{"id":25510,"date":"2023-09-02T13:47:41","date_gmt":"2023-09-02T13:47:41","guid":{"rendered":"https:\/\/wealinternational.com.br\/?p=25510"},"modified":"2023-11-28T13:48:40","modified_gmt":"2023-11-28T13:48:40","slug":"the-economic-potential-of-generative-ai-the-next-productivity-frontier-by-mckinsey","status":"publish","type":"post","link":"https:\/\/wealinternational.com.br\/en\/the-economic-potential-of-generative-ai-the-next-productivity-frontier-by-mckinsey\/","title":{"rendered":"The economic potential of generative AI: The next productivity frontier (By Mckinsey)"},"content":{"rendered":"<p><strong>&#8220;I has permeated our lives<\/strong>\u00a0incrementally, through everything from the tech powering our smartphones to autonomous-driving features on cars to the tools retailers use to surprise and delight consumers. As a result, its progress has been almost imperceptible. 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\u2019s consciousness.<\/p>\n<p>Generative AI applications such as ChatGPT, GitHub Copilot, Stable Diffusion, and others have captured the imagination of people around the world in a way AlphaGo did not, thanks to their broad utility\u2014almost anyone can use them to communicate and create\u2014and preternatural ability to have a conversation with a user. The latest generative AI applications can perform a range of routine tasks, such as the reorganization and classification of data. 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\u2019s impact on business and society but without much context to help them make sense of it.<\/p>\n<div class=\"mck-c-inline-module-container SideBar_mck-c-sidebar__bgimg-wrapper__Tq5_s mck-o-sm-left-span SideBar_mck-c-sidebar__sidebar-wrapper__0Rkai SideBar_mck-c-sidebar__sidebar-wrapper--istablet__682yw mck-u-screen-only mck-c-module-wrapper\" data-module-theme=\"light\" data-module-background=\"lightest-grey\" data-module-category=\"\" data-module-gradient-position=\"bottom-right\" data-layer-region=\"sidebar\" data-testid=\"sidebar-collapsible\">\n<div class=\"SideBar_mck-c-sidebar__dN_sz mck-o-md-center\">\n<div class=\"SideBar_mck-c-sidebar__share-icons-wrapper__r9lUC\"><\/div>\n<div class=\"SideBar_mck-c-sidebar__content-outer__T19Gd\">\n<div class=\"SideBar_mck-c-sidebar__content__nFs_D\"><\/div>\n<\/div>\n<\/div>\n<\/div>\n<p>The speed at which generative AI technology is developing isn\u2019t making this task any easier. ChatGPT was released in November 2022. Four months later, OpenAI released a new large language model, or LLM, called GPT-4 with markedly improved capabilities.<span class=\"FootNote_footnote-holder__7oYio\"><a class=\"FootNote_footnote-wrapper__jPMLi undefined \" data-testid=\"article-footnote\" aria-label=\"footnote\" aria-describedby=\"17618bae-0d3d-44f1-94d2-53bc72a11897\"><sup class=\"FootNote_footnotesup__J92bA\">1<\/sup><\/a><\/span>\u00a0Similarly, by May 2023, Anthropic\u2019s generative AI, Claude, was able to process 100,000 tokens of text, equal to about 75,000 words in a minute\u2014the length of the average novel\u2014compared with roughly 9,000 tokens when it was introduced in March 2023.<span class=\"FootNote_footnote-holder__7oYio\"><a class=\"FootNote_footnote-wrapper__jPMLi FootNote_right__Y5VuZ \" data-testid=\"article-footnote\" aria-label=\"footnote\" aria-describedby=\"4a3d1322-f297-43a6-a380-ac369d2c7034\"><sup class=\"FootNote_footnotesup__J92bA\">2<\/sup><\/a><\/span>\u00a0And 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.<span class=\"FootNote_footnote-holder__7oYio\"><a class=\"FootNote_footnote-wrapper__jPMLi undefined \" data-testid=\"article-footnote\" aria-label=\"footnote\" aria-describedby=\"7fdf533f-49cc-47d2-9237-af9a5a774245\"><sup class=\"FootNote_footnotesup__J92bA\">3<\/sup><\/a><\/span><\/p>\n<p>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. Foundation models are part of what is called deep learning, a term that alludes to the many deep layers within neural networks. 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.<\/p>\n<p>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.<\/p>\n<p>All of us are at the beginning of a journey to understand generative AI\u2019s power, reach, and capabilities. This research is the latest in our efforts to assess the impact of this new era of AI. It suggests that generative AI is poised to transform roles and boost performance across functions such as sales and marketing, customer operations, and software development. In the process, it could unlock trillions of dollars in value across sectors from banking to life sciences. The following sections share our initial findings.<\/p>\n<p>For the full version of this report, \u00a0<a href=\"https:\/\/www.mckinsey.com\/capabilities\/mckinsey-digital\/our-insights\/the-economic-potential-of-generative-AI-the-next-productivity-frontier#\/download\/%2F~%2Fmedia%2Fmckinsey%2Fbusiness%20functions%2Fmckinsey%20digital%2Four%20insights%2Fthe%20economic%20potential%20of%20generative%20ai%20the%20next%20productivity%20frontier%2Fthe-economic-potential-of-generative-ai-the-next-productivity-frontier-vf.pdf\" target=\"_blank\" rel=\"noopener\">download the PDF<\/a>.<\/p>\n<h2><\/h2>\n<h2 class=\"mdc-c-heading___0fM1W_69ef06e SectionHeader_mck-c-section-header__heading__UzdNE\">Key insights<\/h2>\n<p><strong><em>Generative AI\u2019s impact on productivity could add trillions of dollars in value to the global economy.<\/em><\/strong>\u00a0Our 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\u2014by comparison, the United Kingdom\u2019s entire GDP in 2021 was $3.1 trillion. This would increase the impact of all artificial intelligence by 15 to 40 percent. 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.<\/p>\n<p><strong><em>About 75 percent of the value that generative AI use cases could deliver falls across four areas: Customer operations, marketing and sales, software engineering, and R&amp;D.<\/em><\/strong>\u00a0Across 16 business functions, we examined 63 use cases in which the technology can address specific business challenges in ways that produce one or more measurable outcomes. Examples include generative AI\u2019s 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.<\/p>\n<p><strong><em>Generative AI will have a significant impact across all industry sectors.<\/em><\/strong>\u00a0Banking, high tech, and life sciences are among the industries that could see the biggest impact as a percentage of their revenues from generative AI. 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.<\/p>\n<p><strong><em>Generative AI has the potential to change the anatomy of work, augmenting the capabilities of individual workers by automating some of their individual activities.<\/em><\/strong>\u00a0Current generative AI and other technologies have the potential to automate work activities that absorb 60 to 70 percent of employees\u2019 time today. In contrast, we previously estimated that technology has the potential to automate half of the time employees spend working.<span class=\"FootNote_footnote-holder__7oYio\"><a class=\"FootNote_footnote-wrapper__jPMLi undefined \" data-testid=\"article-footnote\" aria-label=\"footnote\" aria-describedby=\"abc9f571-a8ef-4299-ad69-5d862fc90b2a\"><sup class=\"FootNote_footnotesup__J92bA\">4<\/sup><\/a><\/span>\u00a0The acceleration in the potential for technical automation is largely due to generative AI\u2019s increased ability to understand natural language, which is required for work activities that account for 25 percent of total work time. Thus, generative AI has more impact on knowledge work associated with occupations that have higher wages and educational requirements than on other types of work.<\/p>\n<p><strong><em>The pace of workforce transformation is likely to accelerate, given increases in the potential for technical automation.<\/em><\/strong>\u00a0Our updated adoption scenarios, including technology development, economic feasibility, and diffusion timelines, lead to estimates that half of today\u2019s work activities could be automated between 2030 and 2060, with a midpoint in 2045, or roughly a decade earlier than in our previous estimates.<\/p>\n<p><strong><em>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.<\/em><\/strong>\u00a0Generative AI could enable labor productivity growth of 0.1 to 0.6 percent annually through 2040, depending on the rate of technology adoption and redeployment of worker time into other activities. Combining generative AI with all other technologies, work automation could add 0.2 to 3.3 percentage points annually to productivity growth. However, workers will need support in learning new skills, and some will change occupations. If worker transitions and other risks can be managed, generative AI could contribute substantively to economic growth and support a more sustainable, inclusive world.<\/p>\n<p><strong><em>The era of generative AI is just beginning.<\/em><\/strong> Excitement over this technology is palpable, and early pilots are compelling. But a full realization of the technology\u2019s 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.&#8221;<\/p>\n<p>Source: <a href=\"https:\/\/www.mckinsey.com\/capabilities\/mckinsey-digital\/our-insights\/the-economic-potential-of-generative-AI-the-next-productivity-frontier#key-insights\">https:\/\/www.mckinsey.com\/capabilities\/mckinsey-digital\/our-insights\/the-economic-potential-of-generative-AI-the-next-productivity-frontier#key-insights<\/a><\/p>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<p>.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>&#8220;I has permeated our lives\u00a0incrementally, through everything from the tech powering our smartphones to autonomous-driving features on cars to the tools retailers use to surprise and delight consumers. As a result, its progress has been almost imperceptible. Clear milestones, such as when AlphaGo, an AI-based program developed by DeepMind, defeated a world champion Go player [&hellip;]<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"","_et_pb_old_content":"","_et_gb_content_width":"","footnotes":""},"categories":[17,16],"tags":[38,22,32,43],"_links":{"self":[{"href":"https:\/\/wealinternational.com.br\/en\/wp-json\/wp\/v2\/posts\/25510"}],"collection":[{"href":"https:\/\/wealinternational.com.br\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/wealinternational.com.br\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/wealinternational.com.br\/en\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/wealinternational.com.br\/en\/wp-json\/wp\/v2\/comments?post=25510"}],"version-history":[{"count":1,"href":"https:\/\/wealinternational.com.br\/en\/wp-json\/wp\/v2\/posts\/25510\/revisions"}],"predecessor-version":[{"id":25511,"href":"https:\/\/wealinternational.com.br\/en\/wp-json\/wp\/v2\/posts\/25510\/revisions\/25511"}],"wp:attachment":[{"href":"https:\/\/wealinternational.com.br\/en\/wp-json\/wp\/v2\/media?parent=25510"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/wealinternational.com.br\/en\/wp-json\/wp\/v2\/categories?post=25510"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/wealinternational.com.br\/en\/wp-json\/wp\/v2\/tags?post=25510"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}