McKinsey report finds generative AI could add up to $4.4 trillion a year to the global economy

Join top executives in San Francisco on July 11-12, to hear how leaders are integrating and optimizing AI investments for success. Learn More

A bigger impact on an accelerated timeline

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.

Furthermore, McKinsey finds “current generative AI and other technologies have the potential to automate work activities that absorb 60 to 70% of employees’ time today.”

Does this mean massive job loss is inevitable? No, according to Alex Sukharevsky, senior partner and global leader of QuantumBlack, McKinsey’s in-house AI division and report co-author.

“You basically could make it significantly faster to perform these jobs and do so much more precisely than they are performed today,” Sukharevsky told VentureBeat.

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.

However, as the report notes, “workers will need support in learning new skills, and some will change occupations. If worker transitions and other risks can be managed, generative AI could contribute substantively to economic growth and support a more sustainable, inclusive world.”

Also, the advent of accessible GenAI has pushed up McKinsey’s previous estimates for workplace automation: “Half of today’s work activities could be automated between 2030 and 2060, with a midpoint in 2045, or roughly a decade earlier than in our previous estimates.”

Jobs and tasks most likely to be automated by generative AI and AI, generally

While generative AI has captured the public interest and imagination, McKinsey believes other AI applications and technologies will also play a major role in reshaping the global economy.

“When people talk today about GenAI, they sometimes view it an interchangeable with AI and robotics, but it is important to be precise,” Sukharevsky said.

That’s because generative AI and the large language models (LLM) at the center of the uptake of this technology are well-suited for certain kinds of white-collar, so-called “knowledge worker” roles and tasks, as opposed to general AI, robotics, and automation technologies, which may be more useful for more physical tasks such as manufacturing, construction, engineering, transportation, mining and search and rescue.

The former is already here and disrupting the white-collar workforce, while the latter is also here but takes longer to deploy due to the physical machinery required, and will likely have longer-tail impacts further down the road, especially with projections that much of the current workforce will age out over the coming half-century, and there won’t be enough younger people coming up to replace them.

“How do you create a better piece of art? How do you write a better book? How do you produce a better movie? How do you actually create the solution for the world to recover from the worst natural disasters?” Sukharevsky asked, rhetorically, citing some examples of tasks that could be “augmented” by all kinds of AI.

“Many new tasks and jobs will be created,” he continued. “In the short term, we clearly see the prompt engineers [for LLMs], but then in the longer term, I think the full industries will be readjusted here.”

Four tasks with most value add

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.

“Examples include generative AI’s ability to support interactions with customers, generate creative content for marketing and sales and draft computer code based on natural-language prompts, among many other tasks.”

For customer operations, McKinsey said its “research found that roughly half of customer contacts made by banking, telecommunications and utilities companies in North America are already handled by machines, including but not exclusively AI. We estimate that generative AI could further reduce the volume of human-serviced contacts by up to 50%, depending on a company’s existing level of automation.”

For marketing and sales, McKinsey found that creating more personalized and intelligent content with GenAI “could increase the productivity of the marketing function with a value between 5 and 15% of total marketing spending,” and increase the productivity of sales spending 3 to 5% globally.

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.

When it comes to R&D, McKinsey believes generative AI will “help product designers reduce costs by selecting and using materials more efficiently. It can also optimize designs for manufacturing, which can lead to cost reductions in logistics and production.”

AI as a ‘technology catalyst’ for economic growth

Overall, McKinsey views GenAI as a “technology catalyst,” pushing industries further along toward automation journeys, but also freeing up the creative potential of employees.

“I do believe that if anything, we are getting into the age of creativity and the age of creator,” Sukharevsky said.

Asked what types of AI tools he used in particular, Sukharevsky declined to comment specifically, saying he liked to test new ones out nearly every day.

He did confirm that while the data for the report was analyzed and fetched in part by AI, the entire 2023 McKinsey report on the economic impact of AI was written by human authors.