The economic potential of generative AI: The next productivity frontier

The report also explores the quantification of use cases by industries and provides valuable statistics and data on the potential value that generative AI can unlock. What are likely to be the biggest economic applications of the current wave of artificial intelligence technologies? The McKinsey Global Institute takes a shot at answering the question in “The economic potential of generative AI” (June 2023).

I agree with the findings; if you are a marketer, software developer, or R&D professional and aren’t leveraging AI, you will probably not be competitive in the employment market and probably much sooner than one might think. I also believe it’s not a death sentence but an opportunity for those willing to update their skills. The information contained in this article does not constitute a recommendation from any Goldman Sachs entity to the recipient, and Goldman Sachs is not providing any financial, economic, legal, investment, accounting, or tax advice through this article or to its recipient. According to Deloitte, generative AI could reduce the time required for drug discovery by up to 50% and lower the cost by up to 25%. AI has permeated our lives incrementally, through everything from the tech powering our smartphones to autonomous-driving features on cars to the tools retailers use to surprise and delight consumers. Clear milestones, such as when AlphaGo, an AI-based program developed by DeepMind, defeated a world champion Go player in 2016, were celebrated but then quickly faded from the public’s consciousness.

Improved Decision Making vs. Bad Data and Bias

While it is likely to lead to increased efficiency and productivity, it is also expected to lead to job displacement for some workers. Generative AI’s potential in R&D is perhaps less well recognized than its potential in other business functions. Still, our research indicates the technology could deliver productivity with a value ranging from 10 to 15 percent of overall R&D costs.

Many large employment sectors, including government, health care, traditional retail, hospitality, and construction, have critical shortages of workers. And in some countries, such as China, Italy, Japan, and South Korea, overall labor forces are shrinking. Labor markets have also been transformed by the preferences of job seekers in advanced economies, who are choosing employment sectors—and frequently shifting between them—based on flexibility, safety, level of stress, and income. Meanwhile, geopolitical tensions, combined with the shocks of climate change and the pandemic, have led many companies and countries to “de-risk” and diversify their supply chains at great expense for reasons that have nothing to do with reducing costs. The era of building global supply chains entirely on the basis of efficiency and comparative advantage has clearly come to a close. Much recent debate has focused on the dangers that AI poses and the need for international regulations to prevent catastrophic harm.

Similarly, by May 2023, Anthropic’s generative AI, Claude, was able to process 100,000 tokens of text, equal to about 75,000 words in a minute—the length of the average novel—compared with roughly 9,000 tokens when it was introduced in March 2023. And in May 2023, Google announced several new features powered by generative AI, including Search Generative Experience and a new LLM called PaLM 2 that will power its Bard chatbot, among other Google products. As generative AI emerges as the next frontier for productivity, stakeholders must collaborate to navigate its complexities. By addressing challenges, implementing responsible practices, and fostering inclusivity, we can fully leverage generative AI’s potential to drive positive economic and societal change. Generative AI stands as a powerful and versatile technology, unlocking new dimensions of human creativity and productivity.

AI Data Prospecting Platform: Features and Advantages

But it is their ability to write text, compose music, and create digital art that has garnered headlines and persuaded consumers and households to experiment on their own. As a result, a broader set of stakeholders are grappling with generative AI’s impact on business and society but without much context to help them make sense of it. Generating new content based on cumulative data input makes gen AI worthwhile in many industries. 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. Our analysis captures only the direct impact generative AI might have on the productivity of customer operations.

With AI, small businesses are rethinking their approaches to customer experience, productivity, revenue, and growth in both the B2B and the B2C domains. 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. As an example of how this might play out in a specific occupation, consider postsecondary English language and literature teachers, whose detailed work activities include preparing tests and evaluating student work. With generative AI’s enhanced natural-language capabilities, more of these activities could be done by machines, perhaps initially to create a first draft that is edited by teachers but perhaps eventually with far less human editing required. This could free up time for these teachers to spend more time on other work activities, such as guiding class discussions or tutoring students who need extra assistance.

The economic opportunity of Gen AI in India

Generative AI’s ability to understand and use natural language for a variety of activities and tasks largely explains why automation potential has risen so steeply. Some 40 percent of the activities that workers perform in the economy require at least a median level of human understanding of natural language. 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. 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.

But the same principles can be applied to the design of many other products, including larger-scale physical products and electrical circuits, among others. Our analysis of the potential use of generative AI in marketing doesn’t account for knock-on effects beyond the direct impacts on productivity. Generative AI–enabled synthesis could provide higher-quality data insights, leading to new ideas for marketing campaigns and better-targeted customer segments. Marketing functions could shift resources to producing higher-quality content for owned channels, potentially reducing spending on external channels and agencies. 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.

Researchers are trying hard to address these issues, including by using human feedback and other means to guide the generated outputs, but more work is needed. The first was that productivity for the group with the AI assistants was on average 14 percent higher. The second, and even more significant, was that, although everyone in the group with the AI assistant had productivity gains, the effect was much higher for relatively inexperienced agents. In other words, the AI assistant was able to markedly close the gap in performance between new and seasoned agents, suggesting generative AI’s potential to accelerate on-the-job training. By training these new LLMs on billions, and now trillions, of words, and over long periods, they can generate increasingly sophisticated human-like responses when prompted. Unlike many previous AI innovations, which were tailored to specific functions, the LLMs that underlie generative AI have a strong claim to be a truly general-purpose technology.

Technology allows companies and employees a new kind of agility and productivity boost, which our economy desperately needs,” said Anna Katariina Wisakanto, consultant at McKinsey’s office in Helsinki. According to McKinsey , generative AI could deliver value equal to an additional $200 billion to $340 billion annually for the retail industry if the use cases were fully implemented. Because of its digital nature, AI technology will spread; in fact, it would be very hard to stop it from doing so.

But ensuring that it does so in the right way will require new forms of international economic governance. But many emerging economies will also benefit from this technology, and for them, access may be slow and uneven. The extent to which AI can be developed and used in an equitable way worldwide will determine the magnitude of its effect on the global economy. All of us are at the beginning of a journey to understand this technology’s power, reach, and capabilities. If the past eight months are any guide, the next several years will take us on a roller-coaster ride featuring fast-paced innovation and technological breakthroughs that force us to recalibrate our understanding of AI’s impact on our work and our lives. Given the speed of generative AI’s deployment so far, the need to accelerate digital transformation and reskill labor forces is great.

• The era of building global supply chains entirely on the basis of efficiency and comparative advantage has clearly come to a close.
• For example, it used to be that only a limited number of high-caste workers could access the computers.
• This percentage is higher than the company’s previous report, which indicated that generative AI could automate tasks that consume half of the time employees spend performing their jobs.
• Gen AI has potential to increase accuracy, but the technology also comes with vulnerabilities, as its trustworthiness depends heavily on the quality of training datasets, according to the World Economic Forum.

In addition, the industries are heavily customer facing, which offers opportunities for generative AI to complement previously existing artificial intelligence. For example, generative AI’s ability to personalize offerings could optimize marketing and sales activities already handled by existing AI solutions. Similarly, generative AI tools excel at data management and could support existing AI-driven pricing tools.

Leveraging Artificial Intelligence to Uncover Subtle Relationships in Ore Analysis

Generative AI has opened the door to more possibilities and is expected to play a role in tasks requiring creativity, curiosity, and looking at information differently. Therefore, the potential of generative AI lies in its ability to enable people to achieve greater creativity, effectiveness, and efficiency in their work. Tools that use generative AI are able to efficiently process and scan vast volumes of corporate information. This could potentially replace time-consuming tasks for knowledge workers, offering scalable virtual expertise beyond human capabilities for certain industries. Another crucial priority will be to encourage the widest possible spread of AI technologies across the economy.

Lower-wage countries like China, India, and Mexico are predicted to adopt automation more slowly compared to higher-wage counterparts like the United States and Germany. Generative AI can also help cybersecurity firms anticipate future threats and attacks by creating scenarios or simulations that can test the resilience of systems and networks. For example, generative AI can generate adversarial examples or inputs that can fool or bypass security mechanisms. It can also generate attack vectors or strategies that can exploit vulnerabilities or weaknesses. Interestingly, the range of times between the early and late scenarios has compressed compared with the expert assessments in 2017, reflecting a greater confidence that higher levels of technological capabilities will arrive by certain time periods (Exhibit 7). L  Encouragement of innovation, diversity, and inclusion in the development and use of generative AI solutions.

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. Our second lens complements the first by analyzing generative AI’s potential impact on the work activities required in some 850 occupations. We modeled scenarios to estimate when generative AI could perform each of more than 2,100 “detailed work activities”—such as “communicating with others about operational plans or activities”—that make up those occupations across the world economy. This enables us to estimate how the current capabilities of generative AI could affect labor productivity across all work currently done by the global workforce. “Examples include generative AI’s ability to support interactions with customers, generate creative content for marketing and sales and draft computer code based on natural-language prompts, among many other tasks,” the report said.

Has the potential to change the anatomy of work, augmenting the capabilities of individual workers by automating some of their individual activities,” the report said. Drucker is often considered the father of modern management due to his extensive contributions to the field. Central to this philosophy is the view that people are an organization’s most valuable resource and that a manager’s job is preparing and freeing people to perform.

There were also follow-on effects of that job creation, as the boost to aggregate income indirectly drove demand for service sector workers in industries like healthcare, education and food services. Another area in which nascent LLM applications could have a large impact is in ambient intelligence systems. In these, AI technologies are used in conjunction with visual or audio sensors to monitor and enhance human performance.

The output depends on the intended purpose of the AI model, which can be tweaked to suit the needs of individuals and organizations based on several parameters. Previous generations of automation technology were particularly effective at automating data management tasks related to collecting and processing data. Generative AI’s natural-language capabilities increase the automation potential of these types of activities somewhat. But its impact on more physical work activities shifted much less, which isn’t surprising because its capabilities are fundamentally engineered to do cognitive tasks.

L  Development of standards and guidelines for the quality, safety, and ethics of generative AI applications. The first wave of gen AI, conducted especially by LLM models, have seen a huge adoption and experimentation in different contexts. Some start-ups have achieved certain success in developing their own models — Cohere, Anthropic, and AI21, among others, build and train their own large language models (LLMs).

In the case of the earlier digital revolution, a large body of research has documented highly uneven adoption across sectors and firms. In the case of generative AI, small and medium-sized firms deserve special attention, since they may not have the resources to conduct the experiments and develop use cases. It is possible that reductions in the current high costs of AI development and research, as well as competition among the major developers, will lead to affordable AI applications that can be widely implemented, by keeping costs down and spurring entrepreneurial activity.

Tech Policy Trends 2024: Generative AI’s impact on the workforce

Breakthroughs in generative artificial intelligence have the potential to bring about sweeping changes to the global economy, according to Goldman Sachs Research. As tools using advances in natural language processing work their way into businesses and society, they could drive a 7% (or almost $7 trillion) increase in global GDP and lift productivity growth by 1.5 percentage points over a 10-year period. But given their unusual attributes, combined with continuing rapid technical innovations by researchers and the huge amounts of venture capital pouring into AI research, their capabilities will almost certainly grow. Within the next five years, AI developers will introduce thousands of applications built on LLMs and other generative AI models aimed at highly disparate sectors, activities, and jobs. At the same time, generative AI models will soon be used alongside other AI systems, in part to address the current limitations of those systems, but also to expand their capabilities. Examples include adapting LLMs to help with other productivity applications, such as spreadsheets and email, and pairing LLMs with robotic systems to improve and expand the operation of these systems.

Chatbots and virtual assistants powered by generative AI can understand and respond to customer inquiries with a level of nuance that was once thought impossible. This not only improves customer satisfaction but also frees up human resources for more complex and strategic tasks, thereby enhancing overall business efficiency. Generative AI’s impact on productivity could add trillions of dollars in value to the global economy and according to McKinsey and it is already having a significant impact across all industry sectors. Adoption is also likely to be faster in developed countries, where wages are higher and thus the economic feasibility of adopting automation occurs earlier. Even if the potential for technology to automate a particular work activity is high, the costs required to do so have to be compared with the cost of human wages.

The McKinsey report concludes with forecasting the impact of generative AI on the future of work, noting that over the years, machines have given human workers various « superpowers ». The cybersecurity industry is facing a growing number of cyber threats and attacks that are becoming more sophisticated and damaging. Generative AI can help cybersecurity firms defend against these threats by creating adaptive systems that can learn from data and detect novel patterns.

The rush to throw money at all things generative AI reflects how quickly its capabilities have developed. It can also substantially increase labour productivity across the global economy, but that will require continued investments, the report said. In April, Goldman Sachs said the sector could drive a 7 per cent – or almost $7 trillion – increase in global GDP and lift productivity growth by 1.5 percentage points over a 10-year period. While the rapid evolution of AI is expected to automate tasks and boost productivity, experts warn of numerous risks, putting pressure on governments and regulators to accelerate the pace of legislation to match the pace of the industry’s development. Generative AI is estimated to add 15 per cent to 40 per cent to the $11 trillion to $17.7 trillion of economic value that McKinsey estimate non-generative artificial intelligence and analytics could unlock. A new wave of AI systems may also have a major impact on employment markets around the world.

Tools — which exploded onto the tech scene late last year — accelerated the company’s forecast. Ahead of the meeting, major AI companies, including Microsoft and Alphabet’s Google, committed to participating in the independent public evaluation of their systems. The latest estimate is an upgrade from 2017 when the consultancy estimated AI to deliver $9.5 trillion to $15.4 trillion in economic value. StoryLab – StoryLab.ai solves common problems marketers face, such as time constraints, inconsistency in quality, lack of collaboration, and difficulty in capturing attention. If you want your organization to improve at using AI, this is the course to take everyone- especially your non-technical colleagues- to take. Taught by Andrew Ng, a leading Standford researcher on AI and thought l artificial intelligence.

This is because AI assistance helped less-experienced agents communicate using techniques similar to those of their higher-skilled counterparts. Generative AI is a powerful and versatile technology that can create new value for businesses across various industries. By creating novel content and solutions, generative AI can enhance customer experiences, optimize operations, accelerate innovation, and improve security. You can foun additiona information about ai customer service and artificial intelligence and NLP. The economic potential of generative AI is immense, as it can unlock new sources of growth, efficiency, and competitive advantage for businesses.

In this blog post, we will explore how four industries – retail, banking, pharmaceutical companies, and cybersecurity firms – can benefit from generative AI and what are some of the use cases and examples that illustrate its economic potential. The technology’s fraught potential, to bring enormous human and economic gains but also to cause very real harms, is coming sharply into focus. But harnessing the power of AI for good will require more than simply focusing on existential threats and potential damage.

For example, within sectors, so-called frontier firms, which are often the most nimble, have outstripped other firms in using digital technologies. Similarly, the high-tech and financial services sectors have been faster to adopt new technologies than has health care, creating unevenness that can become a barrier to economy-wide productivity gains. Despite the economic potential of generative ai the promise of AI, much of the public debate about it has focused on its controversial aspects and its potential to do harm. Their outputs can sometimes reflect the bias of their training sets, produce erroneous material, or include so-called hallucinations—assertions that sound plausible but do not reflect the reality of the physical world.

Across the banking industry, for example, the technology could deliver value equal to an additional $200 billion to $340 billion annually if the use cases were fully implemented. In retail and consumer packaged goods, the potential impact is also significant at $400 billion to $660 billion a year. Generative AI has several features that suggest its potential economic impact could be unusually large. LLMs now have the capacity to respond to prompts in many different domains, from poetry to science to law, and to detect different domains and shift from one to another, without needing explicit instructions. Many developers of LLMs, including OpenAI, have created APIs—application programming interfaces— that allow others to build their own proprietary AI solutions on the LLM base. The race to create applications for a huge diversity of sectors and professional disciplines and use cases has already begun.

The technical potential curve is quite steep because of the acceleration in generative AI’s natural-language capabilities. These examples illustrate how technology can augment work through the automation of individual activities that workers would have otherwise had to do themselves. Following are four examples of how generative AI could produce operational benefits in a handful of use cases across the business functions that could deliver a majority of the potential value we identified in our analysis of 63 generative AI use cases. In the first two examples, it serves as a virtual expert, while in the following two, it lends a hand as a virtual collaborator.

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. Based on developments in generative AI, technology performance is now expected to match median human performance and reach top-quartile human performance earlier than previously estimated across a wide range of capabilities (Exhibit 6). For example, MGI previously identified 2027 as the earliest year when median human performance for natural-language understanding might be achieved in technology, but in this new analysis, the corresponding point is 2023. While other generative design techniques have already unlocked some of the potential to apply AI in R&D, their cost and data requirements, such as the use of “traditional” machine learning, can limit their application.

However, generative AI also poses ethical and social challenges that need to be addressed, such as ensuring quality, accuracy, fairness, transparency, and accountability of the generated content and solutions. Therefore, businesses should adopt generative AI with caution and responsibility, and follow the best practices and guidelines for its development and deployment. The latest report from McKinsey on the economic potential impact of generative AI points to what may be the next productivity frontier.

This could empower teams to quickly access relevant information, enabling them to rapidly make better-informed decisions and develop effective strategies. Generative AI’s evolution has been gradual, fueled by substantial investments in advanced machine learning and deep learning projects. Foundation models, a key component of generative AI, process large and varied sets of unstructured data, enabling them to perform diverse tasks such as classification, editing, summarization, and content generation. With the ability to generate text, images, and videos, generative AI models can assist in creating compelling and personalized marketing materials.

Generative AI has shown the potential to automate routine tasks, enhance risk mitigation, and optimize financial operations. In the healthcare industry, gen AI is used to analyze medical images and assist doctors in making diagnoses. According to a report by the World Health Organization (WHO), up to 50% of all medical errors in primary care are administrative errors. Gen AI has potential to increase accuracy, but the technology also comes with vulnerabilities, as its trustworthiness depends heavily on the quality of training datasets, according to the World Economic Forum. 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.

Automating repetitive tasks allows human agents to devote more time to handling complicated customer problems and obtaining contextual information. Large technology companies are already selling generative AI for software engineering, including GitHub Copilot, which is now integrated with OpenAI’s GPT-4, and Replit, used by more than 20 million coders. 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.

A study by the World Economic Forum found that adopting AI could lead to a net increase in jobs in some industries, particularly those that require higher levels of education and skills. However, the report also warned that the benefits of AI could be unevenly distributed, with some workers and regions experiencing more significant job displacement than others. 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.

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. 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. Four months later, OpenAI released a new large language model, or LLM, called GPT-4 with markedly improved capabilities.

Applying generative AI to such activities could be a step toward integrating applications across a full enterprise. Generative AI has the potential to revolutionize the entire customer operations function, improving the customer experience and agent productivity through digital self-service and enhancing and augmenting agent skills. The technology has already gained traction in customer service because of its ability to automate interactions with customers using natural language. Crucially, productivity and quality of service improved most among less-experienced agents, while the AI assistant did not increase—and sometimes decreased—the productivity and quality metrics of more highly skilled agents.

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”). 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.