinsey Global Inst
260、itute analysis 850 occupations2,100 activities assessed across all occupationsCapability requirementsSensory Sensory perceptionCognitive Retrieving information Recognizing known patterns and categories(supervised learning)Generating novel patterns and categories Logical reasoning and problem solving
261、 Optimizing and planning Creativity Articulating/display output Coordination with multiple agentsExample:Retail activities Answer questions about products and services Greet customers Clean and maintain work areas Demonstrate product features Process sales and transactions ExamplesPhysical Fine moto
262、r skills and dexterity Gross motor skills Navigation MobilityNatural-language processing Understanding natural language Generating natural languageSocial Social and emotional sensing Social and emotional reasoning Social and emotional output Retail salespeopleFood and beverage service workersHealth
263、practitioners Teachers34The economic potential of generative AI:The next productivity frontierAccelerating the technical potential to transform knowledge workBased on developments in generative AI,technology performance is now expected to match median human performance and reach top quartile human p
264、erformance 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 poin
265、t is 2023.Exhibit 6As a result of generative AI,experts assess that technology could achieve human-level performance in some technical capabilities sooner than previously thought.McKinsey&CompanyTechnical capabilities,level of human performance achievable by technologyComparison made on the business
266、-related tasks required from human workers.Please refer to technical appendix for detailed view of performance rating methodology.Source:McKinsey Global Institute occupation database;McKinsey analysisCoordination with multiple agentsCreativityLogical reasoning and problem solving Natural-language ge
267、nerationNatural-language understandingOutput articulation and presentationGenerating novel patterns and categories Sensory perceptionSocial and emotional outputSocial and emotional reasoningSocial and emotional sensingEstimates post-recent generative AI developments(2023)Estimates pre-generative AI(
268、2017)MedianTop quartileMedianTop quartileLine represents range of expert estimates35The economic potential of generative AI:The next productivity frontierAs a result of these reassessments of technology capabilities due to generative AI,the total percentage of hours that could theoretically be autom
269、ated by integrating technologies that exist today has increased from about 50 percent to 6070 percent.The technical potential curve is quite steep because of the acceleration in generative AIs natural-language capabilities(Exhibit 7).Interestingly,the range of times between the early and late scenar
270、ios 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.Adoption lags behind technical automation potentialOur analysis of adoption scenarios accounts for the time required to
271、 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
272、 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
273、will be made on investments,Exhibit 7The advent of generative AI has pulled forward the potential for technical automation.McKinsey&CompanyTechnical automation potentials by scenario,%Time spent on current work activities11Includes data from 47 countries,representing about 80%of employment across th
274、e world.2017 estimates are based on the activity and occupation mix from 2016.Scenarios including generative AI are based on the 2021 activity and occupation mix.2Early and late scenarios refect the ranges provided by experts(see Exhibit 6).Source:McKinsey Global Institute analysis202020302040205020
275、605060708090100Updated early scenario including generative AI2Updated late scenario including generative AI22017 early scenario22017 late scenario2202336The economic potential of generative AI:The next productivity frontierdeployment,and regulation,among other factors.But they give an indication of
276、the degree to which the activities that workers do each day may shift.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 A
277、Is 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 ac
278、tivities,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 adopt
279、ion 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 2045an acceleration of roughly a decade compared with the previous estimate(Exhibit 8).13 Exhibit 8The midpoint scenario
280、 at which automation adoption could reach 50 percent of time spent on current work activities has accelerated by a decade.McKinsey&CompanyGlobal automation of time spent on current work activities,1%1Includes data from 47 countries,representing about 80%of employment across the world.2017 estimates
281、are based on the activity and occupation mix from 2016.Scenarios including generative AI are based on the 2021 activity and occupation mix.2Early scenario:aggressive scenario for all key model parameters(technical automation potential,integration timelines,economic feasibility,and technology difusio
282、n rates.).3Late scenario:parameters are set for later adoption potential.Source:McKinsey Global Institute analysis20202030204020502060207020802090Updated early scenario including generative AI2Updated late scenario including generative AI32017 early scenario22017 late scenario3100050%20406080Midpoin
283、t 2017Midpoint updated37The economic potential of generative AI:The next productivity frontierDifferent countries,different pace of adoptionAdoption is also likely to be faster in developed countries,where wages are higher and thus the economic feasibility of adopting automation occurs earlier.Even
284、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
285、countries(Exhibit 9).Our analyses of generative AIs impact on work activities and the pace of automation adoption rely on several assumptions and sensitivities(see Box 4,“Limitations of our analyses,key assumptions,and sensitivities”).Exhibit 9Automation adoption is likely to be faster in developed
286、economies,where higher wages will make it economically feasible sooner.McKinsey&CompanyAutomation adoption by scenario for select countries,%1Early scenario:aggressive scenario for all key model parameters(technical automation potential,integration timelines,economic feasibility,and technology difus
287、ion rates.).2Late scenario:parameters are set for the later adoption potential.Source:McKinsey Global Institute analysisChinaGermanyFranceIndiaJapanMexicoUnited States00Early scenarioLate scenario50%50%38The economic potential of generative AI:The next productivity frontierBox
288、41 David Autor et al.,New frontiers:The origins and content of new work,19402018,National Bureau of Economic Research working paper number 30389,August 2022;Jeffrey Lin,“Technological adaptation,cities,and new work,”Review of Economics and Statistics,May 2011,Volume 93,Number 2.Limitations of our an
289、alyses,key assumptions,and sensitivitiesThis analysis considers the potential for automation only of current work activities and occupations.It does not account for how those work activities may shift over time or forecast new activities and occupations.1 Also,the analysis accounts solely for first-
290、order effects.It does not take into account how labor rates could change,and it does not model changes in labor force participation rates or other general equilibrium effects.That said,while these models account for the time it may take for technology to be adopted across an economy,technologies cou
291、ld be adopted much more rapidly in an individual organization.Other research may reach different conclusions.Our assessments of technology capabilities are based on the best estimates of experts involved in developing automation technologies.These assessments could change over time,as they have chan
292、ged since 2017.The technology adoption curves are based on historical findings that technologies take eight to 27 years from commercial availability to reach a plateau in adoption.Some argue that the adoption of generative AI will be faster due to the ease of deploying these technologies.That said,t
293、he case for a minimum of eight years in our earliest scenario for reaching a global plateau in adoption accounts for the pace of adoption of other technologies that have arguably had a faster adoption potentialfor example,social networking as a consumer technology that faced no barriers in enterpris
294、e change management.Our scenario also accounts for the significant role of small and midsize enterprises around the world,in addition to the challenges of incorporating and managing change in larger organizations.In addition,this analysis does not assume that the scale of work automation equates dir
295、ectly to job losses.Like other technologies,generative AI typically enables individual activities within occupations to be automated,not entire occupations.Historically,the activities in many occupations have shifted over time as certain activities are automated.However,organizations may decide to r
296、ealize the benefits of increased productivity by reducing employment in some job categories,a possibility we cannot rule out.Generative AI is likely to have the biggest impact on knowledge work,particularly activities involving decision making and collaboration,which previously had the lowest potent
297、ial for automation.39The economic potential of generative AI:The next productivity frontierGenerative AIs potential impact on knowledge work Previous generations of automation technology were particularly effective at automating data management tasks related to collecting and processing data.Generat
298、ive AIs 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 isnt surprising because its capabilities are fundamentally engineered to do cognitive tasks.As a result,generative AI
299、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
300、,while the potential to automate management and develop talent increased from 16 percent in 2017 to 49 percent in 2023.Generative AIs 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
301、 the activities that workers perform in the economy require at least a median level of human understanding of natural language.Exhibit 10Generative AI could have the biggest impact on collaboration and the application of expertise,activities that previously had a lower potential for automation.McKin
302、sey&CompanyOverall technical automation potential,comparison in midpoint scenarios,%in 2023Note:Figures may not sum,because of rounding.1Previous assessment of work automation before the rise of generative AI.2Applying expertise to decision making,planning,and creative tasks.3Managing and developing
303、 people.4Performing physical activities and operating machinery in unpredictable environments.5Performing physical activities and operating machinery in predictable environments.Source:McKinsey Global Institute analysis58.549.045.090.579.046.073.024.515.524.073.068.045.572.5Activity groupsDecision m
304、aking and collaborationData managementPhysicalApplying expertiseManagingInterfacing with stakeholdersProcessing data Collecting dataPerforming unpredictable physical workPerforming predictable physical workWithout generative AI1With generative AI40The economic potential of generative AI:The next pro
305、ductivity frontierAs 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
306、automation potential was previously expected to emerge later(Exhibit 11).Exhibit 11Advances in technical capabilities could have the most impact on activities performed by educators,professionals,and creatives.McKinsey&CompanyImpact of generative AI on technical automation potential in midpoint scen
307、ario,2023Note:Figures may not sum,because of rounding.Previous assessment of work automation before the rise of generative AI.2Includes data from 47 countries,representing about 80%of employment across the world.Source:McKinsey Global Institute analysisEducator and workforce trainingBusiness and leg
308、alprofessionalsSTEM professionalsCommunity servicesCreatives and arts managementOfce supportManagersHealth professionalsCustomer service and salesProperty maintenanceHealth aides,technicians,and wellnessProduction workFood servicesTransportation servicesMechanical installationand repairAgricultureBu
309、ildersTotal5462576553874443573843827849676353636272945293473704261594951Occupation groupWith generative AIWithout generative AIOverall technical automation potential,comparison in midpoint scenarios,%in 2023Share of global employment,2%45333421710041The economic potential of ge
310、nerative AI:The next productivity frontierLabor 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 opp
311、osite patternit is likely to have the most incremental impact through automating some of the activities of more-educated workers(Exhibit 12).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 h
312、ave advocated for taking a more skills-based approach to workforce development in order to create more equitable,efficient workforce training and matching systems.14 Generative AI could still be described as skill-biased technological change,but with a different,perhaps more granular,description of
313、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 aut
314、omation 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 automatefor example,manipulating fabric or picking delicate fruits.Some labor economists
315、have observed a Exhibit 12Generative AI increases the potential for technical automation most in occupations requiring higher levels of educational attainment.McKinsey&CompanyImpact of generative AI on technical automation potential in midpoint scenario,2023Previous assessment of work automation bef
316、ore the rise of generative AI.Source:McKinsey Global Institute analysis576062646463283645485154Education levelMasters,PhD,or higher With generative AIWithout generative AIOverall technical automation potential,comparison in midpoint scenarios,%in the United States in 2023Share of USemployment,%13229
317、2224Bachelors degree Associates degree Some college High school diploma or equivalentNo high school degree942The economic potential of generative AI:The next productivity frontier“hollowing out of the middle,”and our previous models have suggested that work automation would likely have the biggest m
318、idterm impact on lower-middle-income quintiles.However,generative AIs 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(Ex
319、hibit 13).Exhibit 13Generative AI could have the biggest impact on activities in high-wage jobs;previously,automations impact was highest in lower-middle-income quintiles.McKinsey&CompanyAutomation adoption per wage quintile,%in 2030,midpoint scenarioPrevious assessment of work automation before the
320、 rise of generative AI.Source:McKinsey Global Institute analysisUnited StatesJapanGermanyFranceChinaIndiaMexicoSouth AfricaWithout generative AIWith generative AI802140020Largest increase in automation adoption from generative AILargest automation adoption without generative AIWage quinti
321、lesHigher earnersLower earners020304043The economic potential of generative AI:The next productivity frontierGenerative AI could propel higher productivity growthGlobal economic growth was slower from 2012 to 2022 than in the two preceding decades.15 Although the COVID-19 pandemic was a s
322、ignificant factor,long-term structural challengesincluding declining birth rates and aging populationsare 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 197282 to just 0.8 percent in
323、 201222,largely because of aging.In many large countries,the size of the workforce is already declining.16 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
324、 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.17Exhibit 14Productivity growth,the main engine of GDP growth over the past 30 years,slowe
325、d down in the past decade.McKinsey&CompanyReal GDP growth contribution of employment and productivity growth,19722022,global GDP growth,CAGR,%Source:Conference Board Total Economy database;McKinsey Global Institute analysis Productivity growth bigger contributor to GDP growth22
326、12201222Employment growthProductivity growth0.70.81.72.52.12.52.01.41.30.83.13.12.93.82.844The economic potential of generative AI:The next productivity frontierThe deployment of generative AI and other technologies could help accelerate productivity growth,partially compensating for declining emplo
327、yment 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 0.2 to 3.3 percent from 2023 to 2040 depending on the rate of automation adoptionw
328、ith generative AI contributing to 0.1 to 0.6 percentage points of that growthbut 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
329、of activities will shift;in others,workers will need to shift occupations.Exhibit 15Generative AI could contribute to productivity growth if labor hours can be redeployed efectively.McKinsey&CompanyProductivity impact from automation by scenario,202240,CAGR,%Note:Figures may not sum,because of round
330、ing.1Based on the assumption that automated work hours are reintegrated in work at productivity level of today.2Previous assessment of work automation before the rise of generative AI.3Based on 47 countries,representing about 80%of world employment.Source:Conference Board Total Economy database;Oxfo
331、rd Economics;McKinsey Global Institute analysis Without generative AIAdditional with generative AIChinaEarlyLate3.20.70.63.80.8IndiaEarlyLate1.80.52.30.0MexicoEarlyLate2.30.62.90.0South AfricaEarlyLate1.70.52.30.0United StatesEarlyLate2.90.40.70.33.6Japan4.21.6GermanyEarlyLate3.41.10.63.91.3FranceEa
332、rlyLate3.00.60.73.70.8GlobalDeveloped economiesEmerging economiesEarlyLate2.60.63.30.20.6EarlyLate3.71.40.60.10.20.20.10.145The economic potential of generative AI:The next productivity frontierThe capabilities of generative AI vastly expand the pool of work activities with the potential for technic
333、al automation.That in turn has sped up the pace at which automation may be deployed and expanded the types of workers who will experience its impact.Like other technologies,its ability to take on routine tasks and work can increase human productivity,which has grown at a below-average rate for almost 20 years.18 It can also offset the impact of aging,which is beginning to put a dent in workforce g