AI Productivity Finally Hits the Real Economy

A new report from the Federal Reserve Bank of St. Louis shows that output is trending higher even though headcount has barely moved.

A few years ago, you might have blamed pent-up demand or a lucky sales run. In late 2025, the more honest explanation was that a growing share of your team has a chatbot open in the background. The St. Louis Fed’s national U.S. adoption tracker, built on its Real-Time Population Survey, shows generative AI use jumping by 10 percentage points in a single year. Their new analysis on adoption and productivity argues that those extra minutes are starting to show up in macro data.

Editor’s Note: This is part of an ongoing series examining generative AI and its continuing impact on the business world.

AI Used by Majority of Working-Age Americans

Use of generative AI is already a majority behavior among working-age Americans. The latest Real-Time Population Survey data show that by August 2025, 54.6% of adults aged 18 to 64 had used generative AI, up from 44.6% a year earlier, with work use rising from 33.3% to 37.4% and nonwork use from 36.0% to 48.7%. Three years after launch, this adoption rate is far ahead of personal computers and the early commercial internet at comparable points in their rollout.

An earlier report on adoption, using the same survey, found that nearly 40% of adults were using generative AI by late 2024, with between 1% and 5% of all work hours assisted by the technology.

In other words, what looked like a wave of experimentation has hardened into routine use. For managers, that means your workforce is no longer waiting for a formal AI strategy. They are already automating pieces of their day, even if your policies and metrics have not caught up.

The picture isn’t limited to the United States. A 2024 BCG global employee survey of 13,000+ workers across 15 countries found that about half of employees who use generative AI save at least 5 hours a week, and nearly two-thirds of leaders say they are starting to redesign their organizations around it.

Microsoft and LinkedIn’s 2024 Work Trend Index report similarly found that 75% of knowledge workers worldwide are already using AI, with almost half starting within the previous 6 months and many doing so ahead of any official guidance.

Shadow use is now a structural feature of the workplace. A recent study of “bring your own AI” behavior, based on payroll and survey data, finds that nearly half of U.S. workers use AI tools without telling their employer, and roughly two-thirds of those users pay out of pocket. The combination of high adoption and low formal oversight means leaders who rely only on sanctioned tool metrics are likely underestimating how deeply AI is already woven into everyday work.

AI Productivity Gains

The strongest evidence for productivity gains comes from narrow tasks, and it is no longer limited to lab settings. The St. Louis Fed’s analysis estimates that among workers who used generative AI in the previous week, average time savings were 5.4% of their work hours, with 20.5% of these users saving 4 or more hours per week. When you include nonusers, that still translates into 1.4% of total hours saved across the workforce.

Randomized experiments reinforce these self-reports. In a large customer support experiment with 5,000 agents, access to a generative AI assistant increased the number of issues resolved per hour by 14% on average, with the biggest gains for novice workers and minimal gains for seasoned experts.

In software development, a trio of GitHub Copilot field experiments across Microsoft, Accenture, and a Fortune 100 manufacturer found that developers with access to the tool increased weekly pull requests by about 26%, again with outsized benefits for junior engineers. A separate MIT writing experiment shows that giving knowledge workers access to ChatGPT cut completion times by roughly 40% and improved quality scores by double digits.

The Real-Time Population Survey team at the St. Louis Fed has now connected these micro-level gains to the broader economy. After pooling survey waves from early 2025, they estimate that self-reported time savings from generative AI correspond to 1.6% of all U.S. work hours, implying up to a 1.3% boost in labor productivity since ChatGPT’s release when fed into a standard production model. That estimate lines up with official statistics: labor productivity in the U.S. nonfarm business sector grew at an annualized rate of 2.16% from late 2022 through mid-2025, compared with 1.43% per year in the 2015–2019 period cited in the same analysis.

Not all of that gap comes from chatbots, of course. Some saved time turns into on-the-job leisure rather than extra output, a point emphasized in both the St. Louis Fed work and a separate ITIF commentary on time savings. Yet even if only part of the reported 5% to 25% of task-level improvements is captured as throughput, the cumulative effect on project timelines, service quality, and innovation pipelines is significant.

For professionals managing complex portfolios, that translates into extra cycles for client work, experimentation, and strategic planning that rarely fit into traditional schedules.

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The next phase is less about whether generative AI works and more about how firms convert scattered time savings into durable performance. Global modeling from McKinsey estimates that recent advances in generative AI have raised the share of work hours that are technically automatable from about 50% to as much as 60 to 70% and could add 0.1 to 0.6 percentage points to annual productivity growth between 2023 and 2040, within a broader automation range of 0.5 to 3.4 percentage points.

Those gains only materialize if organizations actually redesign workflows so that freed-up hours are redeployed into high-value activities rather than drowned in meetings and email.

The St. Louis Fed’s new analysis offers an early stress test. By correlating industry-level generative AI time savings with detrended productivity growth, the authors find that industries reporting a single percentage point higher in time savings saw, on average, 2.7 percentage points faster productivity growth relative to their pre-pandemic trend, with a correlation of 0.32 across sectors in their industry-level correlation study.

They are explicit that this pattern is not proof of causality, but it is exactly the sort of relationship you would expect if AI-assisted work were beginning to matter in the aggregate.

At the same time, firm adoption still lags worker behavior. Even among adopters, usage often remains confined to marketing automation and analytics pilots rather than end-to-end process redesign. That gap between individual experimentation and organizational commitment is evident in the Work Trend Index, where high employee usage coexists with the finding that 60% of leaders say their organization lacks a clear AI plan.

Crossing the Threshold

For executives, the implication is blunt. The technology has already crossed the adoption threshold.

The differentiator now is whether your organization treats generative AI as a sanctioned part of core workflows. That means mapping tasks where workers already use AI informally, standardizing prompts and guardrails, investing in targeted training, and tying AI-assisted work to performance metrics rather than leaving it in the shadows.

Companies that take this operational route are more likely to convert scattered time savings into gains in throughput, quality, and innovation. Those that do not may still see happier employees, but they will leave much of the productivity upside on the table.

The information and opinions presented are the author’s own and not those of Vistage Worldwide, Inc.

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