THE ECONOMIC POTENTIAL OF GENERATIVE AI: THE INNOVATIVE PRODUCTIVITY AREA INTRODUCTION

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. All of us are at the beginning of a journey to understand generative AI’s power, reach, and capabilities.

In the past year, organizations using AI most often hired data engineers, machine learning engineers, and Al data scientists—all roles that respondents commonly reported hiring in the previous survey. But a much smaller share of respondents report hiring AI-related-software engineers—the most-hired role last year—than in the previous survey (28 percent in the latest survey, down from 39 percent). Roles in prompt engineering have recently emerged, as the need for that skill set rises alongside gen AI adoption, with 7 percent of respondents whose organizations have adopted AI reporting those hires in the past year. Technology can replace some tasks, but it can also make us more productive performing other tasks, and create new tasks — and new jobs. [Roughly] two-thirds of US occupations are exposed to some degree of automation by AI, and that of those occupations which are exposed, most have a significant—but partial—share of their workload (25-50%) that can be replaced.

How can public sector entities begin to transform their own service delivery?

History suggests that technological advancements lead to the creation of new jobs and long-term economic growth, including the development of roles that can’t even be imagined today. Across Asia, the goal should be to ensure these opportunities are equitably distributed, along with investments to ensure the workforce is adequately prepared. The technology can also streamline class preparation and curriculum planning, enabling teachers to create personalized learning experiences based on an algorithmic analysis of student learning patterns and preferences. According to Access Partnership, this application of generative AI will lead to especially significant reprioritization of work activities for teachers in areas such as biological sciences, nursing, physics, geography, architecture, and computer science.

We’ll be in New York on February 29 in partnership with Microsoft to discuss how to balance risks and rewards of AI applications. In the transportation industry, self-driving vehicles are powered by generative AI, enabling them to navigate roads and make real-time decisions. 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.

THE ECONOMIC POTENTIAL OF GENERATIVE AI: THE INNOVATIVE PRODUCTIVITY AREA

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). We take a first look at where business value could accrue and the potential impacts on the workforce. AI has been imbibed in 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.

By improving the quality and effectiveness of interactions via automated channels, generative AI could automate responses to a higher percentage of customer inquiries, enabling customer care teams to take on inquiries that can only be resolved by a human agent. Our research found that roughly half of the customer contacts made by banking, telecommunications, and utility companies in North America are already handled by machines, including but not exclusively AI. We estimate that generative AI could further reduce the volume of human-serviced contacts by up to 50 percent, depending on a company’s existing level of automation.

Why Generative AI Could Have a Huge Impact on Economic Growth and Productivity

Research from Rem Koning and Rowan Clark, along with colleagues Nicholas G. Otis, Solène Delacourt, and David Holtz from Berkely Haas, delves into the varied effects of Generative AI on the performance of small businesses in Kenya. It explores the differential impact of AI advice on entrepreneurs with varying levels of business success. There is a growing belief that scalable and low-cost AI assistance can improve firmdecision-making and economic performance. However, running a business involvesa myriad of open-ended problems, making it hard to generalize from recent studiesshowing that generative AI improves performance on well-defined writing tasks. Here’s what three of our experts— Alexander Sukharevsky, senior partner and global coleader of QuantumBlack, AI by McKinsey, Michael Chui, and Lareina Yee, a McKinsey senior partner, have learned from the research and from early adopters. Overall, McKinsey views GenAI as a “technology catalyst,” pushing industries further along toward automation journeys, but also freeing up the creative potential of employees.

• It’s been an eye-opening ride from the early days when AI breakthroughs were big news until now when AI is changing how we do business.
• At the same time, they face the heavy burden of monitoring the technology’s downsides and establishing robust guidelines and regulations for its use.
• Growing demand for GenAI use will create mounting pressure to bring down the cost of inference.
• Generalist foundation models will remain in the hands of a handful of very large and powerful tech players because of their extraordinary scale and cost.
• In software engineering, McKinsey sees the technology speeding up the process of “generating initial code drafts, code correction and refactoring, root-cause analysis and generating new system designs,” resulting in a 20 to 45% increased productivity on software spending.

This is because of generative AI’s ability to predict patterns in natural language and use it dynamically. 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. 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.

Companies and business leaders

For one thing, gen AI has been known to produce content that’s biased, factually wrong, or illegally scraped from a copyrighted source. Before adopting gen AI tools wholesale, organizations should reckon with the reputational and legal risks to which they may become exposed. Keep a human in the loop; that is, make sure a real the economic potential of generative ai human checks any gen AI output before it’s published or used. For most of the technical capabilities shown in this chart, gen AI will perform at a median level of human performance by the end of this decade. And its performance will compete with the top 25 percent of people completing any and all of these tasks before 2040.

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. 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. 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.

▶ Fusion power is coming back into fashion – The Economist | Moreover, even if a practical machine does emerge, it will have to find its niche. The story told by the companies is of supplying “baseline” power in support of intermittent sources such as solar and wind—and doing so in a way that avoids the widespread public fear of an otherwise-obvious alternative, nuclear fission. That might work, but it will also have to be cheaper than other alternatives, such as grid-scale energy-storage systems.

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). 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. 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”).

Generative AI can give you “superpowers,” new McKinsey research finds

2023, when we look back, could be the year when this really did hit the new normal and the new balance of in-person versus remote and how we manage those models. We conclude with a suggested eight-step plan for government organizations that are just beginning to implement gen AI use cases. On the other hand, it could provide a time surplus that may be used to create a different work-life balance—it could even be the start of the four-day work week. Developed by Alex Krizhevsky and Ilya Sutskever, who is now a big name at OpenAI, this tool was a game-changer. It made computer vision practical—not just a theory—laying the groundwork for the AI we use today.

A new wave of AI systems may also have a major impact on employment markets around the world. Shifts in workflows triggered by these advances could expose the equivalent of 300 million full-time jobs to automation, Briggs and Kodnani write. It can also enhance performance visibility across business units by integrating disparate data sources.

• 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.
• 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.
• We find that generative AI has the opposite pattern—it is likely to have the most incremental impact through automating some of the activities of more-educated workers (Exhibit 12).

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.