WEBVTT

1
00:00:00.000 --> 00:00:02.310


2
00:00:02.310 --> 00:00:04.500
SHERVIN KHODABANDEH: An
all-you-can-eat sushi buffet

3
00:00:04.500 --> 00:00:08.000
and artificial intelligence
-- how are they related?

4
00:00:08.000 --> 00:00:11.880
Find out today when we talk with
Sarah Karthigan of ExxonMobil.

5
00:00:11.880 --> 00:00:12.380


6
00:00:12.380 --> 00:00:14.930
SAM RANSBOTHAM: Welcome
to Me, Myself, and AI,

7
00:00:14.930 --> 00:00:18.100
a podcast on artificial
intelligence in business.

8
00:00:18.100 --> 00:00:21.840
Each episode, we introduce you
to someone innovating with AI.

9
00:00:21.840 --> 00:00:25.160
I'm Sam Ransbotham, professor
of information systems

10
00:00:25.160 --> 00:00:26.500
at Boston College.

11
00:00:26.500 --> 00:00:29.860
I'm also the guest editor for
the AI and Business Strategy

12
00:00:29.860 --> 00:00:33.450
Big Idea program at MIT
Sloan Management Review.

13
00:00:33.450 --> 00:00:36.050
SHERVIN KHODABANDEH: And
I'm Shervin Khodabandeh,

14
00:00:36.050 --> 00:00:40.100
senior partner with BCG, and
I colead BCG's AI practice

15
00:00:40.100 --> 00:00:41.280
in North America.

16
00:00:41.280 --> 00:00:44.810
Together, MIT SMR
and BCG have been

17
00:00:44.810 --> 00:00:48.230
researching AI for five
years, interviewing hundreds

18
00:00:48.230 --> 00:00:50.580
of practitioners and
surveying thousands

19
00:00:50.580 --> 00:00:54.690
of companies on what it takes
to build and to deploy and scale

20
00:00:54.690 --> 00:00:57.280
AI capabilities across
the organization

21
00:00:57.280 --> 00:01:00.450
and really transform the
way organizations operate.

22
00:01:00.450 --> 00:01:05.280
SAM RANSBOTHAM: Today we're
talking with Sarah Karthigan.

23
00:01:05.280 --> 00:01:08.140
She's the artificial
intelligence for IT operations

24
00:01:08.140 --> 00:01:09.682
manager at ExxonMobil.

25
00:01:09.682 --> 00:01:10.890
Sarah, thanks for joining us.

26
00:01:10.890 --> 00:01:11.350
Welcome.

27
00:01:11.350 --> 00:01:12.560
SARAH KARTHIGAN:
Thanks for having me.

28
00:01:12.560 --> 00:01:14.893
SAM RANSBOTHAM: ExxonMobil
is one of the world's largest

29
00:01:14.893 --> 00:01:17.410
oil and gas companies, and
it's existed since the 1870s,

30
00:01:17.410 --> 00:01:19.153
long before artificial
intelligence.

31
00:01:19.153 --> 00:01:21.070
Sarah, can you tell us
about your current role

32
00:01:21.070 --> 00:01:22.094
at ExxonMobil?

33
00:01:22.094 --> 00:01:23.060


34
00:01:23.060 --> 00:01:26.210
SARAH KARTHIGAN: I am currently
responsible for leading

35
00:01:26.210 --> 00:01:30.220
the design and execution of
self-healing strategies for IT

36
00:01:30.220 --> 00:01:33.240
operations, using
artificial intelligence.

37
00:01:33.240 --> 00:01:36.680
Self-healing, at its
core, is proactively

38
00:01:36.680 --> 00:01:40.020
monitoring, detecting,
and remediating issues

39
00:01:40.020 --> 00:01:41.510
without human intervention.

40
00:01:41.510 --> 00:01:43.510
SAM RANSBOTHAM: How did
you end up in that role?

41
00:01:43.510 --> 00:01:43.910


42
00:01:43.910 --> 00:01:46.450
SARAH KARTHIGAN: My background
is in electrical engineering,

43
00:01:46.450 --> 00:01:50.960
and I started at ExxonMobil
as a technical lead.

44
00:01:50.960 --> 00:01:56.000
I then went down the management
career path, but one of my jobs

45
00:01:56.000 --> 00:01:58.470
took me up to
Clinton, New Jersey,

46
00:01:58.470 --> 00:02:02.490
to support the corporate
strategic research function.

47
00:02:02.490 --> 00:02:05.450
So it is there I
got exposed to data

48
00:02:05.450 --> 00:02:08.130
science, artificial
intelligence, and machine

49
00:02:08.130 --> 00:02:08.630
learning.

50
00:02:08.630 --> 00:02:11.470
I was a part of
several pilots where

51
00:02:11.470 --> 00:02:15.520
we were assessing artificial
intelligence capabilities.

52
00:02:15.520 --> 00:02:18.780
This inspired me to
go back to school,

53
00:02:18.780 --> 00:02:22.730
and I pursued my graduate
certification at Harvard

54
00:02:22.730 --> 00:02:24.090
in data science.

55
00:02:24.090 --> 00:02:28.140
One thing led to another,
and I came back to Houston

56
00:02:28.140 --> 00:02:31.500
to take on my data
science manager role.

57
00:02:31.500 --> 00:02:33.580
SAM RANSBOTHAM: Maybe
let's start with an example

58
00:02:33.580 --> 00:02:35.970
of a project that --
maybe self-healing,

59
00:02:35.970 --> 00:02:39.360
maybe one of these projects --
[is] an example of a concrete

60
00:02:39.360 --> 00:02:43.480
way that you and your team have
used artificial intelligence

61
00:02:43.480 --> 00:02:46.328
in a way that you couldn't
have done before artificial

62
00:02:46.328 --> 00:02:46.870
intelligence.

63
00:02:46.870 --> 00:02:49.220
SARAH KARTHIGAN: There are
plenty of opportunities

64
00:02:49.220 --> 00:02:51.890
for artificial intelligence
in the energy sector,

65
00:02:51.890 --> 00:02:55.010
but before I actually
give you some examples,

66
00:02:55.010 --> 00:02:57.100
I think it's worthwhile
to just understand

67
00:02:57.100 --> 00:02:59.980
the scale of operations
in the energy sector.

68
00:02:59.980 --> 00:03:03.210
So, starting with
the basics here,

69
00:03:03.210 --> 00:03:07.620
energy is constantly
evolving, and when

70
00:03:07.620 --> 00:03:11.750
you think about energy,
it underpins every area

71
00:03:11.750 --> 00:03:13.740
of modern life, right?

72
00:03:13.740 --> 00:03:18.410
So when you think about
mobility or economic prosperity

73
00:03:18.410 --> 00:03:23.300
or social progress, access to
energy underpins all of that.

74
00:03:23.300 --> 00:03:26.800
And at its core, what
we do here at ExxonMobil

75
00:03:26.800 --> 00:03:33.180
is ensure that we are able
to offer reliable, affordable

76
00:03:33.180 --> 00:03:34.520
energy to the masses.

77
00:03:34.520 --> 00:03:38.260
So the scale of energy
itself is quite unimaginable,

78
00:03:38.260 --> 00:03:42.050
and the data that we work
with is also massive.

79
00:03:42.050 --> 00:03:45.170
Big data is not new
to the energy sector,

80
00:03:45.170 --> 00:03:48.880
so we deal with just
huge volumes of data.

81
00:03:48.880 --> 00:03:52.260
Without artificial intelligence,
without data science,

82
00:03:52.260 --> 00:03:53.760
or without machine
learning, you can

83
00:03:53.760 --> 00:03:59.040
imagine the amount of effort
that goes into just processing

84
00:03:59.040 --> 00:04:00.810
and analyzing that data.

85
00:04:00.810 --> 00:04:02.790
And with artificial
intelligence,

86
00:04:02.790 --> 00:04:06.670
it is such a big,
big, big advantage.

87
00:04:06.670 --> 00:04:09.970
The potential that AI
carries with respect

88
00:04:09.970 --> 00:04:13.250
to just overall improving
efficiency and cost

89
00:04:13.250 --> 00:04:15.940
effectiveness is huge.

90
00:04:15.940 --> 00:04:19.410
We also use artificial
intelligence for areas

91
00:04:19.410 --> 00:04:22.100
where we are able to
automate manual tasks,

92
00:04:22.100 --> 00:04:25.560
thereby improving
safety and productivity.

93
00:04:25.560 --> 00:04:29.500
And if we are able to get
people [out] of harm's way,

94
00:04:29.500 --> 00:04:32.070
that's a huge application
for artificial intelligence

95
00:04:32.070 --> 00:04:33.270
in the energy sector.

96
00:04:33.270 --> 00:04:36.310
Additionally, ExxonMobil
is an energy company,

97
00:04:36.310 --> 00:04:39.380
but at its core, again, we
are a technology company,

98
00:04:39.380 --> 00:04:43.770
and so we can use AI
to help our scientists

99
00:04:43.770 --> 00:04:47.380
and engineers in their
decision-making process.

100
00:04:47.380 --> 00:04:50.080
We are able to augment
their decision-making,

101
00:04:50.080 --> 00:04:52.710
connect the dots,
and help discover

102
00:04:52.710 --> 00:04:56.190
insights of value [for]
them at a much faster pace,

103
00:04:56.190 --> 00:04:58.190
so there are plenty
of applications.

104
00:04:58.190 --> 00:05:01.890
My team and I, we have
worked on several use cases.

105
00:05:01.890 --> 00:05:04.880
And again, when you
think about big data,

106
00:05:04.880 --> 00:05:08.130
clearly you can think of
potential applications

107
00:05:08.130 --> 00:05:10.620
of deep learning when it
comes to image processing.

108
00:05:10.620 --> 00:05:13.120
Now whether that's [at] the
front end of the value chain --

109
00:05:13.120 --> 00:05:16.040
you know, you can start with
seismic image processing

110
00:05:16.040 --> 00:05:19.050
to even leak and
flare detection --

111
00:05:19.050 --> 00:05:23.460
so we can use artificial
intelligence for just, again,

112
00:05:23.460 --> 00:05:25.160
plenty of use cases.

113
00:05:25.160 --> 00:05:26.640
So that's one side of things.

114
00:05:26.640 --> 00:05:28.940
You can also use
artificial intelligence --

115
00:05:28.940 --> 00:05:34.620
and we have used it for demand
sensing, for dynamic pricing,

116
00:05:34.620 --> 00:05:36.640
for dynamic revenue management.

117
00:05:36.640 --> 00:05:39.420
Also, we have used
it for trading.

118
00:05:39.420 --> 00:05:42.100
So there [are] just so
many different applications

119
00:05:42.100 --> 00:05:43.720
that my team has
been involved in.

120
00:05:43.720 --> 00:05:48.680
SHERVIN KHODABANDEH: Sarah, tell
us a bit about self-healing.

121
00:05:48.680 --> 00:05:56.230
I think you mentioned building
AI systems that can preempt

122
00:05:56.230 --> 00:06:00.180
issues or problems or
errors or faults --

123
00:06:00.180 --> 00:06:01.930
I don't want to put
words in your mouth --

124
00:06:01.930 --> 00:06:03.700
without human intervention.

125
00:06:03.700 --> 00:06:06.500
Could you give us some
examples of those?

126
00:06:06.500 --> 00:06:09.360
SARAH KARTHIGAN: It all
starts with monitoring, right?

127
00:06:09.360 --> 00:06:11.860
How well can we
monitor our systems,

128
00:06:11.860 --> 00:06:15.700
capture the right type of
data, and then integrate

129
00:06:15.700 --> 00:06:19.240
data, which is probably
sitting across silos today?

130
00:06:19.240 --> 00:06:22.250
It all begins with
that: capturing the data

131
00:06:22.250 --> 00:06:24.620
and bringing it all
together and integrating it

132
00:06:24.620 --> 00:06:26.650
so you're able to
have visibility

133
00:06:26.650 --> 00:06:29.960
across the different silos
that we have in place.

134
00:06:29.960 --> 00:06:31.800
It starts with observability.

135
00:06:31.800 --> 00:06:33.900
And then, once you
have the data in place,

136
00:06:33.900 --> 00:06:36.470
now we are talking about:
How can we utilize the data?

137
00:06:36.470 --> 00:06:38.010
How can we analyze it?

138
00:06:38.010 --> 00:06:40.480
How can we teach a machine?

139
00:06:40.480 --> 00:06:45.210
How can we train a machine
to extract insights out

140
00:06:45.210 --> 00:06:48.550
of that data, to look at
patterns, to see what typically

141
00:06:48.550 --> 00:06:50.910
happens before an
incident occurs?

142
00:06:50.910 --> 00:06:54.230
It is able to look
for those patterns.

143
00:06:54.230 --> 00:06:58.310
It's able to understand the
history and detect anomalies,

144
00:06:58.310 --> 00:07:02.790
and thereby it is able to
prompt -- either an end user,

145
00:07:02.790 --> 00:07:05.040
or you can just go ahead
and close the loop out with

146
00:07:05.040 --> 00:07:08.590
automation altogether --
and kick off the necessary

147
00:07:08.590 --> 00:07:12.000
automations that need to
happen, need to occur,

148
00:07:12.000 --> 00:07:15.430
so we are able to remediate the
issue even before it becomes

149
00:07:15.430 --> 00:07:16.160
an issue.

150
00:07:16.160 --> 00:07:18.650
That is kind of the life
cycle of self-healing.

151
00:07:18.650 --> 00:07:20.830
SHERVIN KHODABANDEH:
Yeah, that's very helpful.

152
00:07:20.830 --> 00:07:24.180
And tell us a bit
about the number

153
00:07:24.180 --> 00:07:25.670
of use cases, if you will.

154
00:07:25.670 --> 00:07:30.010
How big is this group's
span of impact and work?

155
00:07:30.010 --> 00:07:32.560
SARAH KARTHIGAN: There
are multiple groups

156
00:07:32.560 --> 00:07:34.850
within ExxonMobil, because,
as you were saying,

157
00:07:34.850 --> 00:07:37.060
given the scale of
the company, it's

158
00:07:37.060 --> 00:07:40.240
not possible to just centralize
all of the data science

159
00:07:40.240 --> 00:07:44.730
capability in just one group,
so we do have data scientists.

160
00:07:44.730 --> 00:07:48.010
We have AI engineers --
machine learning engineers --

161
00:07:48.010 --> 00:07:51.120
embedded into the different
business functions so they are

162
00:07:51.120 --> 00:07:53.970
able to work very closely
with the business.

163
00:07:53.970 --> 00:07:57.200
And the opportunities
-- there are many.

164
00:07:57.200 --> 00:08:01.400
We are working on a
myriad of those use cases,

165
00:08:01.400 --> 00:08:03.015
and they only continue to grow.

166
00:08:03.015 --> 00:08:04.890
SAM RANSBOTHAM: Who
initiates these projects?

167
00:08:04.890 --> 00:08:06.735
Are these things that
your group comes up

168
00:08:06.735 --> 00:08:08.860
with, or [do] the business
units bring them to you?

169
00:08:08.860 --> 00:08:11.360
What's the working
relationship there?

170
00:08:11.360 --> 00:08:14.800
SARAH KARTHIGAN: The nature
of the AI project, as well as

171
00:08:14.800 --> 00:08:16.800
who initiates them.

172
00:08:16.800 --> 00:08:19.360
It typically comes down
to where a business

173
00:08:19.360 --> 00:08:23.430
line is in their AI adoption
and utilization journey.

174
00:08:23.430 --> 00:08:26.690
If they are in the early
stages, what you will see

175
00:08:26.690 --> 00:08:31.340
is typically they are looking
at a few potential use cases.

176
00:08:31.340 --> 00:08:34.960
They are exploring a few
enterprise-scale opportunities.

177
00:08:34.960 --> 00:08:36.620
That's where it kind of starts.

178
00:08:36.620 --> 00:08:40.860
But as they continue
down that maturity curve,

179
00:08:40.860 --> 00:08:42.640
you will notice that
now we're talking

180
00:08:42.640 --> 00:08:46.290
about systemic introduction
of AI capabilities

181
00:08:46.290 --> 00:08:48.160
into core businesses.

182
00:08:48.160 --> 00:08:51.990
We're talking about true
enterprise-scale opportunities,

183
00:08:51.990 --> 00:08:55.860
so we are able to drive
data-driven decisions.

184
00:08:55.860 --> 00:08:58.500
And so, depending on
where the business line

185
00:08:58.500 --> 00:09:00.630
is in their journey,
that dictates

186
00:09:00.630 --> 00:09:03.200
the nature of the project
as well as who initiates it.

187
00:09:03.200 --> 00:09:05.810
The more mature a
business line is,

188
00:09:05.810 --> 00:09:08.700
the more the business
lines initiate the projects

189
00:09:08.700 --> 00:09:09.200
themselves.

190
00:09:09.200 --> 00:09:11.580
SAM RANSBOTHAM: What's an
example of one that someone has

191
00:09:11.580 --> 00:09:17.270
initiated, or can you give us
just a very specific "Before AI

192
00:09:17.270 --> 00:09:20.500
they were doing X, and then
they came along and we said,

193
00:09:20.500 --> 00:09:23.570
'Hey, let's use artificial
intelligence and then we can do

194
00:09:23.570 --> 00:09:24.710
Y'?"

195
00:09:24.710 --> 00:09:26.100
What's the difference?

196
00:09:26.100 --> 00:09:29.060
And can you give us some
concretes around one of those?

197
00:09:29.060 --> 00:09:32.390
SARAH KARTHIGAN: I'll start
with a simple example.

198
00:09:32.390 --> 00:09:33.930
I touched on this earlier.

199
00:09:33.930 --> 00:09:36.620
ExxonMobil is a very
data-rich company,

200
00:09:36.620 --> 00:09:39.390
so big data is not new to us.

201
00:09:39.390 --> 00:09:42.460
There's data that is
locked up in salt mines,

202
00:09:42.460 --> 00:09:45.060
so we have huge volumes of data.

203
00:09:45.060 --> 00:09:49.060
In the past, some
of our geoscientists

204
00:09:49.060 --> 00:09:53.860
and geophysicists,
they had to process

205
00:09:53.860 --> 00:09:58.100
a lot of unstructured
data, pretty much manually.

206
00:09:58.100 --> 00:10:01.740
And they were the ones who
were connecting the dots.

207
00:10:01.740 --> 00:10:03.600
These were the subject
matter experts,

208
00:10:03.600 --> 00:10:06.570
so they were ingesting all
of this unstructured data,

209
00:10:06.570 --> 00:10:08.420
and they were
connecting the dots,

210
00:10:08.420 --> 00:10:12.880
and they were identifying
the right place for us

211
00:10:12.880 --> 00:10:14.290
to go pursue.

212
00:10:14.290 --> 00:10:16.830
But now, with the introduction
of artificial intelligence,

213
00:10:16.830 --> 00:10:20.110
we were able to build an
intelligent system that,

214
00:10:20.110 --> 00:10:22.180
using natural
language processing,

215
00:10:22.180 --> 00:10:25.140
had us able to ingest
huge volumes of data.

216
00:10:25.140 --> 00:10:28.440
And we're able to
train that system

217
00:10:28.440 --> 00:10:30.730
to look for the right
type of patterns

218
00:10:30.730 --> 00:10:34.420
and to help augment the
decisions that a geoscientist

219
00:10:34.420 --> 00:10:36.140
or a geophysicist would make.

220
00:10:36.140 --> 00:10:40.592
So that is one example of how
we use machine learning insight.

221
00:10:40.592 --> 00:10:42.800
SHERVIN KHODABANDEH: I was
going to ask you, Sarah --

222
00:10:42.800 --> 00:10:46.600
it seems like there is a
large amount of human-AI

223
00:10:46.600 --> 00:10:50.730
collaboration that has to happen
in this example that you gave,

224
00:10:50.730 --> 00:10:55.160
because we've got to imagine
that a series of decisions that

225
00:10:55.160 --> 00:10:59.380
used to be performed by
human experts and geologists

226
00:10:59.380 --> 00:11:03.080
and engineers that
is, over time,

227
00:11:03.080 --> 00:11:07.890
being augmented and maybe
even entirely automatically

228
00:11:07.890 --> 00:11:13.810
performed by AI must have gone
through a pretty robust journey

229
00:11:13.810 --> 00:11:18.160
to get to a level where
those experts are comfortable

230
00:11:18.160 --> 00:11:22.910
and actually seek out the
machine rather than rely

231
00:11:22.910 --> 00:11:23.890
on their judgment.

232
00:11:23.890 --> 00:11:27.600
So comment a bit about
how that process happens

233
00:11:27.600 --> 00:11:32.640
and how you bring the
experts and the geologists

234
00:11:32.640 --> 00:11:35.630
and the engineers and others
from the old-school way

235
00:11:35.630 --> 00:11:36.810
to the new-school way.

236
00:11:36.810 --> 00:11:37.880
What does that feel like?

237
00:11:37.880 --> 00:11:40.740
SARAH KARTHIGAN:
It is a journey,

238
00:11:40.740 --> 00:11:44.540
and it starts with, No.

239
00:11:44.540 --> 00:11:50.450
1, understanding what
is the appetite for new,

240
00:11:50.450 --> 00:11:55.380
emerging solutions
with the end-user base,

241
00:11:55.380 --> 00:11:59.430
because this is not just
a technology challenge;

242
00:11:59.430 --> 00:12:02.520
this is very much a
cultural challenge.

243
00:12:02.520 --> 00:12:09.000
And then, of course, we make
sure that we have advocates

244
00:12:09.000 --> 00:12:15.270
in the business before we start
on any of these AI pilots, AI

245
00:12:15.270 --> 00:12:19.430
solutions because
ultimately, the end

246
00:12:19.430 --> 00:12:21.283
users need to be bought in.

247
00:12:21.283 --> 00:12:22.950
They shouldn't be
fighting the solution.

248
00:12:22.950 --> 00:12:24.900
They should very
much be the ones

249
00:12:24.900 --> 00:12:27.330
who are adopting those
solutions and who

250
00:12:27.330 --> 00:12:30.090
are helping propagate
the changes that this

251
00:12:30.090 --> 00:12:31.120
would produce.

252
00:12:31.120 --> 00:12:36.160
We have seen that having a very
robust management of change

253
00:12:36.160 --> 00:12:40.590
process is crucial for the
adoption of an AI solution,

254
00:12:40.590 --> 00:12:42.950
for it to become a success.

255
00:12:42.950 --> 00:12:46.190
And what we have
also learned is,

256
00:12:46.190 --> 00:12:49.670
giving the end users an
under-the-hood experience

257
00:12:49.670 --> 00:12:52.670
of what the tech actually
does -- what it brings --

258
00:12:52.670 --> 00:12:54.130
is extremely helpful.

259
00:12:54.130 --> 00:12:55.710
They are able to
see that this is

260
00:12:55.710 --> 00:12:57.960
going to augment what they're
doing [and is] not going

261
00:12:57.960 --> 00:12:58.720
to replace them.

262
00:12:58.720 --> 00:13:00.460
SAM RANSBOTHAM: What
is their reaction?

263
00:13:00.460 --> 00:13:02.255
When you give them
this solution that

264
00:13:02.255 --> 00:13:04.630
does a lot of what they have
been used to [doing] before,

265
00:13:04.630 --> 00:13:05.560
what is their reaction?

266
00:13:05.560 --> 00:13:06.210
How do they feel?

267
00:13:06.210 --> 00:13:06.918
What do they say?

268
00:13:06.918 --> 00:13:08.850
SARAH KARTHIGAN:
They actually love it

269
00:13:08.850 --> 00:13:11.320
when they realize that
the machine is actually

270
00:13:11.320 --> 00:13:12.270
helping them.

271
00:13:12.270 --> 00:13:15.130
And sometimes it is
able to even lead them

272
00:13:15.130 --> 00:13:20.840
to areas that they may have
not checked themselves.

273
00:13:20.840 --> 00:13:23.400
I have seen that the
partnership goes really,

274
00:13:23.400 --> 00:13:27.110
really well once they understand
the value that the new solution

275
00:13:27.110 --> 00:13:28.360
is able to bring to the table.

276
00:13:28.360 --> 00:13:31.790
SHERVIN KHODABANDEH: You led,
actually, in your response

277
00:13:31.790 --> 00:13:37.670
to this question with several
nontechnical factors first,

278
00:13:37.670 --> 00:13:38.170
right?

279
00:13:38.170 --> 00:13:43.370
So, "What's your appetite,
what's the openness to change,

280
00:13:43.370 --> 00:13:45.710
and how badly do you want it?"

281
00:13:45.710 --> 00:13:47.180
Which is really
quite insightful,

282
00:13:47.180 --> 00:13:49.590
because over the
last 10 years, it's

283
00:13:49.590 --> 00:13:54.290
just been indexed so much toward
the technical side of things,

284
00:13:54.290 --> 00:13:57.700
and then the change management
becomes an afterthought,

285
00:13:57.700 --> 00:14:00.590
and I was really energized
that you actually

286
00:14:00.590 --> 00:14:03.900
led with the change management:
"Before I do anything,

287
00:14:03.900 --> 00:14:05.830
before I write a
single line of code,

288
00:14:05.830 --> 00:14:08.200
how badly do you want it?"

289
00:14:08.200 --> 00:14:11.090
I want to follow on
the appetite question.

290
00:14:11.090 --> 00:14:15.620
The first time I was offered
sushi, my appetite for it

291
00:14:15.620 --> 00:14:16.550
was zero.

292
00:14:16.550 --> 00:14:21.060
But when somebody effectively
forced me to try it,

293
00:14:21.060 --> 00:14:23.090
then it sort of became
my [go-to] food.

294
00:14:23.090 --> 00:14:30.600
So how do you balance that act
of not forcing the end user,

295
00:14:30.600 --> 00:14:36.760
but also helping them
understand that what they think

296
00:14:36.760 --> 00:14:39.380
their appetite is
before they try

297
00:14:39.380 --> 00:14:42.715
it is going to be different
than what their appetite will

298
00:14:42.715 --> 00:14:43.590
be after they try it?

299
00:14:43.590 --> 00:14:47.130
SARAH KARTHIGAN: When I
first founded the group,

300
00:14:47.130 --> 00:14:51.470
when I had my first
set of data scientists,

301
00:14:51.470 --> 00:14:55.790
we actually met with quite a lot
of skepticism, to your point --

302
00:14:55.790 --> 00:14:59.570
so a lot of people thinking,
"All of this is just hype.

303
00:14:59.570 --> 00:15:00.530
Why are we doing this?

304
00:15:00.530 --> 00:15:01.850
We know what we are doing.

305
00:15:01.850 --> 00:15:05.250
We have done what we
do very successfully.

306
00:15:05.250 --> 00:15:07.380
So why do we have to change it?"

307
00:15:07.380 --> 00:15:09.860
So when we started
out, it really

308
00:15:09.860 --> 00:15:12.680
came down to demonstrating
the art of the possible.

309
00:15:12.680 --> 00:15:17.190
We were knocking [on] a lot
of doors and asking people,

310
00:15:17.190 --> 00:15:19.030
"Hey, just give us your data.

311
00:15:19.030 --> 00:15:21.660
And you don't have to
even engage with us,"

312
00:15:21.660 --> 00:15:25.060
because folks were at
that time a little bit

313
00:15:25.060 --> 00:15:28.470
skeptical about the amount
of time it would require

314
00:15:28.470 --> 00:15:32.980
on their part, and they were not
necessarily ready to offer that

315
00:15:32.980 --> 00:15:34.020
at the get-go.

316
00:15:34.020 --> 00:15:37.190
So we started out with,
"Just give us your data,

317
00:15:37.190 --> 00:15:41.370
and let us come back to you with
what we can discover on our own

318
00:15:41.370 --> 00:15:44.430
and see if that is of
interest to you or not."

319
00:15:44.430 --> 00:15:47.570
SHERVIN KHODABANDEH: And now
you have many sushi restaurants?

320
00:15:47.570 --> 00:15:48.820
SARAH KARTHIGAN: Very much so.

321
00:15:48.820 --> 00:15:51.700
SAM RANSBOTHAM: An
all-you-can-eat buffet.

322
00:15:51.700 --> 00:15:55.780
So, let's say that you've got
these people somewhat convinced

323
00:15:55.780 --> 00:15:57.690
and interested,
and then you start

324
00:15:57.690 --> 00:15:59.990
to put things into production.

325
00:15:59.990 --> 00:16:01.598
How do you keep them going?

326
00:16:01.598 --> 00:16:02.890
How do you keep them improving?

327
00:16:02.890 --> 00:16:05.230
How do you keep them
continuously getting better?

328
00:16:05.230 --> 00:16:07.210
Do you have processes
around that,

329
00:16:07.210 --> 00:16:09.710
and, if so, how
is that organized?

330
00:16:09.710 --> 00:16:11.430
SARAH KARTHIGAN: I'll
tell you this much:

331
00:16:11.430 --> 00:16:14.090
It's been an interesting
learning experience.

332
00:16:14.090 --> 00:16:16.630
Because it's one thing
to go build out a model.

333
00:16:16.630 --> 00:16:19.120
It's one thing to go ahead
and create a prototype

334
00:16:19.120 --> 00:16:20.540
and have everything working.

335
00:16:20.540 --> 00:16:22.410
But it's another
thing altogether

336
00:16:22.410 --> 00:16:24.670
when you're trying
to operationalize it.

337
00:16:24.670 --> 00:16:29.330
After you operationalize AI
solutions, what we have learned

338
00:16:29.330 --> 00:16:30.050
is, No.

339
00:16:30.050 --> 00:16:33.370
1, [in order] to make sure
that it is fully integrated

340
00:16:33.370 --> 00:16:36.050
into the business processes,
there are several things

341
00:16:36.050 --> 00:16:39.750
that you have to be aware
of and keep tabs on.

342
00:16:39.750 --> 00:16:44.130
We ensure, after a solution
has been operationalized,

343
00:16:44.130 --> 00:16:46.440
that it is being monitored.

344
00:16:46.440 --> 00:16:48.140
So that is extremely important.

345
00:16:48.140 --> 00:16:51.450
Now, we learned very quickly
[that] you cannot monitor all

346
00:16:51.450 --> 00:16:54.770
the features of the model, so
there are some features that

347
00:16:54.770 --> 00:16:59.440
you have to home in on that
have the potential to disrupt --

348
00:16:59.440 --> 00:17:02.590
to, I would say, not
necessarily break the model,

349
00:17:02.590 --> 00:17:06.089
but it has the greatest
potential to impact

350
00:17:06.089 --> 00:17:06.920
the predictions.

351
00:17:06.920 --> 00:17:09.210
So we want to home in on
those types of features

352
00:17:09.210 --> 00:17:14.200
and monitor them and see if
concept drift is setting in,

353
00:17:14.200 --> 00:17:18.020
because once a model
moves into production,

354
00:17:18.020 --> 00:17:19.160
it starts degrading.

355
00:17:19.160 --> 00:17:20.200
That's the reality.

356
00:17:20.200 --> 00:17:23.089
So we need to ensure that we are
keeping our eyes on the model

357
00:17:23.089 --> 00:17:26.470
to make sure that the
predictions are still accurate,

358
00:17:26.470 --> 00:17:28.800
that they are still useful.

359
00:17:28.800 --> 00:17:30.960
We also make sure
that our models

360
00:17:30.960 --> 00:17:34.610
are being retrained with the
latest and the greatest data.

361
00:17:34.610 --> 00:17:39.240
We are looking into adopting
a weighting mechanism

362
00:17:39.240 --> 00:17:42.860
so that more recent data
is weighted [more] heavily

363
00:17:42.860 --> 00:17:45.350
in retraining a model
than older data.

364
00:17:45.350 --> 00:17:48.620
And we're also looking into
continuous improvement,

365
00:17:48.620 --> 00:17:51.350
continuous training,
and continuous learning

366
00:17:51.350 --> 00:17:52.800
methodologies for our models.

367
00:17:52.800 --> 00:17:56.020
So these are some things that
we do once a solution has

368
00:17:56.020 --> 00:17:57.218
been productized.

369
00:17:57.218 --> 00:17:59.010
SAM RANSBOTHAM: Within
your organization --

370
00:17:59.010 --> 00:18:01.550
and that's about how
the models get better --

371
00:18:01.550 --> 00:18:04.930
how do you help the
end users get better?

372
00:18:04.930 --> 00:18:08.230
You mentioned initially working
with them to make sure that

373
00:18:08.230 --> 00:18:11.430
it's not too much resistance to
even consider trying a model --

374
00:18:11.430 --> 00:18:14.800
that's Shervin even trying
sushi in the first place --

375
00:18:14.800 --> 00:18:18.490
but how do you get them to
appreciate the finer culinary

376
00:18:18.490 --> 00:18:19.070
aspects?

377
00:18:19.070 --> 00:18:21.020
I mean, maybe for all
we know, Shervin's

378
00:18:21.020 --> 00:18:23.810
stuck on the same piece of
sushi that he started with years

379
00:18:23.810 --> 00:18:26.790
and years ago, but there [are]
lots of other types out there.

380
00:18:26.790 --> 00:18:30.250
How are you growing that
understanding in the user base?

381
00:18:30.250 --> 00:18:32.825
SARAH KARTHIGAN: We have
several efforts in progress

382
00:18:32.825 --> 00:18:34.450
within the company
where we are looking

383
00:18:34.450 --> 00:18:38.440
at upskilling our employees,
making sure that we

384
00:18:38.440 --> 00:18:41.390
are able to train them on
the latest and the greatest

385
00:18:41.390 --> 00:18:43.890
emerging technologies
so they have enough

386
00:18:43.890 --> 00:18:46.610
of an understanding
of what AI offers,

387
00:18:46.610 --> 00:18:49.430
what are the potential
use cases we can consider.

388
00:18:49.430 --> 00:18:52.558
So there's a lot of training
work that is happening.

389
00:18:52.558 --> 00:18:54.350
SAM RANSBOTHAM: What
are you excited about?

390
00:18:54.350 --> 00:18:55.767
I mean, what's new
and what are we

391
00:18:55.767 --> 00:18:58.470
going to read about tomorrow
that ExxonMobil is doing

392
00:18:58.470 --> 00:18:59.880
with artificial intelligence?

393
00:18:59.880 --> 00:19:02.560
What's something you're excited
about, either a technology

394
00:19:02.560 --> 00:19:03.225
or a project?

395
00:19:03.225 --> 00:19:04.212


396
00:19:04.212 --> 00:19:06.170
SARAH KARTHIGAN: What
I'm really excited about,

397
00:19:06.170 --> 00:19:09.100
and what I hope you
get to read about soon,

398
00:19:09.100 --> 00:19:13.120
is this self-healing pilot
that we are gearing up to do.

399
00:19:13.120 --> 00:19:17.050
The self-healing
pilot is looking

400
00:19:17.050 --> 00:19:22.750
at taking an application that
is end-user facing and seeing

401
00:19:22.750 --> 00:19:27.550
how many of these self-healing
wins we can realize.

402
00:19:27.550 --> 00:19:31.340
We have been investing
our time in building out

403
00:19:31.340 --> 00:19:36.830
the foundation, the fabric that
is important to really bring

404
00:19:36.830 --> 00:19:38.910
this whole solution
together, so now we

405
00:19:38.910 --> 00:19:42.530
are very much excited about
testing that out and putting

406
00:19:42.530 --> 00:19:44.580
the strategy into action.

407
00:19:44.580 --> 00:19:47.660
SHERVIN KHODABANDEH: Sarah, as
you think about your own team

408
00:19:47.660 --> 00:19:51.420
-- building and cultivating
and expanding that team --

409
00:19:51.420 --> 00:19:55.240
two questions: What are you
looking for in the candidates

410
00:19:55.240 --> 00:19:56.320
that you're bringing in?

411
00:19:56.320 --> 00:20:00.070
What are some technical and
nontechnical capabilities

412
00:20:00.070 --> 00:20:01.070
you're looking for?

413
00:20:01.070 --> 00:20:02.480
That's my first question.

414
00:20:02.480 --> 00:20:03.350
And No.

415
00:20:03.350 --> 00:20:06.720
2 is, how do you keep them
interested and excited

416
00:20:06.720 --> 00:20:11.120
in data science and AI, with
everything that's going on

417
00:20:11.120 --> 00:20:14.430
and all the other options
out there for them?

418
00:20:14.430 --> 00:20:16.840
SARAH KARTHIGAN: Let
me start with answering

419
00:20:16.840 --> 00:20:17.730
your second question.

420
00:20:17.730 --> 00:20:19.970
So, how do we keep
them interested?

421
00:20:19.970 --> 00:20:22.410
We keep them interested
by exposing them

422
00:20:22.410 --> 00:20:24.920
to diverse use cases.

423
00:20:24.920 --> 00:20:27.880
You don't have to
leave the company

424
00:20:27.880 --> 00:20:30.970
to work on a finance problem.

425
00:20:30.970 --> 00:20:34.300
There are opportunities
here within the company.

426
00:20:34.300 --> 00:20:37.660
And so just the myriad of
use cases that the data

427
00:20:37.660 --> 00:20:40.660
scientist gets to work
on, gets to solve,

428
00:20:40.660 --> 00:20:44.200
is what I have found
that keeps them excited,

429
00:20:44.200 --> 00:20:47.550
that wants them to
continue their career here

430
00:20:47.550 --> 00:20:48.570
within the company.

431
00:20:48.570 --> 00:20:53.960
So that is our secret to
retaining talent internally.

432
00:20:53.960 --> 00:20:56.700
As far as what do I
look for in a candidate?

433
00:20:56.700 --> 00:21:01.090
I am quite keen on diversity.

434
00:21:01.090 --> 00:21:05.840
I don't want a team
that is an echo chamber.

435
00:21:05.840 --> 00:21:09.760
I specifically go
seek out skills

436
00:21:09.760 --> 00:21:13.080
that are in adjacent areas.

437
00:21:13.080 --> 00:21:16.820
I have had data scientists
on my team whose background

438
00:21:16.820 --> 00:21:18.700
is biostatistics.

439
00:21:18.700 --> 00:21:23.520
I have even had people with
English and political majors.

440
00:21:23.520 --> 00:21:26.310
Of course, now I am looking
for people with data science

441
00:21:26.310 --> 00:21:29.310
skills, too, so either they
had an undergrad degree

442
00:21:29.310 --> 00:21:33.060
in that area but then they
also studied data science.

443
00:21:33.060 --> 00:21:35.170
I go seek out those
types of candidates

444
00:21:35.170 --> 00:21:38.080
because it's extremely
important to have

445
00:21:38.080 --> 00:21:40.650
very diverse
viewpoints at the table

446
00:21:40.650 --> 00:21:43.250
when you are trying
to solve a problem.

447
00:21:43.250 --> 00:21:47.240
I am looking for someone who's
curious, who is very much

448
00:21:47.240 --> 00:21:49.510
interested in problem-solving.

449
00:21:49.510 --> 00:21:53.550
And, again, what excites
them is challenging problems,

450
00:21:53.550 --> 00:21:57.990
and we're talking about a scale
that is truly unimaginable.

451
00:21:57.990 --> 00:21:59.690
SHERVIN KHODABANDEH:
Sarah, you've

452
00:21:59.690 --> 00:22:02.300
been named a leader in tech.

453
00:22:02.300 --> 00:22:05.670
You've been named one of [the]
25 most influential women

454
00:22:05.670 --> 00:22:08.370
in energy, in tech.

455
00:22:08.370 --> 00:22:13.990
What do you think companies
could do more of to ensure

456
00:22:13.990 --> 00:22:19.280
a more fair gender balance
in data science roles,

457
00:22:19.280 --> 00:22:23.100
and what do you think data
scientists out there --

458
00:22:23.100 --> 00:22:25.900
female data scientists that are
just starting their careers --

459
00:22:25.900 --> 00:22:28.240
could be doing more of?

460
00:22:28.240 --> 00:22:32.780
SARAH KARTHIGAN: I would say
it all starts with providing

461
00:22:32.780 --> 00:22:34.350
equal opportunities.

462
00:22:34.350 --> 00:22:38.680
I am here because I
got the opportunity

463
00:22:38.680 --> 00:22:42.700
to demonstrate what I can do,
what I'm capable of doing.

464
00:22:42.700 --> 00:22:46.550
Making sure that, that
window of opportunity

465
00:22:46.550 --> 00:22:51.380
is truly open for both
women and men is crucial,

466
00:22:51.380 --> 00:22:53.360
so that's where it all starts.

467
00:22:53.360 --> 00:22:57.970
For an aspiring data
scientist, for girls

468
00:22:57.970 --> 00:23:00.920
in middle school, high school,
who are even considering

469
00:23:00.920 --> 00:23:04.040
pursuing a STEM career,
my encouragement

470
00:23:04.040 --> 00:23:06.200
would be, yes,
absolutely, we need you.

471
00:23:06.200 --> 00:23:10.280
Women bring a perspective
that is so different

472
00:23:10.280 --> 00:23:15.610
and that is extremely needed
in the work environment.

473
00:23:15.610 --> 00:23:19.440
And especially -- we talked
about responsible AI.

474
00:23:19.440 --> 00:23:25.070
It is important to have that
type of a diverse perspective

475
00:23:25.070 --> 00:23:29.700
right from the get-go -- right
from building a strategy,

476
00:23:29.700 --> 00:23:31.260
all the way to execution.

477
00:23:31.260 --> 00:23:33.310
It should not be
an afterthought.

478
00:23:33.310 --> 00:23:37.250
You shouldn't try to slap
on "Hey, let me go ahead

479
00:23:37.250 --> 00:23:40.000
and make sure I
address diversity

480
00:23:40.000 --> 00:23:41.090
and inclusion at the end."

481
00:23:41.090 --> 00:23:42.700
No; that's not how it works.

482
00:23:42.700 --> 00:23:46.930
You start with that,
and that is crucial.

483
00:23:46.930 --> 00:23:49.760
And women play a key role
in making that happen.

484
00:23:49.760 --> 00:23:52.410
SHERVIN KHODABANDEH: And
what do you think women

485
00:23:52.410 --> 00:23:56.760
in data science who are either
just starting their careers

486
00:23:56.760 --> 00:24:00.300
or are in their
academic training, what

487
00:24:00.300 --> 00:24:03.990
do you think they could
be doing to seek out

488
00:24:03.990 --> 00:24:06.100
the right opportunities
for themselves?

489
00:24:06.100 --> 00:24:07.555
What's your advice for them?

490
00:24:07.555 --> 00:24:08.770


491
00:24:08.770 --> 00:24:14.160
SARAH KARTHIGAN: I would say
that ensure you have really

492
00:24:14.160 --> 00:24:20.770
good examples of either a
capstone project or experiences

493
00:24:20.770 --> 00:24:24.823
with internships or
co-op opportunities --

494
00:24:24.823 --> 00:24:26.740
whatever you want to
call those experiences --

495
00:24:26.740 --> 00:24:30.050
with companies where you
have dealt with real data.

496
00:24:30.050 --> 00:24:33.380
I think that absolutely
augments your resume.

497
00:24:33.380 --> 00:24:38.440
And then, on top of that, once
you have found that entry point

498
00:24:38.440 --> 00:24:43.030
into a company, just feel
free to speak up and bring

499
00:24:43.030 --> 00:24:45.850
your solutions very
vocally to the table.

500
00:24:45.850 --> 00:24:46.910
That's what I would say.

501
00:24:46.910 --> 00:24:49.150
SAM RANSBOTHAM: Today
we learned a lot

502
00:24:49.150 --> 00:24:52.400
about starting with the
organizational aspects

503
00:24:52.400 --> 00:24:54.530
of an artificial
intelligence change

504
00:24:54.530 --> 00:24:56.410
versus the technical aspects.

505
00:24:56.410 --> 00:24:59.070
[We] learned about leading
with the idea of showing people

506
00:24:59.070 --> 00:25:01.310
what's possible and what
the potential can be

507
00:25:01.310 --> 00:25:03.400
from artificial intelligence.

508
00:25:03.400 --> 00:25:05.590
We learned about the
many steps in the process

509
00:25:05.590 --> 00:25:08.090
of data that are
fraught with peril

510
00:25:08.090 --> 00:25:10.338
but organizations can overcome.

511
00:25:10.338 --> 00:25:12.130
And I really appreciate
you taking the time

512
00:25:12.130 --> 00:25:13.300
to talk with us today, Sarah.

513
00:25:13.300 --> 00:25:14.080
Thanks for joining us

514
00:25:14.080 --> 00:25:14.850
SHERVIN KHODABANDEH:
Thank you, Sarah.

515
00:25:14.850 --> 00:25:16.080
SARAH KARTHIGAN: It's
been my pleasure.

516
00:25:16.080 --> 00:25:16.780
Thank you.

517
00:25:16.780 --> 00:25:18.890
SAM RANSBOTHAM: In
our next episode,

518
00:25:18.890 --> 00:25:20.840
we'll talk with Doug
Hamilton about how

519
00:25:20.840 --> 00:25:23.690
Nasdaq uses AI to mitigate
high-risk situations.

520
00:25:23.690 --> 00:25:24.730
Please join us.

521
00:25:24.730 --> 00:25:27.410
ALLISON RYDER:
Thanks for listening

522
00:25:27.410 --> 00:25:28.910
to Me, Myself, and AI.

523
00:25:28.910 --> 00:25:31.360
We believe, like you,
that the conversation

524
00:25:31.360 --> 00:25:33.590
about AI implementation
doesn't start and stop

525
00:25:33.590 --> 00:25:34.770
with this podcast.

526
00:25:34.770 --> 00:25:37.260
That's why we've created a
group on LinkedIn, specifically

527
00:25:37.260 --> 00:25:38.380
for leaders like you.

528
00:25:38.380 --> 00:25:41.120
It's called AI for Leaders,
and if you join us,

529
00:25:41.120 --> 00:25:43.140
you can chat with show
creators and hosts,

530
00:25:43.140 --> 00:25:46.770
ask your own questions, share
insights, and gain access

531
00:25:46.770 --> 00:25:49.250
to valuable resources
about AI implementation

532
00:25:49.250 --> 00:25:51.350
from MIT SMR and BCG.

533
00:25:51.350 --> 00:25:56.420
You can access it by visiting
mitsmr.com/AIforLeaders.

534
00:25:56.420 --> 00:25:59.190
We'll put that link
in the show notes,

535
00:25:59.190 --> 00:26:01.620
and we hope to see you there.

536
00:26:01.620 --> 00:26:07.000