Economic potential of Generative AI EY India

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The economic potential of generative AI: The next productivity frontier

the economic potential of generative ai

In particular, our estimates of the primary value the technology could unlock do not include use cases for which the sole benefit would be its ability to use natural language. For example, natural-language capabilities would be the key driver of value in a customer service use case but not in a use case optimizing a logistics network, where value primarily arises from quantitative analysis. 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.

Gen AI is a good fit with finance because its strength—dealing with vast amounts of data—is precisely what finance relies on to function. In the healthcare industry, gen AI is used to analyze medical images and assist doctors in making diagnoses. According to a report by the World Health Organization (WHO), up to 50% of all medical errors in primary care are administrative errors. Gen AI has potential to increase accuracy, but the technology also comes with vulnerabilities, as its trustworthiness depends heavily on the quality of training datasets, according to the World Economic Forum.

for existing Companies

“Skilling programs exist today in pockets across Asia, but too many people are severely underserved because of race, gender, geography, displacement, or other barriers,” Mazhari says. The fact that 61% of students do not receive any digital literacy education at school in ASEAN countries means it is imperative that swift action is taken now—to ensure this technology’s economic impact in the future. The first example is banking, with an estimated total value per industry the economic potential of generative ai of $200 billion to $340 billion, and a value potential increase of 9–15% of operating profits based on average profitability of selected industries in the 2020–22 period. The second example is retail and consumer packaged goods, including auto retail, with an estimated total value per industry of $400 billion–$660 billion, and a value potential increase of 27–44% of operating profits based on average profitability of selected industries in the 2020–22 period.

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. At the same time, demand for smaller, specialized applications will unleash the innovative potential of GenAI’s modular architecture. Consumer companies can leverage Internet-of-things (IoT) datasets to build specialized models for product design; businesses with complex supply chains can capitalize on their logistics data to develop solutions for third parties. The company that, say, makes your dishwasher or manufactures your car could be the next big thing in GenAI. In short, the best specialized GenAI models may not come from “tech” businesses at all today.

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While much is unknown about how generative AI will influence the world economy and society, and it will take time to play out, there are clear signs that the effects could be profound. Each pair of bars is under a different topic, with data representing developer respondent’s feelings with and without the involvement of generative AI in their work. The metrics are whether respondents “felt happy,” were “Able to focus on satisfying and meaningful work,” and were “in a flow state.” In all cases, the more positive responses were, on average, doubled among those using generative AI. Exhibit includes data from 47 countries, representing about 80% of employment around the world.