The question caught Sarah off guard during her interview for Operations Director.

“How would you implement AI in our manufacturing processes?”

After 15 years in manufacturing leadership, she thought she’d heard every interview question. But this was different. This was about the future.

The manufacturing sector stands at a turning point. Those who understand how to lead with AI are seeing results that seemed impossible just months ago. Those who don’t risk being left behind.

Consider two manufacturing leaders who started new roles last quarter.

The first approached AI like another IT project. He delegated it to his technology team and waited for results. Six months later, his team still spends hours each day searching for critical information.

The second, at Freedman Seating Company, took a different approach. Since 1894, they’ve led in seating innovation. But in 2024, they faced a modern challenge: their teams spent an average of 2.5 hours daily hunting for information across systems – from HR documents to customer service data.

Their new leader transformed this overnight. With Seraf, employees now find what they need in under five minutes, whether it’s in Notion, Slack, Jira, or Google Drive. The result? 700 hours saved every month, a fivefold return on investment, and 80% of their workforce embracing the change.

At BMW Group, their new Production Director transformed decision-making overnight. Instead of relying on traditional quality control, she implemented their custom-developed AI platform AIQX (Artificial Intelligence Quality Next). The system uses cameras and sensor technology to analyze data in real-time, sending immediate feedback to production line employees through smart devices.

At Ford, the transformation was equally eye-opening. The automotive pioneer that revolutionized manufacturing with the assembly line is doing it again – their AI-integrated assembly lines now feature robot arms that learn the most efficient ways to assemble parts while performing their tasks.

The most successful manufacturing leaders don’t view AI as another technology implementation. They see it as a fundamental shift in how manufacturing decisions are made.

Take quality control. At Toyota, traditional sampling was catching problems after they occurred. Their shift to AI-powered visual inspection transformed their operation, reducing defects by 30% and cutting inventory costs by 20%.

Or consider maintenance. Rolls-Royce developed a digital twin platform that consolidates data from all their aviation engines, monitoring performance and predicting potential issues before they occur. This approach not only improves operational efficiency but enhances aviation safety and reliability.

The best manufacturing leaders ask better questions.

Not “How can we improve efficiency?” but “Where exactly are we losing time right now?” Research shows that manufacturing teams spend 20% of their workday—nearly one full day per week—searching for critical information across disparate systems.

Not “How’s the quality this month?” but “What patterns predict quality issues?”

Not “When should we maintain this equipment?” but “Which machines are showing early warning signs?”

Back to Sarah. She got the job. Her answer? “I wouldn’t implement AI. I’d use it to solve our three biggest problems: quality, downtime, and waste. Here’s how…”

Three months into her role, she’s following General Electric’s lead. In 2024, GE released their Proficy for Sustainability Insights, allowing manufacturers to integrate operational and sustainability data, maximizing productivity while meeting climate metrics for regulatory compliance.

The tools that powered these transformations exist today. The manufacturing leaders who will excel tomorrow aren’t the ones who know the most about AI technology. They’re the ones who know how to use AI to solve real manufacturing problems.