Locked In by the Algorithm: How AI Is Quietly Rewriting Factory Power

On the surface, the rise of artificial intelligence in manufacturing looks like a triumph of progress: predictive maintenance slashes downtime, computer vision perfects quality control, and entire production lines hum with optimized precision. But as industrial AI quietly becomes the backbone of modern factories, something deeper - and far less discussed - is happening behind the scenes: a seismic shift in who truly controls, owns, and understands the machinery of industry.

The new industrial AI era is not just about smarter machines - it's about new forms of dependence. Traditionally, buying a machine meant owning it, understanding it, and controlling its every move. Now, with AI integrated as a service, factories subscribe to remote software that can be modified, priced, or even revoked by outside vendors. If a company refuses a sudden price hike for predictive maintenance, the alternative may be catastrophic: idle machines and halted production. Ownership, once a physical certainty, is now a contractual gray zone.

This shift is also a data gold rush. Every sensor, camera, and wearable device in the modern factory doesn't just optimize safety or productivity - it also streams a continuous flow of operational data back to distant servers. The knowledge that once resided exclusively within factory walls now feeds the algorithms of tech giants. Who really possesses the know-how when the AI knows your production line better than your engineers?

Legal frameworks are scrambling to keep up. When AI-driven upgrades fundamentally alter a machine’s operation, who’s liable if something goes wrong - the original manufacturer, the AI provider, or the employer? New European regulations acknowledge these dilemmas but offer little immediate clarity. Meanwhile, cyber threats multiply with every new connection between factory floors and external networks. Attacks targeting AI-enabled systems have already caused weeks of costly downtime in Europe, and the full burden of risk falls squarely on factory operators during this legal limbo.

Perhaps most unsettling is the blurry line between safety and surveillance. AI systems designed to prevent accidents also monitor worker behavior, collect biometric data, and generate detailed profiles of staff performance. While the intent may be safety, the effect is often an unprecedented level of oversight - raising thorny questions about privacy, consent, and the gradual transfer of human skill to the algorithm itself. As AI anticipates every move, the distinction between help and control grows ever thinner.

Factories are becoming smarter, but also more opaque, less autonomous, and more vulnerable to both technical and economic shocks. As AI’s grip tightens, the crucial question is not whether these systems work - they do - but whether those who rely on them fully grasp what they’re giving up. In the race for efficiency, the ultimate risk is losing the very sovereignty that once defined industrial power.

WIKICROOK

• Predictive Maintenance: Predictive maintenance uses technology to monitor equipment and forecast failures, enabling timely repairs and preventing costly unplanned outages.
• SaaS (Software as a Service): SaaS (Software as a Service) delivers cloud-based software online, letting users access and manage apps without local installation or maintenance.
• IT/OT Convergence: IT/OT Convergence is the integration of digital information technology with operational technology that manages physical devices and processes.
• Cyber Resilience Act: The Cyber Resilience Act is an EU regulation requiring digital products to meet strict cybersecurity standards, including mandatory SBOMs for transparency and risk reduction.
• Biometric Data: Biometric data is unique physical or behavioral information - like fingerprints or facial features - used for secure identification and authentication in digital systems.