1
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think Prologis

2
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overall has

3
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leaned in really

4
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well.

5
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I think our

6
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leadership has

7
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done an amazing

8
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job of putting

9
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things together,

10
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putting the

11
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right technology

12
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in the hands

13
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of right people,

14
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putting

15
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guardrails

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around it for

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the right

18
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reasons.

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So I think

20
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I'm excited.

21
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We have been

22
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using a lot

23
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of AI in

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our day -to

25
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-day work.

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Actually, the

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whole

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organization is

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encouraging us

30
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to really think

31
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about how can

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we incorporate

33
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more AI.

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So there were

35
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like a whole

36
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AI teams around

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it and

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encouraging us

39
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to do that. So I

40
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think

41
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I'm pretty

42
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excited for that.

43
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I've started

44
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using a lot

45
00:00:30,508 --> 00:00:31,008
for my data

46
00:00:31,008 --> 00:00:31,548
crunching as

47
00:00:31,548 --> 00:00:32,408
well.

48
00:00:32,748 --> 00:00:33,528
Before it would

49
00:00:33,528 --> 00:00:33,788
have been

50
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Tableau or

51
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Excel.

52
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Right now it's

53
00:00:35,588 --> 00:00:36,728
pretty much AI

54
00:00:36,728 --> 00:00:37,288
to help me

55
00:00:37,288 --> 00:00:38,248
with that.

56
00:00:39,308 --> 00:00:40,328
Yeah.

57
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I would expect

58
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it would be

59
00:00:41,828 --> 00:00:42,548
all over your

60
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analysis, for

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00:00:43,388 --> 00:00:44,048
sure.

62
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Yeah, it is

63
00:00:44,928 --> 00:00:45,548
definitely from

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the AI

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perspective,

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from analysis

67
00:00:47,088 --> 00:00:47,628
perspective,

68
00:00:47,628 --> 00:00:48,348
definitely helps

69
00:00:48,348 --> 00:00:49,008
me do a

70
00:00:49,008 --> 00:00:49,748
faster crunch.

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00:00:49,748 --> 00:00:49,988
We are on

72
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a project right

73
00:00:50,548 --> 00:00:51,948
now for network

74
00:00:51,948 --> 00:00:52,868
designing a

75
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whole supply

76
00:00:53,808 --> 00:00:54,328
chain network

77
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for one of

78
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the sporting

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brand.

80
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and the way

81
00:00:57,528 --> 00:00:59,468
with how fast

82
00:00:59,468 --> 00:01:00,388
we are able

83
00:01:00,388 --> 00:01:01,268
to crunch data

84
00:01:01,268 --> 00:01:01,848
is definitely

85
00:01:01,848 --> 00:01:02,508
helping with the

86
00:01:02,508 --> 00:01:03,208
execution speed

87
00:01:03,208 --> 00:01:03,888
on the project

88
00:01:03,888 --> 00:01:05,788
whereas the

89
00:01:05,788 --> 00:01:06,448
project that we

90
00:01:06,448 --> 00:01:06,968
want the

91
00:01:06,968 --> 00:01:07,668
competitors were

92
00:01:07,668 --> 00:01:08,348
at least looking

93
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for 15 to

94
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18 weeks we

95
00:01:10,128 --> 00:01:10,748
are doing that

96
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in nine weeks

97
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just because of

98
00:01:12,028 --> 00:01:13,088
the data the

99
00:01:13,088 --> 00:01:13,728
crunching of

100
00:01:13,728 --> 00:01:14,428
data is pretty

101
00:01:14,428 --> 00:01:15,248
fast for us

102
00:01:15,248 --> 00:01:16,608
so like ai

103
00:01:16,608 --> 00:01:17,148
has broad

104
00:01:17,148 --> 00:01:17,788
efficiencies

105
00:01:17,788 --> 00:01:18,348
which we are

106
00:01:18,348 --> 00:01:19,068
able to pass

107
00:01:19,068 --> 00:01:19,988
on to our

108
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customer in

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terms of speed