WEBVTT

1
00:00:00.000 --> 00:00:02.140


2
00:00:02.140 --> 00:00:04.880
SAM RANSBOTHAM: Launching
a new AI initiative

3
00:00:04.880 --> 00:00:07.460
is quite different from
launching a new technology

4
00:00:07.460 --> 00:00:09.830
platform like an ERP.

5
00:00:09.830 --> 00:00:13.560
Find out the key differences
on today's episode.

6
00:00:13.560 --> 00:00:16.480
NITZAN MEKEL-BOBROV: I'm
Nitzan Mekel-Bobrov from eBay,

7
00:00:16.480 --> 00:00:18.780
and you're listening
to Me, Myself, and AI.

8
00:00:18.780 --> 00:00:22.380
SAM RANSBOTHAM: Welcome
to Me, Myself, and AI,

9
00:00:22.380 --> 00:00:25.190
a podcast on artificial
intelligence in business.

10
00:00:25.190 --> 00:00:28.480
Each episode, we introduce you
to someone innovating with AI.

11
00:00:28.480 --> 00:00:31.500
I'm Sam Ransbotham, professor
of information systems

12
00:00:31.500 --> 00:00:32.700
at Boston College.

13
00:00:32.700 --> 00:00:35.770
I'm also the guest editor for
the AI and Business Strategy

14
00:00:35.770 --> 00:00:38.610
Big Ideas program at MIT
Sloan Management Review.

15
00:00:38.610 --> 00:00:40.770
SHERVIN KHODABANDEH: And
I'm Shervin Khodabandeh,

16
00:00:40.770 --> 00:00:44.760
senior partner with BCG, and I
also colead BCG's AI practice

17
00:00:44.760 --> 00:00:45.740
in North America.

18
00:00:45.740 --> 00:00:48.820
Together, MIT SMR
and BCG have been

19
00:00:48.820 --> 00:00:52.340
researching AI for six years
now, interviewing hundreds

20
00:00:52.340 --> 00:00:54.900
of practitioners and
surveying thousands

21
00:00:54.900 --> 00:00:59.140
of companies on what it takes
to build and deploy and scale

22
00:00:59.140 --> 00:01:01.320
AI capabilities across
the organization

23
00:01:01.320 --> 00:01:04.642
and really transform the
way organizations operate.

24
00:01:04.642 --> 00:01:06.600
SAM RANSBOTHAM: Shervin
and I are excited today

25
00:01:06.600 --> 00:01:09.420
to be talking with Nitzan
Mekel-Bobrov, the chief AI

26
00:01:09.420 --> 00:01:10.415
officer at eBay.

27
00:01:10.415 --> 00:01:12.540
Nitzan, thanks for taking
the time to talk with us.

28
00:01:12.540 --> 00:01:13.040
Welcome.

29
00:01:13.040 --> 00:01:13.640


30
00:01:13.640 --> 00:01:14.570
NITZAN MEKEL-BOBROV:
Thank you so much.

31
00:01:14.570 --> 00:01:15.528
I'm excited to be here.

32
00:01:15.528 --> 00:01:15.780


33
00:01:15.780 --> 00:01:17.220
SAM RANSBOTHAM: You've got
a relatively new position,

34
00:01:17.220 --> 00:01:18.340
within the last year or so.

35
00:01:18.340 --> 00:01:20.090
Can you tell us what
your role is at eBay?

36
00:01:20.090 --> 00:01:21.650
NITZAN MEKEL-BOBROV: Sure.

37
00:01:21.650 --> 00:01:23.870
Before me, my
predecessors, so to speak,

38
00:01:23.870 --> 00:01:26.320
have all been chief
AI scientists,

39
00:01:26.320 --> 00:01:31.020
and the change to a chief
AI officer was actually

40
00:01:31.020 --> 00:01:36.220
a strategic one, with the
recognition that AI is more

41
00:01:36.220 --> 00:01:38.330
than just the machine
learning models --

42
00:01:38.330 --> 00:01:42.950
that AI is the engineering that
it takes to "productionize"

43
00:01:42.950 --> 00:01:45.660
those models at
scale and, of course,

44
00:01:45.660 --> 00:01:48.880
the business impact
and business use cases.

45
00:01:48.880 --> 00:01:52.390
We really think of AI as
an end-to-end experience.

46
00:01:52.390 --> 00:01:54.627
SAM RANSBOTHAM: All
right; so eBay's

47
00:01:54.627 --> 00:01:55.710
pretty excited about this.

48
00:01:55.710 --> 00:01:57.818
What is eBay hoping
to gain from this?

49
00:01:57.818 --> 00:01:59.860
NITZAN MEKEL-BOBROV: I
think [it's] opportunities

50
00:01:59.860 --> 00:02:03.910
in every facet of our business,
as probably most companies

51
00:02:03.910 --> 00:02:06.480
our size would say as well.

52
00:02:06.480 --> 00:02:10.560
I think for us, probably what
we're most excited about,

53
00:02:10.560 --> 00:02:14.360
and the reason that I joined
eBay late last year --

54
00:02:14.360 --> 00:02:21.080
what made me excited -- is the
ability to create AI-led tools,

55
00:02:21.080 --> 00:02:24.230
essentially, that we put into
the hands of our customers,

56
00:02:24.230 --> 00:02:29.320
both buyers and sellers, to
create their own experiences

57
00:02:29.320 --> 00:02:30.940
that they share with each other.

58
00:02:30.940 --> 00:02:37.060
It's not just about us using AI
to build things, but rather us

59
00:02:37.060 --> 00:02:41.930
building AI tooling to enable
our buyers and sellers to build

60
00:02:41.930 --> 00:02:44.000
things, and I think
that's really exciting.

61
00:02:44.000 --> 00:02:46.277
SAM RANSBOTHAM: Give us an
example of one of those.

62
00:02:46.277 --> 00:02:47.860
NITZAN MEKEL-BOBROV:
Most recently, we

63
00:02:47.860 --> 00:02:52.830
rolled out our new 3D
visualization experience,

64
00:02:52.830 --> 00:02:57.750
which is using computer
vision on the back end

65
00:02:57.750 --> 00:03:01.190
to do the processing and
some of the rendering.

66
00:03:01.190 --> 00:03:05.370
It's not about us
using this technology

67
00:03:05.370 --> 00:03:09.070
to create 3D visualizations,
but rather, we're

68
00:03:09.070 --> 00:03:12.030
enabling, through
our mobile app,

69
00:03:12.030 --> 00:03:15.170
our sellers to
create visualizations

70
00:03:15.170 --> 00:03:18.910
of their own items at
scale, and in a sort

71
00:03:18.910 --> 00:03:22.690
of super easy way that doesn't
require professional equipment.

72
00:03:22.690 --> 00:03:24.240
SAM RANSBOTHAM:
What do you think

73
00:03:24.240 --> 00:03:27.540
that eBay's doing uniquely
in artificial intelligence?

74
00:03:27.540 --> 00:03:28.237


75
00:03:28.237 --> 00:03:30.320
NITZAN MEKEL-BOBROV: I'll
start with the approach,

76
00:03:30.320 --> 00:03:33.020
and then I'll get into
an example or two.

77
00:03:33.020 --> 00:03:35.100
From an approach
perspective, I think

78
00:03:35.100 --> 00:03:37.320
we're going at it
in a unique way

79
00:03:37.320 --> 00:03:40.070
because we're a
two-sided marketplace.

80
00:03:40.070 --> 00:03:42.750
We worry about
buyers and sellers,

81
00:03:42.750 --> 00:03:49.690
and so much of our attention
is on building capabilities

82
00:03:49.690 --> 00:03:54.760
for our customers to use versus
building experiences ourselves

83
00:03:54.760 --> 00:03:55.560
directly.

84
00:03:55.560 --> 00:03:59.960
Building tools for our customers
to build experiences I do think

85
00:03:59.960 --> 00:04:01.340
is unique.

86
00:04:01.340 --> 00:04:03.850
It's unique also from a
technology perspective,

87
00:04:03.850 --> 00:04:06.810
because there's a different
level of resilience that's

88
00:04:06.810 --> 00:04:12.180
needed, and you have to test
a far greater number of ways

89
00:04:12.180 --> 00:04:15.780
in which it can fail
when you're not actually

90
00:04:15.780 --> 00:04:17.870
building the
experience yourself;

91
00:04:17.870 --> 00:04:20.170
you're putting tools in
the hands of others to do.

92
00:04:20.170 --> 00:04:24.530
So it's almost like we
are software as a service

93
00:04:24.530 --> 00:04:27.730
within an e-commerce
company, right?

94
00:04:27.730 --> 00:04:30.080
So that's sort of
one aspect of it.

95
00:04:30.080 --> 00:04:35.100
In terms of specific areas of
focus that are maybe unique,

96
00:04:35.100 --> 00:04:40.560
we are [doubling down]
now on visual experiences.

97
00:04:40.560 --> 00:04:43.510
We can say "computer
vision," but I think of it

98
00:04:43.510 --> 00:04:48.420
a little bit more broadly
than computer vision as an AI

99
00:04:48.420 --> 00:04:49.180
approach.

100
00:04:49.180 --> 00:04:52.250
I'm thinking of it as
intelligent visual experiences

101
00:04:52.250 --> 00:04:55.780
that are immersive,
interactive, adaptive, etc.

102
00:04:55.780 --> 00:04:59.750
3D is sort of the
tip of the iceberg,

103
00:04:59.750 --> 00:05:07.150
but as we get deeper into
the "metaverse" and deeper

104
00:05:07.150 --> 00:05:13.370
and deeper into ways in which
our digital platform is more

105
00:05:13.370 --> 00:05:17.940
than just a place to
do the commerce itself,

106
00:05:17.940 --> 00:05:24.630
we think that visual experiences
in real time, live interaction

107
00:05:24.630 --> 00:05:27.850
between people, ways
of visualizing products

108
00:05:27.850 --> 00:05:31.140
that feel like they're in
your hands versus just being

109
00:05:31.140 --> 00:05:33.420
on the screen is
something that will

110
00:05:33.420 --> 00:05:35.450
be transformational for eBay.

111
00:05:35.450 --> 00:05:38.160
SAM RANSBOTHAM: You've got a
very interesting background:

112
00:05:38.160 --> 00:05:41.830
Bookings.com, Capital One,
Hearst, Boston Scientific.

113
00:05:41.830 --> 00:05:44.080
Maybe you can tell us
a little bit about how

114
00:05:44.080 --> 00:05:45.810
you ended up where you are.

115
00:05:45.810 --> 00:05:48.110
And one of the things
that I want to harp on,

116
00:05:48.110 --> 00:05:50.460
perhaps, is that a
lot of our guests

117
00:05:50.460 --> 00:05:52.890
tend to focus on
the artificial part

118
00:05:52.890 --> 00:05:56.060
of artificial intelligence,
but your background is actually

119
00:05:56.060 --> 00:05:59.160
in the intelligence part, like
the actual human intelligence,

120
00:05:59.160 --> 00:06:02.520
with your research on brains
and how brains evolve,

121
00:06:02.520 --> 00:06:05.490
so I think that's a
fascinating, different angle.

122
00:06:05.490 --> 00:06:08.570
You're coming much more
from the intelligence part

123
00:06:08.570 --> 00:06:09.845
than from the technology part.

124
00:06:09.845 --> 00:06:11.220
How did you get
to where you are,

125
00:06:11.220 --> 00:06:12.817
and how did you
learn these lessons?

126
00:06:12.817 --> 00:06:14.400
NITZAN MEKEL-BOBROV:
It's interesting.

127
00:06:14.400 --> 00:06:19.300
I actually was planning on being
a lab biologist or geneticist.

128
00:06:19.300 --> 00:06:22.290
The problem was that I
was terrible at the bench.

129
00:06:22.290 --> 00:06:26.390
Anything that I needed to use
my hands for never worked.

130
00:06:26.390 --> 00:06:29.060
None of my experiments
worked; everything was a flop.

131
00:06:29.060 --> 00:06:34.990
So I quickly learned, "Well, I'm
good with coding and computer

132
00:06:34.990 --> 00:06:38.230
algorithms; maybe I'll focus
on more theoretical aspects

133
00:06:38.230 --> 00:06:39.230
of biology."

134
00:06:39.230 --> 00:06:41.750
And then I got
immersed in the world

135
00:06:41.750 --> 00:06:44.880
of neuroscience and
computational genomics,

136
00:06:44.880 --> 00:06:46.590
and that's, by
the way, how I got

137
00:06:46.590 --> 00:06:48.390
introduced to neural networks.

138
00:06:48.390 --> 00:06:50.130
It's interesting
because me and all

139
00:06:50.130 --> 00:06:55.000
my peers, that's how we
entered what ended up

140
00:06:55.000 --> 00:06:58.800
being machine learning
applications in business,

141
00:06:58.800 --> 00:07:02.870
and I had no awareness that
engineering, essentially,

142
00:07:02.870 --> 00:07:05.540
and computer science were
playing such a big role

143
00:07:05.540 --> 00:07:10.760
on the other side of a
somewhat academic fence.

144
00:07:10.760 --> 00:07:14.440
To me, it seemed like a
very obvious progression.

145
00:07:14.440 --> 00:07:19.270
As I was working on modeling how
real brains, so to speak, work

146
00:07:19.270 --> 00:07:21.240
and how human
intelligence works,

147
00:07:21.240 --> 00:07:26.270
moving from there into
artificial intelligence

148
00:07:26.270 --> 00:07:28.650
seemed like a very
natural progression,

149
00:07:28.650 --> 00:07:29.990
and I wasn't alone in this.

150
00:07:29.990 --> 00:07:33.340
But then I got introduced
to this whole other set

151
00:07:33.340 --> 00:07:36.610
of folks coming at it
from the other end.

152
00:07:36.610 --> 00:07:40.120
The progression -- you're seeing
me jumping around from one

153
00:07:40.120 --> 00:07:43.220
industry to another
-- isn't accidental.

154
00:07:43.220 --> 00:07:45.800
I spent the first half of
my career in health care

155
00:07:45.800 --> 00:07:47.040
because it was very natural.

156
00:07:47.040 --> 00:07:50.340
I did my graduate
work in, essentially,

157
00:07:50.340 --> 00:07:54.040
machine learning methods
for genomic analysis,

158
00:07:54.040 --> 00:07:56.870
because it was the
human genome era, etc.,

159
00:07:56.870 --> 00:07:59.180
and so I stayed in health care.

160
00:07:59.180 --> 00:08:00.770
But at some point,
I really wanted

161
00:08:00.770 --> 00:08:04.400
to see how AI can be
used in other industries,

162
00:08:04.400 --> 00:08:09.570
and so I fairly purposefully
moved from financial services

163
00:08:09.570 --> 00:08:11.810
to online travel to e-commerce.

164
00:08:11.810 --> 00:08:15.220
SHERVIN KHODABANDEH: As you
have traversed the wide array

165
00:08:15.220 --> 00:08:19.510
of sectors and industries,
what did you find

166
00:08:19.510 --> 00:08:24.280
was one of the biggest
hurdles in getting AI at scale

167
00:08:24.280 --> 00:08:25.580
in these organizations?

168
00:08:25.580 --> 00:08:25.962


169
00:08:25.962 --> 00:08:27.420
NITZAN MEKEL-BOBROV:
Always people;

170
00:08:27.420 --> 00:08:28.618
they always get in the way.

171
00:08:28.618 --> 00:08:29.660
SAM RANSBOTHAM: [Laughs].

172
00:08:29.660 --> 00:08:30.850
Spoken like a true engineer.

173
00:08:30.850 --> 00:08:31.350


174
00:08:31.350 --> 00:08:35.490
NITZAN MEKEL-BOBROV: It's hard
to get a whole group of people

175
00:08:35.490 --> 00:08:39.549
with different incentives
to coordinate together

176
00:08:39.549 --> 00:08:42.120
in a way that is needed.

177
00:08:42.120 --> 00:08:45.490
Doing AI at scale,
and in a way that

178
00:08:45.490 --> 00:08:47.560
could drive
transformational value,

179
00:08:47.560 --> 00:08:51.630
does require a much
broader set of players

180
00:08:51.630 --> 00:08:53.250
playing nicely together.

181
00:08:53.250 --> 00:08:57.200
And while typically
everyone is onboard

182
00:08:57.200 --> 00:09:00.280
that it's the right
answer, the prioritization

183
00:09:00.280 --> 00:09:04.370
of that versus the very
immediate-term business

184
00:09:04.370 --> 00:09:07.370
objectives is what
typically ends up faltering.

185
00:09:07.370 --> 00:09:09.830
SAM RANSBOTHAM: Part
of your background

186
00:09:09.830 --> 00:09:13.190
that I was interested in is
that one of your dissertation

187
00:09:13.190 --> 00:09:16.490
findings was that the
human brain is still

188
00:09:16.490 --> 00:09:17.780
evolving and getting smarter.

189
00:09:17.780 --> 00:09:21.680
I think you may be in a
unique position to compare

190
00:09:21.680 --> 00:09:24.390
but I assume that artificial
intelligence is also

191
00:09:24.390 --> 00:09:25.880
getting smarter.

192
00:09:25.880 --> 00:09:27.890
Tell us a little about
what you see happening

193
00:09:27.890 --> 00:09:29.160
in terms of these rates.

194
00:09:29.160 --> 00:09:31.913
Are the machines
getting smarter faster

195
00:09:31.913 --> 00:09:33.830
than humans are getting
smarter, or are humans

196
00:09:33.830 --> 00:09:35.230
still continuing to outpace?

197
00:09:35.230 --> 00:09:37.690
I'm kind of curious what your
perspective is on those two

198
00:09:37.690 --> 00:09:38.560
things.

199
00:09:38.560 --> 00:09:40.310
NITZAN MEKEL-BOBROV:
You really dusted off

200
00:09:40.310 --> 00:09:44.730
some old things when you were
looking at my background.

201
00:09:44.730 --> 00:09:47.210
I appreciate you digging.

202
00:09:47.210 --> 00:09:49.860
I think there's two things
I would pick up there

203
00:09:49.860 --> 00:09:53.270
that I do find interesting on
a sort of philosophical level

204
00:09:53.270 --> 00:09:54.090
almost.

205
00:09:54.090 --> 00:09:58.500
The first one is that when
we look back in history,

206
00:09:58.500 --> 00:10:03.270
both recent and longer term,
we look at these macro changes,

207
00:10:03.270 --> 00:10:06.570
and then we assume that because
those happen over long periods

208
00:10:06.570 --> 00:10:09.150
of time, that they're
almost episodic --

209
00:10:09.150 --> 00:10:12.780
like it's not something that
you would observe day to day.

210
00:10:12.780 --> 00:10:16.160
But as any geneticist
would tell you,

211
00:10:16.160 --> 00:10:20.670
evolution is just population
genetics happening over

212
00:10:20.670 --> 00:10:22.250
a longer time scale.

213
00:10:22.250 --> 00:10:25.710
It's not as if it's
something that is episodic;

214
00:10:25.710 --> 00:10:27.880
it's something that's
actually continuous.

215
00:10:27.880 --> 00:10:33.070
In that sense, I think
it's not surprising

216
00:10:33.070 --> 00:10:37.510
that humans have
continued to evolve

217
00:10:37.510 --> 00:10:39.350
and are continuing to evolve.

218
00:10:39.350 --> 00:10:42.530
It's just the nature of biology.

219
00:10:42.530 --> 00:10:45.260
What's happened in the most
recent history of the human

220
00:10:45.260 --> 00:10:50.940
species is that the variation
in signals and the speed of that

221
00:10:50.940 --> 00:10:55.790
variation being introduced is
just accelerating massively,

222
00:10:55.790 --> 00:11:01.030
and because of that, we're
able to adapt faster and faster

223
00:11:01.030 --> 00:11:04.690
and faster and in that
sense become "smarter" --

224
00:11:04.690 --> 00:11:07.010
maybe "more adaptive" is a
better way [to say this].

225
00:11:07.010 --> 00:11:09.550
And I think it's
the same with what's

226
00:11:09.550 --> 00:11:11.600
happening with technology now.

227
00:11:11.600 --> 00:11:14.250
There's a lot of discussion
about children being exposed

228
00:11:14.250 --> 00:11:16.980
to digital technologies,
etc., and the rate at which

229
00:11:16.980 --> 00:11:20.930
technology is changing, so the
signals that people are getting

230
00:11:20.930 --> 00:11:23.480
-- the variation in signals --
is just getting more and more

231
00:11:23.480 --> 00:11:27.430
and more, and so people's
brains are becoming increasingly

232
00:11:27.430 --> 00:11:28.990
adaptive.

233
00:11:28.990 --> 00:11:32.610
There's somewhat of an analogy
with AI there, where, really,

234
00:11:32.610 --> 00:11:34.910
the amount of data -- the
variation in signals that

235
00:11:34.910 --> 00:11:39.450
we're feeding our models
-- is continuously growing

236
00:11:39.450 --> 00:11:44.030
exponentially, and so of course
the models are becoming more

237
00:11:44.030 --> 00:11:46.610
and more flexible
and adaptive as well.

238
00:11:46.610 --> 00:11:48.100
SHERVIN KHODABANDEH:
Nitzan, you've

239
00:11:48.100 --> 00:11:54.740
been pretty vocal about AI not
being just a fancy technology

240
00:11:54.740 --> 00:11:58.240
or a series of fancy
algorithms just because they're

241
00:11:58.240 --> 00:12:00.470
cool and awesome.

242
00:12:00.470 --> 00:12:00.578


243
00:12:00.578 --> 00:12:01.870
SAM RANSBOTHAM: Which they are.

244
00:12:01.870 --> 00:12:04.120
SHERVIN KHODABANDEH: There
has to be a purpose for it;

245
00:12:04.120 --> 00:12:05.960
there has to be a
real need for it.

246
00:12:05.960 --> 00:12:09.940
Comment more about that
meta framing of AI strategy

247
00:12:09.940 --> 00:12:13.890
and its alignment with
business and corporate strategy

248
00:12:13.890 --> 00:12:16.890
at eBay or, in general,
the purpose of AI,

249
00:12:16.890 --> 00:12:19.227
because I know you've been
pretty vocal about that.

250
00:12:19.227 --> 00:12:20.310
NITZAN MEKEL-BOBROV: Yeah.

251
00:12:20.310 --> 00:12:22.852
I've probably been pretty vocal
because I learned it the hard

252
00:12:22.852 --> 00:12:25.520
way, getting somewhat burned
in [the] earlier years

253
00:12:25.520 --> 00:12:29.793
of my career coming into a new
company and thinking I knew it

254
00:12:29.793 --> 00:12:31.960
all -- that I knew it better
than everyone because I

255
00:12:31.960 --> 00:12:35.470
understood the technology and
what's going on under the hood.

256
00:12:35.470 --> 00:12:39.790
And I learned the
hard way, as I say,

257
00:12:39.790 --> 00:12:44.320
that it's a lot more
nuanced than brute-forcing

258
00:12:44.320 --> 00:12:48.690
a technology onto a
theoretical use case

259
00:12:48.690 --> 00:12:49.940
that you might think of.

260
00:12:49.940 --> 00:12:53.880
It's really important to
understand the business context

261
00:12:53.880 --> 00:12:56.560
in which these
technologies are deployed

262
00:12:56.560 --> 00:13:00.280
and understand it deeply.

263
00:13:00.280 --> 00:13:03.630
So, for example,
early on in my career,

264
00:13:03.630 --> 00:13:05.400
when I was in
financial services,

265
00:13:05.400 --> 00:13:08.810
we were using AI for
a lot of automation --

266
00:13:08.810 --> 00:13:10.040
workflow automation.

267
00:13:10.040 --> 00:13:12.680
There's huge amounts
of savings in this --

268
00:13:12.680 --> 00:13:15.340
hundreds of millions
of dollars a year.

269
00:13:15.340 --> 00:13:20.980
And to me, it felt obvious
that certain applications --

270
00:13:20.980 --> 00:13:24.070
for example, with speech
recognition and intent

271
00:13:24.070 --> 00:13:26.500
detection, etc., in
the call center --

272
00:13:26.500 --> 00:13:28.940
would be an ideal fit.

273
00:13:28.940 --> 00:13:33.900
But it's only after I actually
shadowed a number of agents

274
00:13:33.900 --> 00:13:37.410
and spent probably about
a good month deep-diving

275
00:13:37.410 --> 00:13:40.740
into their workflows that I
understood that there's so much

276
00:13:40.740 --> 00:13:43.110
complexity there;
that it's really

277
00:13:43.110 --> 00:13:45.950
about the interaction between
the human intelligence

278
00:13:45.950 --> 00:13:47.450
and the machine
intelligence, and it

279
00:13:47.450 --> 00:13:49.700
was making assumptions
that just weren't

280
00:13:49.700 --> 00:13:52.220
going to bear out in real life.

281
00:13:52.220 --> 00:13:55.020
So it's that move
from what works

282
00:13:55.020 --> 00:13:59.670
in the lab to what works in real
life that is really critical.

283
00:13:59.670 --> 00:14:02.630
And then, of course,
thinking about what's

284
00:14:02.630 --> 00:14:07.190
important to the business
is not just a matter

285
00:14:07.190 --> 00:14:09.930
of what's important
today but really

286
00:14:09.930 --> 00:14:12.180
what's in the DNA
of the company,

287
00:14:12.180 --> 00:14:16.600
because it takes time to not
just build but deploy and get

288
00:14:16.600 --> 00:14:17.920
these things up and running.

289
00:14:17.920 --> 00:14:20.360
I don't know any one
of my peers who's

290
00:14:20.360 --> 00:14:23.270
ever been able to
get anything up

291
00:14:23.270 --> 00:14:27.930
and running at scale
in a matter of probably

292
00:14:27.930 --> 00:14:30.650
less than a year [before]
you see real impact,

293
00:14:30.650 --> 00:14:33.210
and honestly it's often
quite a bit longer than that.

294
00:14:33.210 --> 00:14:35.260
It's a marathon,
it's not a sprint,

295
00:14:35.260 --> 00:14:37.750
and so you really have
to be conscious of what

296
00:14:37.750 --> 00:14:40.840
will be the business
strategy two, three,

297
00:14:40.840 --> 00:14:44.260
four years down the road, not
just what is on the executive's

298
00:14:44.260 --> 00:14:45.210
mind today.

299
00:14:45.210 --> 00:14:46.793
SHERVIN KHODABANDEH:
I want to pick up

300
00:14:46.793 --> 00:14:49.470
on something you said
about automation, which

301
00:14:49.470 --> 00:14:53.950
is one of the larger themes
that AI's being used [for],

302
00:14:53.950 --> 00:14:56.980
but I think you
also alluded to it.

303
00:14:56.980 --> 00:14:59.680
I think it's unfortunate
that when most people think

304
00:14:59.680 --> 00:15:04.613
about AI, they tend to think of
it as the extreme case of "It's

305
00:15:04.613 --> 00:15:06.530
going to replace humans,"
and you were talking

306
00:15:06.530 --> 00:15:10.880
about the importance of what I
call the middle ground, where

307
00:15:10.880 --> 00:15:16.180
human and AI work together, so
that human-AI interaction is

308
00:15:16.180 --> 00:15:16.780
key.

309
00:15:16.780 --> 00:15:19.900
Perhaps that's also one of the
reasons it's so hard to scale,

310
00:15:19.900 --> 00:15:22.640
because you've got to
figure out how humans

311
00:15:22.640 --> 00:15:24.640
will work differently with AI.

312
00:15:24.640 --> 00:15:28.460
Can you share some
stories or insights

313
00:15:28.460 --> 00:15:30.610
about how you've done that?

314
00:15:30.610 --> 00:15:34.950
Because it requires changing
the mindset of humans and what

315
00:15:34.950 --> 00:15:35.740
they normally do.

316
00:15:35.740 --> 00:15:39.660
NITZAN MEKEL-BOBROV: I can
give you a couple of examples

317
00:15:39.660 --> 00:15:42.380
that I'm seeing at
eBay, but honestly,

318
00:15:42.380 --> 00:15:45.190
a lot of what I'm seeing
at eBay, I've seen before.

319
00:15:45.190 --> 00:15:48.910
The transformation
stage that we're on now

320
00:15:48.910 --> 00:15:51.330
is one that virtually
every company is on,

321
00:15:51.330 --> 00:15:53.960
and no one is fully there yet.

322
00:15:53.960 --> 00:15:59.190
So if you think on the
back-office side, specifically

323
00:15:59.190 --> 00:16:04.000
customer service, I think that
anxiety is typically most acute

324
00:16:04.000 --> 00:16:06.580
there, because frankly
they've seen it

325
00:16:06.580 --> 00:16:10.020
before with other technologies;
this isn't their first rodeo.

326
00:16:10.020 --> 00:16:14.940
And in truth, over the
course of the maturation

327
00:16:14.940 --> 00:16:18.290
of the technology, there
are individual roles

328
00:16:18.290 --> 00:16:20.660
that are no longer needed.

329
00:16:20.660 --> 00:16:21.970
That is true.

330
00:16:21.970 --> 00:16:24.260
But it's not that
humans aren't needed;

331
00:16:24.260 --> 00:16:26.780
it's just that the role
that they do changes.

332
00:16:26.780 --> 00:16:29.010
For example, on the
customer service side,

333
00:16:29.010 --> 00:16:32.740
what we are doing at eBay --
we tried for the past few years

334
00:16:32.740 --> 00:16:35.970
to inject AI in different
places of the flow.

335
00:16:35.970 --> 00:16:39.970
And it was very
challenging, both from

336
00:16:39.970 --> 00:16:42.360
a tech-debt perspective,
because there's just

337
00:16:42.360 --> 00:16:43.980
a lot of tech debt
in different places

338
00:16:43.980 --> 00:16:46.320
that made it hard to do that.

339
00:16:46.320 --> 00:16:49.750
To make it more concrete, I'll
give a real example: intent

340
00:16:49.750 --> 00:16:50.790
detection.

341
00:16:50.790 --> 00:16:54.150
A customer calls, and as
they're talking to you,

342
00:16:54.150 --> 00:16:58.040
there is a model
that's picking up

343
00:16:58.040 --> 00:17:00.510
on what they think that the
customer's trying to answer,

344
00:17:00.510 --> 00:17:03.310
so it's supposed to
help the agent go

345
00:17:03.310 --> 00:17:07.200
to the right pages for help,
surface the right information.

346
00:17:07.200 --> 00:17:09.560
Think about that
poor agent, though:

347
00:17:09.560 --> 00:17:12.130
He's talking to a customer
who, typically, when they call,

348
00:17:12.130 --> 00:17:13.630
it's not because
they're happy; when

349
00:17:13.630 --> 00:17:15.380
a customer calls
customer service,

350
00:17:15.380 --> 00:17:16.950
it's because they're frustrated.

351
00:17:16.950 --> 00:17:19.700
They're trying to help
them while at the same time

352
00:17:19.700 --> 00:17:21.800
getting these messages
on the screen,

353
00:17:21.800 --> 00:17:26.760
and that ability to
multitask and pay attention

354
00:17:26.760 --> 00:17:29.260
to this thing that's
flashing at them with intents

355
00:17:29.260 --> 00:17:31.960
can be more of a
distraction than a help.

356
00:17:31.960 --> 00:17:36.360
What we're doing now is
undertaking a more end-to-end

357
00:17:36.360 --> 00:17:40.540
approach, where we're really
replacing or transforming

358
00:17:40.540 --> 00:17:45.710
customer service on our back end
so that both the systems that

359
00:17:45.710 --> 00:17:50.550
the agents are using and the
systems that the models are

360
00:17:50.550 --> 00:17:54.360
running on are designed
together from the get-go --

361
00:17:54.360 --> 00:17:56.000
so there's a much
better interplay.

362
00:17:56.000 --> 00:17:59.010
And we also have a lot
of our designers --

363
00:17:59.010 --> 00:18:02.890
much more creative than our
AI folks, in many ways --

364
00:18:02.890 --> 00:18:07.310
helping think through what that
interplay should look like,

365
00:18:07.310 --> 00:18:11.880
doing a lot of user research
testing with agents on how they

366
00:18:11.880 --> 00:18:14.480
would interface with customers
and technology at the same

367
00:18:14.480 --> 00:18:14.980
time.

368
00:18:14.980 --> 00:18:18.370
SAM RANSBOTHAM: The verb you
used, inject, was interesting.

369
00:18:18.370 --> 00:18:21.530
You use that as saying that
it didn't work to inject;

370
00:18:21.530 --> 00:18:24.733
it didn't work to Band-Aid
it on or just to paste it on.

371
00:18:24.733 --> 00:18:26.400
SHERVIN KHODABANDEH:
Well, another thing

372
00:18:26.400 --> 00:18:28.977
I wanted to pick up
on, Sam, is, Nitzan,

373
00:18:28.977 --> 00:18:30.560
I think you made a
comment like, "This

374
00:18:30.560 --> 00:18:32.840
is not their first
rodeo with technology;

375
00:18:32.840 --> 00:18:34.670
they've seen other
technologies."

376
00:18:34.670 --> 00:18:41.860
And I'm interested in your views
on how AI might be or how AI is

377
00:18:41.860 --> 00:18:45.400
different compared to
all the prior, let's say,

378
00:18:45.400 --> 00:18:49.710
technologies that came
and transformed functions

379
00:18:49.710 --> 00:18:52.890
or processes, and whether
you think there are any

380
00:18:52.890 --> 00:18:56.280
misconceptions there -- like
people anchoring [on], maybe,

381
00:18:56.280 --> 00:19:00.180
"Oh well, this is just another
ERP technology or like we did

382
00:19:00.180 --> 00:19:02.970
with some other
technologies; it's the same."

383
00:19:02.970 --> 00:19:05.590
And I wonder whether
you agree with that

384
00:19:05.590 --> 00:19:07.600
or whether you think
there's a misconception

385
00:19:07.600 --> 00:19:10.360
to treat it the same,
and what the difference

386
00:19:10.360 --> 00:19:12.240
might be with
anything else people

387
00:19:12.240 --> 00:19:14.700
might be referencing
or anchoring on

388
00:19:14.700 --> 00:19:16.760
based on their
prior experiences.

389
00:19:16.760 --> 00:19:18.220
NITZAN MEKEL-BOBROV:
Yeah, I think

390
00:19:18.220 --> 00:19:21.850
any specific piece
of AI technology

391
00:19:21.850 --> 00:19:26.360
is comparable to an ERP
system or some other version,

392
00:19:26.360 --> 00:19:29.530
but as a paradigm, it's
a much bigger thing.

393
00:19:29.530 --> 00:19:32.960
I think the better analogy is
the digital transformation.

394
00:19:32.960 --> 00:19:36.620
The change from
brick-and-mortar to digital

395
00:19:36.620 --> 00:19:41.740
is probably more at the
level of [an] analogy.

396
00:19:41.740 --> 00:19:43.990
Even where we are
now, I would say,

397
00:19:43.990 --> 00:19:49.380
is a paradigm shift akin to
the digital paradigm shift

398
00:19:49.380 --> 00:19:51.640
much more than just
the introduction

399
00:19:51.640 --> 00:19:53.932
of some new ERP or CRM, etc.

400
00:19:53.932 --> 00:19:55.595


401
00:19:55.595 --> 00:19:57.470
SAM RANSBOTHAM: Nitzan,
we have a new segment

402
00:19:57.470 --> 00:20:00.370
where we ask our guests a
series of rapid-fire questions.

403
00:20:00.370 --> 00:20:01.930
Just answer with
the first response

404
00:20:01.930 --> 00:20:03.010
that comes to your mind.

405
00:20:03.010 --> 00:20:06.280
What's your proudest moment
with artificial intelligence?

406
00:20:06.280 --> 00:20:08.800
NITZAN MEKEL-BOBROV: Probably
on the health care side.

407
00:20:08.800 --> 00:20:10.790
When I was in the
Boston Scientific days,

408
00:20:10.790 --> 00:20:14.590
we rolled out a feature
that was predictive,

409
00:20:14.590 --> 00:20:17.840
basically using signals
from a pacemaker

410
00:20:17.840 --> 00:20:20.150
to predict heart failure.

411
00:20:20.150 --> 00:20:22.110
On average, it was
30 days in advance

412
00:20:22.110 --> 00:20:25.240
of when physicians would
detect it otherwise,

413
00:20:25.240 --> 00:20:28.050
which is a huge
lifesaving benefit.

414
00:20:28.050 --> 00:20:31.680
SAM RANSBOTHAM: What worries you
about artificial intelligence?

415
00:20:31.680 --> 00:20:33.200
NITZAN MEKEL-BOBROV: Misuse.

416
00:20:33.200 --> 00:20:38.650
Misuse by bad actors, whether
it's in military operations

417
00:20:38.650 --> 00:20:42.610
or in other types of
nefarious activities.

418
00:20:42.610 --> 00:20:44.820
If you think of
deepfakes as an example,

419
00:20:44.820 --> 00:20:47.780
just as it becomes more and
more accessible and easy

420
00:20:47.780 --> 00:20:50.540
for everyone to use,
I do have concerns.

421
00:20:50.540 --> 00:20:52.710
SAM RANSBOTHAM: What's
your favorite activity that

422
00:20:52.710 --> 00:20:54.290
involves no technology at all?

423
00:20:54.290 --> 00:20:55.540
NITZAN MEKEL-BOBROV: Oh, crap.

424
00:20:55.540 --> 00:20:56.980
I was about to
say "photography,"

425
00:20:56.980 --> 00:20:59.500
and then I realized that's
not even a good example.

426
00:20:59.500 --> 00:20:59.922


427
00:20:59.922 --> 00:21:01.880
SAM RANSBOTHAM: No,
actually we can count that.

428
00:21:01.880 --> 00:21:03.930
I mean it's not strictly
artificial intelligence,

429
00:21:03.930 --> 00:21:05.110
so we'll give you
credit for that.

430
00:21:05.110 --> 00:21:06.210
SAM RANSBOTHAM:
What did you want

431
00:21:06.210 --> 00:21:07.000
to be when you were a child?

432
00:21:07.000 --> 00:21:08.720
What did you want
be when you grew up?

433
00:21:08.720 --> 00:21:10.370
AI engineer at eBay?

434
00:21:10.370 --> 00:21:13.200
NITZAN MEKEL-BOBROV: I actually
started as a creative writing

435
00:21:13.200 --> 00:21:16.750
major, so I guess writer
was my initial passion.

436
00:21:16.750 --> 00:21:17.250


437
00:21:17.250 --> 00:21:19.380
SAM RANSBOTHAM:
What's your greatest

438
00:21:19.380 --> 00:21:21.880
wish for what we're going to
do with artificial intelligence

439
00:21:21.880 --> 00:21:22.463
in the future?

440
00:21:22.463 --> 00:21:23.750


441
00:21:23.750 --> 00:21:25.400
NITZAN MEKEL-BOBROV:
I think, making

442
00:21:25.400 --> 00:21:31.200
it easier for almost anyone
to make a living pursuing

443
00:21:31.200 --> 00:21:32.180
their passion.

444
00:21:32.180 --> 00:21:34.020
SAM RANSBOTHAM: Nitzan, great
meeting and talking with you

445
00:21:34.020 --> 00:21:34.670
today.

446
00:21:34.670 --> 00:21:36.630
I think one thing that
is going to resonate

447
00:21:36.630 --> 00:21:38.690
with a lot of our
listeners is this idea

448
00:21:38.690 --> 00:21:43.150
that AI implementations
are different than existing

449
00:21:43.150 --> 00:21:47.180
implementations like ERPs; that
that siloed approach that you

450
00:21:47.180 --> 00:21:50.510
might take toward a
monolithic technology

451
00:21:50.510 --> 00:21:53.630
is very different when
you involve lots of users,

452
00:21:53.630 --> 00:21:55.460
and particularly when
you involve your two

453
00:21:55.460 --> 00:21:57.888
platforms, with both your
customers and your sellers.

454
00:21:57.888 --> 00:21:59.680
Thanks for taking the
time to talk with us.

455
00:21:59.680 --> 00:22:01.308
We really appreciate
it; thank you.

456
00:22:01.308 --> 00:22:03.350
SHERVIN KHODABANDEH: Thank
you for being with us.

457
00:22:03.350 --> 00:22:05.660
It's been quite insightful,
and we really, really

458
00:22:05.660 --> 00:22:06.270
appreciate it.

459
00:22:06.270 --> 00:22:07.937
NITZAN MEKEL-BOBROV:
Thank you, Shervin.

460
00:22:07.937 --> 00:22:08.450


461
00:22:08.450 --> 00:22:10.630
SAM RANSBOTHAM: Thank
you for joining us today.

462
00:22:10.630 --> 00:22:14.230
Next time, we'll talk with
Helen Lee, technical fellow

463
00:22:14.230 --> 00:22:16.450
and regional director at Boeing.

464
00:22:16.450 --> 00:22:17.695
Please join us.

465
00:22:17.695 --> 00:22:18.532


466
00:22:18.532 --> 00:22:19.990
ALLISON RYDER:
Thanks for listening

467
00:22:19.990 --> 00:22:21.500
to Me, Myself, and AI.

468
00:22:21.500 --> 00:22:23.950
We believe, like you,
that the conversation

469
00:22:23.950 --> 00:22:26.170
about AI implementation
doesn't start and stop

470
00:22:26.170 --> 00:22:27.297
with this podcast.

471
00:22:27.297 --> 00:22:29.130
That's why we've created
a group on LinkedIn

472
00:22:29.130 --> 00:22:30.960
specifically for
leaders like you.

473
00:22:30.960 --> 00:22:33.700
It's called AI for Leaders,
and if you join us,

474
00:22:33.700 --> 00:22:35.740
you can chat with show
creators and hosts,

475
00:22:35.740 --> 00:22:39.360
ask your own questions, share
insights, and gain access

476
00:22:39.360 --> 00:22:41.840
to valuable resources
about AI implementation

477
00:22:41.840 --> 00:22:43.930
from MIT SMR and BCG.

478
00:22:43.930 --> 00:22:49.060
You can access it by visiting
mitsmr.com/AIforLeaders.

479
00:22:49.060 --> 00:22:51.770
We'll put that link
in the show notes,

480
00:22:51.770 --> 00:22:54.210
and we hope to see you there.

481
00:22:54.210 --> 00:22:59.000