“The ability for the airlines to adopt is a little bit more conservative… You can optimise. But you have to be careful how you do it, because it’s got to be what I would call ‘environmental friendly’ with the labour force, the management force and the passenger.”
A superb episode with Christopher Rospenda, the global aviation leader for AI and AR applications at IBM. As an SME in aviation and transportation, he uses AI to solve some of the biggest airline operations challenges across the globe. A great listen!
Using Optimisation to Accelerate AI Adoption
“The ability for the airlines to adopt is a little bit more conservative… You can optimise. But you have to be careful how you do it, because it’s got to be what I would call ‘environmental friendly’ with the labour force, the management force and the passenger.”
“It’s a fine line that you have to go through. When you say, ‘look, I can reduce your costs by 8%’, you have to make sure you’re not saying it’s a reduction in staff. It’s an incremental optimisation and efficiency statement… You really have to understand that you’re not taking away jobs. You’re just making it easier for you to do what you’re already doing.”
The 3 Tiers of Airline AI-Readiness
“The first tier has the money and the wherewithal. They’ve got the pockets to go after some kind of AI and digital transformation, because those are parallel paths… They’re going to invest in it because they will see a benefit.”
“The second tier has a little bit of money and they’re willing to buy some things off the shelf to improve their operation. But they’re not as cumbered by the competition. And AI is not going to help them that much. They may be government funded. They may be a shorter haul airline.”
“Then you have the ultra low cost carriers that are applying to secondary and tertiary markets that all they care about is their £29 of seats, £4 for a soft drink. And if you want to put a bag on, that’s £10. AI is not going to help them all that much. They already have it in their revenue management. They already have it in some rudimentary planning and scheduling that’s working for them. So they’re not going to invest heavily in that.”
The Challenge of Bad Data
“That problem is, it’s not only getting the data out, but what is the condition of the data? Is the metadata even relevant? Is FLT, flight? Or is FT, flight? We’re seeing all that. I mean those hand-scripted notes, those systems that were built with the myopic view of ‘look, I’m only dealing with fuel planning right now’.”
“… You’ve got to bridge that and balance it out so that the data then is relevant.”
Much More on Transformation In Aviation
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