Ford has hired, promoted or brought back more than 350 experienced engineers after its own automated systems let quality problems slip through. The same company has just topped J.D. Power’s Initial Quality Study among mainstream brands for the first time in 16 years.
Charles Poon, Ford’s vice president of vehicle hardware engineering, did not dress up what went wrong. “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,” he told reporters.
For most of a decade Ford leaned on automation, AI-assisted design and algorithmic decision-making. The tools depended on the data fed to them, and on veterans who knew how cars are built. Many of those veterans had left, some retiring and some moving on, and their pattern recognition went with them. Ford’s systems, trained on incomplete data, could not make up the gap.
The timing was bad. The Explorer and Lincoln Aviator launches went poorly, the pandemic scrambled supply chains, and recalls piled up. Ford currently leads the industry in total recall volume. Chief operating officer Kumar Galhotra said the company’s approach had been fragmented, with departments working in silos and a “find and fix” habit of dealing with defects only after they appeared. “Stop admiring the problem and start solving it,” he said.
What the veterans do
Some of the returning engineers are retraining Ford’s AI models with better, more detailed data. Others mentor younger engineers who were struggling to hold quality standards. “That’s where some of our most experienced engineers have had experience solving and identifying those problems before they creep into the system,” Poon said.
Testing before the customer
Ford also built a dedicated 40-person software quality assurance team and added more than 100,000 AI-powered tests meant to stress software across a wide range of conditions. Software and digital teams now work more closely with vehicle engineering, manufacturing and supply-chain staff.
Because the tests are automated, a late software change can be sent back through the full validation process quickly. “We can rapidly run back through the entire validation process to guarantee it works perfectly well before it reaches the customer,” Poon said.
He also drew a line between cars and consumer gadgets. A “move fast and fix later” approach may work for apps and phones, but a vehicle is safety-critical, and Ford cannot afford it.
