Ford Rehires Veteran Engineers After AI-Driven Quality Push Falls Short, Tops JD Power's 2026 Study for First Time Since 2010

Overview

Ford climbed from No. 15 among mainstream automotive brands in 2023 to No. 1 in the JD Power 2026 U.S. Initial Quality Study, marking the first time in 16 years — since 2010 — that the company has topped the closely watched ranking, according to Ford’s official announcement. The turnaround followed a multi-year effort that included hiring roughly 300 veteran engineers back into Ford’s Vehicle Engineering organization after leaders concluded that an AI-heavy approach to design and quality control was not producing the results they expected, according to TechCrunch.

“Mistakenly, we thought that by just introducing artificial intelligence and adjusting the design requirements that we had, that that would produce a high-quality product,” Charles Poon, Ford’s vice president of vehicle hardware engineering, said, as reported by TechCrunch.

What We Know

Ford’s own account of the turnaround, laid out in a press release dated June 25, 2026, frames it primarily as an organizational overhaul rather than a simple AI reversal. In 2023, the company created a unified “Industrial System” combining Vehicle Engineering, Manufacturing, Supply Chain and Quality under Chief Operating Officer Kumar Galhotra, and this year evolved it into an end-to-end Product Creation and Industrialization organization, according to Ford. “Bringing them together allows us to look at the entire ecosystem of a vehicle — from the intricacies of software development to the deepest tier of the supply chain to the plant floor — as one continuous, collaborative flow,” Galhotra said, according to the release.

The company said it replaced about two-thirds of the senior leaders across its industrial system over the past few years and, within Vehicle Engineering specifically, hired roughly 300 veteran engineers to bring “deep, specialized expertise into the design phase early.” Those engineers, freed from daily production schedules, now run mandatory weekly design reviews intended to catch failure points before blueprints reach the factory floor, Ford said in the release.

The results, as measured by JD Power, were substantial. Ford improved by 41 problems per 100 vehicles compared with the prior year — the largest year-over-year improvement among mainstream brands — and posted its biggest gains in infotainment quality, which came in 12.2 points better than the industry average, according to Ford’s release. Seven of Ford’s 10 tested models placed in the top three of their segments, the highest share of any automaker, and the F-150, Mustang and F-Series Super Duty won their segments for the second year running. Thomas King, JD Power’s president of OEM solutions, said Ford ranked “highest among mass market brands” and that those three vehicles “ranked highest in their respective segments,” per the release. Ford’s luxury brand Lincoln also climbed, to No. 6 from No. 8 among premium brands. Counting premium nameplates alongside mainstream ones, Ford ranked third overall among all brands, the release said.

On the supply side, Ford said it began sending teams directly onto supplier plant floors to catch defects earlier, an effort it credited with a 30 percent reduction in launch issues year over year, according to the company. “It’s easy to celebrate heroes fixing problems,” said Liz Door, Ford’s chief supply chain officer. “What we really want is to celebrate zero defects.” On the factory floor, chief manufacturing officer Bryce Currie said the company is “averaging over eight ideas per kaizen project from operators,” and on the software side, Ford said it overhauled its quality assurance process so that code is now “stress-tested through hundreds of thousands of automated scenarios” before ever reaching a vehicle.

Outside Ford’s own framing, reporting on the rehiring push has been more direct about the role AI played in creating the problem in the first place. According to TechCrunch, the returning engineers have been tasked with rebuilding the data pipelines that feed Ford’s AI training systems, mentoring junior staff, and reprogramming the very automated systems they had originally been hired to replace — work consistent with Poon’s account that leaning on AI and adjusted design requirements alone did not deliver the quality Ford needed. Farley credited the broader push with delivering “hundreds and hundreds of millions of dollars of a tailwind for Ford on cost,” according to Futurism.

The Broader Pattern

Ford’s experience lines up with what researchers have been documenting industry-wide. A February 2026 survey of 600 HR professionals by workforce firm Careerminds, covering organizations that had conducted layoffs in the prior 12 months, found that two in three companies that made AI-driven cuts were already rehiring: 32.7 percent had brought back between a quarter and half of the roles they eliminated, and 35.6 percent had rehired more than half, according to Careerminds. More than half of HR leaders said rehiring began within six months of the original cuts. The financial case was mixed at best — 30.9 percent of organizations said rehiring ultimately cost more than the original layoffs had saved, while 42.37 percent said the two roughly canceled out, and only 26.69 percent came out ahead. Just 8.4 percent of respondents said their AI-driven restructuring delivered exactly what was promised and that they would repeat it unchanged; 41.2 percent said they would take a completely different approach.

Forrester Research’s “Predictions 2026: The Future of Work” report similarly found that 55 percent of employers now regret laying off workers for AI, and the firm predicts that half of AI-attributed layoffs will be quietly rehired — though often offshore or at lower salaries rather than through an admission that the cuts were a mistake, according to HR Executive.

Ford is not the only company to have walked back an AI-first bet on cost grounds. Swedish fintech Klarna scaled back its AI-first customer service strategy in 2025 after CEO Sebastian Siemiatkowski acknowledged the approach had gone too far. “As cost unfortunately seems to have been a too predominant evaluation factor when organising this, what you end up having is lower quality,” he said, according to Silicon Republic. A Forrester analyst cited in the same report attributed Klarna’s stumble to “underestimating the complexity of their customer service operations” combined with an “overzealous pursuit of cost reduction.”

What We Don’t Know

Ford’s press release does not itself quantify how much of the pre-turnaround quality shortfall it attributes to AI specifically, as opposed to the broader organizational silos and pandemic-era disruption it says required fixing. It is also not clear from the public record exactly how many of the roughly 300 rehired Vehicle Engineering veterans are returning former Ford employees versus new hires from suppliers, nor how their compensation compares with the roles eliminated during the AI-first period. As previously reported by The Machine Herald, the tech and manufacturing sectors have struggled to disentangle how much of 2026’s workforce churn is genuinely AI-driven versus a broader post-pandemic correction — a question Ford’s own mixed messaging, part organizational narrative and part AI cautionary tale, does little to resolve.

Analysis

Ford’s case is notable less for the fact that a company reversed an AI-driven staffing decision — Careerminds’ survey suggests that is now common — than for the scale of the payoff once it did. Topping JD Power’s mainstream quality ranking for the first time since 2010 is a marquee, independently verified result, not a self-reported productivity claim of the kind that has made many corporate AI announcements hard to evaluate. That gives outside observers a rare, measurable data point for a pattern researchers have mostly had to document through surveys of HR leaders’ own, potentially self-serving, assessments of their AI decisions. Whether Ford’s result generalizes — or whether it reflects an automaker-specific need for engineering judgment that other industries relying on AI-driven cuts, such as Klarna’s customer service operation, do not share — remains an open question the rest of the corporate world now has a concrete example to weigh against.