A number of recent analyses are highlighting Ford’s missteps on AI because the manufacturer has hired hundreds of engineers who were previously deemed unnecessary as it ramped up the use of AI.
While there’s some cause to call out the automobile giant’s mistakes, it’s more useful to consider the reasons it cited for its AI work not going as planned and its resulting course correction, especially for organizations looking to implement AI in pilots or production use cases.
The automaker said it moved too aggressively to use AI, instead of experienced engineers, to manage core processes in bringing its cars to market. Because of this, Ford has hired, rehired, or promoted 350 experienced technical specialists as part of a sweeping effort – which goes well beyond AI — to shore up quality control over its vehicles. And that effort is delivering strong results.
Company executives said in a public briefing they failed to adequately train AI (a model or models weren’t named explicitly) with knowledge of its engineering practices and instead proceeded with confidence as they introduced AI as they did some level of training. But the effort wasn’t thorough enough.
“Mistakenly, we thought that by just introducing artificial intelligence and ingesting the design requirements that we had, that would produce a high-quality product,” said Charles Poon, Ford’s vice president of vehicle hardware engineering, in a public briefing. He correctly noted that AI is “only as good as the information you use to train it.”
The company also acknowledged that some of its most experienced personnel left Ford before their years of knowledge were fully captured in AI models. Reports of Ford’s travails went so far as to label this a failure of AI (the AI “wasn’t smart enough” and Ford “rehired humans to fix what AI broke”).
From this perspective, the details tell a more nuanced story.
At our Cloud Wars and Agent & Copilot sites, we’ve published numerous analyses laying out the importance of a thoroughly vetted AI plan, training AI models with the best, most comprehensive data, and not making radical moves in a core business function – think automobile quality control — without pilots that prove AI will work as intended. Ford execs’ explanation indicates they factored in none of these requirements or potential stumbling blocks – or at least not proactively enough – and they paid the price.
Now, auto industry veterans (being referred to unfortunately as “gray beards”) are overseeing quality control and managing AI training for quality control systems. This includes technical specialists leading mandatory design reviews.In addition to hiring/rehiring 350 engineers, Ford said it’s taking a more proactive approach to finding problems. It also brought engineering, manufacturing and supply chain teams closer together – that’s precisely the type of process improvement that would have been best worked out before jumping in to AI with both feet.
“We brought back technical specialists and they hunt for failure points before a part ever reaches the plant floor,” said Kumar Galhotra, Ford’s chief operating officer. Those technical specialists are better able to bring judgment to complex problems.
Ford’s move to use AI alongside experienced engineers is a powerful reminder that while AI may replace some people and some repetitive work, functions requiring high levels of expertise and judgment aren’t going away in the near term.
And it’s clear the course correction with humans driving the most challenging work has been successful: Ford’s press briefing coincided with its new status as No. 1 in JD Power’s initial quality ranking among mainstream automakers – the first time that’s happened in 16 years.
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Tom Smith
Analyst
Cloud Wars, Agent and Copilot
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