Artificial Intelligence is transforming almost every industry, with organisations rapidly deploying AI to automate decisions, analyse data, improve customer service and increase productivity.
But while the technology has advanced at an astonishing pace, the governance of AI is still trying to catch up. There are many questions about accountability, transparency, risk and trust which are dominating discussions amongst regulators, business leaders and standards bodies.
The aerospace industry has already faced many of these challenges.
For decades, aerospace organisations have operated in environments where failures can have catastrophic consequences. Every aircraft, component, software update and operational procedure is designed, tested and monitored through rigorous management systems that prioritise safety, reliability and continual improvement. The aerospace industry accepts that humans and technology will occasionally fail, but believes those failures should become opportunities to improve rather than reasons to assign blame.
As businesses begin implementing AI at scale, aerospace provides an excellent blueprint for responsible governance. Many of the principles that have made aviation one of the safest industries in the world can be directly applied to Artificial Intelligence Management Systems, particularly those based upon ISO 42001.
Aerospace understands that failure is inevitable
One of aerospace’s greatest strengths is not that it prevents every failure but that it accepts complex systems will occasionally fail and therefore designs processes that detect, investigate and learn from those failures before they become catastrophic.
Aircraft are built with redundancy. Pilots train continuously for emergency situations they hope never to encounter. Maintenance schedules are based upon evidence and risk rather than convenience. Near misses are investigated with almost the same level of attention as accidents themselves. Every incident becomes another source of knowledge that improves the safety of the entire industry.
This philosophy is very different from the culture found within many organisations implementing Artificial Intelligence today. When AI produces inaccurate outputs, biased recommendations or unexpected behaviour, there is often a temptation to dismiss the event as a technical glitch or simply correct the immediate problem and move on.
Aerospace teaches us that this approach misses the bigger opportunity.
Every AI failure should be viewed as a learning opportunity. The important question is not just what happened but also why the governance framework allowed it to happen and what can be improved to reduce the likelihood of recurrence.
Complex systems rarely fail because of a single mistake
One of the most influential concepts within aviation safety is that serious incidents rarely result from one isolated error. Instead, they occur when several small weaknesses align.
A poorly documented procedure, inadequate training, human fatigue, unclear communication, software limitations and environmental factors may each appear insignificant in isolation. Combined, however, they can produce serious consequences.
Artificial Intelligence operates within similarly complex environments.
An inaccurate AI output may not be caused by the model itself. It could result from poor quality training data, insufficient human oversight, poorly designed prompts, inadequate testing, inappropriate deployment, weak security controls or users placing excessive trust in automated recommendations.
This systems-thinking approach is reflected throughout ISO 42001. The standard encourages organisations to manage AI across its entire lifecycle, recognising that governance extends far beyond the technology itself.
Near misses are opportunities, not inconveniences
Maybe the greatest lesson AI can learn from aerospace is the importance of near misses.
In aviation, an event that almost resulted in an accident is often investigated with the same seriousness as one that actually did. This is because the circumstances that prevented disaster today may not exist tomorrow.
Businesses deploying AI should adopt exactly the same mindset.
For example, imagine an employee identifies an AI-generated report containing fabricated information before it reaches a customer. No external harm has occurred, but the organisation has discovered a weakness in its governance. Equally, if sensitive information is nearly exposed through an AI chatbot or an automated decision demonstrates unintended bias before being implemented, these should not simply be corrected and forgotten.
Each near miss provides evidence that governance processes can be strengthened. Organisations that capture and analyse these events will improve more rapidly than those that only react after serious incidents.
Trust is built through transparency
The aerospace industry has spent decades developing reporting cultures that encourage openness rather than concealment. Engineers, pilots and maintenance personnel are expected to report concerns because the objective is organisational learning rather than personal blame.
This concept of a “Just Culture” has become fundamental to aviation safety.
Artificial Intelligence needs to have a similar environment.
Employees must feel able to report unexpected AI behaviour, biased outputs or ethical concerns without fearing criticism for using the technology. If organisations discourage reporting or treat AI incidents as embarrassing failures, valuable learning opportunities will be lost.
Building trust in AI depends not only upon sophisticated technology but also upon creating governance processes that encourage transparency, accountability and continual improvement.
AS9100 demonstrates governance in practice
The aerospace standard AS9100 embodies many of these principles.
Although best known as the quality management standard for aerospace manufacturers, AS9100 extends well beyond product quality. It requires organisations to think proactively about risk, product safety, configuration management, operational planning, supplier control, corrective action and continual improvement.
Importantly, AS9100 expects organisations to investigate non-conformities properly, identify genuine root causes and implement effective corrective actions rather than superficial fixes.
This philosophy has obvious parallels with Artificial Intelligence governance.
When an AI system behaves unexpectedly, organisations should not just apply a fix. They should examine the wider governance framework asking: Were risks properly assessed? Was sufficient testing undertaken? Were appropriate approval processes followed? Was ongoing monitoring adequate? Were users competent to rely upon the technology?
ISO 42001 adopts many of the same principles
Although developed for a very different purpose, ISO 42001 reflects many of the lessons that aerospace has demonstrated over decades.
The standard expects organisations to establish policies, define responsibilities, assess AI risks, monitor system performance and continually improve governance arrangements throughout the lifecycle of AI systems.
This mirrors the way aerospace organisations manage safety.
Neither discipline assumes perfection. Instead, both recognise that responsible organisations identify weaknesses early, learn from operational experience and continually strengthen their management systems.
Organisations implementing ISO 42001 should therefore resist viewing certification as the end of the journey. Like AS9100, the real value lies in creating an organisational culture that continually learns from evidence.
Every management system benefits from this mindset
The principles that underpin aerospace are not unique to aviation or Artificial Intelligence.
ISO 9001 encourages organisations to investigate customer complaints and process failures so they can improve quality. ISO 27001 requires information security incidents to be analysed so that vulnerabilities are addressed before they are exploited again. ISO 14001 promotes learning from environmental incidents and near misses, while ISO 45001 places significant emphasis on reporting hazards before injuries occur. Business continuity under ISO 22301 relies on testing, exercising and learning from simulated failures before genuine disruption takes place.
Each of these standards reflects the same philosophy. Organisations become more resilient when they treat failure as information rather than embarrassment.
Artificial Intelligence applications should be no different.
Looking beyond compliance
Many organisations still approach AI governance as another compliance exercise. They focus on producing policies, completing risk assessments and demonstrating conformity with regulatory expectations.
While these activities are important, the aerospace industry, as an example, demonstrates that the safest and most successful organisations are those that continually question their own assumptions, actively seek evidence that controls are failing and create environments where lessons are shared openly across the business.
As AI becomes increasingly integrated into business-critical operations, organisations that adopt this learning mindset will almost certainly outperform those that simply seek to comply with minimum requirements.
How Assent Risk Management can help
At Assent Risk Management, we help organisations build practical management systems that deliver genuine business value rather than simply satisfying certification requirements.
Our consultants support organisations implementing ISO 42001, AS9100, ISO 9001, ISO 27001, ISO 14001, ISO 45001 and many other internationally recognised standards. Through consultancy, internal auditing, training and ongoing compliance support, we help businesses establish governance frameworks that promote continual improvement, manage emerging risks and build confidence in new technologies.
Artificial Intelligence may represent the future of business, but many of the principles needed to govern it successfully have already been proven within aerospace. Organisations that embrace those lessons today will be better prepared for tomorrow’s opportunities and challenges.
