Why SMS Isn't Enough: The Systemic Risk Gap

In 1998, I started developing a mathematical model to map causal patterns in aviation accidents. Not just events—but how failures connect and cascade. Twenty-five years later, after enhancing it with AI capabilities, one pattern is clear: Safety Management Systems (SMS) excel at documenting known risks but fail at surfacing systemic exposure before it materializes.

The SMS Paradox

SMS frameworks were built for a specific problem: standardizing safety processes across distributed operations. They do this well. But they were designed in an era when:

• Failure modes were known — you could build checklists for predictable scenarios
• Operations were relatively stable — changes happened incrementally, not exponentially
• Human error was the primary concern — before AI, automation, and algorithmic decision-making
• Regulatory compliance equaled safety — if you followed the rules, you were "safe"

Today's aviation environment has outgrown these assumptions.

What SMS Doesn't See

The gap isn't about SMS being "bad." It's about what it was never designed to do:

1. Systemic Interconnection

SMS treats risks as independent events. But modern aviation operates as a complex system where failures in one area cascade through others. Your SMS might flag:

• Late maintenance sign-offs (tracked)
• Increased pilot schedule changes (tracked)
• Weather deviation frequency (tracked)

But it won't show you that these three trends are mathematically correlated and heading toward a convergence point in Q2 when seasonal weather, fleet utilization, and crew availability collide.

2. Leading Indicators vs. Lagging Metrics

SMS excels at lagging indicators: incidents reported, audits completed, training sessions conducted. These tell you what happened.

What you need are leading indicators: patterns that predict what's about to break before the incident occurs.

3. Algorithmic Risk (The AI Blindspot)

If you're integrating AI into operations—predictive maintenance, route optimization, automated decision support—your SMS wasn't built to assess algorithmic failure modes. Questions like:

• What happens when the AI model encounters data it wasn't trained on?
• How do you validate predictions when ground truth is unavailable?
• What's the human override protocol when automation confidence is ambiguous?

These aren't SMS questions. But they're existential operational questions.

What Fills the Gap?

You don't abandon SMS. You augment it with causality modeling and predictive intelligence.

The Question for Leaders

Your SMS compliance might be perfect. But that's not the question.

The question is: Can you see systemic risk converging before your board, regulators, or an accident investigation asks why you didn't?

If the answer is no—or if you're not certain—you're operating with visibility gaps that SMS was never designed to fill.