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The economic potential of generative AI: The next productivity frontier

As the development of generative artificial intelligence (GenAI) progresses, there is enormous potential for the global economy. A new study by the McKinsey Global Institute (MGI), the economics think tank of management consultants McKinsey & Company, shows: GenAI technologies such as ChatGPT or DALL-E can theoretically enable annual productivity gains of $2.6 to $4.4 trillion.

This is in the order of magnitude of the UK’s gross domestic product in 2021 of around $3.1 trillion. Compared to previous manifestations of artificial intelligence and analytics, such as machine learning and deep learning, this would represent an additional increase of 10 to 40 percent. The actual impact could be even higher if GenAI were integrated into software such as word processors or chatbots, allowing freed-up work time to be used for other tasks.

About 75 percent will be created in customer service, marketing and sales, software development, and research and development – and thus in areas that are heavily knowledge- and people-based.

Marketing function could change fundamentally

GenAI has the potential to fundamentally change the marketing function – from storyboarding to creative content to customization for different media channels and audiences. Our analysis shows that GenAI could increase marketing productivity by about 9 percent.

Not included in our analysis are examples that go beyond the direct impact of GenAI on productivity. For example, GenAI-powered synthesis could provide higher-quality data insights that lead to new ideas for marketing campaigns and better-tailored customer segments. Marketers:could then focus their resources on creating higher-quality content for their own channels, potentially reducing spending on external channels and agencies.

Weighing opportunities and risks

The introduction of GenAI in marketing should be carefully weighed. For one, there is a risk that models trained with publicly available data may infringe copyrights. Sufficient safeguards should be put in place in advance for this. Second, virtual “try-on” applications could produce distorted representations of certain demographic groups due to limited or biased training data. Therefore, a significant amount of human supervision is required for conceptual and strategic thinking tailored to the needs of the specific organization.

Authors: Kai Vollhardt, Sascha Lehmann Jerome Königsfeld, Oliver Gediehn

The economic potential of generative AI

The next productivity frontier

Source: McKinsey
Image: somchairakin via stock.adobe.com

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