The problem to solve
Gate management or a stand plan looks tidy on paper or on Gannt charts. In reality, it rarely holds. Arrivals bunch, wide-bodies run long, gates close, and what seemed settled at dawn needs reshuffling by mid-morning.
At slot-restricted airports, the pressure is sharper. The runway schedule is already stretched. Slots decide who gets to land and when, but they do not decide where those aircraft should go once they touch down. That is when stand conflicts appear, pushbacks run late, and tows stack up.
Over the years I have watched planners work through this reality across three horizons. The seasonal plan sets the framework months ahead. Rolling planning, six to twelve weeks out, absorbs the churn of charters, reschedules and works.
Then there is the day of operations, when weather and delays can undo even the neatest plan. AI now has a role in all three, for different reasons.
From slots to stands
The slot file was designed for the runway. It often says little about the stand plan. An A321 with a quick turn can end up parked in by a wide-body that needs twice the time. That gap between the slot view and the ground reality is where inefficiency creeps in.
I saw it at one hub running close to 60 movements an hour.
Conflict rates hit around three percent during the morning peak. That is nearly two aircraft every hour competing for the same gate. Each clash added about ten minutes of primary delay, and the knock-ons hit connections and crew duty limits.
With AI in the mix, the slot file becomes airport-aware. Forecasts of real off-block times, rather than averages, flag overlaps well before they happen. The number of conflicts is halved and multiple turns are saved in a single rush period.
Seasonal gate planning: starting strong
Seasonal plans are fixed for half a year.
They are not set and forget, but once agreed they often sit untouched until the first ripple of operational reality a few weeks out. Risks stay hidden such as buffers being too generic, gate allocations that look fine in theory but fail in practice and curfews quietly pushed.
AI strengthens the plan at the start. It learns how turns really behave by fleet, by gate, by time of day.
At one airport averaging 40 tows a day, that insight can cut the number required by 20 percent. Eight fewer moves daily meant less tug time, less fuel burned, and smoother first-wave performance. Small changes, multiplied across a season, made a measurable difference.
Rolling planning: staying ahead
Schedules never sit still. Charters appear, airlines reschedule, stands close for works. I have seen planners spend long nights in spreadsheets, shifting flights and hoping the structure would hold.
AI makes those adjustments easier.
- Instead of tearing up the plan, it re-optimises as it goes.
- AI helps preserves what matters such as connections, airline agreements and operational guardrails, while absorbing the changes.
- Planners no longer rebuild from scratch. They review a shortlist of swaps, ranked for impact and sign off what makes sense.
Day of operations: recover faster
This is where the pressure is sharpest. A wave of arrivals lands close together. A stand suddenly closes. A long-haul overruns and pushes the next turn into conflict. In the past, these issues often surfaced too late, sometimes when the tug was already on its way.
AI pushes the warning upstream.
- Models trained on weather, upstream delays and turnaround history flag the likely clashes hours earlier.
- Instead of a last-minute scramble, supervisors get credible alternatives with the reasoning laid out.
- A conflict spotted at pushback is a crisis. The same conflict flagged at midday for the evening peak is just another adjustment.
One case sticks with me. Two A320s were planned, using a generic 30 minute buffer on the same gate. The second was feeding a raft of long -haul transfers. The model predicted the first would run 42 minutes late, the system suggested a new gate for the second hours earlier. Ground crews adjusted, passengers made their connections and the airline avoided the far greater cost of missed bags, rebooked itineraries and compensation payouts.
Why AI in gate management matters
The real appeal of AI is not hype. It is precision and lead time. It strips away the false comfort of averages and shows when a gate will actually be free. It shifts the problem from minutes to hours in advance, turning disruption into a choice rather than a surprise.
‘AI does not replace the planner. It gives them a head start.’
In airport operations, that is often the difference between a day that holds together and one that unravels.
Connecting the horizons
For years, airports have worked with separate systems: one for slots, another for the seasonal schedule, and a third for the day itself. Each solved part of the problem, but they left planners stitching things together by hand.
AI shifts that balance. It connects the horizons into one flow and pulls in data that planners never had at their fingertips before, stand use, real turn behaviour, upstream delays, weather impacts. Suddenly, what used to be hidden becomes visible, and what used to be reactive becomes manageable.
I like to think of it as giving planners space to plan rather than patch. One connected view that holds from the first draft of the season through to the last departure of the day.
