Generative AI Could Raise Global GDP by 7%

But leaders of government agencies must be aware that this endeavor requires considerable investment of time and resources. The many barriers to entry include the availability of talent to build, train, and maintain gen AI models; the necessary computing power; and experience in addressing potential risks inherent in building and serving gen AI foundation models. Almost all current work in these models is led by a few large private sector tech companies (Cohere, Google, Meta, and others) and by open-source initiatives that are quickly becoming popular (such as Hugging Face, Stability AI, and Alpaca).

Multiple industries will be completely revamped by using AI to lower the barriers to entry, to disintermediate company structure, or to deliver the same value propositions at a fraction of the current cost. The way organizations define the opportunities and the execution speed will differentiate the future winners. Organizations continue to see returns in the business areas in which they are using AI, and

they plan to increase investment in the years ahead. We see a majority of respondents reporting AI-related revenue increases within each business function using AI. And looking ahead, more than two-thirds expect their organizations to increase their AI investment over the next three years.

Let’s start with the big question: What is McKinsey doing about generative AI right now?

The competitive advantage will go to the organizations that are first to use generative AI to accelerate their business priorities, innovations, and company growth. Based on developments in generative AI, technology performance is now expected to match median human performance and reach top-quartile human performance the economic potential of generative ai 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.

However, generative models use Natural Language Interfaces (NLIs) to interpret text as opposed to code. NLIs reduce the technical learning curve and widen the potential user base, empowering a much larger number of people to utilize the model effectively. Our analysis captures only the direct impact generative AI might have on the productivity of customer operations. Companies, policy makers, consumers, and citizens can work together to ensure that generative AI delivers on its promise to create significant value while limiting its potential to upset lives and livelihoods. The time to act is now.11The research, analysis, and writing in this report was entirely done by humans. 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).

Democratized Usability Via Natural Language Interfaces

Like many private sector organizations, government agencies face challenges with gen AI’s transparency and with the difficulty of explaining the conceptual underpinnings of gen AI, as well as the logic of the models’ decisions and output. Consequences might include low public acceptance of gen-AI-powered government services and unclear liability when unintended effects occur. And like all organizations, government entities run the risk that criminals may misuse gen AI to carry out powerful cybersecurity attacks.

Our analysis suggests that implementing generative AI could increase sales productivity by approximately 3 to 5 percent of current global sales expenditures. 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. We then estimated the potential annual value of these generative AI use cases if they were adopted across the entire economy. For use cases aimed at increasing revenue, such as some of those in sales and marketing, we estimated the economy-wide value generative AI could deliver by increasing the productivity of sales and marketing expenditures. Tools like ChatGPT, Midjourney and Stable Diffusion became mainstream, bringing AI into everyday conversations. The trend started with language models and is now expanding to visual AI tools, changing the landscape of various industries.

Gen AI implementations could streamline a broad range of services that governments typically provide, in areas such as education, healthcare, defense and intelligence, and urban development (see sidebar “Potential applications of gen AI in government functions and services”). Across all of those areas, we have seen government agencies implement gen AI use cases in both external and internal operations that fall within the categories of our framework (see Exhibits 3 and 4). For example, in customer-facing applications, gen AI can help the public navigate government services and get access to real-time language translation. Internally, gen AI can draft creative content such as speeches and official correspondence, simplify complex official documents, and consistently generate financial reports and KPIs on schedule. While the use of gen AI tools is spreading rapidly, the survey data doesn’t show that these newer tools are propelling organizations’ overall AI adoption.

Some government organizations have started ongoing awareness programs among stakeholders—especially end users—about gen AI’s risks and how to address them. For example, the United Kingdom’s Central Digital and Data Office has released a guide for civil servants on safe and informed use of gen AI tools. Similarly, Australia’s Digital Transformation Agency and its Department of Industry, Science and Resources provide interim guidance to government agencies on responsibly using publicly available gen AI platforms, with emphasis on ethical AI usage, security, and human oversight. One received standard business advice, while the other benefited from AI-generated guidance through a GPT-4 powered mentor. Intriguingly, businesses that were already performing well saw considerable improvement with AI assistance. In contrast, businesses that were not performing as well experienced a downturn when using AI advice.

Companies like OpenAI, Alphabet’s Google, and Meta continue to plough resources into generalist models of extraordinary power and size (so-called “foundation models”), and they will continue to be at the forefront of technological innovation in GenAI. Yet the driving economic force of the B2B GenAI industry is set to move “downstream,” towards smaller, more cost-efficient models tailored to specific business purposes. The impetus for this shift will be the growing demand for high-performing GenAI systems that are cheaper to use than the large language or multimodal models (LLMs and LMMs) of today, such as OpenAI’s GPT-4 or Google’s Gemini. Previous waves of automation technology mostly affected physical work activities, but gen AI is likely to have the biggest impact on knowledge work—especially activities involving decision making and collaboration. Professionals in fields such as education, law, technology, and the arts are likely to see parts of their jobs automated sooner than previously expected.

These smaller models can, collectively, decouple performance from the cost of inference, unleashing GenAI adoption at scale. A similar pattern is currently taking hold in the GenAI industry, and some of the major players are already anticipating what comes next. Open AI, for instance, has announced an app store of its own where new models will be marketed and purchased.

Unleashing Economic Growth: How Generative AI Is Shaping The Future Of Prosperity

Even when such a solution is developed, it might not be economically feasible to use if its costs exceed those of human labor. Additionally, even if economic incentives for deployment exist, it takes time for adoption to spread across the global economy. Hence, our adoption scenarios, which consider these factors together with the technical automation potential, provide a sense of the pace and scale at which workers’ activities could shift over time. The pace of workforce transformation is likely to accelerate, given increases in the potential for technical automation. Our updated adoption scenarios, including technology development, economic feasibility, and diffusion timelines, lead to estimates that half of today’s work activities could be automated between 2030 and 2060, with a midpoint in 2045, or roughly a decade earlier than in our previous estimates. Breakthroughs in generative artificial intelligence have the potential to bring about sweeping changes to the global economy, according to Goldman Sachs Research.

In the first two examples, it serves as a virtual expert, while in the following two, it lends a hand as a virtual collaborator. The survey results show that AI high performers—that is, organizations where respondents say at least 20 percent of EBIT in 2022 was attributable to AI use—are going all in on artificial intelligence, both with gen AI and more traditional AI capabilities. These organizations that achieve significant value from AI are already using gen AI in more business functions than other organizations do, especially in product and service development and risk and supply chain management. These organizations also are using AI more often than other organizations in risk modeling and for uses within HR such as performance management and organization design and workforce deployment optimization.

At the same time, they face the heavy burden of monitoring the technology’s downsides and establishing robust guidelines and regulations for its use. We estimate that about percent of the time people spend working has the theoretical potential to be transformed by a combination of generative AI with other technologies. In fact, we updated our models for the rate at which these technologies might be deployed, and in some scenarios, generative AI could accelerate the pace of adoption by a decade, compared to our previous estimates.

• Over the last six years, I’ve seen generative AI (GenAI) evolve from a niche idea to a major industry.
• But high tech and banking will see even more impact via gen AI’s potential to accelerate software development.
• We see a majority of respondents reporting AI-related revenue increases within each business function using AI.
• For instance, generative AI’s ability to identify leads and follow-up capabilities could uncover new leads and facilitate more effective outreach that would bring in additional revenue.
• Properly managing the workforce changes posed by generative AI could raise the global GDP by 7% in just 10 years.

When that innovation seems to materialize fully formed and becomes widespread seemingly overnight, both responses can be amplified. The arrival of generative AI in the fall of 2022 was the most recent example of this phenomenon, due to its unexpectedly rapid adoption as well as the ensuing scramble among companies and consumers to deploy, integrate, and play with it. All of us are at the beginning of a journey to understand this technology’s power, reach, and capabilities.

Deep learning has powered many of the recent advances in AI, but the foundation models powering generative AI applications are a step-change evolution within deep learning. Unlike previous deep learning models, they can process extremely large and varied sets of unstructured data and perform more than one task. Our latest survey results show changes in the roles that organizations are filling to support their AI ambitions.

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). To address those risks, many countries—such as the United States, Australia, and China—have launched initiatives to create frameworks of regulations and policies for AI, and some have expanded their existing AI regulations to explicitly include gen AI, too. The European Union is leading a global effort to build safeguards for any product or service that uses an AI system. The findings suggest that hiring for AI-related roles remains a challenge but has become somewhat easier over the past year, which could reflect the spate of layoffs at technology companies from late 2022 through the first half of 2023.