Generative AI can give you superpowers, new McKinsey research finds

Notably, the potential value of using generative AI for several functions that were prominent in our previous sizing of AI use cases, including manufacturing and supply chain functions, is now much lower.5Pitchbook. 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. There’s research that’s coming out, including from our McKinsey Health Institute [MHI], that shows that working for longer improves your health and overall well-being. I’m hopeful that in 2024 and beyond we will start to see more workers aged 70 and older in the workforce, not because they economically need to be there but because they want to be there. One of the pieces of data that we’ve seen through our Women and Race in the Workplace reports is that when you’re promoted to the managerial level, regardless of your race, you are more likely to think that your race is holding you back from the next level of promotion.

As an example of how this might play out in a specific occupation, consider postsecondary English language and literature teachers, whose detailed work activities include preparing tests and evaluating student work. With generative AI’s enhanced natural-language capabilities, more of these activities could be done by machines, perhaps initially to create a first draft that is edited by teachers but perhaps eventually with far less human editing required. This could free up time for these teachers to spend more time on other work activities, such as guiding class discussions or tutoring students who need extra assistance. Across the 63 use cases we analyzed, generative AI has the potential to generate $2.6 trillion to $4.4 trillion in value across industries. Its precise impact will depend on a variety of factors, such as the mix and importance of different functions, as well as the scale of an industry’s revenue (Exhibit 4).

Which Companies Are Using Generative AI?

The $2.6 trillion to $4.4 trillion economic impact figure marks a huge increase over McKinsey’s previous estimates of the AI field’s impact on the economy from 2017, up 15 to 40% from before. This upward revision is due to the incredibly fast embrace and potential use cases of GenAI tools by large and small enterprises. At the same time, advances in AI are expected to have far-reaching implications for the global enterprise software, healthcare and financial services industries, according to a separate report from Goldman Sachs Research. With well-known tech giants poised to roll out their own generative AI tools, the enterprise software industry appears to be embarking on the next wave of innovation, after the development of the internet, mobile and cloud computing transformed the ways we operate as a society. Gen AI tools can already create most types of written, image, video, audio, and coded content.

The modeled scenarios create a time range for the potential pace of automating current work activities. The “earliest” scenario flexes all parameters to the extremes of plausible assumptions, resulting in faster automation development and adoption, and the “latest” scenario flexes all parameters in the opposite direction. These examples illustrate how technology can augment work through the automation of individual activities that workers would have otherwise had to do themselves.

Generative A.I. Can Add $4.4 Trillion in Value to Global Economy, Study Says

The second way generative AI can deliver major economic impact is by accelerating the process of scientific and educational discovery. That might include reducing the cost of research—the technology’s capabilities to interrogate vast data sets, for example, can help develop and test hypotheses quickly and more cost-efficiently. In software engineering, McKinsey sees the technology speeding up the process of “generating initial code drafts, code correction and refactoring, root-cause analysis and generating new system designs,” resulting in a 20 to 45% increased productivity on software spending. Specifically, McKinsey’s report found that four types of tasks — customer operations, marketing and sales, software engineering and R&D — were likely to account for 75% of the value add of GenAI in particular.

In some cases, workers will stay in the same occupations, but their mix of activities will shift; in others, workers will need to shift occupations. The analyses in this paper incorporate the potential impact of generative the economic potential of generative ai AI on today’s work activities. They could also have an impact on knowledge workers whose activities were not expected to shift as a result of these technologies until later in the future (see sidebar “About the research”).

It demonstrates that the impact of AI is not universally positive and depends significantly on the pre-existing condition of the business. The findings emphasize the necessity of tailoring AI solutions to the unique needs and capabilities of each business to ensure equitable and effective outcomes. Another key realization for me was that our role wasn’t just to sell a product but to educate about AI’s possibilities. Part of our journey was showing the potential of AI to those who hadn’t considered it before.

• Vicuna, a model trained by fine-tuning Meta’s LLaMa, reportedly achieves 90% of the quality of ChatGPT and Google Bard, with “just” 13 billion parameters and with a total cost of retraining of $300.
• Shifts in workflows triggered by these advances could expose the equivalent of 300 million full-time jobs to automation, Briggs and Kodnani write.
• Crucially, productivity and quality of service improved most among less-experienced agents, while the AI assistant did not increase—and sometimes decreased—the productivity and quality metrics of more highly skilled agents.
• My role in this evolution has been as much about learning and adapting as it has been about leading a company in this field.

But this also entails a profound workforce shift, changing the processes of production within the economy and, in turn, the types of tasks that are undertaken and the skills needed to succeed. 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. While it’s difficult to say for certain, global consulting leader McKinsey and Company — where GenAI is already in use by roughly half the workforce — has attempted to quantify the trend in a new report, The economic potential of generative AI. A recent study by economist David Autor cited in the report found that 60% of today’s workers are employed in occupations that didn’t exist in 1940.

🤖 Why Goldman Sachs thinks generative AI could have a huge impact on economic growth and productivity

Instead, AI will likely serve as a complement to existing workflows rather than a substitute for an entire occupation. According to the same research by Goldman Sachs, only 7% of U.S. jobs risk automation, while 63% will leverage AI-enabled augmentation, and roughly 30% will remain unaffected. When you combine the broader capabilities of generative models with the democratization of access provided by NLIs, the explosive rise of ChatGPT and massive generative AI market predictions become more understandable. Global economic growth was slower from 2012 to 2022 than in the two preceding decades.8Global economic prospects, World Bank, January 2023. Although the COVID-19 pandemic was a significant factor, long-term structural challenges—including declining birth rates and aging populations—are ongoing obstacles to growth.

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