Gartner told CIOs this week that about 30 percent of workers laid off and replaced by AI will be rehired by 2029, often at a higher price than it cost to let them go. The forecast rests on a plain problem: cuts made to bank AI savings drain the experienced staff and institutional knowledge a company still needs. Separately, the New York Fed counted what firms in its region are doing, and the picture is one of retraining: 4 percent of service firms laid anyone off because of AI, while a third retrained the workers they have.
By the Numbers
30 percent: the share of AI-displaced workers Gartner expects to be rehired by 2029, often at a higher cost than the savings that justified the cut.
75 percent: organizations booking AI productivity gains as cost savings that Gartner expects to be outrun by 2027 by rivals who reinvest those gains.
61 percent: service firms in the New York Fed’s region now using AI, up from 40 percent a year ago and 25 percent in 2024.
4 percent: service firms that laid off workers because of AI in the past six months, versus just over a third that retrained them.
Reskilling and Education
Gartner expects a third of AI layoffs to reverse by 2029
Gartner’s new Future of Work research, released September 9 alongside its IT Symposium, predicts that by 2029 about 30 percent of employees laid off because AI replaced them will be rehired, often at a higher cost. VP analyst Tori Paulman’s argument is that executives who treat AI mainly as a way to cut costs risk cutting too deep and too soon, losing the experienced staff and institutional knowledge they need to compete as the technology matures. Gartner’s alternative is a “talent remix,” using AI to reshape roles and move people off less productive work onto new opportunities. A second forecast sharpens the point: by 2027, organizations that book AI productivity gains as savings will be outrun by competitors that put those gains into new products, modernization, and training.
Gartner, September 9, 2026. Read the report.
Why it matters: The rehiring forecast puts a price on layoffs pitched as AI efficiency: the same workers, hired back later, at a premium. For HR leaders, the cheaper move is to plan the role change before the cut rather than pay for the rehire after it.
New York Fed survey finds retraining far outpacing AI layoffs
The New York Fed’s August business survey, published September 1, found AI use now widespread across the New York and Northern New Jersey region: 61 percent of service firms reported using it, up from 40 percent a year ago and 25 percent in 2024, with manufacturers at 51 percent. Layoffs stayed rare. Four percent of service firms said they let workers go because of AI in the past six months, and no manufacturers did. Retraining was the common response: just over a third of service firms and more than a fifth of manufacturers retrained existing workers, mostly to use AI in the jobs they already hold. The economists, Jaison Abel, Richard Deitz, Natalia Emanuel, and Nick Montalbano, flag one caveat: entry-level workers may still lose the routine tasks that once got them in the door.
Federal Reserve Bank of New York, Liberty Street Economics; Abel, Deitz, Emanuel, and Montalbano; September 1, 2026 (9 days old). Read the report.
Why it matters: The survey covers one region, so read it as a live count of behavior rather than a national tally. What it shows is employers spending on the workers they already have, which is the choice Gartner is urging companies to make on purpose.
What Workforce Leaders Are Watching
Whether your own AI business case counts the cost of rehiring in 2027 and 2028, or only the savings booked this year.
Which roles you would reshape rather than cut if you took Gartner’s “talent remix” seriously, and who owns that call, HR or the CIO.
Whether the retraining your firm offers moves people into new work, or only teaches them to use AI in the job they already hold, which is mostly what the New York Fed found.
Where the entry-level door stands once AI takes the routine tasks that used to train new hires, a caveat both the Fed economists and Stanford’s researchers keep raising.
This briefing was prepared automatically by the Workforce Rewired research assistant. All stories include direct source links.
The institutional-knowledge piece is easy to underestimate. A headcount spreadsheet can quantify salary saved immediately; it rarely captures the accumulated judgment, context and exception-handling that disappears with the person. AI can remove a lot of tasks. That doesn’t automatically mean removing the person who understood why those tasks existed is equally low-risk.