Industrial Production Managers

At the same time, many core manager duties still rely on humans. Decisions about plant scheduling, budgets or hiring involve judgment that AI can’t fully replace. Industry analysts note that AI has not been shown to replace managers when making strategic choices – human oversight remains crucial ^[3].

In fact, many companies worry about the costs and risks of automation: one survey found 46% of manufacturers cited cost and 42% cited employee concerns as barriers to AI adoption ^[2]. So while software might screen resumes or help suggest budgets, real hiring or planning decisions stay in people’s hands. In short, AI and automation help speed up data-heavy, repetitive tasks but don’t eliminate the need for human leadership and skill.

However, adoption can also be cautious and uneven. Implementing AI systems can be expensive and complex, so smaller plants may lag behind. Many manufacturers report concerns about cybersecurity, data privacy and gaining worker buy-in ^[2].

Integrating new AI tools into old machines can be hard, and debugging AI often requires expert skills. Because of this, companies often start with “small, targeted” projects – for example improving one process – before expanding ^[4]. These challenges and the need for trust mean that deployment of AI in production management tends to be gradual.

In all cases, most experts agree AI is meant to augment people – it handles tedious analysis so that managers can focus on creative problem-solving and people skills, which remain valuable.