Artificial intelligence (AI) is becoming as integral to industrial operations as any piece of core machinery, yet many organisations are rushing ahead without the safeguards needed to manage its risks. When AI fails, it rarely does so quietly. It can topple customer trust, trigger regulatory pressure, and derail entire business units. Despite estimates that 70–80% of AI projects ultimately fail, companies continue accelerating adoption with little structured protection.
AI is now embedded across manufacturing — from maintenance forecasting to production optimisation — but most organisations are deploying systems much faster than they are preparing for their consequences. Only about
25% of organisations have meaningful governance in place, leaving a disconnect between awareness and action. The challenge is that AI risks don’t reveal themselves early. They sit inside datasets and model logic until real-world conditions expose them, often when damage is already unfolding.
A structured approach to AI governance is starting to resemble the way the sector treats safety protocols: layered, systematic, and impossible to ignore. Clear principles guide how AI interacts with machinery and workflows, and dedicated platforms for
AI risk management offer visibility into system performance and compliance. As one executive put it: “If someone isn’t accountable for AI ethics and compliance, then no one is.”
Technical rigour has become essential. Validating models before deployment is now treated like commissioning equipment: test thoroughly and repeatedly. Clean data is the fuel that determines whether AI performs reliably or destabilises operations. Explainability tools help organizations understand why a system made a decision, which is increasingly critical when AI influences quality, safety, or regulatory outcomes. Monitoring for model drift has become as important as monitoring vibration or temperature in high-value assets, and version control allows fast rollback when updates misfire.
Building an effective governance framework starts with identifying where AI already exists in the organisation. Many companies discover far more models in use than expected—inside maintenance platforms, quality systems, or external vendor tools. Once that inventory is clear, legal, compliance, engineering, and data teams can align on policies and incident response plans. As one risk leader noted, “Panic during a crisis breeds terrible decisions,” and preparedness is the only antidote.
None of this works without leadership commitment. Too few CEOs personally oversee AI governance, which is one reason programs often stall. Cross‑functional collaboration has become essential; when engineers, compliance experts, and operators review systems together, they catch issues earlier and reduce risks that any single group might miss. Organisations that reward teams for identifying vulnerabilities see governance become part of culture rather than a bureaucratic burden.
Measuring progress is now part of operational discipline. Manufacturers track how often AI systems encounter issues, how long fixes take, how many models pass validation, and how accuracy behaves over time. External audits and regulatory interactions provide a reality check, while softer signals—such as employee confidence and cultural readiness—reveal whether responsible AI practices have taken root. Companies at higher maturity levels sustain projects far longer, with “45% keeping projects alive for three years or more compared to just 20% among lower-maturity peers”.
Across the machinery sector, the direction is clear: structured AI risk management is becoming a core pillar of operational resilience. Those who implement it gain stability, fewer disruptions, and stronger trust with stakeholders. Those who delay find themselves exposed just as global expectations tighten. As one industry leader said: “The question isn’t whether you need AI risk management. It is whether you implement it now, or after a failure forces your hand.”