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Data: The Partnership for Public Service; Chart: Axios Visuals

The adoption of AI technologies over the coming decade is likely to eliminate work, and in some cases entire jobs, currently being done by more than 130,000 federal agency employees in more than 80 occupations.

The big picture: Re-training hundreds of thousands of other federal government employees will take time and resources that haven't yet been allocated on a large-scale.

By the numbers: A report from the Partnership for Public Service and the IBM Center for the Business of Government found that this shift is likely to impact agencies with financial responsibilities — including the Department of the Treasury, the IRS, the Securities and Exchange Commission and the Pension Benefit Guaranty Corporation —where technology could be used for data analysis.

The jobs that are likely to be at least partially automated include an estimated:

  • 20,000 IRS tax examiners and agents
  • 13,000 accountants
  • 12,000 auditors
  • 5,600 paralegals
  • 3,000 food inspectors

Only about 3% of the 130,000 jobs are blue collar.

What we're watching: President Trump recently issued an executive order urging agencies to promote AI research and development in addition to making government data widely available to industry and reducing regulatory barriers to innovation.

  • The Department of Defense has taken an early lead on this, and already has an AI strategy to transform functions and maintain U.S. preeminence on the battlefield.
  • Yes, but: Understanding and working with AI will require technical, digital and data literacy that much of the workforce currently lacks. There is currently no cross-agency plan in place to retrain the federal workforce.

The bottom line: AI technologies are likely to play a significant role in helping federal agencies to more efficiently provide services, but they will reshape that workforce dramatically.

Max Stier is the president and CEO of the Partnership for Public Service.

Go deeper: Reskilling workers due to automation will cost $34 billion

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