Ford learns how people and machines can work better together
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That’s what happened recently at Ford, according to What happens when a company cuts jobs in the name of AI, then discovers AI can’t cut jobs and executives are forced to rehire the people they cut? bloomberg. This is a story about rethinking the role of people and AI in business transformation and why executives need to think beyond efficiency gains and cost reductions to stay competitive. AI Darwinism.
AI has seduced too many executives into believing the wrong things. But AI itself is not a strategy. The mistake many executives make is believing that because AI can automate tasks, it can also replace the human judgment, context, empathy, and hard-earned expertise that makes those tasks valuable in the first place.
This realization could make leadership and AI very costly.
The early promise of generative AI was exciting. Faster service. Reduce costs. There are few people. More output. Less friction. Suddenly, “efficiency” became a substitute for strategy, and AI became a convenient answer to questions leaders weren’t asking enough.
But speed and quality are not the same. Automation is not the same as intelligence. And replacing a person is not the same as reinventing a job.
Ford is now offering one of its clearest lessons yet.
The Trap: Treating AI as a Labor Replacement
Ford has reportedly concluded that AI and automated quality systems alone won’t deliver the results it needs, and has hired, promoted or recalled approximately 350 experienced technology professionals. These veteran engineers, sometimes referred to within the company as “Gray Beards,” are now mentoring younger staff, leading design reviews and helping Ford improve the AI systems it uses to spot defects early. business insider.
This may sound like a story about AI failure, but it’s a story about leadership learning and trying again. And let that be your mantra for AI business reinvention. We cannot blame mistakes. We must right the wrongs and celebrate companies that lead the way by learning and growing publicly.
In this case, Ford modified its operating model around AI.
Kumar Galhotra, Ford’s chief operating officer, said the company has “increasingly relied on automated quality systems” without achieving the desired results. “We brought back our technical experts,” and “they look for failures before the parts even reach the factory floor.” ford authority.
This sentiment reconfigures AI from being a replacement to an enhancement.
These experts were brought back to raise quality standards and expand wisdom in ways that make humans matter, even in AI.
The value of AI is determined by the knowledge it has access to
Charles Poon, Ford’s vice president of vehicle hardware engineering, was refreshingly frank.
“Artificial intelligence is a great tool, but its effectiveness is only as good as the information you use to train it,” Poon said.bloomberg. “We mistakenly believed that simply introducing artificial intelligence and incorporating the design requirements we had would result in a high-quality product.”
He added: “We recognized that in order to power some of our automation, machine learning, and artificial intelligence tools, we needed to ensure that those tools were trained by the most experienced individuals.” ford authority.
It should be mandatory reading in every boardroom and conference room.
The problem wasn’t the AI. The problem was the assumption that AI could produce superior results from incomplete inputs, disconnected workflows and data, and organizational knowledge residing in the minds of the company’s most experienced experts.
Every company has its own version of this problem. It depends on the judgment of knowledge workers. This is an exception that should never be included in process documentation. Handover between departments. It’s the difference between what the workflow tells you to do and what actually happens.
If that knowledge leaves your organization, AI can’t magically recover it. Simply automate your out-of-office time.
Khurana learned a similar lesson
Ford isn’t alone.
According to Klarna, Klarna became one of the most cited examples of AI efficiency after its AI assistant built with OpenAI handled 2.3 million conversations in its first month, managed two-thirds of customer service chats, and performed the equivalent of 700 full-time agents. OpenAI.
Those results were, and still are, impressive.
However, Klarna has since reversed its stance, emphasizing the need to reinvest in human customer service and ensure customers can reach out when they need to. CEO Sebastian Siemiatkowski acknowledged that cost had become “too much of a dominant evaluation factor,” leading to a “deterioration in quality,” and said that “real investment in the quality of human support is the way forward.” sifted.
A Klarna spokesperson said the takeaway: “AI brings speed. Human talent brings empathy. Together, AI can deliver fast when it matters and personal service when it matters.”
Again, this is not a reason to mock Klarna. This is a moment not to celebrate a change in direction, but to double down on failure.
Klarna is learning in public what many companies still have to learn in private. AI can handle speed, volume, and repeatability. People deal with ambiguity, emotion, trust, accountability, and brand-defining moments.
Endless lesson: Redesign work, don’t just automate it
This is the point that Dave Wright and I make in our new book Infinite: How visionary leaders can transform today’s businesses into AI-advanced enterprises.
Adopting AI is not the same as advancing AI.
The introduction of AI installs tools into existing organizations and existing workflows. Leadership driving AI will redesign how value is created, how knowledge is compounded, how humans and agents work together, and how businesses learn faster than they can change themselves.
Companies that win with AI are not those that simply cut headcount. They will enhance the influence of humans and agents.
This requires leaders to move from organizational charts to work charts.
An organizational chart shows who reports to whom. Work charts show how value actually flows between functions, systems, data, decisions, approvals, exceptions, agents, and humans. These reveal where AI can accelerate outcomes, where it can remove friction, where it should not act autonomously, and where human judgment should remain central.
Ford’s quality reset is a lesson in workbooks. The breakdown was not limited to one team. They lived at the intersection of design, manufacturing, software, hardware, supply chain, and quality. AI can help detect problems, but it required experienced talent to understand why those problems occur and how to prevent them upstream.
The measure is value, not reduction in number of people.
The results of Ford’s revival of “Greybeard” are eloquent and worth careful study.
The 2026 JD Power US Initial Quality Study ranks Ford highest among mass-market brands, improving to 152 problems per 100 vehicles. Reuters. Ford also said it was the first time since 2010 that it was ranked No. 1 among mainstream brands in the study. ford.
Ford improved by combining intelligent systems with experienced humans, cross-functional collaboration, and a preventive mindset.
That’s leadership…knowing when to right the ship even after being told to take a wrong turn.
What leaders should do now
First, stop thinking about where AI can replace humans. Consider where AI can double your expertise.
Second, redesign your work before automating it. If your workflow is fragmented, AI will scale the fragmentation. If your processes are built around search and fix, AI can help you find and fix faster. But real progress is prevention, prediction, and reinvention.
Third, treat knowledge as infrastructure. Not enough data. Prompts alone are not enough. Not enough requirements. AI requires context, edge cases, feedback loops, institutional memory, and human judgment.
Fourth, measure AI by value creation rather than activity or headcount reduction. The goal is better products, faster learning, stronger customer experiences, lower risk, and new growth capabilities.
Finally, build to leverage humans and agents. The future is not man versus machine. Agents and humans, systems and experts, automation and judgment, scale and wisdom.
Ford and Klarna have learned how to use AI better, which is a lesson in leadership.
With the right talent, AI can be worth scaling.
