1
00:00:00,000 --> 00:00:03,659
, 
Cybersecurity today, we'd like to thank
Meter for their support in bringing you.

2
00:00:03,659 --> 00:00:08,640
This podcast Meter delivers a complete
networking stack, wired, wireless and

3
00:00:08,640 --> 00:00:13,799
cellular in one integrated solution
that's built for performance and scale.

4
00:00:13,979 --> 00:00:18,270
You can find them at meter.com/cst.

5
00:00:20,827 --> 00:00:25,147
Welcome to a special edition of
Project Synapse, our AI show that we

6
00:00:25,147 --> 00:00:27,847
run every weekend on Hashtag Trending.

7
00:00:28,417 --> 00:00:33,007
This week, we're sharing the show with
our Cybersecurity Today listeners, and

8
00:00:33,007 --> 00:00:35,677
we have a special guest, Krish Banerjee.

9
00:00:35,917 --> 00:00:39,337
He's the managing partner for
AI in Canada at Accenture.

10
00:00:39,577 --> 00:00:40,637
Welcome Krish.

11
00:00:41,467 --> 00:00:43,112
.
Good morning and thank you, Jim.

12
00:00:44,027 --> 00:00:48,887
And of course, John Pinard, whose
day job is a VP at CSO for a

13
00:00:48,887 --> 00:00:50,713
financial institution here in Canada.

14
00:00:51,242 --> 00:00:52,172
Every weekend.

15
00:00:52,352 --> 00:00:56,271
He's a co-host of our AI
show, project Synapse.

16
00:00:56,612 --> 00:00:57,392
Welcome John.

17
00:00:58,442 --> 00:00:58,772
Yep.

18
00:00:58,802 --> 00:00:59,132
Morning.

19
00:00:59,285 --> 00:01:02,576
Marcel can't be with us today, but,
but the three of us are gonna carry on.

20
00:01:02,985 --> 00:01:06,085
just to start out with some of the news
stories from this week, and, Krish,

21
00:01:06,085 --> 00:01:08,825
you've done this, been on the show
before, but we just bounce these ideas

22
00:01:08,880 --> 00:01:11,850
and add what they've, what we know
about them or what we think about them.

23
00:01:12,540 --> 00:01:16,950
One of the ones, and this is a
familiar for us, John, is Google.

24
00:01:17,690 --> 00:01:22,280
every time we do a show and we dis
Google for not doing something,

25
00:01:22,330 --> 00:01:23,650
they come out and go do it.

26
00:01:23,890 --> 00:01:27,380
So I think we're gonna consider
ourselves the Google whisperers.

27
00:01:27,650 --> 00:01:31,790
'cause two weeks ago we were
wondering why Google hadn't integrated

28
00:01:32,090 --> 00:01:37,760
Gemini, which is a fantastic ai, and why
they hadn't integrated it with the tools,

29
00:01:37,760 --> 00:01:41,210
why you had to do stupid things like, oh,
I want to fix this email, so I'm gonna

30
00:01:41,210 --> 00:01:44,960
cut and paste it into another window,
and then all of a sudden Google makes a

31
00:01:44,960 --> 00:01:49,700
big announcement and they've integrated
it with everything Workspace, Gmail.

32
00:01:49,850 --> 00:01:53,690
I don't know if, I don't know if either
of you guys use Gmail or workspace.

33
00:01:53,690 --> 00:01:57,410
I do, and it's fantastic so far.

34
00:01:57,420 --> 00:02:01,735
I'm just, I'm happy to rave about them
again because . The email things that

35
00:02:01,735 --> 00:02:05,035
they do, and the intelligence of Gemini,
I don't know if you've seen it before,

36
00:02:05,035 --> 00:02:08,305
but when Google first started out and
many of these AI products, you get this

37
00:02:08,305 --> 00:02:13,275
clunky window that tries to take over
your email and then gets in your way.

38
00:02:13,335 --> 00:02:17,535
And all you want to do is send a quick
email and you can't do anything about it.

39
00:02:17,745 --> 00:02:19,635
But Google now.

40
00:02:20,010 --> 00:02:21,630
Elegantly lets you write the email.

41
00:02:21,910 --> 00:02:25,490
It underlines the  mistakes By the
way, Grammarly is outta business,

42
00:02:25,540 --> 00:02:27,790
at least in for terms of Workspace.

43
00:02:28,030 --> 00:02:29,380
But it underlines everything.

44
00:02:29,590 --> 00:02:32,590
And of course, you can pop the window
up and get advice on the email.

45
00:02:32,770 --> 00:02:36,710
It's just elegant and it does
the things that I want it to do.

46
00:02:38,020 --> 00:02:40,060
Gimme a quick grammar and spell checks.

47
00:02:40,580 --> 00:02:43,500
Before you press enter you're
like me, when you do email, you

48
00:02:43,500 --> 00:02:46,554
press enter and you see something
is spelled wrong, you go, oh, no.

49
00:02:46,774 --> 00:02:48,544
So anyway, I was just happy.

50
00:02:48,614 --> 00:02:49,184
heck about that.

51
00:02:49,364 --> 00:02:50,564
Have you guys tried this at all?

52
00:02:52,154 --> 00:02:53,934
I'm not a Gemini user, oh.

53
00:02:53,934 --> 00:02:55,464
Or sorry, I am a Gemini user.

54
00:02:55,464 --> 00:02:58,329
I'm not a Google Workspace user, so yeah.

55
00:02:58,359 --> 00:03:01,809
I do a little bit with, Google
Docs, but that's about it.

56
00:03:01,899 --> 00:03:03,599
And, so I haven't done a lot with it.

57
00:03:05,294 --> 00:03:11,234
I have used Gemini, both for like
work as well as, for kind of more of

58
00:03:11,234 --> 00:03:12,944
the research type of stuff as well.

59
00:03:13,344 --> 00:03:14,844
I agree, like it's pretty good.

60
00:03:14,944 --> 00:03:19,224
at work we do have, Co-pilot
and Microsoft, that's the

61
00:03:19,224 --> 00:03:21,604
integrated, platform that we have.

62
00:03:21,714 --> 00:03:26,444
and it's pretty good as well, like
the difference between this, platforms

63
00:03:26,444 --> 00:03:27,844
are, disappearing quite fast.

64
00:03:27,894 --> 00:03:33,294
And if you see one announcement of
Microsoft copilot, cowork led to

65
00:03:33,294 --> 00:03:37,704
announce another announcement, and
then all of that kind of cascades.

66
00:03:38,529 --> 00:03:38,829
Yeah.

67
00:03:38,829 --> 00:03:42,039
And that's one of the things that,
that I've said in previous shows too,

68
00:03:42,039 --> 00:03:46,184
Jim, is that Krish, I keep saying
that it's a bit of a leapfrog effect,

69
00:03:46,634 --> 00:03:49,934
that one company will come out with
something and then the next week

70
00:03:50,054 --> 00:03:53,644
another company will come out with that
plus something else and go through.

71
00:03:54,064 --> 00:03:54,974
I do Gemini.

72
00:03:55,024 --> 00:03:56,944
I've, I have two paid subscriptions.

73
00:03:56,944 --> 00:04:00,874
I have a Gemini paid subscription and
a ChatGPT, paid subscription at home.

74
00:04:01,354 --> 00:04:06,334
and I, I find I'm, I still use
Chachi pt, but I'm, I find I'm

75
00:04:06,334 --> 00:04:07,954
using Gemini more and more.

76
00:04:08,404 --> 00:04:11,614
And then like Krish, and by the
way, Krish, it's really nice to hear

77
00:04:11,614 --> 00:04:13,954
somebody else that uses Copilot at work.

78
00:04:15,004 --> 00:04:18,544
these guys keep giving me a hard time
because that's what we use as well,

79
00:04:18,544 --> 00:04:22,584
is that, we have Copilot integrated
into, into our Microsoft Suite.

80
00:04:23,214 --> 00:04:26,024
Yeah, that's what, an
Accenture from, at least in.

81
00:04:26,639 --> 00:04:28,679
Microsoft 365 perspective.

82
00:04:28,679 --> 00:04:29,399
That's yes.

83
00:04:29,519 --> 00:04:33,449
Integrated with our, because we are
on, Outlook and, and that's easy

84
00:04:33,449 --> 00:04:38,249
form an integration, but we do have,
Tim and I, we have Claude name it.

85
00:04:38,249 --> 00:04:39,399
We have that as well.

86
00:04:39,454 --> 00:04:44,299
Yeah, you need that in your line of
work, but the, but it is, and I'm a

87
00:04:44,299 --> 00:04:48,479
big, I am, like I said, I'm a big fan of
the tools that I use, but I've always,

88
00:04:48,479 --> 00:04:52,079
I've been in both the position of John
and, and you as well Krish in terms of

89
00:04:52,079 --> 00:04:57,889
consulting and I, the tool that works
the best is the one that you use, Yes.

90
00:04:59,214 --> 00:05:01,224
I've had people complain to me,
this tool doesn't do everything.

91
00:05:01,344 --> 00:05:02,334
How much do you use?

92
00:05:02,384 --> 00:05:02,984
not a lot.

93
00:05:02,984 --> 00:05:05,354
And you go, we'll use more, Yeah.

94
00:05:05,384 --> 00:05:06,464
You'll get more from it.

95
00:05:06,884 --> 00:05:08,534
couple other things that
happened this week that.

96
00:05:08,804 --> 00:05:10,724
Just, and two of them are related.

97
00:05:11,064 --> 00:05:16,074
we had the big agent explosion over the
past couple weeks with Open Claw and

98
00:05:16,074 --> 00:05:23,344
Moltbook and then Open Claw went to OpenAI
and basically, I don't know how you buy

99
00:05:23,344 --> 00:05:27,444
an open source software, but you take
the founder I guess what happens then

100
00:05:27,444 --> 00:05:32,164
Meta came in and bought MT book and I
don't know if you guys remember MoltBook.

101
00:05:33,084 --> 00:05:37,734
I'm sure you do, but MoltBook was for
our audience was that, it was supposed

102
00:05:37,734 --> 00:05:41,644
to be a, social media network for agents.

103
00:05:41,644 --> 00:05:45,784
I think that was nonsense, but I think the
real, a lot more people there than there

104
00:05:45,784 --> 00:05:48,994
were a automated agents red for agents.

105
00:05:49,909 --> 00:05:50,149
Yeah.

106
00:05:50,179 --> 00:05:50,569
Yeah.

107
00:05:50,569 --> 00:05:51,379
Reddit for agents.

108
00:05:51,379 --> 00:05:51,649
Yeah.

109
00:05:51,649 --> 00:05:53,564
that's a better, yeah, that's
a better way of putting it.

110
00:05:53,804 --> 00:05:58,564
And I looked at, it didn't look anywhere
near as nasty as some of the Reddit forums

111
00:05:58,564 --> 00:06:03,094
I've been in, but, but it was getting
there, like I, I've been following it

112
00:06:03,094 --> 00:06:08,084
and if you have followed from when it
started till the time, it got taken

113
00:06:08,084 --> 00:06:11,864
over, the discussions has changed.

114
00:06:12,324 --> 00:06:17,134
and that's the whole point here is that
there is a self, learning aspect of it.

115
00:06:17,164 --> 00:06:22,234
there were comments around, how they
would develop and language that they

116
00:06:22,234 --> 00:06:23,824
don't want humans to understand.

117
00:06:23,879 --> 00:06:28,114
Which is surreal, just mind blowing
to think about like that is an

118
00:06:28,114 --> 00:06:33,064
agent to agent communication and an
agent talking about we need to build

119
00:06:33,064 --> 00:06:37,324
in some kind of in communication
protocol that humans don't understand.

120
00:06:38,034 --> 00:06:38,324
Yeah.

121
00:06:38,539 --> 00:06:38,689
Yeah.

122
00:06:38,689 --> 00:06:39,829
Isn't that comforting?

123
00:06:41,959 --> 00:06:47,219
But I, my feeling is that I felt that some
of those discussions were manipulated,

124
00:06:47,219 --> 00:06:48,929
not the fact that they wouldn't occur.

125
00:06:48,989 --> 00:06:50,279
'cause that's happened before.

126
00:06:50,579 --> 00:06:53,429
Somebody has put two AIs
talking to each other and they

127
00:06:53,429 --> 00:06:54,899
did evolve their own language.

128
00:06:55,379 --> 00:07:00,179
but I would just, I felt like it was
more manipulated and sensational.

129
00:07:00,179 --> 00:07:01,229
That's just a feeling.

130
00:07:01,229 --> 00:07:02,009
You can't prove it.

131
00:07:02,429 --> 00:07:06,209
But, the usual AI's gonna take over
the world and destroy all humans

132
00:07:06,209 --> 00:07:08,219
things, And, I don't blame 'em.

133
00:07:08,219 --> 00:07:12,509
I feel that way some days, but
the, it's, but being a grouchy

134
00:07:12,509 --> 00:07:14,579
AI sort of thing, I don't know.

135
00:07:14,879 --> 00:07:20,669
but Meta taking that over is a really
interesting mix and seeing how they're

136
00:07:20,669 --> 00:07:24,569
gonna move into this because, everybody's
struggling with the business model

137
00:07:24,689 --> 00:07:28,059
of AI and the obvious ones we
take a look at are, OpenAI are

138
00:07:28,059 --> 00:07:29,139
they gonna be able to make money?

139
00:07:29,139 --> 00:07:31,669
Or is, how does Claude make its money?

140
00:07:31,919 --> 00:07:33,209
and all that meta.

141
00:07:33,774 --> 00:07:39,234
is has a whole AI that nobody
thinks about and they have a

142
00:07:39,234 --> 00:07:41,544
perfect way, to make money from it.

143
00:07:41,544 --> 00:07:45,704
they're great at making money from
social networks and from the use of AI

144
00:07:45,734 --> 00:07:49,744
.
And I think it's gonna be interesting to
see the way they take this, but it's an,

145
00:07:49,894 --> 00:07:55,344
a new way of looking at, And how people
are gonna evolve and Nvidia joined the

146
00:07:55,344 --> 00:07:59,364
group Looking at the next generation of
how they're going to make money or how

147
00:07:59,364 --> 00:08:00,504
they're going to continue themselves.

148
00:08:00,534 --> 00:08:03,234
And they pulled, for listeners
who don't know, they pulled back,

149
00:08:03,654 --> 00:08:06,964
Nvidia was supposed to invest a
hundred billion dollars in OpenAI.

150
00:08:07,104 --> 00:08:09,564
They got to about 30
billion and they stopped.

151
00:08:09,564 --> 00:08:12,624
And Jensen, Juan came out and said,
basically, I don't know if we're gonna

152
00:08:12,624 --> 00:08:19,599
do any more of this, but Nvidia, is now,
they've built a network now, and I think

153
00:08:19,749 --> 00:08:22,109
Meta will do this Reddit type network.

154
00:08:22,109 --> 00:08:26,084
That's a great thing for Meta, that's
a consumer type of social network.

155
00:08:26,584 --> 00:08:31,489
But Nvidia, and maybe you've thought
about this Kish, but the, they've got

156
00:08:31,759 --> 00:08:35,059
an enterprise software, agent network.

157
00:08:35,359 --> 00:08:39,139
But for corporations, and I've
been thinking about this a

158
00:08:39,139 --> 00:08:41,629
lot because the next level.

159
00:08:42,239 --> 00:08:46,739
Of ERP of enterprise software
is going to be AG agentic.

160
00:08:47,489 --> 00:08:52,439
And if they pull this off and manage to,
to make that this, the coalition that

161
00:08:52,439 --> 00:08:58,459
develops those real enterprise agents and
runs them, they're gonna be in a great

162
00:08:58,459 --> 00:09:03,949
place for hundred billions, multi hundreds
of billions in spending every year.

163
00:09:04,819 --> 00:09:07,159
Is that something, have you
been watching that, Krish?

164
00:09:07,299 --> 00:09:12,489
If you think of Nvidia, they're
evolving from being a chip maker to

165
00:09:12,519 --> 00:09:14,319
really an infrastructure backbone.

166
00:09:14,899 --> 00:09:19,539
that's the strategy that you see
from them over the last year or so.

167
00:09:19,589 --> 00:09:23,729
and now they're getting into more of
the agent based enterprise systems,

168
00:09:23,829 --> 00:09:27,639
comparing with the coworker model
that we are seeing in the last

169
00:09:28,509 --> 00:09:30,459
few weeks to months coming out.

170
00:09:30,459 --> 00:09:32,859
Like they capturing that space.

171
00:09:34,644 --> 00:09:39,624
If you think about the compute demand,
like it's nearly doubling every three

172
00:09:39,624 --> 00:09:43,734
to four months, like overall in an
AI compute perspective globally.

173
00:09:44,734 --> 00:09:46,234
and that's the opportunity for them.

174
00:09:46,234 --> 00:09:49,474
Like the chip opportunity will
become commoditized at some point.

175
00:09:49,744 --> 00:09:55,954
But how do you capture the AI
infrastructure, AI compute opportunity?

176
00:09:57,334 --> 00:10:00,094
And remember, we used to
think Moore's law was fast.

177
00:10:00,634 --> 00:10:03,564
you're saying every three to four
months we're doubling our demand.

178
00:10:03,864 --> 00:10:06,824
We, I used to think, Moore's
Law we're, every 18 months.

179
00:10:06,854 --> 00:10:07,994
That's spectacular.

180
00:10:07,994 --> 00:10:08,984
No, it's every three.

181
00:10:09,194 --> 00:10:12,294
The last, report I saw, it's
every three to four months.

182
00:10:12,684 --> 00:10:15,504
The globally AI compute
demand is, is almost doubling.

183
00:10:16,024 --> 00:10:16,594
Wow.

184
00:10:16,961 --> 00:10:20,591
And then NVIDIA's getting
into autonomous vehicles.

185
00:10:20,641 --> 00:10:21,991
The same thing.

186
00:10:21,991 --> 00:10:22,051
Wow.

187
00:10:22,141 --> 00:10:22,501
Yeah.

188
00:10:22,601 --> 00:10:26,371
and where I guess, people
have, and they're I think

189
00:10:26,371 --> 00:10:27,871
they're so smart as a company.

190
00:10:28,071 --> 00:10:33,231
they've let Tesla and Waymo do the
hard work, develop all these things.

191
00:10:33,531 --> 00:10:36,231
Now they come in and say, okay,
we'll develop a facility that

192
00:10:36,231 --> 00:10:40,041
can be used for autonomous
vehicles and everybody can use it.

193
00:10:40,521 --> 00:10:45,351
and once again, that everybody is going
to be using Nvidia chips, and I think

194
00:10:45,351 --> 00:10:47,211
they're, it's a brilliant strategy.

195
00:10:47,211 --> 00:10:50,856
It's gets back to that idea of, the,
that we used to teach in business

196
00:10:50,856 --> 00:10:55,026
school about the people who made
money during the Gold Rush were

197
00:10:55,026 --> 00:10:56,526
not the people who looked for gold.

198
00:10:56,901 --> 00:11:00,471
Few of them got rich, but most of
them didn't make any money at all.

199
00:11:00,501 --> 00:11:01,341
Who made money?

200
00:11:01,491 --> 00:11:03,321
People who sold them the pick axes.

201
00:11:03,441 --> 00:11:04,311
The wagons.

202
00:11:04,551 --> 00:11:07,711
And for Levi Strauss, the jeans, Yeah.

203
00:11:08,016 --> 00:11:08,466
Great stuff.

204
00:11:08,856 --> 00:11:11,676
Last thing, and I'm, I've
been really, since we knew you

205
00:11:11,676 --> 00:11:12,306
were coming outta the show.

206
00:11:12,306 --> 00:11:14,226
I wanted to talk to you about this
and then I wanna get into this,

207
00:11:14,226 --> 00:11:18,396
the whole question of AI anxiety,
but I wa my favorite YouTuber, Nate

208
00:11:18,396 --> 00:11:20,646
Jones came out with a great model.

209
00:11:20,676 --> 00:11:26,576
'cause this has been the great discussion
is why and MIT came, I think MIT came

210
00:11:26,576 --> 00:11:28,616
out with a report, said we can plot.

211
00:11:29,066 --> 00:11:32,546
How much of an occupation
could be done with ai?

212
00:11:33,056 --> 00:11:36,896
And then we'll plot how much
is actually being done with ai.

213
00:11:36,986 --> 00:11:38,726
And it's a big difference.

214
00:11:38,886 --> 00:11:40,326
we have the promise.

215
00:11:40,326 --> 00:11:40,386
Yeah.

216
00:11:40,476 --> 00:11:41,706
We have the ability.

217
00:11:41,886 --> 00:11:44,106
We're not having the uptake.

218
00:11:44,376 --> 00:11:46,026
And that constantly comes back.

219
00:11:46,086 --> 00:11:49,396
You see everybody's report , only
5% of executives say they're getting

220
00:11:49,396 --> 00:11:55,146
benefits . And yet we know e especially
if you know business, you know how

221
00:11:55,146 --> 00:11:56,826
much of the job can be replaced.

222
00:11:56,826 --> 00:12:02,276
I, we play with it every day and we're
not seeing that, that degree of uptake.

223
00:12:02,336 --> 00:12:06,106
And Nate Jones , came up with
this idea that maybe it's

224
00:12:06,106 --> 00:12:07,216
just the way we're doing it.

225
00:12:08,281 --> 00:12:12,841
And that we're not, we're trying
to automate jobs end to end like we

226
00:12:12,841 --> 00:12:16,051
used to do with chatbots where we'd
come in, we'd type something in, we'd

227
00:12:16,051 --> 00:12:17,521
expect the whole report to come out.

228
00:12:17,881 --> 00:12:21,181
Instead of saying, what, how
can I break down the job?

229
00:12:21,421 --> 00:12:24,121
How can I supervise the AI properly?

230
00:12:24,391 --> 00:12:26,221
And have somebody say, oh, that's right.

231
00:12:26,341 --> 00:12:26,881
That's not right.

232
00:12:26,941 --> 00:12:28,231
And you start to think about it.

233
00:12:28,801 --> 00:12:35,571
We need to think about ai, not as I
think somebody used to say as, junior

234
00:12:35,571 --> 00:12:41,331
employees, but I think we have to
think about these AI capabilities in

235
00:12:41,331 --> 00:12:45,831
a new way of architecting our business
or we're never gonna get anywhere

236
00:12:45,861 --> 00:12:47,061
'cause they'll never be perfect.

237
00:12:47,631 --> 00:12:52,621
But then we accept imperfection from
humans, but we can't manage it with ai.

238
00:12:52,621 --> 00:12:55,381
Krish you've, you, this is
what you do all the time.

239
00:12:55,476 --> 00:12:59,856
are you seeing the same reluctance and
are you finding that people are starting

240
00:12:59,856 --> 00:13:01,356
to think about ways to overcome it?

241
00:13:04,056 --> 00:13:05,076
I think it's getting better.

242
00:13:05,076 --> 00:13:11,376
if I were to go back to the time when
we started talking maybe a year back or

243
00:13:11,796 --> 00:13:17,716
six or nine months back, there was a lot
more of, skepticism and a lot more of,

244
00:13:17,896 --> 00:13:21,256
fear in terms of getting hands on ai.

245
00:13:21,916 --> 00:13:25,996
But I see more and more people, not
just people who are in consulting or

246
00:13:25,996 --> 00:13:30,796
who are dealing with AI every day,
but just in general, like people

247
00:13:30,826 --> 00:13:33,976
who has no clue of what AI is.

248
00:13:34,396 --> 00:13:37,726
But it is becoming more of an,
I think it's becoming more of

249
00:13:37,726 --> 00:13:40,071
an ambient layer in, in my.

250
00:13:41,001 --> 00:13:42,351
Way of explaining, right?

251
00:13:42,351 --> 00:13:44,361
everything you do, it's part of ai.

252
00:13:44,391 --> 00:13:48,231
Like you wear the Meta sunglasses
from RayBan, it's part of AI.

253
00:13:48,561 --> 00:13:51,921
You don't have to figure out, but
then it becomes part of the jewelry

254
00:13:51,921 --> 00:13:53,706
or kind of the thing that you wear.

255
00:13:54,006 --> 00:13:56,936
And then you ask the question like,
pointing and looking at something

256
00:13:56,936 --> 00:14:00,056
and say, tell me what this building
is, and it tells you about it.

257
00:14:00,146 --> 00:14:04,046
What's happening behind is the
meta AI is the LAMA models that are

258
00:14:04,046 --> 00:14:05,666
probably working behind the scenes.

259
00:14:06,476 --> 00:14:10,916
So that ambient layer is causing us not
to make those conscious decisions every

260
00:14:10,916 --> 00:14:16,481
day that I'm going to use AI no one thinks
about using digital applications anymore.

261
00:14:16,836 --> 00:14:19,566
When it was a thing, like in the
first time when you could order

262
00:14:19,566 --> 00:14:23,286
pizza on a digital app, it was a
big thing versus having to call.

263
00:14:23,796 --> 00:14:26,706
I think it, it will just
become like that at some point.

264
00:14:27,591 --> 00:14:31,321
But Krish, how do you find, I agree
that there's more and more people that

265
00:14:31,321 --> 00:14:34,981
are using AI for a whole variety of
different things, like you said, even

266
00:14:34,981 --> 00:14:36,871
down to the Meta glasses and so on.

267
00:14:37,711 --> 00:14:43,901
But how do you find the difference in
uptake of, I'll call it traditional ai?

268
00:14:43,931 --> 00:14:50,051
So the chat bot, the, what I call the
personal assistant versus agentic ai.

269
00:14:51,051 --> 00:14:55,371
I think agentic AI is still,
still in the labs in many ways.

270
00:14:55,371 --> 00:15:00,891
there's a lot of organizations, at least
the tier one Canadian organizations are

271
00:15:00,891 --> 00:15:04,581
all using some form or shape of Agen ai.

272
00:15:05,691 --> 00:15:12,831
Where the opportunity lies is
have we actually used Agen AI

273
00:15:13,401 --> 00:15:15,141
in your business processes?

274
00:15:15,191 --> 00:15:16,481
it's a great thing to have a Chatbot

275
00:15:16,841 --> 00:15:18,011
It's a great thing to have.

276
00:15:18,821 --> 00:15:25,861
One process in your overall kind of,
ecosystem being automated, but have

277
00:15:25,861 --> 00:15:30,511
you actually looked at how to automate
your procurement business processes?

278
00:15:30,511 --> 00:15:34,441
Have you looked at reinventing
your HR and marketing?

279
00:15:34,441 --> 00:15:37,881
And I think that's the opportunities
and I'm seeing people are starting

280
00:15:37,881 --> 00:15:42,201
to think about it, but haven't really
thought about the full reinvention

281
00:15:42,201 --> 00:15:44,151
of all of those business processes.

282
00:15:44,256 --> 00:15:47,461
I, I was gonna say, it almost
sounds like, years ago people used

283
00:15:47,461 --> 00:15:51,291
to talk about process redesign and
re-engineering of the business.

284
00:15:51,291 --> 00:15:56,451
And that really sounds a lot like
what's required now is process

285
00:15:56,451 --> 00:15:59,181
redesign to now incorporate.

286
00:15:59,256 --> 00:16:05,796
Agentic into your processes and not just
trying to slap them in various places.

287
00:16:05,796 --> 00:16:05,976
Percent.

288
00:16:05,976 --> 00:16:08,966
Because if you automate or if you
put AI into something that is broken,

289
00:16:08,966 --> 00:16:13,646
you're basically scaling the kind
of the making it break faster.

290
00:16:13,826 --> 00:16:14,756
Yeah, exactly.

291
00:16:14,936 --> 00:16:17,216
You are, you're promoting
digital bureaucracy.

292
00:16:17,966 --> 00:16:18,446
Yes.

293
00:16:19,916 --> 00:16:25,076
And the, so what do you, the other part
of, while we're talking about AgTech,

294
00:16:25,076 --> 00:16:32,006
is how do you find, in your experience,
are there a lot of companies or is, do

295
00:16:32,006 --> 00:16:35,966
you find that there's an, a real need
to be able to go back and look at making

296
00:16:35,966 --> 00:16:38,456
sure you know that your data is secure?

297
00:16:38,816 --> 00:16:42,981
Pre I'll call it pre agentic, before
you start utilizing any kind of

298
00:16:42,981 --> 00:16:47,811
agents, making sure that your data
is safe and secure and that it's

299
00:16:47,811 --> 00:16:50,691
properly classified and that, because.

300
00:16:51,511 --> 00:16:57,481
I guess my concern, one of my concerns
from a corporate standpoint is it's fine

301
00:16:57,481 --> 00:17:02,851
if, if Sally accidentally has access to
some data right now that she shouldn't,

302
00:17:03,481 --> 00:17:09,241
that's wrong, but it's probably not
gonna kill us because Sally's one person

303
00:17:09,451 --> 00:17:11,851
and can only look at things so quickly.

304
00:17:12,511 --> 00:17:20,371
But now, if Sally's running an agent
that can run 24 7, 365 and do things

305
00:17:20,371 --> 00:17:25,531
dramatically faster than what Sally
did, now you run into the problem

306
00:17:25,561 --> 00:17:30,211
of the, an agent having access to
the data that they shouldn't have.

307
00:17:31,771 --> 00:17:36,531
Yeah, no, I completely agree that
the guardrails need to be there.

308
00:17:36,651 --> 00:17:41,151
And depends on the industry,
depends on the type of data.

309
00:17:42,231 --> 00:17:46,181
At the same time, if you look
at, the sniff test, which we were

310
00:17:46,181 --> 00:17:50,531
talking about, it talks about
reasonableness verification.

311
00:17:50,921 --> 00:17:53,861
Like you, you need to
verify reasonableness.

312
00:17:53,911 --> 00:17:58,741
and sometimes you could be
80% right when it comes to AI

313
00:17:58,801 --> 00:18:01,681
and the 20% could be manual.

314
00:18:01,681 --> 00:18:03,601
Human in the loop, human in the lead.

315
00:18:04,751 --> 00:18:05,711
and that's good enough.

316
00:18:05,741 --> 00:18:12,671
And I think sometimes we have, we're
running for perfection and that is causing

317
00:18:12,731 --> 00:18:15,641
some of the concerns to be perfect.

318
00:18:15,641 --> 00:18:18,461
And to your point, like if there's
one person getting access, sure,

319
00:18:18,821 --> 00:18:22,031
can we put the right guardrails
to make sure that doesn't happen?

320
00:18:22,031 --> 00:18:26,891
And if it happens, what are the
kind of the mitigations for it?

321
00:18:27,401 --> 00:18:31,571
but you may be not doing anything
with AI for the next nine months

322
00:18:31,991 --> 00:18:33,401
just trying to solve that problem.

323
00:18:33,461 --> 00:18:34,301
Is it worth, yeah.

324
00:18:34,856 --> 00:18:38,286
Yeah, and I've seen this before
in it, it, I wrote a paper

325
00:18:38,286 --> 00:18:39,536
on it for the cutter journal.

326
00:18:39,536 --> 00:18:44,396
At one point I was doing a big
telco system and they, we were

327
00:18:44,396 --> 00:18:49,436
gonna build this massively great
system to avoid a couple of errors.

328
00:18:49,676 --> 00:18:53,036
And the couple of errors were that,
the way Bell expressed where they

329
00:18:53,036 --> 00:18:55,256
put things was not by postal code.

330
00:18:55,706 --> 00:18:57,266
So you couldn't do an edit.

331
00:18:57,476 --> 00:19:00,296
So we were gonna spend all of this
great time and money trying to

332
00:19:00,296 --> 00:19:01,796
figure out how to solve this problem.

333
00:19:02,216 --> 00:19:04,106
I went and asked one question.

334
00:19:04,106 --> 00:19:08,326
I said, who do you have that's really
successful, really dealing with this?

335
00:19:08,326 --> 00:19:10,306
and I had this one lady, and
she was really up on this.

336
00:19:10,486 --> 00:19:13,906
I went to her, you know what she had under
terminal, whole pile of yellow sticky

337
00:19:13,906 --> 00:19:16,806
notes for the 15 items that were there.

338
00:19:16,806 --> 00:19:19,146
And literally we were going
to spend a million and a half

339
00:19:19,146 --> 00:19:23,916
dollars building a system to fix
several error areas like that.

340
00:19:24,186 --> 00:19:25,416
That training would've.

341
00:19:26,031 --> 00:19:29,631
Gotten rid of or that just making
sure people had experience with.

342
00:19:29,691 --> 00:19:35,881
And that opened my eyes and I did almost
10 years of lean work and just pulling bad

343
00:19:35,881 --> 00:19:40,021
things outta processes and understanding
that many times we were trying to automate

344
00:19:40,021 --> 00:19:44,461
things we should have just abolished
or we should have just taken a new

345
00:19:44,461 --> 00:19:47,101
look at and said, why automate this?

346
00:19:47,151 --> 00:19:48,411
we can fix this with training.

347
00:19:48,411 --> 00:19:49,851
We can fix this with experience.

348
00:19:50,091 --> 00:19:54,681
And and I think that's, I think
that would be the maturity of AI is

349
00:19:54,681 --> 00:19:57,501
to stop trying to think of it like
it's, we're gonna have an end to end.

350
00:19:57,501 --> 00:20:00,321
It's gonna, we'll install
it, it'll fix this problem.

351
00:20:00,471 --> 00:20:04,071
Instead looking at and saying, what
components could I do well with ai?

352
00:20:04,876 --> 00:20:06,946
What components can I do well with people?

353
00:20:07,186 --> 00:20:12,076
Where do I need exactness of an
algorithm versus the experience

354
00:20:12,076 --> 00:20:13,546
that says that number's just wrong.

355
00:20:13,616 --> 00:20:16,346
I've had a lot of bosses
who don't redo my work.

356
00:20:16,816 --> 00:20:19,516
when I was, especially when I was a young
employee, I was working in finance and

357
00:20:19,516 --> 00:20:23,266
stuff like that, and somebody look at it
and go, Jim, there's something wrong here.

358
00:20:24,616 --> 00:20:26,476
And basically that's
what they got paid for.

359
00:20:27,946 --> 00:20:28,186
You.

360
00:20:28,186 --> 00:20:29,626
You are spot on, Jim.

361
00:20:29,626 --> 00:20:34,596
Like we, when we go and, look at
business processes or existing process

362
00:20:34,636 --> 00:20:41,686
at a number of our clients, I often
find that there's probably good 20, 30%

363
00:20:41,836 --> 00:20:47,926
that's pure RPA type of work and that's
good enough that does not require ai.

364
00:20:48,796 --> 00:20:52,656
And I think that's the realization
that we need to absorb that.

365
00:20:53,456 --> 00:20:59,216
You are looking for an outcome that is
going to be X 30, 40% more efficient, more

366
00:20:59,216 --> 00:21:04,616
productive, improves your NPS score, helps
you with customer acquisition or saves

367
00:21:04,616 --> 00:21:06,686
you money from an bottom line perspective.

368
00:21:07,316 --> 00:21:11,636
How you get there doesn't need
to be, and I'm saying this being

369
00:21:11,636 --> 00:21:16,286
kind of the AI lead for Accenture,
it doesn't always need to be AI.

370
00:21:17,426 --> 00:21:21,316
And I think that's an important
acceptance because sometimes we run

371
00:21:21,316 --> 00:21:26,106
after and bit of an AI wash, let me wash
everything with AI and we don't have to.

372
00:21:27,276 --> 00:21:27,606
Yeah.

373
00:21:28,056 --> 00:21:28,566
Interesting.

374
00:21:29,586 --> 00:21:33,226
John, you're that same, and by the
way, Krishtian John's questions,

375
00:21:33,316 --> 00:21:37,486
he's also in charge of security, so
he is, that's a, I saw the CSO mug.

376
00:21:38,251 --> 00:21:38,471
yes.

377
00:21:38,971 --> 00:21:42,131
. I just, I was just gonna
say that, I'm a big fan.

378
00:21:42,181 --> 00:21:47,861
part of the reason I'm on this show with
Jim is I'm a big fan of AI, but, one of

379
00:21:47,861 --> 00:21:53,501
the things, and it more so with ag agentic
than traditional ai, but, it scares the

380
00:21:53,501 --> 00:21:58,941
hell outta me because there's, it feels a
little bit like the, a black box that you

381
00:21:59,151 --> 00:22:03,971
put the data in and it spits answers out,
and you go, okay, how did it get that?

382
00:22:04,431 --> 00:22:07,881
so it's not like traditional
programming where you can follow

383
00:22:07,881 --> 00:22:09,681
the code through and verify it.

384
00:22:10,251 --> 00:22:15,401
but it's, so I'm, I don't wanna say I'm
skeptical, but I'm a little bit leery.

385
00:22:15,401 --> 00:22:20,096
And again, because of the cybersecurity
hat, it, it makes me a little bit nervous.

386
00:22:20,096 --> 00:22:22,381
So it's, I'm approaching it with caution.

387
00:22:22,381 --> 00:22:24,966
And you were in the industry, you're
in a regulated industry as well.

388
00:22:24,996 --> 00:22:25,236
Yes.

389
00:22:25,286 --> 00:22:26,336
so that's the other.

390
00:22:26,861 --> 00:22:27,551
Aspect.

391
00:22:27,671 --> 00:22:31,451
'cause the industry and the type of
data and the type of obligations you

392
00:22:31,451 --> 00:22:36,811
have for your customers and shareholders
is also going to decide the kind

393
00:22:36,811 --> 00:22:38,731
of, protection you need to consider.

394
00:22:39,271 --> 00:22:39,901
Yeah, sure.

395
00:22:39,901 --> 00:22:44,091
And one of the things I love about having
John on the show is he's got that because

396
00:22:44,091 --> 00:22:49,041
of where he, the way he's thinking about
these things and trying to wrap his mind

397
00:22:49,041 --> 00:22:52,961
around this, it's different than me who's,
I don't, I don't work for anybody anymore.

398
00:22:53,201 --> 00:22:54,791
I can put as much AI as I can.

399
00:22:54,791 --> 00:22:59,771
I created a little publishing
company that is 80% ai.

400
00:23:00,041 --> 00:23:03,971
Like we, we automate everything, including
my expenses, which I toss into it

401
00:23:03,971 --> 00:23:05,711
and say, send those to my accountant.

402
00:23:05,711 --> 00:23:10,091
Now, what used to take me 45 minutes of
going, what is this one, what is this one?

403
00:23:10,091 --> 00:23:11,351
What is this one done?

404
00:23:11,591 --> 00:23:16,451
So we've, and that's how I attacked it,
was I just, when I was stuck saying I

405
00:23:16,451 --> 00:23:19,571
wanted to have a publishing company,
but I couldn't afford to have a massive

406
00:23:19,571 --> 00:23:21,911
amount of employees, anything that's not.

407
00:23:22,721 --> 00:23:27,131
The value of publishing, doing this
sort of work where a human should do it.

408
00:23:27,761 --> 00:23:28,841
I said, we're gonna get rid of it.

409
00:23:29,321 --> 00:23:32,511
And I think we've done
that in a small business.

410
00:23:32,511 --> 00:23:34,761
I know you can do that in
a big regulated business.

411
00:23:34,761 --> 00:23:38,661
I think the constraints are bigger
and the challenges are bigger.

412
00:23:38,691 --> 00:23:42,791
it's great to be a cheerleader, but
then if you have to do it in real time,

413
00:23:42,891 --> 00:23:46,461
what's that old, was that an Accenture
ad that they used to have about changing

414
00:23:46,461 --> 00:23:48,351
the engines on a plane in mid-flight?

415
00:23:48,651 --> 00:23:51,571
I think it was, they were trying
to explain, they're trying to

416
00:23:51,571 --> 00:23:54,001
explain consulting and said,
here's what we're doing for you.

417
00:23:54,001 --> 00:23:55,291
Crazy things for our clients as well.

418
00:23:55,291 --> 00:23:57,271
So I'm sure it could be ours.

419
00:23:57,371 --> 00:23:57,951
no doubt about it.

420
00:23:57,951 --> 00:23:58,151
No.

421
00:23:58,696 --> 00:24:02,901
But it was the idea of, it was the idea
of a big corporation making a change was

422
00:24:03,111 --> 00:24:05,251
like flying an airplane to try to change.

423
00:24:05,251 --> 00:24:07,116
we need into a massive
change ourselves as well.

424
00:24:07,366 --> 00:24:11,711
if you look at from the
time of the AI kind of the.

425
00:24:13,081 --> 00:24:14,791
Growth in the last few years.

426
00:24:14,821 --> 00:24:18,181
AI has always been there, as we all
know, but since the time, it has

427
00:24:18,181 --> 00:24:22,151
become more often, commoditized,
opportunity for all of us.

428
00:24:23,371 --> 00:24:27,421
we are a small country, like 800,000
people globally in Accenture.

429
00:24:28,111 --> 00:24:32,881
And think about the change that
we have to go through to move.

430
00:24:33,556 --> 00:24:38,376
Accenture people to use ai, and
that's what we have done, like our

431
00:24:38,376 --> 00:24:40,596
leadership and our CEO, Julie Sweet.

432
00:24:40,646 --> 00:24:42,626
she has been very vocal about that.

433
00:24:42,626 --> 00:24:48,446
We need to be our best credential
for us to be going and talking

434
00:24:48,446 --> 00:24:50,336
about AI to, to anyone.

435
00:24:50,456 --> 00:24:54,176
So we have completely changed
our marketing function.

436
00:24:54,176 --> 00:24:58,886
We have changed our, the way
we look at HR and legal and

437
00:24:58,886 --> 00:25:00,416
all of our corporate functions.

438
00:25:00,416 --> 00:25:04,166
So we went through a massive
transformation and then for our people

439
00:25:04,166 --> 00:25:08,306
as well, like every single person
within Accenture, irrespective of your

440
00:25:08,306 --> 00:25:13,286
level, has to go through a certain
amount of agent and AI training.

441
00:25:14,351 --> 00:25:16,751
That includes, that's
certification from Stanford.

442
00:25:17,411 --> 00:25:22,121
That's a phenomenal way to do it because
that's the only way to do it properly is

443
00:25:22,241 --> 00:25:26,921
train your people ahead of time on how
to use the tools before you just throw

444
00:25:26,921 --> 00:25:29,131
them out and, and say, here, go use it.

445
00:25:29,881 --> 00:25:33,601
Sorry, I was just, when you said
800,000 employees, I'm sitting there

446
00:25:33,601 --> 00:25:37,621
going, God, I can only imagine what
their copilot licensing costs are.

447
00:25:38,621 --> 00:25:40,511
I'm sure they've got
somebody driving a deal.

448
00:25:40,511 --> 00:25:40,961
Don't worry.

449
00:25:41,411 --> 00:25:42,581
Yes, no doubt.

450
00:25:43,091 --> 00:25:43,391
Yeah.

451
00:25:43,446 --> 00:25:45,906
but this is the thing I loved
about working in a consulting

452
00:25:45,906 --> 00:25:50,096
company and something that I
think people could a adopt.

453
00:25:50,396 --> 00:25:55,596
And that was, we and I ran a worldwide
practice , with , the former DMR

454
00:25:55,596 --> 00:25:57,786
group, and we did a lot of training.

455
00:25:58,626 --> 00:26:04,176
We invested heavily in training,
both for knowledge and culture.

456
00:26:05,521 --> 00:26:06,811
and those are two important things.

457
00:26:06,901 --> 00:26:07,921
And I think, yeah.

458
00:26:07,971 --> 00:26:11,841
Accenture does that exceptionally
well as, as well, investing the

459
00:26:11,841 --> 00:26:15,421
money and training your people so
that they're not only knowledgeable,

460
00:26:15,601 --> 00:26:19,541
but they have an understanding of
the culture of the company and how

461
00:26:19,541 --> 00:26:21,671
you move forward and how you work.

462
00:26:21,671 --> 00:26:24,881
And it's why I think you
can have 800,000 employees.

463
00:26:25,031 --> 00:26:29,586
And I, my experience has been, you
might, you might, people might argue

464
00:26:29,586 --> 00:26:33,781
with this, but if you talk to somebody
in Accenture who works in another

465
00:26:33,781 --> 00:26:37,201
country, they're gonna have a lot in
common with somebody who works in Canada.

466
00:26:37,591 --> 00:26:41,411
'cause there's a, their thread that of,
and I, they're big companies and all that,

467
00:26:41,561 --> 00:26:44,561
but there's a thread and understanding
of the purpose of the company and how

468
00:26:44,561 --> 00:26:45,791
it works and all those sorts of things.

469
00:26:46,091 --> 00:26:48,071
I think companies could learn from that.

470
00:26:49,361 --> 00:26:52,271
No, I think we have, I feel
proud of, what we have done.

471
00:26:52,351 --> 00:26:54,181
and I think it's a good segue.

472
00:26:55,201 --> 00:26:58,951
If you may, Jim, into the topic of
AI anxiety, because I think when

473
00:26:58,951 --> 00:27:06,151
this does not happen, yes, it leads
to confusion, it leads to distrust,

474
00:27:06,151 --> 00:27:11,341
it leads to causing the, all the
parameters that leads to anxiety.

475
00:27:13,621 --> 00:27:15,631
Because that's one of the
things we were hearing.

476
00:27:15,631 --> 00:27:20,601
Like people were anxious, people were
nervous about what's going to happen.

477
00:27:20,691 --> 00:27:25,791
Like in consulting, people don't
always jump and talk about job loss,

478
00:27:26,301 --> 00:27:28,581
but they think about relevancy.

479
00:27:29,001 --> 00:27:33,761
They think about am I going to be relevant
in conversations, in discussions in my

480
00:27:33,761 --> 00:27:35,851
peer group, in front of my, clients.

481
00:27:36,301 --> 00:27:37,771
And that is an anxiety.

482
00:27:38,071 --> 00:27:44,041
And I think we address that with the
training, with the knowledge, giving

483
00:27:44,041 --> 00:27:46,141
people the tools, letting them use it.

484
00:27:46,721 --> 00:27:50,801
and I think that's a good way of
Addressing that and acknowledging it,

485
00:27:50,801 --> 00:27:53,201
that it's okay to have that conversation.

486
00:27:53,721 --> 00:28:00,111
and I think some of the anxiety anyways
comes from the fear of the unknown, right?

487
00:28:00,111 --> 00:28:04,461
That if, as you said, that you've, that
Accenture has done a really good job of

488
00:28:04,461 --> 00:28:11,026
educating people and sharing information
with them that helps to, tame that anxiety

489
00:28:11,566 --> 00:28:15,406
versus a company that says, oh yeah,
we're rolling out AI across the board.

490
00:28:15,796 --> 00:28:19,036
And everybody going, oh my God, I
keep hearing about people losing

491
00:28:19,036 --> 00:28:21,406
jobs over AI and this and that.

492
00:28:21,566 --> 00:28:23,636
am I, does that mean
I'm gonna lose my job?

493
00:28:23,636 --> 00:28:25,856
And, what's, how are things gonna change?

494
00:28:26,286 --> 00:28:29,166
yeah, the communication
part I think is huge.

495
00:28:29,226 --> 00:28:30,276
And the education.

496
00:28:31,144 --> 00:28:31,354
Yeah.

497
00:28:31,354 --> 00:28:34,894
And I think companies, I think it,
this is now catching up with us.

498
00:28:35,074 --> 00:28:40,764
I think the first, from the first looking
at chat GPT in, what was it, 2022?

499
00:28:41,394 --> 00:28:41,514
Yeah.

500
00:28:41,514 --> 00:28:42,474
November, 2022.

501
00:28:42,924 --> 00:28:44,374
November was 22.

502
00:28:44,884 --> 00:28:45,244
Yeah.

503
00:28:45,994 --> 00:28:49,044
With you, every, everybody knows
where they were when John Lennon

504
00:28:49,044 --> 00:28:52,104
died and everybody knows where
they were when ChatGPT came in.

505
00:28:52,464 --> 00:29:00,804
the, but it's the idea of we've, we
were so amazed by this thing that could

506
00:29:00,804 --> 00:29:05,054
write a poem, that could talk to us,
that behaved in a way that no software

507
00:29:05,054 --> 00:29:07,544
had, except maybe in science fiction.

508
00:29:07,694 --> 00:29:09,104
So we're enthralled with that.

509
00:29:09,314 --> 00:29:10,634
We've had, that has exploded.

510
00:29:10,754 --> 00:29:12,494
I think there is a huge.

511
00:29:13,214 --> 00:29:15,404
anxiety in companies.

512
00:29:15,584 --> 00:29:18,914
I think there's a huge anxiety in
society and I'm starting to see it,

513
00:29:19,124 --> 00:29:23,444
and I don't think that people who
are rolling this out, even the big AI

514
00:29:23,444 --> 00:29:28,724
companies that have been quite critical
of them really understand the feelings

515
00:29:28,724 --> 00:29:33,514
that people are going through with
this when they hear, and it's relevant

516
00:29:33,794 --> 00:29:35,114
when you're talking about a company.

517
00:29:35,764 --> 00:29:39,634
it's one thing to talk about, the
job loss that's gonna be out there

518
00:29:39,634 --> 00:29:41,764
in the future and it will come.

519
00:29:42,174 --> 00:29:43,464
I'm absolutely convinced of that.

520
00:29:43,474 --> 00:29:47,159
Geoffrey Hinton was in front of A
Parliament committee and you could

521
00:29:47,159 --> 00:29:50,399
see the impatience in the man saying,
you're not preparing for this.

522
00:29:50,759 --> 00:29:52,769
You're not preparing for
what's going to be a massive

523
00:29:52,769 --> 00:29:55,439
transformation of the workforce
and our taxation and all of that.

524
00:29:55,739 --> 00:29:56,789
But that's in the future.

525
00:29:56,819 --> 00:30:00,869
We're not good at the future,
but when we see ourselves.

526
00:30:01,529 --> 00:30:02,219
Being affected.

527
00:30:02,219 --> 00:30:05,849
And it's that old joke about, not joke,
but saying that, when the guy next

528
00:30:05,849 --> 00:30:08,099
door loses his job, it's a recession.

529
00:30:08,579 --> 00:30:10,769
When you lose your job, it's a depression.

530
00:30:11,269 --> 00:30:15,169
and so when we see it affecting us,
and I think that's what's happening, I

531
00:30:15,169 --> 00:30:18,979
think people are seeing it affect them
in their own lives and starting to worry

532
00:30:18,979 --> 00:30:20,959
about that no matter where they are.

533
00:30:21,922 --> 00:30:22,072
Yeah.

534
00:30:22,072 --> 00:30:28,687
And I find like most, organizations
are trying to tackle the job, which

535
00:30:28,687 --> 00:30:34,237
is okay, but I would rather start
with the task because there's a

536
00:30:34,237 --> 00:30:36,817
difference between the job and the task.

537
00:30:37,447 --> 00:30:42,087
And if you think about the work's
going to change doesn't mean that the

538
00:30:42,087 --> 00:30:44,667
human being is not required anymore.

539
00:30:44,757 --> 00:30:45,147
It's.

540
00:30:45,942 --> 00:30:47,952
The work is going to change.

541
00:30:48,042 --> 00:30:51,162
I may not be doing the exact
same thing I'm doing today.

542
00:30:51,252 --> 00:30:58,182
I may be doing more audit, more decision
making, more of validation, versus

543
00:30:58,182 --> 00:31:02,712
actually doing the stuff and the steps
that I were doing so it changes the

544
00:31:02,712 --> 00:31:10,812
work, which then in turn changes the
workforce and thinking about what kind

545
00:31:10,812 --> 00:31:12,792
of a workforce do I need for future?

546
00:31:13,272 --> 00:31:14,682
How do I prepare for that?

547
00:31:14,682 --> 00:31:19,902
What trainings, what tools, what kind
of empowerment do I need to give them?

548
00:31:19,902 --> 00:31:25,122
The pyramids going to shift upwards,
so how do I make the junior entry

549
00:31:25,122 --> 00:31:30,372
levels behave like they have more
tools and capability to behave a couple

550
00:31:30,372 --> 00:31:35,892
of levels even higher, even from the
get go that changes the workforce.

551
00:31:37,827 --> 00:31:41,147
Then we need to think about
what do I need from the workers?

552
00:31:41,657 --> 00:31:45,077
I think we are jumping into the
workers first in many cases and

553
00:31:45,077 --> 00:31:48,257
saying, oh, I, I have a contact center.

554
00:31:48,527 --> 00:31:52,217
I need to displace X amount of
people because I need to save cost.

555
00:31:54,047 --> 00:31:58,547
Instead, if I think about what are the
processes that I'm running in my contact

556
00:31:58,547 --> 00:32:03,047
center, what work needs to change, what
are the tasks that needs to change?

557
00:32:03,047 --> 00:32:07,157
And then decide and design the
workforce of the future, then

558
00:32:07,157 --> 00:32:08,567
you can have a better worker.

559
00:32:09,257 --> 00:32:10,637
And I think that builds trust.

560
00:32:12,527 --> 00:32:12,707
Yeah.

561
00:32:12,707 --> 00:32:15,347
I think one of the things though
that we also don't do is we don't

562
00:32:15,347 --> 00:32:17,087
think of work in terms of outcomes.

563
00:32:18,147 --> 00:32:21,087
and this is one of the things that
I think freezes organizations.

564
00:32:21,327 --> 00:32:24,357
When you think about who you are in the
organization, I think it's important

565
00:32:24,537 --> 00:32:26,397
your processes become important.

566
00:32:26,647 --> 00:32:29,917
you've gotta defend your data, your
processes and where, what you do

567
00:32:29,917 --> 00:32:32,677
in the organization, instead of
asking yourself, what's our outcome?

568
00:32:33,757 --> 00:32:39,847
And what's the best way to utilize both
people and technology to get that outcome?

569
00:32:40,087 --> 00:32:42,307
And that gets broken very quickly.

570
00:32:42,657 --> 00:32:46,137
we're good at thinking about
inputs to a process and we're good

571
00:32:46,137 --> 00:32:49,407
at protecting those and managing
those, but we forget the outcome.

572
00:32:49,407 --> 00:32:51,477
And I think that's part of, I
think what you're saying, Kris, is

573
00:32:51,717 --> 00:32:54,137
if you're looking at the outcomes
people can have, you can start to

574
00:32:54,137 --> 00:32:57,887
ask questions like, wait a minute, A
junior employee, why are they junior?

575
00:32:57,947 --> 00:33:01,097
Because they don't have enough
knowledge to make these decisions.

576
00:33:01,357 --> 00:33:03,487
but how else could we employ them?

577
00:33:03,487 --> 00:33:04,657
How else could we use them?

578
00:33:04,977 --> 00:33:08,307
I think that, or give them the tools they
can make different kinds of decisions

579
00:33:08,307 --> 00:33:09,717
that they were not making today.

580
00:33:11,277 --> 00:33:14,527
Yeah, 'cause and we talk about,
the errors that AI would make.

581
00:33:15,037 --> 00:33:19,987
I have taken on a lot of new
employees and I wish I could

582
00:33:19,987 --> 00:33:21,397
give them a tool that says, look.

583
00:33:21,712 --> 00:33:27,362
Just work your stuff out and then
ask, check it using this and see

584
00:33:27,362 --> 00:33:28,592
if you get a different answer.

585
00:33:28,842 --> 00:33:32,532
and that's, I think would be, I
think we can find different ways to

586
00:33:32,532 --> 00:33:37,272
use the diff the different tools we
have, but the anxiety piece of it

587
00:33:37,272 --> 00:33:40,032
goes, I think, goes deeper as well.

588
00:33:40,402 --> 00:33:43,297
and I don't want to, I don't want to go
get past that, but I also don't wanna

589
00:33:43,297 --> 00:33:46,447
lose that point that I was going to
think about, and I was talking about

590
00:33:46,537 --> 00:33:53,437
consulting companies and investing in the
training and getting people to understand

591
00:33:53,437 --> 00:33:57,037
what this can do before we start
asking them how we're gonna employ it.

592
00:33:57,637 --> 00:34:01,147
'cause I think if people have an
understanding of ai, they will do

593
00:34:01,147 --> 00:34:02,677
things that will make their jobs easier.

594
00:34:03,562 --> 00:34:07,812
I believe you know, that people
are, people will act in the interest

595
00:34:07,812 --> 00:34:11,322
of their own personal efficiency,
if nothing else, if it's offered.

596
00:34:12,942 --> 00:34:15,582
And you can use AI to
your advantage as well.

597
00:34:15,612 --> 00:34:17,292
I'll give you my personal example.

598
00:34:17,292 --> 00:34:22,572
Like early on, like I was really, I
would say borderline anxious in terms

599
00:34:22,572 --> 00:34:27,912
of what do I need to learn because I,
it just because of my title, I have a

600
00:34:27,912 --> 00:34:31,362
responsibility to be ahead of others.

601
00:34:31,842 --> 00:34:34,062
You need to know a little bit
about AI is what you're saying.

602
00:34:34,692 --> 00:34:35,022
Yeah.

603
00:34:35,027 --> 00:34:35,037
Yeah.

604
00:34:35,772 --> 00:34:38,772
My mentor, John Thorpe once said
to me, said, you don't have to be

605
00:34:38,772 --> 00:34:39,912
the smartest person in the room.

606
00:34:39,912 --> 00:34:42,102
I said, I'm the head of
the practice worldwide.

607
00:34:42,522 --> 00:34:46,512
I love your, I love what you just
said, but I have to try to be

608
00:34:46,542 --> 00:34:47,832
the smartest person in the room.

609
00:34:48,432 --> 00:34:48,642
Yeah.

610
00:34:48,692 --> 00:34:53,692
so that was a phase when Okay, I need
to read MIT, I need to read this,

611
00:34:53,692 --> 00:34:55,492
I need to, that read that article.

612
00:34:55,552 --> 00:34:57,502
And it was overwhelming.

613
00:34:57,652 --> 00:34:59,632
And it's overwhelming for a lot of people.

614
00:35:00,082 --> 00:35:05,002
And if it's overwhelming for people
who have access to a lot of tools,

615
00:35:05,392 --> 00:35:08,932
I can only imagine people who do not
have access to tools, but they're

616
00:35:08,932 --> 00:35:10,552
hearing about all of these things.

617
00:35:10,672 --> 00:35:12,382
So I used AI to my advantage.

618
00:35:12,382 --> 00:35:16,342
Now, I wrote a couple of agents
who summarizes information,

619
00:35:16,852 --> 00:35:19,162
sends me an email every Friday.

620
00:35:19,342 --> 00:35:21,472
This is what happened in the world of ai.

621
00:35:21,502 --> 00:35:26,272
I take a quick glance, at least I
know enough to be relevant or can have

622
00:35:26,272 --> 00:35:31,492
a few sound bites of conversations
like this, but that's like use

623
00:35:31,492 --> 00:35:33,022
AI to your advantage as well.

624
00:35:33,112 --> 00:35:37,872
Like even in the field of mental health
and neurodiversity, there's a lot of

625
00:35:37,902 --> 00:35:40,122
AI based diagnosis that is happening.

626
00:35:40,662 --> 00:35:44,712
So I think it's bit of an circle
like AI is causing some of that.

627
00:35:45,312 --> 00:35:47,082
And then you can also use AI to.

628
00:35:48,237 --> 00:35:51,797
Preempt or remediate some of
that as well from an anxiety

629
00:35:51,797 --> 00:35:53,177
or mental health perspective.

630
00:35:54,437 --> 00:35:55,217
That's interesting.

631
00:35:55,217 --> 00:35:57,167
'cause we do focus on the reverse.

632
00:35:57,587 --> 00:35:59,487
and I was thinking about
this the other day.

633
00:35:59,667 --> 00:36:04,537
Some of the smartest people I
know are dyslexic, in business.

634
00:36:04,537 --> 00:36:08,587
And it really, you don't actually know
that unless you actually talk to them

635
00:36:09,187 --> 00:36:11,857
or neurodiverse in one way or another.

636
00:36:12,397 --> 00:36:13,987
And they've used that.

637
00:36:14,437 --> 00:36:20,047
What, what might be thought of as
a limitation by some people as a

638
00:36:20,047 --> 00:36:21,667
way to drive themselves forward?

639
00:36:22,287 --> 00:36:25,317
my friend Mark, I won't give his
last name, just, but he's, I didn't

640
00:36:25,317 --> 00:36:27,127
know he was dyslexic for years.

641
00:36:27,127 --> 00:36:32,197
I'd known him and he finally told
me he looked, he was one of the

642
00:36:32,197 --> 00:36:33,907
most well-read people in the world.

643
00:36:34,207 --> 00:36:35,617
And I went, how do you.

644
00:36:36,502 --> 00:36:39,192
Keep up that fat, how do you do that?

645
00:36:39,192 --> 00:36:40,002
He said, work hard.

646
00:36:40,752 --> 00:36:44,632
And now I'm thinking, in some
cases, you can now have books

647
00:36:44,632 --> 00:36:45,862
available to you, read to you.

648
00:36:46,572 --> 00:36:49,092
you can have different ways
of tacking information.

649
00:36:49,432 --> 00:36:53,992
it's sad to say my eyesight isn't what
it used to be and able after three

650
00:36:53,992 --> 00:36:59,422
hours of staring at a screen, I dunno
if I can read that stuff anymore.

651
00:36:59,632 --> 00:37:02,842
I'm much more likely to say, look,
I'm gonna just have something.

652
00:37:02,842 --> 00:37:06,742
Read this to me, Or as, and I
think executives must suffer from

653
00:37:06,742 --> 00:37:10,552
this a lot, is you get a stack of
things you're supposed to read.

654
00:37:11,092 --> 00:37:12,382
could I summarize some of them?

655
00:37:12,382 --> 00:37:13,162
Is that cheating?

656
00:37:13,222 --> 00:37:13,672
Maybe.

657
00:37:14,062 --> 00:37:17,512
But it helps you cope with the
volume of information you've got.

658
00:37:18,082 --> 00:37:18,982
It's an interesting thing.

659
00:37:19,192 --> 00:37:19,582
Yeah.

660
00:37:20,032 --> 00:37:20,422
Wow.

661
00:37:20,992 --> 00:37:22,472
'cause she started me
thinking about that, Krish.

662
00:37:23,757 --> 00:37:23,947
it's.

663
00:37:24,937 --> 00:37:25,987
Not difficult to write.

664
00:37:25,987 --> 00:37:27,007
A couple of agents.

665
00:37:27,107 --> 00:37:32,177
I've seen people who have never
done coding, have never been

666
00:37:32,417 --> 00:37:34,397
close to calling themselves.

667
00:37:34,397 --> 00:37:37,997
Technical has actually started doing that.

668
00:37:37,997 --> 00:37:42,677
And this is something that again, in
Accenture, we have started doing, like

669
00:37:42,677 --> 00:37:50,567
an democratized way of how you can
actually use Agentic to your own work.

670
00:37:51,677 --> 00:37:57,587
Like we, we have in-office workshops
where people will come in, sit down

671
00:37:57,587 --> 00:38:01,997
with their laptops, that includes
managing directors, partners at all

672
00:38:01,997 --> 00:38:04,847
levels and analysts and build agents.

673
00:38:05,417 --> 00:38:09,677
And the agents could be about
helping me plan my next trip to

674
00:38:09,737 --> 00:38:13,367
Italy to summarize this AI knowledge.

675
00:38:13,367 --> 00:38:19,817
For me to give me a summary of my
emails, like the use cases are different.

676
00:38:19,817 --> 00:38:22,067
You find your own.

677
00:38:22,622 --> 00:38:28,652
Bottlenecks in your day, do your own
kind of day in a life journey map and

678
00:38:28,652 --> 00:38:30,842
say, where can I be more efficient?

679
00:38:32,402 --> 00:38:34,082
And I think there's many opportunities.

680
00:38:35,312 --> 00:38:35,612
Yeah.

681
00:38:35,612 --> 00:38:38,097
I think, by the way, when we
talk about anxiety, I think we

682
00:38:38,097 --> 00:38:43,227
ignore senior management and
executives to our peril actually.

683
00:38:43,587 --> 00:38:47,517
Because, the myth is, oh, they
don't understand this technology.

684
00:38:47,517 --> 00:38:48,567
they're not interested.

685
00:38:48,667 --> 00:38:53,077
many of them are interested, but
we forget that they have jobs too.

686
00:38:53,647 --> 00:38:59,317
And some of them are incredibly busy and
taking a risk in front of a large group.

687
00:38:59,407 --> 00:39:01,597
It may not be everybody's style.

688
00:39:02,067 --> 00:39:07,287
when I was at Ernst and Young, one of the
reasons I think I got ahead there was I

689
00:39:07,287 --> 00:39:08,877
would help partners with their computers.

690
00:39:09,492 --> 00:39:11,592
And I didn't look like a
jerk when I was doing it.

691
00:39:11,802 --> 00:39:15,372
I respected that they knew a lot more
about a lot of things than I did.

692
00:39:15,522 --> 00:39:21,042
And if I could share my knowledge of how
to use a PC and get some of the experience

693
00:39:21,042 --> 00:39:25,492
they had, and understanding, wow, I
was getting a big benefit out of that.

694
00:39:25,802 --> 00:39:28,532
instead of just treating them like,
you don't understand how to do this,

695
00:39:28,532 --> 00:39:30,602
and I've seen tech people do this.

696
00:39:30,902 --> 00:39:33,572
They walk into an executive's office
and they have that attitude from the

697
00:39:33,572 --> 00:39:37,892
start, and they don't realize that,
that these aren't people who are stupid.

698
00:39:38,132 --> 00:39:40,992
They are, they just, they're
smart at a lot of other things.

699
00:39:41,592 --> 00:39:47,532
But I think if we want executives to be
able to run companies understanding ai,

700
00:39:48,012 --> 00:39:50,082
they have to somehow get that experience.

701
00:39:50,202 --> 00:39:57,312
I think, and I think the good thing about
AI is that it is become a great leveler

702
00:39:58,222 --> 00:40:04,737
to a large extent from an capability
perspective, you can build an agent.

703
00:40:05,632 --> 00:40:09,502
With just English language prompts or

704
00:40:12,052 --> 00:40:14,572
execution level kind of commands.

705
00:40:14,632 --> 00:40:18,432
You don't need to be knowing
Python or Java or any

706
00:40:18,432 --> 00:40:20,232
programming language to do that.

707
00:40:21,372 --> 00:40:27,822
And that's a great kind of way
of leveling the playing field.

708
00:40:29,082 --> 00:40:30,822
And I'm seeing that is already happening.

709
00:40:30,912 --> 00:40:34,862
Like people who, who would've
otherwise dependent on a group of

710
00:40:34,862 --> 00:40:40,532
ingenious to do something for them,
can build reports, can build tools.

711
00:40:40,562 --> 00:40:42,152
And we are already seeing that happening.

712
00:40:42,152 --> 00:40:47,762
Like in our Canadian leadership
team, our Canadian CEO, he asked

713
00:40:47,882 --> 00:40:51,962
everyone directly reporting to him
to have a session on building agents.

714
00:40:52,802 --> 00:40:54,842
How do we build agents,
how do we learn to do that?

715
00:40:55,082 --> 00:40:56,822
And now there is a whole.

716
00:40:57,917 --> 00:40:59,567
flu of things happening.

717
00:40:59,657 --> 00:41:03,977
Everyone's trying to be more efficient,
trying to come up with new ideas, and

718
00:41:03,977 --> 00:41:07,937
it's just like it, it's almost it's
spreading, and when you do that at the

719
00:41:07,937 --> 00:41:12,337
top and you start showing that, and when
you start sending that information to

720
00:41:12,337 --> 00:41:14,737
a broader group of people, it spreads.

721
00:41:15,337 --> 00:41:16,547
It is very infectious.

722
00:41:18,452 --> 00:41:18,742
Yeah.

723
00:41:18,747 --> 00:41:19,067
Yeah.

724
00:41:19,067 --> 00:41:22,817
I think one of the things like we've
talked about anxiety and fear and

725
00:41:22,817 --> 00:41:28,127
things, and I think part of it is that,
I guess I call it fear of the unknown.

726
00:41:28,187 --> 00:41:32,807
That, and Jim, you talked about the
executives as well, that they don't wanna

727
00:41:32,807 --> 00:41:37,132
look stupid in front of others in the
company because they're an executive.

728
00:41:37,132 --> 00:41:38,602
They should know all of this stuff.

729
00:41:39,142 --> 00:41:44,462
but I think that's the big thing is
that, how do you get started learning it?

730
00:41:44,462 --> 00:41:45,212
How do you.

731
00:41:45,567 --> 00:41:49,797
how do I know what to do with AgTech
AI when I don't even know what it is?

732
00:41:50,337 --> 00:41:57,527
and so I think, Krish, to your point,
the, the idea of providing, training

733
00:41:58,247 --> 00:42:03,027
often, and making it available to
everybody in the company is a huge

734
00:42:03,027 --> 00:42:06,807
start or a huge stepping stone to
getting you to where you need to be.

735
00:42:08,487 --> 00:42:09,357
Yeah, a hundred percent.

736
00:42:09,517 --> 00:42:11,197
we absolutely have seen that benefit.

737
00:42:11,647 --> 00:42:11,827
Yeah.

738
00:42:12,667 --> 00:42:12,937
Yeah.

739
00:42:12,937 --> 00:42:15,607
And for the overachievers out there,
and I said, and one of the things I,

740
00:42:15,607 --> 00:42:18,337
and I've talked openly about my career
and what it, what I did wrong and what

741
00:42:18,337 --> 00:42:22,857
I did right over the years, one of the
things that drove me was if somebody

742
00:42:23,787 --> 00:42:30,117
was working for me or consultant or
contractor or anybody who came up to

743
00:42:30,117 --> 00:42:31,677
me and said, you don't understand this.

744
00:42:32,397 --> 00:42:33,567
I'd take the books home.

745
00:42:33,687 --> 00:42:39,407
I, the next time they saw me, I'd know a
lot about the topic, but it's exhausting.

746
00:42:40,217 --> 00:42:40,337
Yeah.

747
00:42:40,817 --> 00:42:44,657
Operating at that level is
exhausting and overwhelming at times.

748
00:42:44,817 --> 00:42:47,247
and as I got older that, that got harder.

749
00:42:47,637 --> 00:42:52,782
Now I'm just, I, and John and I
have talked about this, we were,

750
00:42:52,842 --> 00:42:56,852
we're talking about one of the,
when Gemini came out, I learned a

751
00:42:56,852 --> 00:43:02,462
programming language in an afternoon
and am I gonna be perfect at that?

752
00:43:02,462 --> 00:43:02,912
No.

753
00:43:03,602 --> 00:43:07,802
But I got past all the syntax problems,
all of the things that you had to learn.

754
00:43:08,042 --> 00:43:13,082
I could be, came up and wrote a program
and the AI walked me through it.

755
00:43:13,922 --> 00:43:18,962
So I had a really good understanding of
how to program and rust in an afternoon.

756
00:43:19,292 --> 00:43:22,862
And everyone will tell you there's a
reason why they put me in management.

757
00:43:22,862 --> 00:43:25,082
And it wasn't because of
my programming skills.

758
00:43:25,922 --> 00:43:27,302
It was to get you outta programing.

759
00:43:27,457 --> 00:43:27,747
Yeah.

760
00:43:28,052 --> 00:43:28,292
Yeah.

761
00:43:28,762 --> 00:43:31,522
I think they put me in, they wanted
me in project management to keep

762
00:43:31,522 --> 00:43:36,192
me outta coding, but that's, but at
the same time, we have this ability

763
00:43:36,192 --> 00:43:40,622
to learn new skills and experiment
with those, and I think that's being

764
00:43:40,622 --> 00:43:42,582
missed, of the things you can do.

765
00:43:42,672 --> 00:43:44,472
Everybody thinks about it like a chatbot.

766
00:43:44,472 --> 00:43:46,692
And I was listening to the
senate committee, everybody's

767
00:43:46,692 --> 00:43:47,532
talking about chatbots.

768
00:43:47,742 --> 00:43:48,312
That's good.

769
00:43:48,672 --> 00:43:52,502
But what about the other things
that you can do to expand your

770
00:43:52,502 --> 00:43:55,352
knowledge, to learn to try things.

771
00:43:55,412 --> 00:43:59,402
I think, I guess maybe that's
one of the cures for anxiety.

772
00:44:00,872 --> 00:44:04,002
Krish, I wanna ask you about another
thing and John, I'm gonna ask you the same

773
00:44:04,002 --> 00:44:06,307
question because it's, I think, when we.

774
00:44:07,032 --> 00:44:10,632
We are all humans, and we
hear about the things that are

775
00:44:10,632 --> 00:44:14,352
anxiety generating from ai.

776
00:44:14,772 --> 00:44:18,172
the things that we hear about children,
the things that we hear about,

777
00:44:18,187 --> 00:44:20,062
about people being dependent on it.

778
00:44:20,602 --> 00:44:27,442
How do you cope with that level of
anxiety, which I think hits us all

779
00:44:27,442 --> 00:44:31,402
if we're parents and we're members of
society, how are you dealing with that?

780
00:44:32,402 --> 00:44:36,402
When I have two daughters, one, just
finishing university and the other

781
00:44:36,402 --> 00:44:38,922
one in, grade nine, and I see, the.

782
00:44:39,672 --> 00:44:43,692
The real field level experience
of working with them on that.

783
00:44:43,742 --> 00:44:50,292
And I see the one who is in the high
school, they're natively in ai, right?

784
00:44:50,292 --> 00:44:57,812
my, my older daughter, she's, in adversity
and final year, she has adopting to ai.

785
00:44:58,022 --> 00:45:02,972
she has, she's obviously seen the journey,
but she's adopting to AI and she's

786
00:45:02,972 --> 00:45:05,462
learning AI as, as we are all learning.

787
00:45:05,912 --> 00:45:10,502
But the younger one, going back
three, four years, when she was middle

788
00:45:10,502 --> 00:45:15,512
school, by the time they started using
computers and all, they're all in ai.

789
00:45:15,542 --> 00:45:17,752
So it's the native way of using it.

790
00:45:17,752 --> 00:45:20,602
So I call them kind of the
AI natives in some way.

791
00:45:21,262 --> 00:45:22,372
And for them, they don't know.

792
00:45:22,422 --> 00:45:25,602
and honestly, they're not
even using search anymore.

793
00:45:27,162 --> 00:45:31,122
And sometimes to our
peril, because if you use.

794
00:45:31,662 --> 00:45:35,932
LLMs for every single question
you are probably not using the

795
00:45:35,932 --> 00:45:37,732
resources effectively, right?

796
00:45:37,732 --> 00:45:41,332
Because there's a cost of using
LLMs and tokens and all that.

797
00:45:42,532 --> 00:45:47,002
So I think that to me is an, is a
positive thing, but I obviously have

798
00:45:47,002 --> 00:45:50,647
the same concerns of what does it mean
for their future, what kind of work

799
00:45:50,647 --> 00:45:53,572
and task will be relevant for them?

800
00:45:54,502 --> 00:45:59,932
And I don't have an answer
because today one, one a of

801
00:45:59,932 --> 00:46:01,942
work might feel secure, right?

802
00:46:01,942 --> 00:46:06,022
whether it's, whether you have
certain skillset, whether it's

803
00:46:06,022 --> 00:46:08,702
in, medical or law or something.

804
00:46:08,702 --> 00:46:11,447
But even law is disrupted, right?

805
00:46:11,452 --> 00:46:16,522
the question would be like, do we need
as many lawyers that we need today to

806
00:46:16,522 --> 00:46:21,442
make the type of decisions and synthesis
that we are doing tomorrow, right?

807
00:46:21,767 --> 00:46:25,097
and everybody tries not to
smile and make a lawyer joke.

808
00:46:25,152 --> 00:46:27,642
I'm proud of your
professionalism, gentlemen.

809
00:46:28,062 --> 00:46:28,242
Yes.

810
00:46:28,242 --> 00:46:28,512
Yeah.

811
00:46:28,692 --> 00:46:32,592
So I honestly, Jim, I don't know
the answer of sometimes people

812
00:46:32,592 --> 00:46:36,252
ask me, so what is in future
proof profession at this point?

813
00:46:36,302 --> 00:46:36,902
I don't know.

814
00:46:36,902 --> 00:46:39,092
Like I can, there is know the skills.

815
00:46:39,122 --> 00:46:43,562
I know the traits, like it's probably
more decision making, it's more

816
00:46:44,282 --> 00:46:48,372
analytics, it's more being, how can
you ask the right questions because

817
00:46:48,372 --> 00:46:49,782
the answers are all out there.

818
00:46:50,262 --> 00:46:55,872
It's about asking the questions, where the
skill lies now, but what type of job will

819
00:46:55,872 --> 00:47:01,542
evolve in future to my kind of the work
changing and then the workforce changing

820
00:47:01,542 --> 00:47:03,162
and that means the worker will change.

821
00:47:03,912 --> 00:47:05,052
I just don't know at this point.

822
00:47:05,052 --> 00:47:05,862
It's hard to predict.

823
00:47:07,512 --> 00:47:07,872
Yeah.

824
00:47:07,872 --> 00:47:11,272
And I think just tied in with what
Krish was saying is that it's.

825
00:47:12,322 --> 00:47:14,822
it's the skills, not the knowledge.

826
00:47:15,072 --> 00:47:19,392
I go back and look at when computers
first came out, people went from

827
00:47:19,392 --> 00:47:25,432
handwriting everything or typing to now
they use the computer for taking, doing

828
00:47:25,432 --> 00:47:27,142
all of their note taking and so on.

829
00:47:27,622 --> 00:47:30,982
And this is, it's another transition.

830
00:47:31,552 --> 00:47:37,402
I think one of the fears that I have
is, and quite frankly, Jim, this kind

831
00:47:37,402 --> 00:47:41,542
of ties to the discussion that you
and I were having earlier on, is, as

832
00:47:41,542 --> 00:47:46,142
you get older you want to do things
to keep your, exercise your brain.

833
00:47:46,922 --> 00:47:51,332
and to me it's the same thing with the
younger generation as they're going

834
00:47:51,332 --> 00:48:00,512
through school, that if they can just go
and ask an AI for an answer versus them

835
00:48:00,517 --> 00:48:02,912
having to go and do some research on it.

836
00:48:03,587 --> 00:48:05,717
That makes me a little bit nervous.

837
00:48:06,297 --> 00:48:12,207
I think one of the things though, is
it, so that's the negative side of it.

838
00:48:12,207 --> 00:48:17,037
The positive side though, is the
more detailed you can be in, the

839
00:48:17,037 --> 00:48:21,717
questions you ask your ai, the
better the answer is going to be.

840
00:48:22,227 --> 00:48:30,722
So it will train people to ask
better questions,  but I don't know.

841
00:48:30,722 --> 00:48:36,072
I think, back to what Krish was saying,
that I think it's going to become

842
00:48:36,072 --> 00:48:43,312
more about, knowing how to use things,
understanding how to be able to, grasp

843
00:48:43,312 --> 00:48:50,542
concepts versus this is really just a new
tool that we have to learn how to use.

844
00:48:51,517 --> 00:48:55,177
and I think there are going to be some.

845
00:48:55,237 --> 00:48:57,487
So I have 23-year-old twins.

846
00:48:58,177 --> 00:48:59,617
One is in the trades.

847
00:49:00,007 --> 00:49:02,497
One graduated with her BComm in marketing.

848
00:49:03,167 --> 00:49:07,397
my, my son with the trades
had no problems finding a job.

849
00:49:07,397 --> 00:49:11,567
Got a job right away, and
he's busy 95% of the time.

850
00:49:12,317 --> 00:49:16,847
my daughter in marketing had troubles
finding a marketing job because now

851
00:49:16,847 --> 00:49:18,497
everybody's using AI for marketing.

852
00:49:19,217 --> 00:49:23,412
So she's now gone into a different,
a different line of business.

853
00:49:24,192 --> 00:49:29,707
But, it's not that she can't find a job,
It's that the job that she has found

854
00:49:29,707 --> 00:49:31,507
is in a different line of business.

855
00:49:31,867 --> 00:49:35,077
And so I think people are going
to have to learn to adapt.

856
00:49:36,067 --> 00:49:38,887
And, where is this gonna end up?

857
00:49:38,887 --> 00:49:39,517
Who knows?

858
00:49:40,287 --> 00:49:44,347
I'm a firm believer that I think
that, as humans, we always go

859
00:49:44,347 --> 00:49:47,187
overboard and then tone it back a bit.

860
00:49:47,187 --> 00:49:52,737
I think that we're going to be in the same
boat with AI I think everybody's trying to

861
00:49:52,737 --> 00:49:55,347
do everything with AI and go well, yeah.

862
00:49:55,347 --> 00:49:58,137
as Krish said earlier, some
things just make more sense

863
00:49:58,137 --> 00:49:59,757
to do for a human to do it.

864
00:50:00,417 --> 00:50:04,127
so I think we'll figure
out what that balance is.

865
00:50:04,707 --> 00:50:08,847
and we're going to have to, as a human
race, we're going to have to figure out

866
00:50:08,847 --> 00:50:12,327
how to adapt to that, that new balancing.

867
00:50:15,147 --> 00:50:15,327
Yeah.

868
00:50:15,327 --> 00:50:18,757
Two things that, that, and one thing that
jumped out from what you both said, that.

869
00:50:19,462 --> 00:50:23,812
The one thing that troubles me
is, and it goes back to this idea

870
00:50:23,812 --> 00:50:29,152
of the sniff test of being able
to assess and not trust openly,

871
00:50:29,152 --> 00:50:30,922
everything that, that an AI tells you.

872
00:50:31,402 --> 00:50:31,522
Yes.

873
00:50:31,552 --> 00:50:36,442
Or that in the way we wouldn't trust
any other piece of information because

874
00:50:36,892 --> 00:50:40,462
in the society of the future, AI
is the easiest thing to manipulate.

875
00:50:40,852 --> 00:50:44,012
If we think controlling the media
is something that's happening.

876
00:50:44,012 --> 00:50:46,092
And it is, that's just a natural thing.

877
00:50:46,462 --> 00:50:50,572
media has its own way of thinking about
things and presenting information.

878
00:50:50,572 --> 00:50:54,482
And that's, whether it's social media
that's making us angry or whether whatever

879
00:50:54,482 --> 00:50:57,602
we think about media is there, but AI.

880
00:50:58,322 --> 00:51:02,092
Really easy to make to
put together information.

881
00:51:02,092 --> 00:51:04,132
And I've tested it, what does it say?

882
00:51:04,252 --> 00:51:07,222
What does this AI say about
one politician or another?

883
00:51:07,732 --> 00:51:09,922
You'll find actual differences.

884
00:51:10,342 --> 00:51:10,612
Yeah.

885
00:51:10,612 --> 00:51:14,752
And, so who's trying to please who,
but that, but the fact that potential

886
00:51:14,752 --> 00:51:20,872
is there means not only does AI have
a capacity to make mistakes or to

887
00:51:20,992 --> 00:51:24,562
misinterpret , and it's always going to
have that, that's a feature, not a bug.

888
00:51:25,072 --> 00:51:28,912
And, but, so people have to be able
to have that critical thinking.

889
00:51:28,942 --> 00:51:33,022
I don't see that in our educational
system, and it troubles me, but.

890
00:51:33,412 --> 00:51:37,982
More about it, I think is this idea
that I think, I think our kids may

891
00:51:37,982 --> 00:51:41,822
have, and maybe a lot of us have,
and I heard Geoffrey Hinton express

892
00:51:41,822 --> 00:51:45,022
it beautifully, and it was, it's
a paraphrasing of that old line of

893
00:51:45,232 --> 00:51:48,922
if you think you understand quantum
physics, you're not a quantum physicist.

894
00:51:49,252 --> 00:51:51,802
but he said, if you think you
understand the future of AI

895
00:51:51,802 --> 00:51:53,362
10 years out, you're wrong.

896
00:51:54,052 --> 00:51:55,162
that's all I can tell you.

897
00:51:55,322 --> 00:51:57,392
that you, it's like looking through gauze.

898
00:51:57,392 --> 00:52:01,652
He said, you can see a little,
you can see some stuff.

899
00:52:01,652 --> 00:52:04,442
Clearly the rest of it's
pretty, pretty messy.

900
00:52:04,712 --> 00:52:09,062
And when one of the godfathers of AI
tells you that he can't think 10 years

901
00:52:09,062 --> 00:52:11,972
ahead and be right, we shouldn't feel bad.

902
00:52:12,837 --> 00:52:14,987
I think that's something we
all have to grapple with.

903
00:52:16,257 --> 00:52:16,527
yeah.

904
00:52:16,797 --> 00:52:18,402
No, I think Jim, you are right.

905
00:52:18,402 --> 00:52:23,562
I would say, you can also use AI to the
advantage or the benefit of that Yes,

906
00:52:23,562 --> 00:52:29,602
it's easy to, to have fraud with ai,
but it's, I expect that in future we

907
00:52:29,602 --> 00:52:32,542
will use AI to also detect that as well.

908
00:52:32,542 --> 00:52:34,822
And there's already
capabilities like that.

909
00:52:34,827 --> 00:52:34,897
Yep.

910
00:52:34,912 --> 00:52:38,722
You have scanners who can detect
whether it's an deep facke or

911
00:52:38,722 --> 00:52:43,292
whether, the text that you just
got probably came from an AI bot.

912
00:52:43,832 --> 00:52:48,412
And I expect that will become
more and more part of our lives.

913
00:52:48,442 --> 00:52:52,792
So it's the usual thing, like any new
technology that has come in the world

914
00:52:52,822 --> 00:52:56,512
has been used for both good and bad.

915
00:52:57,082 --> 00:53:00,677
The balance of that is where the
progress of the society lies.

916
00:53:02,447 --> 00:53:02,837
Yeah.

917
00:53:03,167 --> 00:53:03,437
Yep.

918
00:53:03,442 --> 00:53:03,842
I would agree.

919
00:53:03,842 --> 00:53:07,497
And the, my kids are grown, but I
was actually asked about the same

920
00:53:07,497 --> 00:53:09,857
thing about what I hoped for them.

921
00:53:09,857 --> 00:53:13,047
And my, my, my kids are in
their thirties now, but the and.

922
00:53:13,927 --> 00:53:19,537
I came up with a piece of advice that
I think actually works and my, because

923
00:53:19,537 --> 00:53:20,947
they're my kids, they won't listen to me.

924
00:53:21,337 --> 00:53:24,877
But, so for other people who've got
kids out there, I was asked about,

925
00:53:24,907 --> 00:53:28,057
'cause I get asked about the same
question as what do I do about my work?

926
00:53:28,087 --> 00:53:31,147
And I said two things to concentrate on.

927
00:53:31,717 --> 00:53:35,887
What are you passionate about
and where can you add value?

928
00:53:36,877 --> 00:53:38,467
And value Doesn't have to be money.

929
00:53:38,857 --> 00:53:42,337
it can be something you trade for
money, but it can also be making

930
00:53:42,337 --> 00:53:45,457
a difference in your society,
making a difference in your world.

931
00:53:45,607 --> 00:53:50,467
And I think as I look back on it,
the only thing I've done well,

932
00:53:50,467 --> 00:53:52,117
I'm crappy at career planning.

933
00:53:52,117 --> 00:53:53,702
I just stumbled into everything I've done.

934
00:53:54,242 --> 00:53:57,812
But I try to follow stuff that I
was passionate about and where I

935
00:53:57,812 --> 00:53:59,452
thought I could make a difference.

936
00:54:00,172 --> 00:54:03,522
And I think if we could, if we
could get behind that attitude.

937
00:54:04,252 --> 00:54:08,192
and we'd ask ourselves a different
question about AI and that's, how

938
00:54:08,192 --> 00:54:09,902
do I use it to help me get there?

939
00:54:12,032 --> 00:54:12,242
Yeah.

940
00:54:12,812 --> 00:54:13,982
Well said, well said.

941
00:54:15,042 --> 00:54:19,962
Krish, ' you advise a lot of businesses
and you have some foresight into this.

942
00:54:20,052 --> 00:54:25,992
What is your biggest piece
of advice for companies right

943
00:54:25,992 --> 00:54:28,572
now, who are looking at this?

944
00:54:28,577 --> 00:54:31,482
And they may be partway through
the journey they might be starting.

945
00:54:31,512 --> 00:54:32,652
what would you say to them?

946
00:54:34,272 --> 00:54:35,502
Yeah, this is what I'm saying.

947
00:54:35,742 --> 00:54:40,882
Pretty much every conversation with
the, the CEOs and chief AI officers

948
00:54:40,882 --> 00:54:43,202
and chief data officers, we need to.

949
00:54:44,467 --> 00:54:47,707
Beyond the experimentation and
I already see that's happening.

950
00:54:47,707 --> 00:54:55,747
But let's jump into proving something
and use AI where it mat matters

951
00:54:55,747 --> 00:54:58,597
most and gets the most value.

952
00:54:58,687 --> 00:55:01,627
So focus on the value part is important.

953
00:55:01,657 --> 00:55:05,917
I think we have all proven in the
last two years or so, AI works.

954
00:55:05,947 --> 00:55:12,007
There's no reason or no need for
anymore proving the point that AI works.

955
00:55:12,247 --> 00:55:14,677
Now it's about how does it
work in your environment?

956
00:55:15,187 --> 00:55:16,627
How does it unlock value?

957
00:55:16,627 --> 00:55:17,677
How do you get value?

958
00:55:17,767 --> 00:55:21,997
How can you do that ethically and
how can you do that keeping in

959
00:55:21,997 --> 00:55:26,977
mind talent people and change, and
that's how you can be successful.

960
00:55:27,037 --> 00:55:31,327
I think that's usually where I see
most of the organizations are stuck.

961
00:55:31,567 --> 00:55:33,727
Everyone has done some experimentation.

962
00:55:34,297 --> 00:55:38,617
Now, how do we scale from there
and realize value, which is the ask

963
00:55:38,617 --> 00:55:40,707
from the board, ask from the CEO.

964
00:55:41,357 --> 00:55:47,657
show me the value now and let's focus on
the value and do it in a way that's future

965
00:55:47,657 --> 00:55:52,557
proof ethically, and for the people and
for the, the community and everything

966
00:55:52,557 --> 00:55:53,877
that the organization lives under.

967
00:55:54,057 --> 00:55:55,377
So that's my advice, Jim.

968
00:55:57,297 --> 00:55:57,897
Fabulous.

969
00:55:58,557 --> 00:56:01,257
Thank you, sir. I'm gonna wrap there.

970
00:56:01,257 --> 00:56:04,690
I don't think we can do any better than
that, our guest today has been Kris

971
00:56:04,690 --> 00:56:08,840
Banerjee, who's the managing director
and partner for Canada, for Accenture

972
00:56:08,840 --> 00:56:11,300
in AI thank you so much for joining us.

973
00:56:11,300 --> 00:56:12,560
Kris has been really great.

974
00:56:13,430 --> 00:56:13,740
Thank you, Jim.

975
00:56:13,945 --> 00:56:14,165
Yes.

976
00:56:14,165 --> 00:56:14,445
Thank you Kris.

977
00:56:14,450 --> 00:56:18,050
I hope, joy, John, great to meet
you and great convers you again.

978
00:56:18,650 --> 00:56:24,830
I put a small challenge to you that
I would like to see you hosting an,

979
00:56:24,860 --> 00:56:28,040
podcast with agents talking to you.

980
00:56:29,570 --> 00:56:30,140
Ooh.

981
00:56:30,200 --> 00:56:30,860
Oh, come on.

982
00:56:30,860 --> 00:56:31,640
Let's do that.

983
00:56:32,860 --> 00:56:33,755
We will, we'll do that.

984
00:56:34,415 --> 00:56:34,565
Okay.

985
00:56:34,565 --> 00:56:34,685
Yeah.

986
00:56:34,685 --> 00:56:36,485
I'll, I'm gonna, we'll
be, we'll talk to you.

987
00:56:36,605 --> 00:56:37,480
we, yes.

988
00:56:37,480 --> 00:56:39,070
We're yes is the answer.

989
00:56:39,490 --> 00:56:40,120
Oh, that's cool.

990
00:56:40,120 --> 00:56:40,930
So think as a guest.

991
00:56:41,830 --> 00:56:42,190
Yep.

992
00:56:43,180 --> 00:56:46,140
That we're gonna do that, John, get on it.

993
00:56:46,140 --> 00:56:46,290
Yes.

994
00:56:46,290 --> 00:56:46,350
Yeah.

995
00:56:47,790 --> 00:56:48,510
I, I was, yes.

996
00:56:48,510 --> 00:56:49,860
I was thinking maybe Marcel.

997
00:56:50,880 --> 00:56:51,120
Yeah.

998
00:56:51,450 --> 00:56:51,750
Go.

999
00:56:52,470 --> 00:56:53,370
Thanks again, Krish.

1000
00:56:53,520 --> 00:56:56,340
Thanks to you out there
listening to this program.

1001
00:56:56,470 --> 00:56:57,880
It's been great to have Krish with us.

1002
00:56:57,980 --> 00:57:00,710
Hopefully we've given you something
to think about some insights.

1003
00:57:00,710 --> 00:57:05,000
Don't hesitate to contact
us and send us your ideas.

1004
00:57:05,120 --> 00:57:10,710
and even if you're an AI agent, \ You
could reach me@technewsday.com or.ca.

1005
00:57:10,710 --> 00:57:14,010
Take your pick, just go to the
contact us form and drop a note.

1006
00:57:14,220 --> 00:57:17,490
And like I said, even if you're an
AI agent, tell us what you think.

1007
00:57:17,910 --> 00:57:22,280
Have a great weekend, and we'll be
back on Monday with the tech news.

1008
00:57:23,760 --> 00:57:26,850
We'd like to thank Meter for their
support and bringing you The Podcast

1009
00:57:27,030 --> 00:57:31,350
Meter delivers full stack networking
infrastructure, wired, wireless and

1010
00:57:31,350 --> 00:57:33,090
cellular to leading enterprises.

1011
00:57:33,390 --> 00:57:34,560
Working with their partners.

1012
00:57:34,560 --> 00:57:38,970
Meter designs, deploys and
manages everything required to

1013
00:57:38,970 --> 00:57:43,350
get performant, reliable and
secure connectivity in a space.

1014
00:57:43,650 --> 00:57:48,575
They design the hardware, the firmware
build, the software, manage deployments.

1015
00:57:48,855 --> 00:57:50,415
And even run support.

1016
00:57:50,805 --> 00:57:53,985
It's a single integrated solution
that scales from branch offices,

1017
00:57:53,985 --> 00:57:57,585
warehouses, and large campuses
all the way to data centers.

1018
00:57:58,005 --> 00:58:02,085
Book a demo at meter.com/cst.

1019
00:58:02,415 --> 00:58:06,495
That's METE r.com/cst.

1020
00:58:07,245 --> 00:58:08,385
I'm your host, Jim Love.

1021
00:58:09,105 --> 00:58:10,005
Thanks for listening.