The economic potential of generative AI: The next productivity frontier
Generative AI possesses the power to create human-like content instantaneously, unlocking new levels of productivity across various sectors of our economy. As this technology develops, I believe it will continue to empower the transcendence of previous capabilities. The era of generative AI is just beginning, and fully realizing the enormous benefits of the technology will take time. But business leaders should begin implementing generative AI use cases as soon as possible rather than waiting on the sidelines as the performance gap between laggards and early adopters will widen quickly.
- What that translates to is an addition of “0.2 to 3.3 percentage points annually to productivity growth” to the entire global economy, he said.
- One European bank has leveraged generative AI to develop an environmental, social, and governance (ESG) virtual expert by synthesizing and extracting from long documents with unstructured information.
- 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.
- 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.
- Numerous case studies and reports have pointed to AI’s impact on various industries, the economy, and the workforce.
With its ability to leverage vast amounts of data and predict outcomes, AI can significantly improve decision making, optimize production, enhance product quality, and reduce waste. Tools — which exploded onto the tech scene late last year — accelerated the company’s forecast. The report from McKinsey comes as a debate rages over the potential economic effects of A.I.-powered chatbots on labor and the economy.
Factors for retail and CPG organizations to consider
Now, the generative AI market is expected to grow from $40 billion in 2022 to $1.3 trillion over the next 10 years. In this article, I aim to demystify how generative AI constitutes a distinct revolution and explore the prospective economic impacts of deploying this technology across diverse sectors. Generative A.I., which includes chatbots such as ChatGPT that can generate text in response to prompts, can potentially boost productivity by saving 60 to 70 percent of workers’ time through automation of their work, according to the 68-page report, which was published early Wednesday.
To comprehend what lies ahead needs a consideration of the discoveries that have enabled the rise of generative AI, which were decades in the making. For the purposes of this report, we describe generative AI as claims typically built using foundation models. These models contain expansive artificial neural networks inspired by the billions of neurons connected in the human brain. Foundation the economic potential of generative ai models are part of what is called DL, a term that indicates the many deep layers within neural networks. DL has powered many of the recent advances in AI, but the foundation models powering generative AI applications are a step-change evolution within DL. Unlike previous DL models, they can process extremely large and varied sets of unstructured data and perform more than one task.
How can public sector entities begin to transform their own service delivery?
To stay on top of the talent market, organizations should develop excellent talent management capabilities, delivering rewarding working experiences to the gen AI–literate workers they hire and hope to retain. “This includes increasing the level of productivity through direct efficiency gains as well as accelerating the rate of innovation and future productivity growth,” Korinek says. Numerous case studies and reports have pointed to AI’s impact on various industries, the economy, and the workforce. For example, generative AI can help retailers with inventory management and customer service, both cost concerns for store owners. Gen AI can also help retailers innovate, reduce spending, and focus on developing new products and systems.
Nearly all industries will see the most significant gains from deployment of the technology in their marketing and sales functions. But high tech and banking will see even more impact via gen AI’s potential to accelerate software development. Banking, a knowledge and technology-enabled industry, has already benefited significantly from previously existing applications of artificial intelligence in areas such as marketing and customer operations.1“Building the AI bank of the future,” McKinsey, May 2021. For example, the life sciences and chemical industries have begun using generative AI foundation models in their R&D for what is known as generative design. Foundation models can generate candidate molecules, accelerating the process of developing new drugs and materials.
Responses show many organizations not yet addressing potential risks from gen AI
The technology could also monitor industries and clients and send alerts on semantic queries from public sources. The model combines search and content creation so wealth managers can find and tailor information for any client at any moment. Generative AI could have a significant impact on the banking industry, generating value from increased productivity of 2.8 to 4.7 percent of the industry’s annual revenues, or an additional $200 billion to $340 billion.
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. We also surveyed experts in the automation of each of these capabilities to estimate automation technologies’ current performance level against each of these capabilities, as well as how the technology’s performance might advance over time. Specifically, this year, we updated our assessments of technology’s performance in cognitive, language, and social and emotional capabilities based on a survey of generative AI experts.
A waterfall graph shows the potential additional value that could be added to the global economy by new generative AI uses cases. An initial $11.0 trillion–$17.7 trillion could come from advanced analytics, traditional machine learning, and deep learning. And additional $2.6 trillion–$4.4 trillion of incremental economic impact could be added from new generative AI use cases, resulting in a total use-case-driven potential of $13.6 trillion–$22.1 trillion. Previous generations of automation technology often had the most impact on occupations with wages falling in the middle of the income distribution. For lower-wage occupations, making a case for work automation is more difficult because the potential benefits of automation compete against a lower cost of human labor.
Bloomberg leveraged the open-source LLM BLOOM to build BloombergGPT with less than one third the number of parameters. To thrive in a world of generative AI, people will have to apply the technology across a range of situations and work tasks. In both India and the Philippines, there are important initiatives underway to improve digital literacy across the whole population. When it comes to the ability to generate, arrange, and analyze content, generative AI is a gamechanger—one with transformative social and economic potential.
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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. In this section, we highlight the value potential of generative AI across business functions. Our updates examined use cases of generative AI—specifically, how generative AI techniques (primarily transformer-based neural networks) can be used to solve problems not well addressed by previous technologies. There is still a portion of the workforce saying, “If we’re required to come in more, we’ll leave.” Even if that number is 10 percent, 10 percent of folks with a scarce skill can make the difference between a successful product launch and an unsuccessful one.