Picture a wet Tuesday evening at a major hub. Visibility is borderline, cloud base is sitting uncomfortably low, and a queue of heavies is stacked up waiting to punch through. This is the environment where aviation’s oldest instrument landing technology earns its keep — and it’s also, quietly, where artificial intelligence is beginning to do some of its most consequential work in aviation.
The Instrument Landing System has been guiding aircraft down to the threshold since the 1930s. The core physics haven’t changed: a localiser beam for lateral guidance, a glideslope beam for vertical guidance, and marker beacons or DME to keep the crew oriented along the approach. It is elegant, robust, and deeply trusted. But it has limits. The beams are sensitive to ground interference. A large aircraft taxiing across the localiser signal can momentarily corrupt it. Signal bending near hilly terrain is a well-documented headache. And the system itself produces no prediction — it only tells you where you are right now, never where the approach is trending.
That last limitation matters more than it might sound. A skilled crew reads an ILS approach as a continuous story, catching deviations early and understanding the difference between a momentary needle flicker and the beginning of a real drift. That interpretive skill, built over years of flying, is exactly the kind of pattern recognition that modern AI systems are starting to replicate — and in some cases, meaningfully extend.
Several avionics developers and airline research programs have been working on systems that layer machine learning models over raw ILS data. Instead of simply displaying where the aircraft sits relative to the beams, these systems analyse the rate of change, cross-reference it with airspeed, wind component, and aircraft energy state, and flag developing instability before it becomes an unstable approach in the traditional sense. Think of it as the difference between a thermometer and a weather forecast. The ILS tells you the temperature. The AI layer tells you which way it’s heading.
The practical upshot is subtle but meaningful. Crews flying approaches into difficult airports — think crosswind-prone runways, terrain-constrained glidepaths, or aerodromes where wind shear is a routine visitor — can receive earlier, more nuanced alerting than the traditional warning thresholds allow. The system isn’t second-guessing the pilots; it’s giving them more data, earlier, so their own judgement can operate with a fuller picture. The human is still flying the aircraft. The AI is just reading the story a few sentences ahead.
There’s also work being done on the ground side. AI models trained on years of ILS signal logs can identify anomalous signal behaviour that might indicate a developing calibration problem or a new source of interference — a construction project, a new taxiway configuration — before it degrades to a level where it would show up on routine flight checks. Predictive ground infrastructure monitoring isn’t as glamorous as cockpit technology, but for the engineers who keep these systems certified and trusted, it represents a genuine step forward.
None of this makes the ILS obsolete. If anything, it underscores why the system has lasted nearly a century: it provides a stable, physical foundation that newer technologies can build on rather than replace. GBAS and RNP approaches are expanding the precision approach landscape, and AI is enhancing those too. But there is something almost poetic about watching machine learning breathe new capability into a system old enough to have guided the first jets down through the weather.
The approach might look exactly the same from the window. But the intelligence behind it is quietly deepening — and on a bad-weather night at a busy airport, that matters an enormous amount.