Why judgment matters more, not less, in the AI era

For a few years now, the headlines have become familiar: yet another company laying off workers as a result of AI. But in recent months, we’ve seen a new twist. Some of those same companies are now reconsidering staffing decisions after recognizing the continued need for experienced professionals.

Ford offers a telling example. In 2023, the company leaned heavily into AI-driven quality control, relying on more than 900 AI-assisted cameras to identify defects at scale. Leaders believed the technology, combined with revised design requirements, would be enough to maintain product quality. It wasn’t. As defects and quality challenges persisted, Ford concluded it had overcorrected in its push toward automation and technology-led processes. The company ultimately rehired 350 veteran engineers, often referred to as “gray beards,” over a three-year period, recognizing that experience, judgment and deep institutional knowledge remained essential.

The company was careful to say this wasn’t a total about-face. According to Ford, it wasn’t a decision to replace AI but to retrain it, catch failure points early and mentor younger staff. As Poon put it, “AI is a powerful tool for catching potential quality issues, but it’s only as good as the people using it.”

And Ford’s results suggest that this approach is working. CEO Jim Farley credited the move with generating “hundreds and hundreds of millions of dollars” in warranty and recall savings. Ford also topped the mainstream automotive brand rankings in J.D. Power’s 2026 Initial Quality Study, its strongest performance in 16 years.

Rather than viewing this as a failure, I see it as an example of organizational courage. In a moment when many leaders are racing to adopt AI, it takes confidence to experiment, evaluate the results honestly and course-correct when the outcomes don’t meet expectations. In my view, this is a more complex and cautionary tale about what separates AI adoption from AI success at a time when we are all trying to figure it out. AI can accelerate analysis, surface patterns and scale knowledge. But judgment remains the capability that turns those inputs into decisions, action and business value. That’s good news for us humans. As AI becomes more capable, our judgment and critical thinking skills become more important, not less.

AI is a force multiplier, not a replacement for expertise

There was an important realization at the center of Ford’s decision: their AI-driven computer vision systems could identify potential anomalies at scale, but experienced engineers were still needed to determine which signals mattered, what risks they represented and what actions should follow.

Research suggests this dynamic extends well beyond the automotive industry. In a recent study by Harvard Business School and UC Berkeley, a field experiment of more than 600 entrepreneurs, AI access widened the gap between high and low performers. Researchers found that the difference stemmed from how participants selected and implemented the AI advice they received. In the same vein, last year, MIT found that AI models use more confident language precisely when they are hallucinating — a worrying behavior that reinforces why human verification and discernment are non-negotiable.

This isn’t just an academic conclusion, nor is it unique to Ford. Other major employers, including Google, Booz Allen Hamilton and CSX, have recently increased hiring in key areas as companies gain a clearer understanding of AI’s capabilities and limitations. Executives now say they still need experienced professionals to deploy, oversee and complement AI, particularly in complex functions like engineering, cybersecurity and cloud computing.

In the project management field, I see this, too. Teams that treat AI outputs as inputs to a judgment process outperform those that treat AI outputs as answers. The organizations winning with AI are pairing it with, not substituting it for, seasoned expertise.

The rising value of complex problem solving and discernment

When everyone has access to the same tools, differentiation comes from human judgment and critical evaluation. A recent roundtable hosted by Accenture and iResearch came to the same conclusion, positing that 15% of content is now AI slop.

In fact, people who can position themselves as so-called “AI orchestrators”, combining AI fluency with deep domain expertise, are earning more, while the value of low-level, unedited AI output is declining. This underscores why these professionals are more valuable than ever to their organizations. The Ford example reinforces this. The engineers who were rehired weren’t brought back to do manual labor that AI could replace; they were rehired for their domain expertise and judgment built over decades that AI hasn’t replicated.

For CIOs, this new dynamic is likely to change how you evaluate talent and design roles and teams. Judgment and critical thinking should be explicitly assessed and cultivated, not assumed.

What this means for how CIOs build teams and lead

There’s also a practical shift happening, and a broader lesson we can learn from Ford: AI success is rarely determined by the model alone. It depends on the goals, governance, expertise, talent and decision-making systems surrounding it.

As the PMI Standard for Artificial Intelligence in Portfolio, Program and Project Management demonstrates, there is a growing realization among leading organizations: the most successful AI systems combine AI capabilities with structured human judgment, governance and accountability.

In practice, leaders can take actionable steps to build structured checkpoints at which experienced staff review and validate AI outputs, especially in high-stakes workflows, such as those reflected in PMI Certified Professional in Managing AI’s (PMI-CPMAI) phased governance approach.

From a culture and team-building perspective, Ford’s veteran engineers also played a critical, irreplaceable role. They were training and coaching the next generation. That’s another reason why CIOs should treat experienced talent as a retention and knowledge transfer priority, not a cost center to shrink. This is an investment in the future health of your organization.

Avoiding the overcorrection

The answer isn’t to replace human-driven workflows with AI, nor to reject AI altogether. The right path lies somewhere in the middle. Rejecting AI entirely out of fear is unwise when the technology can unlock so much business potential both now and in the future.

The sweet spot is augmented intelligence: knowing where AI adds speed and scale, and where human judgment must have the final say. Bring AI into the workflow as a collaborator because your competitors certainly will. Then make human judgment the differentiator.

Judgment is the skill that scales

My call to action, plain and simple: judgment is not optional. AI didn’t fail Ford, necessarily, but the company’s transformation struggled because judgement wasn’t sufficiently integrated at scale. As AI adoption accelerates, CIOs should treat project management skills, judgment, critical thinking and decision-making as core competencies to hire for, train for and protect. These are the capabilities that will determine whether AI delivers business value at scale. The organizations that win with AI won’t be the ones with the most AI tools, or those that hastily cut the most jobs. They will be the ones that best integrate AI into decision-making and business outcomes and know exactly where to keep humans in the loop.