We’re at a point where artificial intelligence (AI) has successfully crept into nearly every facet of our lives. Some cringe at such a thought, while others embrace the ease at which we navigate our surroundings shepherded by AI. The last two years have been dominated by generative AI applications and their ability to create digital art, write really impressive text, and even compose music. Stable Diffusion, GitHub Copilot, and ChatGPT are paving the way and a recent report by McKinsey aims to investigate the economic potential of generative AI and workforce impacts.
In “The economic potential of generative AI: The next productivity frontier,” the McKinsey authors looked at the Retail and Consumer Packaged Goods, Banking, and Pharma and Medical Products industries.
Retail and Consumer Packaged Goods
Generative AI has the potential of producing an additional $400 billion to $660 billion for the Retail and Consumer Packaged Goods industry. This would arrive via productivity increases of 1.2 to 2% of annual revenues. Inventory and supply chain management, customer service, and marketing and sales functions could be streamlined and automated in the same way that traditional AI helped many companies manage data across extensive warehousing and supply chain networks.
Banking
McKinsey estimates increased productivity of 2.8 to 4.7% of the Banking industry’s annual revenues with generative AI. This would result in an additional $200 billion to $340 billion. Banking is a white-collar industry and there is a significant amount of time spent writing emails, putting together presentations, and similar tasks. Generative AI could automate these tasks as well as the tasks of service representatives (call-center agents, etc).
Pharma and Medical Products
A remarkable amount of revenue (roughly 20%) is spent on Research and Development within the Pharma and Medical Products industry. A new drug takes anywhere from 10 to 15 years to bring to market and generative AI could vastly improve the quality and speed of this process. This, along with other gains, could equate to additional revenues of $60 billion to $110 billion (2.6 to 4.5% of annual revenues). Improving the automation of preliminary screening and enhancing indication findings (diseases or symptoms that justify the use of a medication or treatment) are two areas that hold the most value for generative AI.
Lastly, the paper’s authors rightly note that productivity growth has slowed over the past decade. The main engine of GDP growth, the successful deployment of generative AI could automate some individual work activities translating to annual productivity boosts of 0.2 to 3.3% from now (2023) to 2040. Yet, this is highly dependent on the individuals affected by AI technology shifting to other work activities while maintaining their 2022 productivity levels.
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