Ford spent years trying to speed up its engineering and production processes by handing more decisions to automated computer systems. It did not work the way the company expected. Quality fell. Recalls rose. And the carmaker eventually had to go back and rehire more than 350 engineers it had allowed to leave.
Now, having rebuilt that human foundation, Ford has returned to the top of one of the most closely watched vehicle quality rankings in the American car industry. The lesson it learned along the way has implications not just for the car business but for any organisation that has moved too fast in replacing skilled workers with automated tools.
The J.D. Power initial quality study is published annually and is widely regarded as one of the most reliable measures of how well a vehicle holds up in its first few months on the road. Ford’s return to the top of the rankings among mainstream brands this year marks a significant reversal from the difficulties it experienced over the past several years.
Charles Poon, who serves as vice president of vehicle hardware engineering at Ford, was direct in explaining what went wrong. “Mistakenly, we thought that by just introducing artificial intelligence and adjusting the design requirements that we had, that would produce a high-quality product,” he told journalists in a briefing this week.
The problem was not simply that the automated systems underperformed. The deeper issue was what those systems were missing. As experienced engineers left Ford over the years, they took with them decades of practical knowledge. That knowledge was rarely written down. It lived in the heads of people who had spent careers solving problems on production lines and in development cycles. When those engineers left, the knowledge went with them.
The automated systems that were supposed to take over could only work with the data they were given. But that data had gaps. It reflected what had been recorded and documented. It did not capture the quieter, unwritten understanding that senior engineers carried. Problems that those engineers would have caught early instead went undetected until they became visible defects in finished vehicles.
“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. Ford’s response was to bring those engineers back. More than 350 of them were rehired or promoted into active roles. Their job is not simply to mentor younger staff. They are now directly involved in shaping how data is gathered, organised and fed into the company’s automated systems. They are, in effect, filling the gaps that their absence had created.
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The quality problems were not caused by automation alone. Ford executives described a company where different teams, covering software, hardware, manufacturing and supply chain, had been working in isolation from one another. That separation meant that defects were often not caught until late in the development process, when fixing them was expensive and disruptive.
The company’s chief operating officer, Kumar Galhotra, put it plainly. “We’re moving from that find-and-fix mentality to preventing issues before they occur,” he said. “We’re focused on enablers and early indicators versus outputs. Stop admiring the problem and start solving it.”
Part of that shift involved rethinking how software development fits into the car-making process. In the past, software defects often surfaced late in the development cycle. Ford could not simply push updates to customers after a car had been sold, the way a phone or laptop maker might. Vehicles need their software to work correctly from day one. Getting it wrong has safety consequences.
To address this, Ford created a dedicated 40-person team focused entirely on software quality at the earliest stages of development. Their job is to find and prevent defects before they are built into vehicles rather than after.
As reported, automated testing still plays a central role at Ford. The company has added more than 100,000 automated checks designed to probe its systems under a wide range of conditions and catch problems that might otherwise be missed. But crucially, these systems now run with experienced engineers guiding them rather than operating independently.
When software changes are made late in the development process, engineers can now run the full battery of automated checks quickly. “Even if we have a late change in the software, we can rapidly run back through the entire validation process to guarantee it works perfectly well before it reaches the customer,” Poon said.
The wider lesson from Ford’s experience is one that many organisations are beginning to confront. Automated systems can handle enormous volumes of work quickly. They can identify patterns across huge amounts of data. But they depend entirely on the quality and completeness of the information they are given. When that information has gaps, the systems produce flawed results. And filling those gaps requires exactly the kind of human experience and judgement that automated tools are supposed to replace.
Ford tried the shortcut. It found the gap. Now it is rebuilding with a clearer understanding of what machines can and cannot do on their own.
