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SAM RANSBOTHAM: Many
tasks could benefit

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from advanced technologies,
but how can organizations

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use emerging technologies
in high-stakes situations?

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Find out how the
aerospace industry

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is using AI in today's episode.

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HELEN LEE: I'm Helen
Lee from Boeing,

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and you're listening
to Me, Myself, and AI.

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SAM RANSBOTHAM: Welcome
to Me, Myself, and AI,

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a podcast on artificial
intelligence in business.

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Each episode, we introduce you
to someone innovating with AI.

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I'm Sam Ransbotham, professor
of analytics at Boston College.

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I'm also the AI and business
strategy guest editor

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at MIT Sloan Management Review.

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SHERVIN KHODABANDEH: And
I'm Shervin Khodabandeh,

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senior partner with BCG, and
I colead BCG's AI practice

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in North America.

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Together, MIT SMR and BCG have
been researching and publishing

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on AI for six years,
interviewing hundreds

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of practitioners and
surveying thousands

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of companies on what it takes
to build and to deploy and scale

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AI capabilities and
really transform

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the way organizations operate.

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SAM RANSBOTHAM:
Shervin and I are

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excited to be talking today with
Helen Lee, regional director

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of air traffic management
and airport programs

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in China for The Boeing Company.

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Helen, thanks for taking
the time to talk with us.

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Welcome.

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HELEN LEE: Thank
you for having me.

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SAM RANSBOTHAM:
Let's get started.

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Helen, can you tell us about
your current role at Boeing?

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HELEN LEE: I'm currently working
at Boeing China in the Beijing

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office.

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My main responsibility
is to oversee

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Boeing's airport and
air traffic management

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[ATM] programs in China.

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What we try to do here is to
improve airport and airspace

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operational efficiency
and at the same time

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enhance flight safety.

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I've been doing consulting for
airports and ATM for 10 years,

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and the interesting
part is every project

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is very different; I'm able
to work with different groups

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of people.

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Air traffic management is not
the core business for Boeing,

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but what we really want
is to help our customers.

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And because airlines
are our customers,

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we want them operate
more efficiently

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and also enhance
their flight safety.

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Other than the design and
manufacture of aircraft,

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we also provide
a lot of services

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to our airline customers.

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It's kind of like
the entire ecosystem

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that Boeing and
actually other partners

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are working on together.

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For example, we provide flight
planning to airline customers,

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and that will help them to
better plan their flights

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and optimize their flight
paths, and that also

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will reduce their
fuel consumption

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and emission of carbon dioxide.

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SHERVIN KHODABANDEH:
Air traffic management

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has always been quite
fascinating to me

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because I always think,
there's tens of thousands

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of aircraft at any
given time in the air,

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and you have different
systems trying

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to do air traffic
management or air traffic

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control for that aircraft.

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It feels like it's
very data centric.

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It's also somewhat,
maybe, chaotic,

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where unpredictable
things can happen.

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And then you probably have --

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I don't know, you tell us
-- some interaction effects

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between different ATMs, or air
traffic management systems,

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but can you just educate us and
our audience a bit on what it

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is and how it works and how
sophisticated and complicated

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it is?

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HELEN LEE: So you probably
all know that a flight usually

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takes six phases:
testing on the ground

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and either testing in or
testing out; and then takeoff

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from the runway; and then
climbing; and then en route;

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and then start
descent; and landing.

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So there are six phases of it.

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And in each phase, there
are air traffic controllers

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controlling the aircraft.

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General aviation in the
U.S. is very different.

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[A plane] could fly in
controlled airspace,

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but for most of our
commercial flights,

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you need to go through
the six phases.

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First, you have a
controller at the ground,

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and you probably see the
airport's high tower --

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that's the ATC tower.

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They have the ground controller
that controls the ground

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movement, and then there's the
tower controller, who controls

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the takeoff and landing.

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And once the aircraft climbs
to a certain altitude,

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you will be handed over
to an approach controller,

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or departure controller;
you can say that.

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So that controller will
handle the aircraft

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to about 30 to 60 nautical
miles from the airport,

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and then you are passed to
the en route controller.

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And then the en route
controller usually

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has to go through several
centers, and in each center,

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there could be many sectors,
and each sector will usually

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be managed by one controller.

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And so they hand off
one by one, all the way

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through to the landing site.

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So that's how that works.

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But I would have to
say, AI is still not

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widely used in ATM systems.

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There are many, many challenges;
we can talk about that later.

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They're still using voice
communication mostly.

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And so that's the
part that hasn't

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been replaced by this
data-link communication yet.

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But we're moving
in that direction.

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One application that's
widely being used

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is computer vision,
like image recognition.

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And so, for example, what
we are doing right now is,

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in one of the studies, we're
[using] that to recognize wake

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turbulence -- the wake vortex
right after a landing aircraft,

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or maybe for an
aircraft in approach.

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And so we are using a lidar
machine to observe that wake,

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and then we use the AI algorithm
to help us to capture those.

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And so we can train the
machine to recognize

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the location and the
strength of that wake vortex.

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That would be something we
might be applying in the future

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to shorten the wake
turbulence separation.

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Another one is, as
I mentioned earlier,

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about speech recognition.

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That is something that we are
doing a lot of research on --

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and not just Boeing, but
other parts of the industry --

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to speech-recognize the
cockpit conversation with

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the controller.

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Because some of the
instructions from the controller

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are kind of the same from one
aircraft to another aircraft,

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that part may be able
to be digitized and just

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use a data form and have a
display in the cockpit instead

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of having the controller
repeat it all the time.

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And if an aircraft has to
be rerouted to another path,

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that would be another
thing that can be digitized

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using speech recognition.

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The benefit of it is,
if it's all digitized,

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it's all coming
from the controller,

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and then we don't need
the pilot to punch

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in the paths, the
waypoints, into the system

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and they can be uploaded
directly to the flight

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management system.

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So they will avoid some of
the errors made by the pilots.

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SHERVIN KHODABANDEH:
So we're hearing

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there are a lot of
potential applications

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but not yet --widespread use.

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What are some of the
reasons for that?

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You said voice recognition,
for example, right?

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HELEN LEE: Yes.

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SHERVIN KHODABANDEH:
Like my Siri

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doesn't have any problems
recognizing my voice,

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although I have heard,
on YouTube, some

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of the conversations between
air traffic control and pilots,

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and it's as strange
to me as deciphering

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my doctor's prescription.

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But tell us more:
Why is that hard?

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HELEN LEE: One is
the reliability.

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You know, how
reliable [are they]

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when they recognize the voice?

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Because it cannot
make any mistakes.

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In the air traffic
management world,

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there's zero tolerance
for mistakes.

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So it's not like
your Siri, where

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if you make a mistake, that's
fine; you just say it again.

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But not here; you cannot do it.

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And so that's one thing.

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And the other one
that I was saying,

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probably with data
link communication,

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is the security -- whether we
have a very secure environment

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that nobody could hack
into or things like that.

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That's why so far, we haven't
seen any application being

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certified yet.

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SHERVIN KHODABANDEH: If I'm
understanding you correctly,

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one of the biggest hurdles
is the need for absolute

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precision, zero error tolerance,
and just how much is at stake

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that it's --

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HELEN LEE: Correct.

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SHERVIN KHODABANDEH: -- maybe
unlike many other things.

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SAM RANSBOTHAM: Yes,
Shervin, I think a lot

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that we talk about is
corporate application, where

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people make a recommendation
or a loan approval.

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It's not real time; it's
not critical in the moment,

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whereas this is a very
different scenario.

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But not all the scenarios
are very different.

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Like, for example,
the wake turbulence

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is something that doesn't
have to be real time.

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That could be an
after-the-fact analysis;

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runway configuration
could be after the fact.

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SHERVIN KHODABANDEH: And it's
not just real time, either.

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It's like, if you think about
algorithmic trading that's

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going on, it's near real
time, or the credit card

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authorization is real time.

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I think it's real time, but
also, how much is at stake --

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like, [what is] the
cost of being wrong?

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Like with image recognition,
for example, or video,

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like in medical applications,
you still have a doctor,

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and if there's a mistake,
God forbid, it's one life;

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it's not hundreds of lives.

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Are we getting it,
that it's really

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the gravity of the situation?

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Is that what
prevents these things

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from being widely adopted
in the flight life cycle?

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HELEN LEE: Actually, in
the last couple decades,

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the industry has been really
preparing for this technology

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to be applied in this
industry, so a lot of work

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has been done to [support]
the automation of the system.

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So that's one part.

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Now we know most of the
aircraft, especially

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new aircraft the advantage
to using this in China

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is, they have almost all
new aircraft in China,

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because most of their aircraft
is less than 10 years old.

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So that means they're all
equipped with the latest

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technology onboard.

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And so that's one thing.

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And also, their
control surfaces --

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they use the most advanced
technology as well.

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I would say the
basics for AI to be

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applied is to have some of the
automation system be there.

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One thing that we
are also doing you

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know, aircraft can
do [autopilot].

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Before, many times
we used the procedure

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of landing and departing
from an airport.

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But now, we are really promoting
performance-based navigation.

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And since most of
aircraft already

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have that navigation
system equipped,

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we are able to give an aircraft
a more precise route for it

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to climb out or
descend to an airport.

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And that means it'll be
much easier later on if we

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try to manage those aircraft.

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So that's one thing: Kind
of create a base that so we

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can build upon it and use
more advanced technology

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in this system.

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And there are many new
studies coming out,

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and there are road
maps and plans

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for using AI in air
traffic management.

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So I would say in
the next decade,

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we'll probably see a lot
more things come up using AI.

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I would have to say
another challenge we

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have in using AI
is, usually the AI,

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if you apply it to an air
traffic management system,

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we might rely on a
knowledge-based expert system.

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So it is very hard to
build a good expert system;

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especially in
different environments,

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their expert system may
be completely different

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because their
operation is different.

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They may have a
[different] terrain,

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they may have different runway
configurations and all that.

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So that's another part: You
cannot just build one expert

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system to use everywhere.

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SAM RANSBOTHAM: So there's
a lot of preparation work,

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and what struck
me as particularly

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interesting about
what you are saying

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is how coordinated that
needs to be with lots

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of different people, lots
of different organizations,

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different airlines.

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This isn't just a thing that one
organization can put in place

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and dictate to their
people that they use.

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It's something that has
to coordinate across lots

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of different organizations.

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With equipment like airplanes,
you can't just [say],

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"Oh, well, let's just all get
new airplanes next week so they

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have the new technology."

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So it's complicated.

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HELEN LEE: You're
very right about that.

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And that's why
everybody's working

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on what we call SWIM, that's
"systemwide information

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management."

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So that means we are
able to share information

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between different players;
that could be the airline,

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could be pilots in
the cockpit, could

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be air traffic controllers.

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And then we will have
the meteorology data,

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and everything
will come together.

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So everything will be
shared within the system.

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Different players will
able to see the information

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and data they need to better
operate their own system.

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SAM RANSBOTHAM: You pointed to
a lot of forward-looking aspects

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of artificial intelligence.

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Is there something
you're using right now?

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Is there something that Boeing
is doing right now that maybe

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we don't know about or that
is behind the scenes that's

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hard for people to see?

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What kinds of
artificial intelligence

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are currently in use right now?

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HELEN LEE: One
thing that's coming

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very close to application
is using image recognition.

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I listened to one of
your peer's [episodes] --

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[Gina Chung] from DHL.

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She mentioned a
similar technology.

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So, for example, when an
aircraft is coming in, we can

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use a robot camera and to
take pictures of the aircraft,

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to take pictures
of the fuselage,

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to see if there's any damage
and then use AI to recognize

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whether that's an important
thing that we need to take care

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of -- whether it needs to go
into the hangar to be fixed,

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you know, things like that.

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SAM RANSBOTHAM: That seems
like a great application.

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HELEN LEE: Yes, but
before, you know,

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you have to have a human
being walk around the aircraft

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to identify all those
and then make decisions.

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SAM RANSBOTHAM: I think
what's difficult about some

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of those things is
what you don't notice

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is when they don't happen.

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Let's say you do a great job of
inspecting the plane beforehand

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and finding a problem
and preventing it,

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or recognizing a part
needs service before people

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are actually on the plane.

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These are not things
that people notice.

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You only notice when
it doesn't work.

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HELEN LEE: Correct!

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SAM RANSBOTHAM: It's the
classic engineering problem.

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HELEN LEE: You're
absolutely right.

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We are in the process
of collecting data

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because we need a lot of
data to train the machine.

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And the most important part
is to collect all those data.

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And nowadays, with the new
aircraft, like the 787,

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there's a lot of
data we can collect.

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It's not like the older 747
that was built decades ago.

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But the new aircraft
that we make today,

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we are able to
have a lot of data,

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and then those data
will help us to analyze

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the health of the aircraft.

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SAM RANSBOTHAM:
That seems great.

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So, Helen, we have
a new segment,

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and we ask our guests a series
of rapid-fire questions.

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00:16:26.080 --> 00:16:27.900
So just answer
the first response

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that comes to your mind.

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00:16:28.940 --> 00:16:30.690
You don't have to think
about it too much.

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Just [go with] your
first reaction.

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So, what's been your
proudest moment of using

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artificial intelligence?

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HELEN LEE: It's hard, because
I don't use that every day.

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[Laughs.]

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SHERVIN KHODABANDEH: I
think that was the response.

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[Laughs.]

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SAM RANSBOTHAM: Exactly.

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That may be your answer.

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SHERVIN KHODABANDEH: No
response is the response.

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HELEN LEE: Yeah.

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SAM RANSBOTHAM: OK.

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Well, what worries you about
artificial intelligence?

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HELEN LEE: The challenge
we were talking about.

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You know, how safe it can be if
it really is applied to the air

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traffic control environment.

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00:17:01.424 --> 00:17:01.717


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SAM RANSBOTHAM: What's
your favorite activity that

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00:17:03.800 --> 00:17:05.790
involves no technology?

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00:17:05.790 --> 00:17:08.290
HELEN LEE: Oh, that's
something I couldn't do now:

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I used to do kayaking when
I was living in Atlanta.

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00:17:12.422 --> 00:17:13.380
SAM RANSBOTHAM: Oh, OK.

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Well, I'm actually
from Atlanta, so yeah.

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HELEN LEE: Oh!

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SAM RANSBOTHAM: We probably
kayaked the same waters then.

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00:17:17.950 --> 00:17:21.010
I see a picture of you
snowboarding on your background

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00:17:21.010 --> 00:17:21.510
there.

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HELEN LEE: Yes,
that's in Beijing.

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I used to do snowboarding when
I was living in the D.C. area.

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SAM RANSBOTHAM: What was
your first career that you

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00:17:30.242 --> 00:17:31.450
wanted when you were a child?

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00:17:31.450 --> 00:17:35.640
HELEN LEE: I would have to
say mechanical engineer,

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00:17:35.640 --> 00:17:37.740
because that was
what my mom had been

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00:17:37.740 --> 00:17:40.390
doing, because she designed
household electrical

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00:17:40.390 --> 00:17:41.340
appliances.

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00:17:41.340 --> 00:17:45.730
I used to watch her draw those
engineering drawings when

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I was little, and I thought,
"Oh, that's amazing."

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And then you can see the
product, and that's fun.

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So that's why my
major in college

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was mechanical engineering,
before I changed to aerospace

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engineering.

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SAM RANSBOTHAM: Both Shervin
and I are chemical engineers,

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00:18:01.420 --> 00:18:03.378
and so I have to bring
that up every [episode].

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And so we believe chemical
engineering is better

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00:18:05.540 --> 00:18:06.934
than all the other engineering.

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00:18:06.934 --> 00:18:07.428


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00:18:07.428 --> 00:18:09.970
HELEN LEE: [Laughs.] Well, yeah,
in some of the universities,

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00:18:09.970 --> 00:18:13.120
aerospace engineering is part
of the mechanical engineering

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00:18:13.120 --> 00:18:13.950
department.

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SAM RANSBOTHAM:
What's your greatest

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00:18:15.450 --> 00:18:16.960
wish for AI in the future?

395
00:18:16.960 --> 00:18:18.430


396
00:18:18.430 --> 00:18:20.180
HELEN LEE: I wish all
the aircraft would

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be able to be controlled by AI,
and also that the air traffic

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00:18:23.940 --> 00:18:27.760
control would be conducted
by AI so it's always

399
00:18:27.760 --> 00:18:31.580
just machine-to-machine
talk, and so there would

400
00:18:31.580 --> 00:18:36.160
be fewer errors, less
chance for mistakes,

401
00:18:36.160 --> 00:18:37.950
and [greater efficiency].

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00:18:37.950 --> 00:18:40.840
SAM RANSBOTHAM: I think
we all want those things.

403
00:18:40.840 --> 00:18:43.320
Helen, it was great meeting
you and talking with you.

404
00:18:43.320 --> 00:18:45.400
I think one thing that
impressed me about this

405
00:18:45.400 --> 00:18:47.900
is what a complex
environment you're in,

406
00:18:47.900 --> 00:18:50.380
coordinating lots of
different organizations

407
00:18:50.380 --> 00:18:52.710
with equipment that's
really out of your control.

408
00:18:52.710 --> 00:18:55.000
And it's a very
difficult situation

409
00:18:55.000 --> 00:18:57.410
compared to a lot of the
people who we talk to.

410
00:18:57.410 --> 00:18:59.340
Thank you for taking the
time to talk with us.

411
00:18:59.340 --> 00:19:00.100
We really enjoyed it.

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00:19:00.100 --> 00:19:00.570
Thanks.

413
00:19:00.570 --> 00:19:01.700
SHERVIN KHODABANDEH:
Thank you very much.

414
00:19:01.700 --> 00:19:02.950
It's been really enlightening.

415
00:19:02.950 --> 00:19:04.840
HELEN LEE: Thank
you for having me.

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00:19:04.840 --> 00:19:07.590
SAM RANSBOTHAM: Next time,
Shervin and I talk with Sowmya

417
00:19:07.590 --> 00:19:10.360
Gottipati, vice president
of global supply chain

418
00:19:10.360 --> 00:19:12.650
technologies at the
Estée Lauder Company.

419
00:19:12.650 --> 00:19:14.252
We hope you can join us.

420
00:19:14.252 --> 00:19:15.710
ALLISON RYDER:
Thanks for listening

421
00:19:15.710 --> 00:19:17.170
to Me, Myself, and AI.

422
00:19:17.170 --> 00:19:19.670
We believe, like you,
that the conversation

423
00:19:19.670 --> 00:19:21.890
about AI implementation
doesn't start and stop

424
00:19:21.890 --> 00:19:23.007
with this podcast.

425
00:19:23.007 --> 00:19:24.840
That's why we've created
a group on LinkedIn

426
00:19:24.840 --> 00:19:26.680
specifically for
leaders like you.

427
00:19:26.680 --> 00:19:29.430
It's called AI for Leaders,
and if you join us,

428
00:19:29.430 --> 00:19:31.440
you can chat with show
creators and hosts,

429
00:19:31.440 --> 00:19:34.150
ask your own questions,
share your insights,

430
00:19:34.150 --> 00:19:36.820
and gain access to
valuable resources about AI

431
00:19:36.820 --> 00:19:39.650
implementation from
MIT SMR and BCG.

432
00:19:39.650 --> 00:19:44.770
You can access it by visiting
mitsmr.com/AIforLeaders.

433
00:19:44.770 --> 00:19:47.490
We'll put that link
in the show notes,

434
00:19:47.490 --> 00:19:49.930
and we hope to see you there.

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00:19:49.930 --> 00:19:55.000