Generative AI: the economic value potential and the next productivity frontier by Martina Caronte Jan, 2024

Our estimate of the technical potential to automate the application of expertise jumped 34 percentage points, while the potential to automate management and develop talent increased from 16 percent in 2017 to 49 percent in 2023. 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). 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.

A larger challenge will be addressing the uneven effects of the new technologies, both within and between countries. Within countries, productivity growth is likely to be concentrated in white-collar jobs rather than blue-collar jobs because of generative AI’s particular impact on the knowledge economy. To achieve a similar productivity surge in the industrial economy, however, will require additional major advances in robotics. Despite good progress on that front, technological challenges remain, with the result that automation and augmentation in manufacturing, logistics, and autonomous vehicles are proceeding more slowly.

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Their estimate is that these technologies could add $2.6 trillion to $4.4 trillion annually to the global economy; for perspective, total world GDP is about $100 trillion. We’re at the dawn of the generative AI era which holds immense potential for transforming roles, enhancing performance across various sectors, and could generate trillions of dollars in value. However, this technology also poses certain challenges, including risk management, determining future workforce skills, and rethinking business processes such as skills development and retraining. McKinsey & Company’s ongoing research aims to comprehend and gauge the influence of this transformative AI. Generative AI’s impact on productivity could add trillions of dollars in value to the global economy.

This shows how such a technology could make time to perform more critical actions, which could lead to not only improved productivity, but also an increase in revenue. The report indicated that these estimates are based on 16 business functions with 63 use cases that have been evaluated. The aforementioned figure could even double if the potential of generative AI is embedded in software utilized by industries other than those that have been assessed. Generative AI, a subset of artificial intelligence, is revolutionizing the way machines learn and create. Unlike traditional AI, which relies on predefined rules, generative AI has the ability to generate new, original content.

Internationally, the recent breakthroughs and innovations in AI have clearly been led by the United States, with China in second place. These two countries are also home to the AI platform companies with enough computing power to train advanced LLMs. By contrast, the European Union has fallen behind the United States and China in AI, cloud computing, and other related areas. The question, then, is how quickly advanced AI applications can be implemented throughout the global economy. Under the open model that prevailed for several decades after World War II, technology could spread quite rapidly across borders.

In June 2023 alone, the ChatGPT website was visited by 1.6 billion users, a convincing signal of the low barrier to entry and the breadth of interest in the technology. In this context, the McKinsey & Company report indicated that generative AI can achieve this feat by automating tasks that consume 60% to 70% of the employees’ workday. 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.

McKinsey & Company

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.

With the acceleration in technical automation potential that generative AI enables, our scenarios for automation adoption have correspondingly accelerated. These scenarios encompass a wide range of outcomes, given that the pace at which solutions will be developed and adopted will vary based on decisions that will be made on investments, deployment, and regulation, among other factors. But they give an indication of the degree to which the activities that workers do each day may shift (Exhibit 8). Generative AI is poised to impact various industries, with banking, high tech, and life sciences expected to experience significant transformations. McKinsey identifies customer operations/service, marketing and sales, software engineering, and R&D as the most valuable business functions likely to benefit from generative AI. In reality, generative AI can substantially increase labor productivity across the economy, but that will require investments to support workers as they shift work activities or change jobs.

But the specific manner of using the technology still mostly involved using a more-or-less intuitive piece of software or an app. We are now moving into a space where it will be possible to use the technology with “natural language”–not just for searching on a map, but for a range of less structured tasks. To my previous point about skills development in the 21st century, I think it behooves us all to learn as much as we can to ensure future employment and improve our prospects around that employment. You will examine three fundamental forces that enable AI in marketing strategies – Algorithms, Networks, and Data – and gain a deeper understanding of how businesses in various industries can get the most out of this exciting technology.

In contrast, one foundation model can perform both of these functions and generate content as well. Foundation models amass these capabilities by learning patterns and relationships from the broad training data they ingest, which, for example, enables them to predict the next word in a sentence. The report predicts that half of today’s work activities could be automated between 2030 the economic potential of generative ai and 2060, with the midpoint of 2045 expected to arrive a decade earlier than McKinsey’s previous estimates. The tools — some of which can also generate images and video, and carry on a conversation — have started a debate over how they will affect jobs and the world economy. Will displace people from their work, while others have said the tools can augment individual productivity.

Thus, significant human oversight is required for conceptual and strategic thinking specific to each company’s needs. Despite the excitement over this technology, a full realization of the technology’s benefits will take time, and leaders in business and society still have considerable challenges to address. The McKinsey’s updated adoption scenarios, lead to estimates that half of today’s work activities could be automated between 2030 and 2060, with a midpoint in 2045, or roughly a decade earlier than in our previous estimates. [Deep learning models] can, for example, either classify objects in a photo or perform another function such as making a prediction.

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).

The technology enables businesses to automate content creation, from writing compelling articles to designing engaging visuals. With personalized content becoming increasingly important, generative AI algorithms can analyze user preferences and deliver tailor-made experiences. This level of customization not only enhances user satisfaction but also drives customer loyalty and revenue growth.

While this could lead to job displacement, the report also noted that just because AI could automate a job doesn’t necessarily mean that it will, as cost, regulations, and social acceptance can also be limiting factors. For one thing, mathematical models trained on publicly available data without sufficient safeguards against plagiarism, copyright violations, and branding recognition risks infringing on intellectual property rights. A virtual try-on application may produce biased representations of certain demographics because of limited or biased training data.

A newly developed decision-making AI data mineral exploration system

The AI component could then produce a summary of its findings for review by the medical staff. According to some estimates, doctors currently spend about a third of their time writing up reports and the decisions made; such a system could reduce that time by up to 80 percent. In countries that account for over 75 percent of global economic output, aging populations have limited the growth of the labor supply, increasing dependency ratios—the number of nonworkers relative to the working-age population in a given country—and creating fiscal stress.

But policymakers must be diligent in creating rules that ensure that such competition results in broad diffusion and use of the technologies. Activities such as bookkeeping, filing, and accounting, much of consumer banking, and the control systems for entire supply chains were partially and sometimes completely automated. In parallel, most information came to be stored and transmitted in digital form, making it cheaper and easier to access and use. An abundance of free and low-cost web-based services also transformed the consumer economy and social interaction. After all, digital technologies have been transforming the economy in measurable ways for at least three decades. One explanation for the excitement is that unlike earlier digital innovations, the AI revolution has extended the impact of digital technologies well beyond so-called codifiable work—routine tasks that can be reduced to a precise series of instructions.

This surge in job creation is a positive driver for economic growth, fostering a workforce that is adaptive to the evolving technological landscape. As we stand on the cusp of a new year, the buzz surrounding generative artificial intelligence (AI) is reaching a crescendo. The year 2024 promises to be a groundbreaking period for businesses and economies worldwide, as the economic potential of generative AI takes center stage. In this blog post, we will explore the transformative power of generative AI and its potential to reshape industries, drive innovation, and fuel economic growth. These tools have the potential to create enormous value for the global economy at a time when it is pondering the huge costs of adapting and mitigating climate change. At the same time, they also have the potential to be more destabilizing than previous generations of artificial intelligence.

The National Fourth Industrial Revolution (4IR) Policy document identifies AI as one of the key technologies that ‘’are foundational to the nation’s 4IR agenda’’ and stresses the need to develop ethical use of AI for transforming the economy. To guide AI deployment and governance, the government also published a National AI Roadmap in 2021, aimed at making Malaysia a nation where AI enhances jobs, drives competitiveness, encourages innovation and entrepreneurship, and improves people’s well-being. The pharmaceutical industry is facing high costs, long timelines, and low success rates in drug discovery and development. Generative AI can help pharmaceutical companies accelerate the process by creating novel molecules that have desired properties and effects. For example, generative AI can generate chemical structures that are likely to bind to specific targets or receptors in the human body.

Such a divergence in productivity growth between the knowledge economy, the wide service sector, and industrial sectors could further contribute to unequal distribution of AI gains. Its tech stack, consisting of data extraction, data analysis, natural language processing (NLP), and natural language generation (NLG) tools, all seamlessly work together to produce content quickly and at scale. In this way, Narrativa supports the growth of businesses across a variety of industries, while also saving them both time and money. For example, our analysis estimates generative AI could contribute roughly $310 billion in additional value for the retail industry (including auto dealerships) by boosting performance in functions such as marketing and customer interactions. By comparison, the bulk of potential value in high tech comes from generative AI’s ability to increase the speed and efficiency of software development (Exhibit 5). The field of artificial intelligence (AI) has seen significant advancements in recent years, particularly in the area of generative AI.

Generative A.I. Can Add $4.4 Trillion in Value to Global Economy, Study Says

Companies, policy makers, consumers, and citizens can work together to ensure that generative AI delivers on its promise to create significant value while limiting its potential to upset lives and livelihoods. 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). 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.

Generative AI refers to AI models that have the capability to create new content, such as text, images, or even videos. This technology has the potential to revolutionize various industries and drive economic growth. In a recent report by McKinsey, titled “The Economic Impact of Generative AI,” the authors explore the potential economic benefits and implications of this emerging technology. In addition, jobs displaced by automation have historically been offset by the creation of new jobs, and the emergence of new occupations following technological innovations accounts for the vast majority of long-run employment growth, according to the report. For example, information-technology innovations introduced new occupations such as webpage designers, software developers and digital marketing professionals.

The complex and increasingly restrictive constraints on flows of technology and capital—whether from the war in Ukraine, sanctions, or rising tensions between China and the United States—have created new barriers to international diffusion. As numerous examples have shown, generative AI platforms occasionally get things wrong or hallucinate—that is, make things up. For example, an LLM given a prompt to write an article on inflation not only produced the article but concluded with a list of additional reading that included five articles and books that do not exist. Obviously, in applications that require factual accuracy, made-up answers pose a major concern. Even when not hallucinating, LLMs can produce bad, seriously biased, silly, or obnoxious predictions that require human review.

Our latest research estimates that generative AI could add the equivalent of $2.6 trillion to $4.4 trillion annually across the 63 use cases we analyzed—by comparison, the United Kingdom’s entire GDP in 2021 was $3.1 trillion. This 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. Given its remarkable capabilities and range, where will the main economic impact of generative AI occur? When Sundar Pichai, the CEO of Alphabet, Google’s parent company, was asked a version of this question, he responded that it would come in the “knowledge economy.” This seems exactly right. One could substitute the term “information economy,” but across fields from scientific research to software development and a host of service functions, the potential economic benefits of LLM-based applications seem extremely large. 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 most valuable business functions

But a full realization of the technology’s benefits will take time, and leaders in business and society still have considerable challenges to address. These include managing the risks inherent in generative AI, determining what new skills and capabilities the workforce will need, and rethinking core business processes such as retraining and developing new skills. 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. Researchers examined 63 use cases across 16 business functions in which the technology can address specific business challenges in ways that produce one or more measurable outcomes. 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.

You can foun additiona information about ai customer service and artificial intelligence and NLP. Occupations in these fields will not go away, but they will require new skills as people do their jobs in collaboration with capable machines. 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.

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”).

• The economic impact of such collaborations is not only cultural but extends to new revenue streams and market opportunities.
• To guide AI deployment and governance, the government also published a National AI Roadmap in 2021, aimed at making Malaysia a nation where AI enhances jobs, drives competitiveness, encourages innovation and entrepreneurship, and improves people’s well-being.
• Under the open model that prevailed for several decades after World War II, technology could spread quite rapidly across borders.
• Occupations in these fields will not go away, but they will require new skills as people do their jobs in collaboration with capable machines.

It can also generate synthetic data that can augment existing data sets and improve the accuracy of predictive models. From enhancing customer experiences to automating tasks, AI is creating new value for businesses across various sectors. One of the most promising and exciting developments in AI is the emergence of generative AI, which can create novel content such as text, images, music, and code from large and diverse data sources. With its broad scope and its ease of use, generative AI could do much to counter these forces. Moreover, the AI revolution has unleashed an intense period of experimentation and innovation that could add much more value to the economy. Governments, companies, and researchers will need to prioritize augmenting human skills rather than replacing them.

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.

In other cases, generative AI can drive value by working in partnership with workers, augmenting their work in ways that accelerate their productivity. Its ability to rapidly digest mountains of data and draw conclusions from it enables the technology to offer insights and options that can dramatically enhance knowledge work. This can significantly speed up the process of developing a product and allow employees to devote more time to higher-impact tasks.

It holds the potential for a digitally enabled surge in productivity that could restore growth momentum by easing the supply-side constraints—especially the shrinking labor pool in many countries—that have been holding the global economy back. It must be driven primarily by value-added growth, in which firms and sectors expand value-added output, thereby contributing to a rise in GDP, rather than simply by reducing inputs, such as labor, while keeping the growth in output weak or flat. According to the McKinsey & Company report, the impact of generative AI on productivity could result in gains of up to $4.4 trillion per year. To put that in perspective, this is about 1/5 of the United States gross domestic product (GDP) during the year 2022, based on numbers from the World Bank.

Gen AI is expected to help address this shortage through increased efficiency, allowing fewer workers to serve more patients. 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.

For example, they will produce first drafts in media and marketing applications and produce much of the basic code needed for a variety of programming, thus dramatically speeding up the work of advanced-software developers. In many professions, an AI system’s ability to absorb and process vast amounts of literature at superhuman speed will also accelerate both the pace and the dissemination of research and innovation. Because they are designed to respond to ordinary language and other ubiquitous inputs, LLMs can be readily used by nonspecialists who lack technical skills. At the same time, the models’ use of the vast material on the Internet or any other corpus for training means that they can acquire expertise in almost any field of knowledge. These two features give LLMs far more extensive potential uses than previous digital technologies, even those involving AI.

Just as the steam engine and the cotton gin revolutionized the 19th-century economy, AI and machine learning are set to redefine the 21st-century job market. There are concerns about job displacement due to automation, and these fears are not unfounded. However, history has shown us that while technology can render specific jobs obsolete, it also creates new ones in its wake.

“We identified 63 generative AI use cases spanning 16 business functions that could deliver total value in the range of $2.6 trillion to $4.4 trillion in economic benefits annually when applied across industries,” the report states. For reference, the entire GDP of the UK – the second strongest economy in Europe – is $3.1 trillion. For many industries, generative AI, trust, data security and improved digital experiences are key priorities to improve the overall customer experience. To learn more about the potential impact of generative AI on the economy, you can review the robust McKinsey report here.

Narrativa has experience across multiple industries, like life sciences, finance, marketing, entertainment, and many others. We have partnered with leading companies in these areas, like the Leukemia & Lymphoma Society (LLS), Microsoft, and the Wall Street Journal. 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. Labor economists have often noted that the deployment of automation technologies tends to have the most impact on workers with the lowest skill levels, as measured by educational attainment, or what is called skill biased.

It can also create audit trails, documentation, and evidence that demonstrate compliance with regulatory standards. The retail industry is undergoing a digital transformation, as consumers demand more convenience, choice, and personalization. Generative AI can help retailers meet these expectations by creating tailored content and recommendations for each customer, based on their preferences, behavior, and context. For example, generative AI can generate product descriptions, reviews, images, videos, and ads that are relevant and engaging for each shopper.

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.

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