Ford Pairs 350 Senior Engineers With AI to Catch Quality Problems Earlier
A loose bolt, a marginal transmission trace and an underspecified fuel-system component call for different inspection methods. Ford’s quality program therefore combines three layers: mobile tools for assembly checks, machine learning for high-volume test data and 350 senior engineers tasked with challenging design and manufacturing decisions. The automation narrows where people need to look; experienced engineers still decide what the signal means and what should change.
Ford deployed the technical specialists as troubleshooters and advisers, bringing some back from retirement after an earlier outflow of experience. Charles Poon, Ford’s vice president of engineering, described their role as a “red team” for younger design groups: examine material choices, dimensions, assumptions and possible failure modes before they become production problems.
The staffing model matters because vehicle engineering is increasingly distributed across specialized teams, suppliers and software disciplines. A design can satisfy its immediate requirements while still creating trouble at an interface, in production variation or under an operating condition that was not adequately considered. Ford’s senior reviewers are intended to provide the accumulated pattern recognition that formal requirements and automated checks may miss.
Red teams put experience back into design reviews
Ford can assign several specialists to one system. Poon cited fuel-system reviews in which five or six experienced specialists evaluate work presented by two or three design engineers. Their mission is not to take over the design but to pressure-test it: identify weak points, question failure assumptions and determine whether the selected materials and dimensions provide an adequate margin.
That structure also addresses an organizational problem. There was an impact of an outflow of experience probably three or four years ago, Poon said. Ford subsequently placed experienced engineers in senior positions with authority to review both product designs and manufacturing processes. Authority is important here; an advisory group has limited value if schedule or cost pressures can routinely bypass its findings.
The human-review program operates alongside a broader restructuring of Ford’s industrial organization. Engineering, manufacturing, supply chain and quality functions were brought under a more integrated system, while suppliers were moved earlier into design validation. Ford says the supplier work contributed to a 30% year-over-year reduction in launch issues, although that figure does not isolate the effect of any single process change.
AI handles inspection scale, not final engineering judgment
At Ford’s Kentucky Truck Plant, an iPhone-based inspection system checks electrical connectors and installation of critical bolts. If an operator finds a loose bolt, the tool can analyze a photograph and compare it with the vehicle’s parts catalog to help identify where it belongs. This is a practical use of computer-assisted inspection: it puts a searchable reference and image recognition at the point of assembly without requiring a fixed inspection station for every check.
Powertrain testing presents a different data problem. Ford runs every transmission through a functional test, collecting time-based traces for pressure, force response, speed and torque output. Machine-learning systems examine hundreds of thousands of those traces and flag subtle anomalies for engineers. A suspect engine or transmission can then be disassembled, with inspections sometimes finding contamination or blocked hydraulic passages.
The distinction between detection and diagnosis is central. An algorithm can compare a new trace with a large population of prior tests more consistently than a person can inspect every waveform. It does not automatically establish the physical cause, the production exposure or the appropriate corrective action. Those steps still require test engineers, manufacturing specialists and component inspection.
Ford uses internally developed neural-network, generative-AI and agentic-AI capabilities as well as commercial tools. Poon framed them as engineering support rather than labor replacement: The idea is that there are many tools like that that augment the engineers. They don’t necessarily replace them.
Initial quality is evidence of progress, not long-term proof
Ford ranked first among mass-market brands in the 2026 J.D. Power U.S. Initial Quality Study, improving by 41 reported problems per 100 vehicles from the previous year. Ford, the F-150, Mustang and Super Duty led their respective categories, while seven of the 10 Ford models evaluated placed in the top three of their segments.
That result measures owner-reported problems during the first 90 days, so it is not a verdict on long-term durability. Ford and Lincoln remained below the industry average in the 2026 U.S. Vehicle Dependability Study, which examines older vehicles. Ford also continues to manage recalls involving earlier model years. Those boundaries make sustained launch performance, warranty experience and dependability over several years more demanding tests of the program.
Nor does the ranking prove that the 350 specialists or AI systems independently produced the improvement. Ford also changed leadership, supplier collaboration, software testing and manufacturing processes. The defensible conclusion is narrower: Ford has built a quality system in which automation expands inspection coverage while senior engineers supply skepticism, context and failure experience. Its next challenge is maintaining that discipline as new vehicles move from design reviews through launch and into years of customer use.
By Robert McKinney — Editor-in-Chief for AMI’s automotive and mobility coverage, with a mechanical engineering background and a decade reporting on powertrain systems, EV innovation, and global vehicle manufacturing.
