What exactly is artificial intelligence—and why are governments and companies scrambling to harness it? One reason AI is so heralded is that it is not a single technology, but many. Machine learning, natural-language processing, computer vision, and other AI technologies combine to analyze vast quantities of data, to offer previously unavailable insights and guidance.

Huge productivity breakthroughs of the kind promised by AI have occurred before. Think of transformative innovations like the wheel, the printing press, or the steam engine. Yet such technologies spread slowly. Today, innovations are diffused fast, allowing AI to be scaled across millions of workers in little time.

To quantify AI’s potential to boost workers’ productivity, Accenture previously developed a cross-industry econometric model that synthesized labor force data from 14 countries in the G20, a club of the world’s largest economies (this model defined productivity as the amount of value added per hour worked).i That research suggested that the impact of augmentation and automation will be sweeping, affecting up to 90% of the time workers spend on daily tasks in the coming decades. It also indicated that organizations (and workers) are poorly prepared for such change, with up to $11.5 trillion in foregone economic growth, if skill-building fails to catch up with the new kinds of work that AI will create.

This paper refocuses that model, to quantify how varying levels of AI investment by the U.S. government— “slow”, “baseline” (i.e., current trends), and “intensive”—would increase the productivity of its workforce (Figure 1). We use 2018 as our baseline year and project that ongoing AI investments will grow at variable rates, as described; anticipated productivity increases build upon this baseline and reflect the cumulative effect, within the specified year, of investments to date. (For more on the assumptions behind our model, see Modeling AI’s effect on federal workers’ productivity). Here’s what we found.

AI Investment projections and estimated productivity gains.

Figure 1

In the slow (most conservative) scenario, we estimate that $439 million invested in AI in 2021 would yield $49 billion in productivity gains that same year; $634 million invested in 2023 would yield $87 billion in gains in 2023; and $1.6 billion invested in 2028 would deliver $205 billion in productivity gains in 2028.

In the baseline scenario, we estimate that $600 million invested in AI in 2021 would yield $84 billion in productivity gains that same year; $976 million invested in 2023 would yield $143 billion in gains in 2023; and $3.8 billion invested in 2028 would deliver $364 billion in productivity gains in 2028.

In the intense (most optimistic) scenario, we estimate that $697 million invested in AI in 2021 would yield $104 billion in productivity gains that same year; $1.4 billion invested in 2023 would yield $196 billion in gains in 2023; and $7.4 billion invested in 2028 would deliver $532 billion in productivity gains in 2028.

Such a productivity surge would confer enormous benefits. To name only a few: it would generate vast savings, which could be reinvested in innovation and R&D; and it would reduce delivery backlogs, allowing citizens to receive services more promptly.

Consider, again, the benefits for tax collection. In 2017, the Internal Revenue Service fielded just 53% of the calls to its public hotline, with callers waiting 17 minutes for service, on average (That year, an answered call cost the IRS $41). Investing in AI-powered chatbots to answer calls would cut waiting times sharply, as chatbots fielded routine questions and human operators took the most difficult queries.

Whether at the IRS or the U.S. Copyright Office or the Veterans Benefits Administration, more productive workers would deliver better services, improving lives and increasing citizens’ satisfaction with their government.

Productivity, considered

To put productivity gains of $532 billion (our high-end estimate) in perspective, consider that the amount would equal about 2.4% of America’s GDP of $21.73 trillion in 2019; and it would be 1.7 times greater than the U.S. government’s civilian payroll of $298 billion in fiscal year 2020.

When thinking about our productivity estimates, two caveats are also in order. The first is that productivity gains from AI may reveal themselves in non-conventional ways. “Intangibles such as better responsiveness to customers and increased coordination with suppliers do not always increase the amount or even intrinsic quality of output,” observed MIT’s Erik Brynjolfsson, in an influential 1994 paper, The Productivity Paradox of Information Technology. “But they do help make sure it arrives at the right time, at the right place, with the right attributes for each customer.” What applied to past IT breakthroughs may well apply to future AI breakthroughs.

The second caveat: our productivity projections assume that federal workers and executives are widely empowered to make the most of the new AI tools at their disposal. At present, this is not the case.

1 This model also included other “intelligent technologies” like virtual reality and Internet of Things that were omitted from our U.S. federal government study.

About the Authors

Christina Bone

Senior Manager – Accenture Federal Services, Growth & Strategy


Ira Entis

Managing Director – Accenture Federal Services, Growth & Strategy


Bryan Rich

Managing Director – Accenture Federal Services, Applied Intelligence Lead


Sangita Shaha

Senior Manager – Accenture Federal Services, Growth & Strategy


Kristen Vaughan

Managing Director – Accenture Federal Services, Human Capital Lead

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