The productivity potential of GenAI US

Chatbots can help investment professionals easily leverage the firm’s scale by sorting through its prior internal work on the subsector or market. They can crawl through more data—both internal and external—than deal teams could ever do on their own. Consider that, for most midsize target companies, you can’t find a published source of Net Promoter ScoreSM data or any other objective measure of customer loyalty.

Marketing functions could shift resources to producing higher-quality content for owned channels, potentially reducing spending on external channels and agencies. While generative AI is an exciting and rapidly advancing technology, the other applications of AI discussed in our previous report continue to account for the majority of the overall potential value of AI. Traditional advanced-analytics and machine learning algorithms are highly effective at performing numerical and optimization tasks such as predictive modeling, and they continue to find new applications in a wide range of industries. However, as generative AI continues to develop and mature, it has the potential to open wholly new frontiers in creativity and innovation. It has already expanded the possibilities of what AI overall can achieve (see sidebar “How we estimated the value potential of generative AI use cases”).

If the past eight months are any guide, the next several years will take us on a roller-coaster ride featuring fast-paced innovation and technological breakthroughs that force us to recalibrate our understanding of AI’s impact on our work and our lives. Given the speed of generative AI’s deployment so far, the need to accelerate digital transformation and reskill labor forces is great. Generative AI could still be described as skill-biased technological change, but with a different, perhaps more granular, description of skills that are more likely to be replaced than complemented by the activities that machines can do. 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.

For use cases aimed at increasing revenue, such as some of those in sales and marketing, we estimated the economy-wide value generative AI could deliver by increasing the productivity of sales and marketing expenditures. As companies rush to adapt and implement it, understanding the technology’s potential to deliver value to the economy and society at large will help shape critical decisions. We have used two complementary lenses to determine where generative AI, with its current capabilities, could deliver the biggest value and how big that value could be (Exhibit 1). Banking, high tech, and life sciences are among the industries that could see the biggest impact as a percentage of their revenues from generative AI.

However, the rise of the production of specialized chips has led to an evolving landscape for generative AI. NVIDIA must adapt and innovate within the changing demands of the AI chip industry to maintain its position as a leading player. Although the technology promises to do away with drudgery, some people worry that it may ultimately replace them. A survey by BCG, a consultancy, finds that front-line workers are more likely to be concerned, and less likely to be optimistic, about generative ai than managers or leaders are. In some cases, unions may act to slow the adoption of the technology; some may go as far as the writers’ guild in Hollywood, which was on strike for much of 2023, in part because of concerns about AI’s impact on jobs. Some businesses are taking a cautious approach, since much about the technology still needs ironing out.

With generative AI’s enhanced natural-language capabilities, more of these activities could be done by machines, perhaps initially to create a first draft that is edited by teachers but perhaps eventually with far less human editing required. This could free up time for these teachers to spend more time on other work activities, such as guiding class discussions or tutoring students who need extra assistance. Our analysis suggests that implementing generative AI could increase sales productivity by approximately 3 to 5 percent of current global sales expenditures. 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. The latest generative AI applications can perform a range of routine tasks, such as the reorganization and classification of data.

AI-enabled automation of tasks can empower employees to focus more on highly cognitive tasks, boosting overall output. Simultaneously, many of the new jobs created by the rise of AI are likely to contain higher-level work worthy of higher compensation, further boosting GDP. When you combine the broader capabilities of generative models with the democratization of access provided by NLIs, the explosive rise of ChatGPT and massive generative AI market predictions become more understandable.

Case Studies and Reports About AI

Outside the tech world, only a third of global managers tell McKinsey they are regularly using generative AI for work; about half have tried the technology but have decided not to use it, and about a fifth have had no exposure to it all. EY refers to the global organization, and may refer to one or more, of the member firms of Ernst & Young Global Limited, each of which is a separate legal entity. Ernst & Young Global Limited, a UK company limited by guarantee, does not provide services to clients.

Several studies and analyses have examined the impact of generative AI on the economy, with estimates ranging from $14 trillion to $15.7 trillion in economic contribution by 2030. The potential economic benefits of generative AI include increased productivity, cost savings, new job creation, improved decision making, personalization, and enhanced safety. However, there are also important questions about the distribution of those benefits and the potential impact on workers and society. Generating new content based on cumulative data input makes gen AI worthwhile in many industries.

Fostering a culture of sharing advice and prompts also helps constantly reinforce your habit with new tips and tricks. When we start to embed AI into our daily work and share what we’ve done with it, it helps others see the potential and start to explore the technology for themselves. Like exercising or mastering a new language, working effectively with AI takes developing new habits (plus unlearning some old ones). Research shows those habits pay off with better productivity, creativity, work-life balance, and enjoying your job more. The technology will augment insurance agents’ capabilities and help customers self-serve for simpler transactions.

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. For one firm targeting an IT services company, that meant determining whether generative AI could make key functions more efficient. But the potential new owner wanted to project whether future growth could be made more profitable using AI.

The varying stages of chip development and innovation promise a competitive environment for these companies that is conducive to growth. For example, we helped Intel build downloadable AI reference kits to share with the open-source community, enabling developers to quickly optimize code to meet the increasing demands of AI workloads. Within 15 months, the joint team trained about 220 developers on the enterprise potential of AI analytics technology. Similarly, HPE broadened its GreenLake portfolio, partnering with German AI company Aleph Alpha to offer GreenLake for LLMs. This allows customers to build their own AI models using their own data without paying for supercomputers because the offering runs on HPE’s own Cray XD supercomputers, which have an AI-native architecture. But the benefits are not limited to optimizing operations; they can help drive revenue growth as well.

For example, generative AI’s ability to personalize offerings could optimize marketing and sales activities already handled by existing AI solutions. Similarly, generative AI tools excel at data management and could support existing AI-driven pricing tools. Applying generative AI to such activities could be a step toward integrating applications across a full enterprise. With the acceleration in technical automation potential that generative Chat PG AI enables, our scenarios for automation adoption have correspondingly accelerated. These scenarios encompass a wide range of outcomes, given that the pace at which solutions will be developed and adopted will vary based on decisions that will be made on investments, deployment, and regulation, among other factors. But they give an indication of the degree to which the activities that workers do each day may shift (Exhibit 8).

In a previous article, I discussed GenAI use cases and best practices for high-tech companies to start adopting the technology. In this piece, I’ll dive deeper into the potential for GenAI to transform the entire high-tech value chain and the imperatives to follow to successfully implement the revolutionary technology. Within two months 100m users were posing all sorts of entertaining queries (“Write me a rap song using references to SpongeBob SquarePants”). The number of people Googling “artificial intelligence” surged, and the mania set off investors’ enthusiasm for all manner of AI projects. Yet the real promise, these investors and entrepreneurs are betting, lies with its use in business.

These include managing the risks inherent in generative AI, determining what new skills and capabilities the workforce will need, and rethinking core business processes such as retraining and developing new skills. Foundation models have enabled new capabilities and vastly improved existing ones across a broad range of modalities, including images, video, audio, and computer code. AI trained on these models can perform several functions; it can classify, edit, summarize, answer questions, and draft new content, among other tasks. The speed at which generative AI technology is developing isn’t making this task any easier. The tool was designed to transcribe and summarize data to create efficiency in the customer’s workflow.

Foundation models can generate candidate molecules, accelerating the process of developing new drugs and materials. Entos, a biotech pharmaceutical company, has paired generative AI with automated synthetic development tools to design small-molecule therapeutics. But the same principles can be applied to the design of many other products, including larger-scale physical products and electrical circuits, among others. Generative AI could have an impact on most business functions; however, a few stand out when measured by the technology’s impact as a share of functional cost (Exhibit 3). Our analysis of 16 business functions identified just four—customer operations, marketing and sales, software engineering, and research and development—that could account for approximately 75 percent of the total annual value from generative AI use cases. Some of this impact will overlap with cost reductions in the use case analysis described above, which we assume are the result of improved labor productivity.

Notably, TFP accounted for about half of the decade-long acceleration in labor productivity growth during the 1990s and early 2000s. Disappointingly though, productivity growth has been sluggish in both advanced and developing countries over the past decade. In the US, labor productivity growth has averaged only 1.4% per year since 2013, less than half the rate of the previous decade. Generative AI has the potential to automate certain tasks, displacing some workers, and it can also create new jobs and industries.

Although the COVID-19 pandemic was a significant factor, long-term structural challenges—including declining birth rates and aging populations—are ongoing obstacles to growth. Pharma companies that have used this approach have reported high success rates in clinical trials for the top five indications recommended by a foundation model for a tested drug. This success has allowed these drugs to progress smoothly into Phase 3 trials, significantly accelerating the drug development process. Our updates examined use cases of generative AI—specifically, how generative AI techniques (primarily transformer-based neural networks) can be used to solve problems not well addressed by previous technologies. One surprising thing I’ve learned about bringing AI to work is the viral nature of it. Show one coworker a fun, new prompt or a stellar AI-generated presentation and you’ve got them hooked.

For now, however, foundation models lack the capabilities to help design products across all industries. Our analysis of the potential use of generative AI in marketing doesn’t account for knock-on effects beyond the direct impacts on productivity. Generative AI–enabled synthesis could provide higher-quality data insights, leading to new ideas for marketing campaigns and better-targeted customer segments.

While generative AI is here to leave a lasting impact on the technological world, it is important to recognize the major players in the industry. As trends, ideas, and innovation are the focus of leading names within the chip industry, following their progress provides insights into the economic potential of generative AI. Overall, GenAI has the potential to transform the entire high-tech industry value chain by optimizing processes, reducing costs, improving customer satisfaction and driving revenue growth. As the technology continues to advance, high-tech companies that embrace and integrate generative AI into their operations will have a competitive advantage in the market as both adopters and enablers of the technology. Broadly speaking, forecasters are predicting that generative AI will boost economic productivity and growth in advanced economies.

Generative AI’s potential impact on knowledge work

Investors are already using these technologies to transform companies, make better decisions, and boost returns. The idea is that AI will make millions of “knowledge workers”, like scientists, editors, lawyers and doctors, more productive within a few years. But the truth is that these things are hard to predict and assess, especially as the output of such workers is notoriously difficult to measure. History lessons and scenarios provide clues about the magnitude of GenAI’s future impact. Generative AI is improving operations and ensuring employees are following the proper steps. It can also enhance performance visibility across business units by integrating disparate data sources.

They could also have an impact on knowledge workers whose activities were not expected to shift as a result of these technologies until later in the future (see sidebar “About the research”). The McKinsey Global Institute began analyzing the impact of technological automation of work activities and modeling scenarios of adoption in 2017. At that time, we estimated that workers spent half of their time on activities that had the potential to be automated by adapting technology that existed at that time, or what we call technical automation potential. We also modeled a range of potential scenarios for the pace at which these technologies could be adopted and affect work activities throughout the global economy. GenAI systems are expected to permeate wide segments of business operations in coming years with significant implications for a wide range of activities, such as customer support, marketing and sales, business operations and software programming.

These examples illustrate how technology can augment work through the automation of individual activities that workers would have otherwise had to do themselves. Generative AI tools can draw on existing documents and data sets https://chat.openai.com/ to substantially streamline content generation. These tools can create personalized marketing and sales content tailored to specific client profiles and histories as well as a multitude of alternatives for A/B testing.

The speed with which this technology can create content can help employees develop more content in less time and/or work more efficiently. This can reduce the need for human labor, raising concerns about job displacement and income inequality. Generative AI (Gen AI) is a type of artificial intelligence designed to generate new content without human intervention, such as text, images, and even music. This technology uses complex algorithms and machine learning models to memorize patterns and rules from existing data and generate new content similar in style and structure. Of CEOs surveyed by IBM, 75% believe businesses leveraging the most advanced generative AI will garner a distinct competitive advantage. The technology’s ability to widen the range of tasks AI can automate has already led to a reduction in time-consuming work and a subsequent surge in productivity.

Generative AI possesses the power to create human-like content instantaneously, unlocking new levels of productivity across various sectors of our economy. As this technology develops, I believe it will continue to empower the transcendence of previous capabilities. As a result of these reassessments of technology capabilities due to generative AI, the total percentage of hours that could theoretically be automated by integrating technologies that exist today has increased from about 50 percent to 60–70 percent. The technical potential curve is quite steep because of the acceleration in generative AI’s natural-language capabilities.

Based on a historical analysis of various technologies, we modeled a range of adoption timelines from eight to 27 years between the beginning of adoption and its plateau, using sigmoidal curves (S-curves). This range implicitly accounts for the many factors that could affect the pace at which adoption occurs, including regulation, levels of investment, and management decision making within firms. We also surveyed experts in the automation of each of these capabilities to estimate automation technologies’ current performance level against each of these capabilities, as well as how the technology’s performance might advance over time. Specifically, this year, we updated our assessments of technology’s performance in cognitive, language, and social and emotional capabilities based on a survey of generative AI experts. In the lead identification stage of drug development, scientists can use foundation models to automate the preliminary screening of chemicals in the search for those that will produce specific effects on drug targets. To start, thousands of cell cultures are tested and paired with images of the corresponding experiment.

Gen AI’s impact on consumption patterns has made it easier for companies to personalize their marketing and advertising efforts. This has led to a more targeted approach to advertising, which can be beneficial but also problematic from a privacy perspective. I look back over the last year and can’t believe I used to work without AI, manually scrolling through every email (or missing important ones) and staring at blank documents and a blinking cursor. At the same time, I know that learning never stops, especially with new AI innovations that open up new possibilities happening all the time.

Gen AI can also help retailers innovate, reduce spending, and focus on developing new products and systems. Goldman Sachs estimates that generative AI could automate tasks that take up to one-fourth of employees’ time today. These assessments have sparked concerns about job displacement and an uncertain future of work. Would automate half of all work between 2035 and 2075, but the power of generative A.I.

The time to act is now.11The research, analysis, and writing in this report was entirely done by humans. In the banking industry, generative AI has the potential to improve on efficiencies already delivered by artificial intelligence by taking on lower-value tasks in risk management, such as required reporting, monitoring regulatory developments, and collecting data. In the life sciences industry, generative AI is poised to make significant contributions to drug discovery and development. In 2012, the McKinsey Global Institute (MGI) estimated that knowledge workers spent about a fifth of their time, or one day each work week, searching for and gathering information.

NLIs reduce the technical learning curve and widen the potential user base, empowering a much larger number of people to utilize the model effectively. However, generative AI’s impact is likely to most transform the work of higher-wage knowledge workers because of advances in the technical automation potential of their activities, which were previously considered to be relatively immune from automation (Exhibit 13). Generative AI’s ability to understand and use natural language for a variety of activities and tasks largely explains why automation potential has risen so steeply.

At the forefront, the development of its generative AI models is leading to innovation and growth. While these projects lead to the consumption of AI chips from companies like NVIDIA, Google AI contributes to the development of AI chips through research and collaboration. The transformative capability of generative artificial intelligence (GenAI) to augment human work and unlock efficiency will likely have far-reaching implications for the macroeconomic and business landscape.

Education is widely viewed as a sector with potential generative AI applications, and CVC saw that Multiversity was uniquely positioned to develop a strong AI-enabled business model. The company had robust market share, fully accredited content, and participation in a highly regulated university landscape. Even before generative AI came along, it was developing a set of initiatives to improve everything from how students enrolled in classes to how they interacted with professors. While Intel has been a long-standing name in the semiconductor industry, it is a new player within the AI chip industry. Some of its strategic initiatives as an AI chip industry player include the acquisition of Habana Labs which provided them expertise in the AI chip technology. According to recent research by my company, Accenture, in which I’m the semiconductor industry global lead, 77% of high-tech executives see generative AI (GenAI) as a key lever in their reinvention strategy.

The model combines search and content creation so wealth managers can find and tailor information for any client at any moment. While we have estimated the potential direct impacts of generative AI on the R&D function, we did not attempt to estimate the technology’s potential to create entirely novel product categories. These are the types of innovations that can produce step changes not only in the performance of individual companies but in economic growth overall. In other cases, generative AI can drive value by working in partnership with workers, augmenting their work in ways that accelerate their productivity.

While generative AI will impact a wide variety of industries, 75% of its potential value spans just four sectors. When that innovation seems to materialize fully formed and becomes widespread seemingly overnight, both responses can be amplified. The arrival of generative AI in the fall of 2022 was the most recent example of this phenomenon, due to its unexpectedly rapid adoption as well as the ensuing scramble among companies and consumers to deploy, integrate, and play with it. An important phase of drug discovery involves the identification and prioritization of new indications—that is, diseases, symptoms, or circumstances that justify the use of a specific medication or other treatment, such as a test, procedure, or surgery. Possible indications for a given drug are based on a patient group’s clinical history and medical records, and they are then prioritized based on their similarities to established and evidence-backed indications.

The exact impact of AI on jobs is difficult to predict and will likely vary depending on the industry and the specific tasks involved. A study by Accenture found that artificial intelligence could add $14 trillion to the global economy by 2035, with the most significant gains in China and North America. The study also predicted that AI could increase labor productivity by up to 40% in some industries. The use of gen AI in finance is expected to increase global gross domestic product (GDP) by 7%—nearly $7 trillion—and boost productivity growth by 1.5%, according to Goldman Sachs Research. Gen AI is a good fit with finance because its strength—dealing with vast amounts of data—is precisely what finance relies on to function.

Improved Decision Making vs. Bad Data and Bias

Third, generative AI’s natural-language translation capabilities can optimize the integration and migration of legacy frameworks. Generative AI tools can facilitate copy writing for marketing and sales, help brainstorm creative marketing ideas, expedite consumer research, and accelerate content analysis and creation. The potential improvement in writing and visuals can increase awareness and improve sales conversion rates. But a full realization of the technology’s benefits will take time, and leaders in business and society still have considerable challenges to address.

Across the 63 use cases we analyzed, generative AI has the potential to generate $2.6 trillion to $4.4 trillion in value across industries. Its precise impact will depend on a variety of factors, such as the mix and importance of different functions, as well as the scale of an industry’s revenue (Exhibit 4). Treating computer languages as just another language opens new possibilities for software engineering. Software engineers can use generative AI in pair programming and to do augmented coding and train LLMs to develop applications that generate code when given a natural-language prompt describing what that code should do. We estimate that generative AI could increase the productivity of the marketing function with a value between 5 and 15 percent of total marketing spending. Notably, the potential value of using generative AI for several functions that were prominent in our previous sizing of AI use cases, including manufacturing and supply chain functions, is now much lower.5Pitchbook.

Initial case studies provide evidence that GenAI will likely provide substantial productivity boosts in four major realms. In this installment, we explore the economic impact of GenAI through a productivity lens and quantify the extent to which the productivity potential of GenAI could bolster overall economic prospects in the next decade. A report by McKinsey & Company found that AI could automate up to 45% of the tasks currently performed by retail, hospitality, and healthcare workers.

One Bain & Company tool can ingest 10,000 customer reviews, print charts, and summarize findings within minutes. Another can summarize interviews with customers and market participants, converting unstructured text data into structured formats. These tools widen the aperture to more information, more quickly, so deal teams can focus on generating insights and testing their investment theses.

Sales teams, for example, can use gen AI assistants to bring up the most current and relevant information about customers, ranging from recent communications across channels to past transactions and stated intents, effectively improving opportunity conversions. Similarly, customer support interactions can be turned into opportunities by having assistants bring up relevant new services for cross-selling and upselling. These benefits can extend to downstream organizations by delivering deep insights that can help guide customer success managers and SMB partners.

This is largely explained by the nature of generative AI use cases, which exclude most of the numerical and optimization applications that were the main value drivers for previous applications of AI. In this section, we highlight the value potential of generative AI across business functions. Large language models swept the globe, and deepfakes were becoming ever more pervasive. Zeroing in on the handful of companies and initiatives that will create real impact is the best way to start turning these disruptive technologies to your advantage.

The latest EY 2023 Work Reimagined Survey indicates that 84% of employers say they expect to have implemented GenAI within 12 months. And a net 33% of employees and employers see potential benefits for productivity and new ways of working. As such, the ability of business leaders to reimagine business models and consider how best to augment workers’ skills will be a key determinant of how powerful the productivity lift from GenAI is. Using the ICT period as a reference, we created three scenarios – trend, revival (our baseline) and boom – that correspond to three different productivity outcomes for the next decade.

The modeled scenarios create a time range for the potential pace of automating current work activities. The “earliest” scenario flexes all parameters to the extremes of plausible assumptions, resulting in faster automation development and adoption, and the “latest” scenario flexes all parameters in the opposite direction. First, they can draft code based on context via input code or natural language, helping developers code more quickly and with reduced friction while enabling automatic translations and no- and low-code tools. Second, such tools can automatically generate, prioritize, run, and review different code tests, accelerating testing and increasing coverage and effectiveness.

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. You can foun additiona information about ai customer service and artificial intelligence and NLP. First, we estimated a range of time to implement a solution that could automate each specific detailed work activity, once all the capability requirements were met by the state of technology development. Second, we estimated a range of potential costs for this technology when it is first introduced, and then declining over time, based on historical precedents. The potential of technological capabilities in a lab does not necessarily mean they can be immediately integrated into a solution that automates a specific work activity—developing such solutions takes time. Even when such a solution is developed, it might not be economically feasible to use if its costs exceed those of human labor. Additionally, even if economic incentives for deployment exist, it takes time for adoption to spread across the global economy.

I believe the time is now for businesses to think about how to capitalize on generative AI to augment workflows, gain a competitive advantage and create their ideal future. Interacting with most discriminative AI models requires the use of specific syntax or knowledge of a programming language. This takes time to adapt to and greatly limits the range of people capable of using the model. However, generative models use Natural Language Interfaces (NLIs) to interpret text as opposed to code.

McKinsey & Company

In addition to the potential value generative AI can deliver in function-specific use cases, the technology could drive value across an entire organization by revolutionizing internal knowledge management systems. Generative AI’s impressive command of natural-language processing can help employees retrieve stored internal knowledge by formulating queries in the same way they might ask a human a question and engage in continuing dialogue. This could empower teams to quickly access relevant information, enabling them to rapidly make better-informed decisions and develop effective strategies. Capabilities for the iPhone would be the latest example of its filling a gap in Apple’s products. Apple’s effort to develop its own large language model, the technology behind chatbots like ChatGPT and Gemini, has been running behind, two people familiar with its development said. Moreover, the development of new AI chip designs has increased the variety of options for consumers.

• Chatbots are prone to “hallucinations”, or making up things that sound dangerously plausible.
• As trends, ideas, and innovation are the focus of leading names within the chip industry, following their progress provides insights into the economic potential of generative AI.
• Also, the time saved by sales representatives due to generative AI’s capabilities could be invested in higher-quality customer interactions, resulting in increased sales success.
• Pharma companies that have used this approach have reported high success rates in clinical trials for the top five indications recommended by a foundation model for a tested drug.
• Tools like ChatGPT and Google’s Bard, with tech companies and venture capitalists investing billions of dollars in the technology.

Additionally, some of the tasks performed in lower-wage occupations are technically difficult to automate—for example, manipulating fabric or picking delicate fruits. Some labor economists have observed a “hollowing out of the middle,” and our previous models have suggested that work automation would likely have the biggest midterm impact on lower-middle-income quintiles. Banking, a knowledge and technology-enabled industry, has already benefited significantly from previously existing applications of artificial intelligence in areas such as marketing and customer operations.1“Building the AI bank of the future,” McKinsey, May 2021. For example, the life sciences and chemical industries have begun using generative AI foundation models in their R&D for what is known as generative design.

Gauging the productivity potential of GenAI

The model answers complex questions based on a prompt, identifying the source of each answer and extracting information from pictures and tables.

In addition, generative AI could automatically produce model documentation, identify missing documentation, and scan relevant regulatory updates to create alerts for relevant shifts. Generative AI could have a significant impact on the banking industry, generating value from increased productivity of 2.8 to 4.7 percent of the industry’s annual revenues, or an additional $200 billion to $340 billion. On top of that impact, the use of generative AI tools could also the economic potential of generative ai enhance customer satisfaction, improve decision making and employee experience, and decrease risks through better monitoring of fraud and risk. Generative AI tools can enhance the process of developing new versions of products by digitally creating new designs rapidly. A designer can generate packaging designs from scratch or generate variations on an existing design. This technology is developing rapidly and has the potential to add text-to-video generation.

The economic insights can highlight new investment avenues by informing policymakers and business leaders of the changing economic landscape timely. Previous generations of automation technology often had the most impact on occupations with wages falling in the middle of the income distribution. For lower-wage occupations, making a case for work automation is more difficult because the potential benefits of automation compete against a lower cost of human labor.

If generative AI could take on such tasks, increasing the efficiency and effectiveness of the workers doing them, the benefits would be huge. To grasp what lies ahead requires an understanding of the breakthroughs that have enabled the rise of generative AI, which were decades in the making. For the purposes of this report, we define generative AI as applications typically built using foundation models. These models contain expansive artificial neural networks inspired by the billions of neurons connected in the human brain.

We analyzed only use cases for which generative AI could deliver a significant improvement in the outputs that drive key value. In particular, our estimates of the primary value the technology could unlock do not include use cases for which the sole benefit would be its ability to use natural language. For example, natural-language capabilities would be the key driver of value in a customer service use case but not in a use case optimizing a logistics network, where value primarily arises from quantitative analysis.

A generative AI bot trained on proprietary knowledge such as policies, research, and customer interaction could provide always-on, deep technical support. Today, frontline spending is dedicated mostly to validating offers and interacting with clients, but giving frontline workers access to data as well could improve the customer experience. The technology could also monitor industries and clients and send alerts on semantic queries from public sources.