Building on our previous insights into how operational transparency shapes on-time performance, today we turn to one of the most persistent friction points in airline-ground coordination: delay coding. The new AHM732 standard promises better granularity and fewer disputes, but with added complexity and a higher demand for accuracy. In this blog, we look beyond just the code list and explore how smarter tools, especially AI, can support more reliable, collaborative, and context-rich delay attribution.

AHM732 is precise, but also more demanding

Instead of logging a generic “Code 93” for aircraft rotation, AHM732 asks for a breakdown: Which process failed? What caused it? Who was involved? This is a win for transparency and root-cause analytics, but also a challenge in the middle of a high-pressure turnaround.

At Cosmos, we’ve seen how even the best station teams struggle to consistently tag delays, especially under time pressure or when the root cause isn’t clear-cut. Miscoding doesn’t just skew metrics, it erodes trust and delays action.

AI-assisted coding: real-time suggestions, fewer mistakes

With the introduction of AHM732, accurate delay attribution is becoming both more critical and more complex. Real-time data sources, such as staff check-ins, service timestamps, and aircraft status, can now be used to support smarter, faster delay classification.

One emerging approach is to use machine learning not to replace human judgment, but to offer context-aware code suggestions during the turnaround. By highlighting the most likely delay codes based on operational patterns, these tools reduce friction without taking control away from station staff.

The result is not automation for its own sake, but fewer miscoded delays, greater consistency across stations, and ultimately more reliable performance data to support both reporting and collaboration.

Faster feedback loops, smarter decisions

When code data is clean, everything gets better:

• Analytics reveal real patterns, not noise

• SLAs reflect true performance, not gaps in reporting

• Ops meetings shift from what happened to why it happened, and how to fix it

No more post-facto guessing

The least effective moment to understand a delay is several days after it occurred, when memories fade, context is lost, and the pressure to move on is high. That’s why smarter delay management is shifting toward real-time attribution. When delay codes are proposed and confirmed during the turnaround, they’re more accurate, more defensible, and more aligned with operational truth.

Modern systems now track not just the primary cause, but also contributing factors, along with edit histories and source data. Integrated with operational tools, from EFBs to chat logs, this approach reduces ambiguity and improves both internal analysis and external alignment.

Making delay codes smarter

As AHM732 rolls out across the industry, early adopters will gain a real edge, but only if they can handle the complexity. Cosmos helps teams:

• Transition from 2-digit to triple-letter codes

• Embed accountability without blame

• Turn delay data into operational improvement

The future isn’t just more data. It’s better data. And AI can help us get there, one smart delay code at a time.

Still managing delays with outdated tools? Get in touch and explore our collaborative real-time approach.