1
00:00:06,910 --> 00:00:11,220
Hi, everyone! Welcome to a new episode of Pharma Insights.

2
00:00:11,280 --> 00:00:14,620
My name is Juliana Kreisel and
I will be one of your hosts for today.

3
00:00:15,140 --> 00:00:17,320
As you may know,

4
00:00:17,440 --> 00:00:20,440
Pharma Insights is a talk show created by Platforce

5
00:00:20,640 --> 00:00:25,000
with the aim to connect, network and exchange ideas

6
00:00:25,080 --> 00:00:28,000
from the pharma industry from all over the world.

7
00:00:28,380 --> 00:00:32,920
Today we will explore how to use AI to enhance Commercial Excellence.

8
00:00:33,200 --> 00:00:38,280
Our panel of experts will disclose
some key points regarding these new tools

9
00:00:38,580 --> 00:00:42,500
such as how is AI changing our go-to market pharma,

10
00:00:42,550 --> 00:00:44,720
and what will happen with omni channel.

11
00:00:45,600 --> 00:00:48,960
This episode will not be only moderated by me,

12
00:00:49,020 --> 00:00:54,200
I'm going to invite my colleague Stefan Repin to the stage.

13
00:00:54,320 --> 00:00:57,080
Hello, Stef. How are you doing?

14
00:00:57,200 --> 00:00:58,840
Hey! I'm doing great.

15
00:00:58,940 --> 00:01:01,400
It's finally a good weather here in Budapest, where I'm at.

16
00:01:01,460 --> 00:01:03,480
and we have the show today

17
00:01:03,560 --> 00:01:05,400
and I'm really stoked about the guests we have.

18
00:01:05,500 --> 00:01:07,660
It's like all superstars, you know?

19
00:01:07,780 --> 00:01:11,280
So just let's introduce the other superstars.

20
00:01:11,820 --> 00:01:14,700
Sure sure. Then our first guest

21
00:01:14,800 --> 00:01:19,640
for today is Florent Edouard.
Hello! How are you doing today?

22
00:01:19,700 --> 00:01:21,220
Hello! I'm good.

23
00:01:21,240 --> 00:01:25,200
I'm in the Netherlands and it's not warm, as usual

24
00:01:25,480 --> 00:01:29,180
Hahaha I love it!

25
00:01:29,280 --> 00:01:30,960
So, guys, don't go there!

26
00:01:31,110 --> 00:01:34,920
If you're looking for warm weather, then it's not your option.

27
00:01:35,460 --> 00:01:37,420
Then I would like to introduce

28
00:01:37,440 --> 00:01:40,380
our second guest for today Manuel Mitola.

29
00:01:40,480 --> 00:01:41,760
Hello, Manuel! How are you doing?

30
00:01:41,860 --> 00:01:44,480
Hello, Juliana. Hi, Stefan. Hi, everyone!

31
00:01:44,580 --> 00:01:46,660
So all's good from my side.

32
00:01:46,740 --> 00:01:51,060
I mean, actually I'm from Italy and today,
I don't want to brag, but the weather is great.

33
00:01:51,200 --> 00:01:54,720
So... haha I'm very glad to be here.

34
00:01:54,900 --> 00:01:59,340
Great! So, you heard:
go to Italy, don't go to Netherlands.

35
00:01:59,500 --> 00:02:01,320
Yeah, for food and for weather.

36
00:02:01,460 --> 00:02:05,780
There you go! And last but not least,
we have Marco Andre. Hello, Marco!

37
00:02:05,860 --> 00:02:07,200
How are you doing today?

38
00:02:07,320 --> 00:02:11,360
Hi, everyone! Don't come to Switzerland.
Summer hasn't arrived here yet.

39
00:02:11,520 --> 00:02:13,880
I believe winter is coming here.

40
00:02:16,700 --> 00:02:20,840
So, guys, we do not only bring you
information about the pharma industry

41
00:02:20,920 --> 00:02:24,420
but we also bring weather reports from all over the world.

42
00:02:24,680 --> 00:02:30,940
So, you know, just take our advice
and go to Italy ha ha ha

43
00:02:33,940 --> 00:02:37,940
Ok, before we start I want to remind our audience

44
00:02:38,060 --> 00:02:40,900
that we would love to hear from you

45
00:02:41,000 --> 00:02:42,900
so please leave your comments and questions

46
00:02:42,980 --> 00:02:45,240
below in the comment section

47
00:02:45,300 --> 00:02:47,900
and we will bring it up here to the panel.

48
00:02:48,080 --> 00:02:51,520
also I want to let you know that
we would love to hear from you

49
00:02:51,640 --> 00:02:54,800
in terms of what topics do you want to hear after

50
00:02:54,900 --> 00:03:00,900
so my friend, my colleague, and social media guru
Malén D'Urso will be on the comments.

51
00:03:01,060 --> 00:03:05,140
So, she's going to drop the form, you can fill it out,

52
00:03:05,220 --> 00:03:10,520
and we will plan our next topics regarding your thoughts.

53
00:03:10,720 --> 00:03:16,220
So, without any further delay
I would love start this talk show

54
00:03:16,320 --> 00:03:20,360
with this incredible panel.
Stef, how are you feeling today?

55
00:03:20,440 --> 00:03:22,980
Yes, I'm feeling great!

56
00:03:23,060 --> 00:03:26,880
In fact, we have so many topics to start with

57
00:03:26,920 --> 00:03:31,120
and we have just discussed this,
before we started the webinar of ours,

58
00:03:31,120 --> 00:03:35,320
that we want to go more into commercial AI

59
00:03:35,400 --> 00:03:37,900
when it relates to GTM, go to market,

60
00:03:38,060 --> 00:03:42,760
and since Marco was the one to bring it on,
maybe he should start it on haha

61
00:03:47,500 --> 00:03:49,860
I'm thinking... I'm thinking, well,

62
00:03:50,430 --> 00:03:51,760
you have AI now, right?

63
00:03:51,860 --> 00:03:55,840
So I was reading this book about the go to market strategy

64
00:03:55,860 --> 00:03:59,240
and I was thinking about AI at the same time.

65
00:03:59,500 --> 00:04:03,900
How can AI help you with your go-to-market strategy?

66
00:04:04,000 --> 00:04:05,220
But when it comes to pharma,

67
00:04:05,280 --> 00:04:06,800
we just mentioned that for example

68
00:04:06,880 --> 00:04:12,500
when you do a research on Google,
you don't get much outcome nowadays

69
00:04:12,620 --> 00:04:14,760
but for example with tools like Perplexity

70
00:04:15,070 --> 00:04:17,560
when you're looking for your ICP, ideal client persona,

71
00:04:17,780 --> 00:04:22,040
it can give you like a way more, and a better outcome

72
00:04:22,160 --> 00:04:26,280
than Google coule ever give you, you know?

73
00:04:26,360 --> 00:04:28,980
and that, you know, if you have your ideal customer persona,

74
00:04:29,120 --> 00:04:31,470
that's already the beginning of your go-to-market strategy.

75
00:04:31,470 --> 00:04:35,220
But you have many other things to incorporate there

76
00:04:36,900 --> 00:04:40,840
So my question is: what's a part of...

77
00:04:40,840 --> 00:04:45,100
What's a basic part of the go-to-Market strategy for pharma

78
00:04:45,200 --> 00:04:47,500
when it comes to, well, particularly pharma?

79
00:04:47,580 --> 00:04:50,220
What are the building blocks
nowadays when it comes to AI?

80
00:04:50,380 --> 00:04:52,500
Anyone can take the question.

81
00:04:53,190 --> 00:04:56,500
I just, so when I need go-to-market,

82
00:04:56,580 --> 00:05:00,140
I know this conversation me and Manuel had last week.

83
00:05:00,980 --> 00:05:03,700
Literally the best way to think about go-to-market is

84
00:05:03,800 --> 00:05:05,480
where the market is going?

85
00:05:06,280 --> 00:05:10,660
And where the market is going,
if you think about HCPs nowadays

86
00:05:11,220 --> 00:05:16,040
we have reports that they're using
these technologies to get answers

87
00:05:16,280 --> 00:05:20,440
much faster and much better.
Patients the same.

88
00:05:20,700 --> 00:05:24,560
So if we think about the immediacy, and the speed

89
00:05:24,700 --> 00:05:27,540
and how they're using these tools to get those answers

90
00:05:28,220 --> 00:05:31,000
It's unlikely that will go to other sources

91
00:05:31,080 --> 00:05:32,480
so I always give this example

92
00:05:32,560 --> 00:05:35,300
a lot of us having the industry HCP portals,

93
00:05:35,600 --> 00:05:37,980
which need to be gated,
which people need to put a password

94
00:05:38,040 --> 00:05:39,520
need to look for the information,

95
00:05:39,620 --> 00:05:42,800
need to download some information, and scan for it.

96
00:05:43,680 --> 00:05:49,080
Will HCPs in six months, in one year actually be using that?

97
00:05:49,340 --> 00:05:51,720
It's like any of us when we started to use

98
00:05:52,060 --> 00:05:54,680
desktop, and then we pivoted to mobile

99
00:05:54,860 --> 00:05:57,900
How many hours a day do we
spend in mobile versus desktop?

100
00:05:58,140 --> 00:06:00,880
So that's the first thing I think we should look at.

101
00:06:01,120 --> 00:06:03,620
It's how we adapt our strategy based on

102
00:06:03,780 --> 00:06:09,220
where the behavior of our cost...
in this case our HCP and our patient is going.

103
00:06:12,700 --> 00:06:17,240
I think I would even take it earlier than that,

104
00:06:17,320 --> 00:06:20,720
I think I'm very lucky in the world of commercial excellence

105
00:06:20,980 --> 00:06:24,420
in a sense that you know when joined Grünenthal in 2018,

106
00:06:24,540 --> 00:06:28,940
I was able to completely shape
the whole commercial excellence organization.

107
00:06:29,240 --> 00:06:31,700
The only thing I got was the people

108
00:06:31,800 --> 00:06:36,740
like there were people, there was no structure,
no mission, no vision.

109
00:06:37,160 --> 00:06:40,480
and one of the things we have established very early in comx,

110
00:06:40,540 --> 00:06:42,680
is that we need to be the ones

111
00:06:42,760 --> 00:06:45,580
who are going to drive the creation of the insights.

112
00:06:45,880 --> 00:06:50,140
in the organization, specifically around the go-to-Market model

113
00:06:50,420 --> 00:06:53,340
so right now for instance I got a beautiful asset

114
00:06:53,500 --> 00:06:55,080
that we want to launch

115
00:06:55,310 --> 00:06:57,420
in a specific therapeutic area

116
00:06:57,560 --> 00:06:59,020
and the key question is

117
00:06:59,080 --> 00:07:01,980
which physician do we need to talk to?

118
00:07:02,520 --> 00:07:07,000
because they have the patients
that will benefit from this treatment

119
00:07:07,180 --> 00:07:08,800
and that's going to generate

120
00:07:08,870 --> 00:07:11,080
the right Revenue stream for the company.

121
00:07:11,800 --> 00:07:14,940
And this is really for me the starting point

122
00:07:15,040 --> 00:07:16,600
where AI can help us.

123
00:07:16,880 --> 00:07:19,760
And for instance if you take
just an example from the US

124
00:07:19,980 --> 00:07:22,300
you got the claims data, which are

125
00:07:22,460 --> 00:07:26,500
billions of rows of prescription by individual in the US,

126
00:07:26,680 --> 00:07:28,420
which is data that you can access.

127
00:07:28,700 --> 00:07:31,680
The only way to manipulate that data,

128
00:07:31,800 --> 00:07:33,980
to identify where those patients

129
00:07:34,160 --> 00:07:36,380
are being treated along their patient journey

130
00:07:36,440 --> 00:07:38,780
is to use AI models

131
00:07:39,080 --> 00:07:42,660
and once you got that,
you can then know where those patients

132
00:07:42,800 --> 00:07:45,320
are being treated and you can

133
00:07:46,120 --> 00:07:49,960
interact with the customers at that point in time

134
00:07:50,080 --> 00:07:51,760
where they will be facing a patient.

135
00:07:51,920 --> 00:07:54,540
And that's opening completely new grounds

136
00:07:54,760 --> 00:07:56,980
in terms of possibility to be

137
00:07:57,400 --> 00:08:00,420
to be accurate, to be relevant

138
00:08:00,680 --> 00:08:02,640
with the physician and with the patient

139
00:08:02,900 --> 00:08:05,360
so really for me it starts you know at the beginning

140
00:08:05,480 --> 00:08:07,540
and then all along the commercial journey

141
00:08:07,680 --> 00:08:11,020
AI is going to reshape the way we do commercial excellence.

142
00:08:12,760 --> 00:08:16,100
Yeah, if I may add, Florent,

143
00:08:16,240 --> 00:08:19,200
a couple of items let's say

144
00:08:19,400 --> 00:08:22,760
came to my mind where you and Marco were speaking.

145
00:08:23,040 --> 00:08:26,100
The first one is related to the the speed

146
00:08:26,260 --> 00:08:29,680
because I think that when it comes with AI,

147
00:08:29,760 --> 00:08:31,360
this is an element that

148
00:08:31,590 --> 00:08:33,980
is going to be changed massively.

149
00:08:34,150 --> 00:08:35,500
We were speaking before

150
00:08:35,620 --> 00:08:37,400
how much time do you need to do that?

151
00:08:37,540 --> 00:08:41,100
how much time do you need to get an Insight from the data?

152
00:08:41,200 --> 00:08:44,140
so on, so I think speed also from our side,

153
00:08:44,260 --> 00:08:46,480
let's say from the pharmaceutical industry,

154
00:08:46,680 --> 00:08:50,140
if we were thinking about a go-to marketing strategy

155
00:08:50,240 --> 00:08:52,100
with a certain lifespan in the past

156
00:08:52,240 --> 00:08:55,240
now we are I think shortening it.

157
00:08:55,460 --> 00:08:58,260
so that's one key element.

158
00:08:58,520 --> 00:09:03,040
The other key element that it is let's say close

159
00:09:03,140 --> 00:09:05,620
also to my own experience.

160
00:09:05,680 --> 00:09:10,500
I have been working into
the Omni Channel marketing field

161
00:09:10,580 --> 00:09:13,120
for more than eight years and

162
00:09:13,300 --> 00:09:17,320
and I think we have been always saying

163
00:09:17,500 --> 00:09:19,580
you know, right channel,

164
00:09:19,660 --> 00:09:22,380
right customer, right target, right timing,

165
00:09:22,460 --> 00:09:25,620
and so on, we have been always hearing

166
00:09:25,600 --> 00:09:27,960
this kind of thing I don't know how many times.

167
00:09:28,200 --> 00:09:29,760
I think that

168
00:09:30,160 --> 00:09:32,580
I mean to be honest we never went there

169
00:09:32,680 --> 00:09:36,520
we never really went there but I think

170
00:09:36,560 --> 00:09:40,260
that AI maybe will be and is the

171
00:09:40,240 --> 00:09:44,080
thing that taking the data from multiple sources,

172
00:09:44,140 --> 00:09:46,480
having the power of the

173
00:09:46,760 --> 00:09:50,440
instilling Insight from this multisource data

174
00:09:50,620 --> 00:09:54,680
maybe will be finally

175
00:09:54,800 --> 00:09:59,520
able to enable a true Omni channel so

176
00:09:59,950 --> 00:10:02,100
I would also say that maybe

177
00:10:02,180 --> 00:10:04,840
the Omni Channel as is today

178
00:10:04,920 --> 00:10:07,360
will also stop existing somehow

179
00:10:07,500 --> 00:10:11,460
it will be a completely different and new thing so

180
00:10:11,620 --> 00:10:15,500
in my mind I have these two things top of my mind.

181
00:10:17,620 --> 00:10:20,900
Yeah, I agree. I'm totally with you

182
00:10:21,040 --> 00:10:23,300
and one on the speed

183
00:10:23,500 --> 00:10:25,420
like you know

184
00:10:25,720 --> 00:10:28,980
it's not impossible to imagine tomorrow

185
00:10:29,160 --> 00:10:33,160
that we will be able to monitor
in real time the social media

186
00:10:33,320 --> 00:10:37,540
to detect adverse event and
address them in real time as well.

187
00:10:37,780 --> 00:10:39,420
Today if you tell that to

188
00:10:39,480 --> 00:10:40,800
the people in the company,

189
00:10:40,860 --> 00:10:42,940
everyone's going to tell you you're completely nuts.

190
00:10:43,020 --> 00:10:45,580
I mean, that's going to cost us a fortune,

191
00:10:45,700 --> 00:10:48,600
and we don't even know if that data is accurate,

192
00:10:48,760 --> 00:10:50,660
but with good AI models,

193
00:10:50,760 --> 00:10:54,020
we are not that far away from being able to monitor that

194
00:10:54,220 --> 00:10:57,500
and from the Omni Channel standpoint, it's the same thing.

195
00:10:58,510 --> 00:11:01,580
The good old days we were segmenting customer

196
00:11:01,740 --> 00:11:04,940
on potential and adoption

197
00:11:05,140 --> 00:11:07,240
and they had to be in the right box

198
00:11:07,340 --> 00:11:10,540
high potential and high adoption or you had to move them there

199
00:11:10,660 --> 00:11:11,920
That was very rudimentary,

200
00:11:12,000 --> 00:11:13,500
but that was better than nothing.

201
00:11:13,780 --> 00:11:15,760
Today with the tools that we have

202
00:11:15,900 --> 00:11:18,420
with the capture of all the omni channel interactions,

203
00:11:18,540 --> 00:11:22,400
we can segment customers on 200 and plus dimensions

204
00:11:22,640 --> 00:11:27,220
and delivering really One to One content interactions

205
00:11:27,300 --> 00:11:31,180
and think that they really need
in the format that they like.

206
00:11:31,540 --> 00:11:32,980
So, yes, I'm totally with you.

207
00:11:33,360 --> 00:11:36,980
Especially when they don't have the GDPR haha

208
00:11:37,500 --> 00:11:39,020
Yeah, sorry, Marco.

209
00:11:39,120 --> 00:11:42,060
And just on Omni Channel thinking about go to market

210
00:11:42,520 --> 00:11:45,660
we might be facing a future
in which there's no Omni Channel

211
00:11:45,830 --> 00:11:47,560
we might be facing a future in which

212
00:11:47,640 --> 00:11:50,520
each one of us interacts through only one

213
00:11:50,620 --> 00:11:54,760
you know form of Jarvis or assistant

214
00:11:55,120 --> 00:11:57,800
in which we will go through all of the other things

215
00:11:57,840 --> 00:12:00,480
but actually we might be moving towards convergence.

216
00:12:00,740 --> 00:12:02,800
and I think a lot of this is guess work,

217
00:12:02,880 --> 00:12:04,840
because a lot of, as you said Florent,

218
00:12:04,920 --> 00:12:05,940
there's the insights

219
00:12:06,020 --> 00:12:08,400
and everything is moving at such a speed

220
00:12:08,600 --> 00:12:10,540
but one thing is for sure

221
00:12:10,660 --> 00:12:13,780
either on the applied AI side or generative AI side

222
00:12:13,880 --> 00:12:16,620
our industry is going to have to be much faster

223
00:12:16,760 --> 00:12:20,000
and how we create but also then

224
00:12:20,140 --> 00:12:23,840
send to Market, that
I have absolutely no doubt.

225
00:12:28,460 --> 00:12:30,440
Sorry, you can go, Stef.

226
00:12:31,240 --> 00:12:33,020
No, I saw that we have a question.

227
00:12:33,060 --> 00:12:34,840
So I guess we all know Claude.

228
00:12:34,900 --> 00:12:38,940
Claude Weddington, another influencer in the pharma space.

229
00:12:39,120 --> 00:12:42,220
and Claude has a question, right?

230
00:12:42,380 --> 00:12:46,180
So, Marco Andre on the spot, right?

231
00:12:46,400 --> 00:12:50,620
Marco Andre, Novartis appears to have the most significant

232
00:12:50,840 --> 00:12:52,540
patent cliff to overcome

233
00:12:52,560 --> 00:12:55,520
considering those present in the conversation.

234
00:12:56,140 --> 00:12:59,880
This is mainly due to the loss of exclusivity for Entresto

235
00:12:59,940 --> 00:13:02,960
and the settlement-related patent cliff for Humira,

236
00:13:03,070 --> 00:13:05,220
how active is the Novartis organization

237
00:13:05,280 --> 00:13:07,820
leveraging AI for medical affairs excellence

238
00:13:07,920 --> 00:13:11,680
in particular to combat the upcoming generics competition?

239
00:13:11,780 --> 00:13:14,740
Yeah, we were discussing generics recently with Claude

240
00:13:14,900 --> 00:13:18,100
and I think next year is going to be like

241
00:13:18,240 --> 00:13:20,020
in two years there's going to be

242
00:13:20,060 --> 00:13:21,400
like a ton of...

243
00:13:21,540 --> 00:13:25,020
patents are coming... are expiring so

244
00:13:25,160 --> 00:13:27,620
a ton of generics will flood the market so

245
00:13:27,830 --> 00:13:29,880
this is I guess a question related to that.

246
00:13:29,940 --> 00:13:31,440
So, Marco, the stage is yours.

247
00:13:31,760 --> 00:13:35,520
Claude, good to see you.
It's always a good excuse to go to Portugal.

248
00:13:35,620 --> 00:13:37,880
There is definitely better weather than Switzerland. Always.

249
00:13:38,680 --> 00:13:42,600
So, listen. Obviously I cannot comment directly on your question.

250
00:13:42,600 --> 00:13:44,100
Because there are some things

251
00:13:44,200 --> 00:13:46,600
I absolutely can't comment on in public.

252
00:13:46,700 --> 00:13:50,580
I can... I'm sorry. I know that's not the answer you were looking for.

253
00:13:50,740 --> 00:13:53,760
I think what I can tell you is

254
00:13:54,040 --> 00:13:55,740
how I am thinking about generative AI

255
00:13:55,820 --> 00:13:57,700
and what I'm doing in Novartis

256
00:13:57,780 --> 00:14:00,860
and it has to do a bit with what Florent and Manuel

257
00:14:01,060 --> 00:14:02,920
what we're talking about is

258
00:14:04,560 --> 00:14:06,540
if we think about this

259
00:14:07,080 --> 00:14:10,140
theme of generative AI 18 months ago

260
00:14:10,520 --> 00:14:12,260
probably none of us

261
00:14:13,260 --> 00:14:15,520
knew what it was. I didn't know for sure.

262
00:14:17,360 --> 00:14:21,020
We always see this graph of

263
00:14:21,080 --> 00:14:23,080
generative AI that chatGPT

264
00:14:23,200 --> 00:14:26,340
took two months to get to 100 million users

265
00:14:27,040 --> 00:14:28,760
but that's compared with other apps,

266
00:14:28,900 --> 00:14:30,260
right, with like Uber

267
00:14:30,580 --> 00:14:33,020
but the one stat I found

268
00:14:33,080 --> 00:14:38,420
is that it took 37 years for electricity
to get to half of US households

269
00:14:38,760 --> 00:14:41,080
and smartphones it took 21 years.

270
00:14:41,720 --> 00:14:43,480
ChatGPT took 10 months.

271
00:14:44,860 --> 00:14:49,560
So I don't say this in a fluffy way that
we need to be kind towards each other is

272
00:14:49,880 --> 00:14:51,720
this is a massive earthquake.

273
00:14:51,900 --> 00:14:54,700
My friend says this is the biggest

274
00:14:55,040 --> 00:14:58,620
systemic technological change that
any generation has gone through

275
00:14:59,000 --> 00:15:00,140
So I think

276
00:15:00,180 --> 00:15:02,420
even before we start thinking about

277
00:15:02,480 --> 00:15:04,140
patent cliffs, and next developments

278
00:15:04,180 --> 00:15:06,360
in medical, in applications in medical affairs

279
00:15:06,390 --> 00:15:09,720
or commercial Excellence or R&D

280
00:15:09,860 --> 00:15:11,900
there's a work to be done

281
00:15:11,980 --> 00:15:13,820
and that's what I've been trying to do is

282
00:15:14,180 --> 00:15:15,900
showing this to people,

283
00:15:16,100 --> 00:15:20,480
showing it's not the implication
of a video being done very quickly

284
00:15:20,600 --> 00:15:23,180
it's not so much that the video is done so quickly.

285
00:15:23,380 --> 00:15:24,860
It is the way you interact,

286
00:15:24,900 --> 00:15:26,480
as Florent was mentioning earlier,

287
00:15:26,660 --> 00:15:29,420
the way you interact with your agency,
with your partners

288
00:15:29,480 --> 00:15:32,340
and what you expect of them,
and of your own teams

289
00:15:32,420 --> 00:15:34,080
changes, so that's the work

290
00:15:34,160 --> 00:15:35,700
I've been doing in Novartis.

291
00:15:35,840 --> 00:15:38,760
First, talking with everyone that I can

292
00:15:38,840 --> 00:15:41,240
to make them at the most senior level

293
00:15:41,760 --> 00:15:44,120
to show them that this is not a fad

294
00:15:44,300 --> 00:15:47,380
it's not going to go away and
it's going to have massive impact

295
00:15:47,560 --> 00:15:48,780
in whatever they do

296
00:15:48,880 --> 00:15:51,300
so I know I'm not answering the question.

297
00:15:51,380 --> 00:15:53,820
You're gonna understand why

298
00:15:54,040 --> 00:15:56,360
but I wanted to give you a bit of my approach.

299
00:15:57,280 --> 00:16:00,060
Then we have another question.

300
00:16:00,100 --> 00:16:02,000
This is why people don't like pharma.

301
00:16:02,060 --> 00:16:04,100
Guys, remember hahaha

302
00:16:04,180 --> 00:16:05,940
ha ha ha

303
00:16:06,040 --> 00:16:09,640
No one has said that you know
having a patent cliff is a good problem

304
00:16:09,680 --> 00:16:12,080
because that means you have a lot of revenue for now

305
00:16:12,160 --> 00:16:14,260
so at least, you know, you should say that.

306
00:16:14,340 --> 00:16:16,020
ha ha ha

307
00:16:16,100 --> 00:16:17,960
And then we have another question:

308
00:16:18,000 --> 00:16:23,240
Can you enlight us more on the best
AI models that can be applied and how?

309
00:16:23,820 --> 00:16:25,100
Who wants to tackle that?

310
00:16:26,260 --> 00:16:27,320
Maybe I can.

311
00:16:27,500 --> 00:16:28,740
Yeah, please!

312
00:16:28,860 --> 00:16:32,200
Just to give my two cents here,

313
00:16:32,200 --> 00:16:35,720
I am not let's say a developer

314
00:16:35,840 --> 00:16:40,720
but I saw a study from the Stanford University

315
00:16:40,960 --> 00:16:45,760
and also again the Stanford University has a website

316
00:16:45,900 --> 00:16:49,100
where they are continuously testing the model

317
00:16:49,220 --> 00:16:51,100
so I think the answer is

318
00:16:51,160 --> 00:16:53,580
it depends on the task that you have to do

319
00:16:53,840 --> 00:16:56,460
and basically you have different performances

320
00:16:56,540 --> 00:17:00,180
for different, for different models.

321
00:17:00,240 --> 00:17:02,480
I saw very good performances

322
00:17:02,520 --> 00:17:04,300
generally speaking for Llama,

323
00:17:04,420 --> 00:17:07,500
for example, I saw how to add

324
00:17:07,580 --> 00:17:10,580
with the GPT and so on but it...

325
00:17:10,760 --> 00:17:13,320
when you dive deep into the thing that

326
00:17:13,400 --> 00:17:16,220
you have to do with the model then it is

327
00:17:16,320 --> 00:17:18,340
then it is...

328
00:17:18,420 --> 00:17:22,620
a different scenario, so it depends let's say

329
00:17:22,700 --> 00:17:23,460
in short.

330
00:17:23,840 --> 00:17:27,020
I would agree. I think it's difficult to

331
00:17:27,160 --> 00:17:30,120
to say, you know, there is a winner per category

332
00:17:30,320 --> 00:17:32,540
but depending on the problem that you have,

333
00:17:32,640 --> 00:17:35,180
you will use a generative AI model

334
00:17:35,260 --> 00:17:37,640
like GPT or Dall-e or whatever

335
00:17:37,940 --> 00:17:41,460
If it's a compliance issue,

336
00:17:41,760 --> 00:17:44,240
you may use another type of models

337
00:17:44,420 --> 00:17:49,220
uh more NLP or you know image models

338
00:17:49,400 --> 00:17:50,860
If you're talking about

339
00:17:51,280 --> 00:17:55,580
you need to manipulate a lot of sales and market data

340
00:17:55,700 --> 00:17:58,240
to understand where your market opportunities are

341
00:17:58,400 --> 00:18:00,180
it's going to be sales predictive models.

342
00:18:00,260 --> 00:18:02,020
I think there are really a lor of models.

343
00:18:02,160 --> 00:18:04,960
The only thing for me is really

344
00:18:05,080 --> 00:18:07,300
it depends also on how much money you have

345
00:18:07,620 --> 00:18:08,980
and how many people

346
00:18:09,320 --> 00:18:12,160
which means if you're a mid pharma company like us,

347
00:18:12,400 --> 00:18:14,600
we don't have the luxury to have

348
00:18:14,680 --> 00:18:16,120
tons of data scientists

349
00:18:16,120 --> 00:18:18,640
manipulating and creating models and stuff like that.

350
00:18:18,740 --> 00:18:21,120
So for instance when we go next best action,

351
00:18:21,220 --> 00:18:23,920
we use models that, from partners

352
00:18:24,020 --> 00:18:25,800
like Salesforce or others

353
00:18:25,960 --> 00:18:27,620
because those guys can invest

354
00:18:27,920 --> 00:18:30,320
in the tech and develop the model and stuff

355
00:18:30,700 --> 00:18:32,440
and we can't afford that

356
00:18:32,520 --> 00:18:34,480
I imagine that when you're on Novartis for instance

357
00:18:34,840 --> 00:18:36,760
it's easier for you to build your own

358
00:18:36,760 --> 00:18:38,980
personal model on your own data set

359
00:18:39,200 --> 00:18:42,520
and be able to really protect your IP in that field.

360
00:18:42,620 --> 00:18:45,180
I think it doesn't come cheap

361
00:18:45,240 --> 00:18:46,840
the good models I think.

362
00:18:47,420 --> 00:18:49,740
Can I add just on that, Florient

363
00:18:49,760 --> 00:18:52,040
I think it might be good but I think

364
00:18:52,160 --> 00:18:54,500
some of the decisions we need to make

365
00:18:54,600 --> 00:18:56,820
and that's even outside the industry is

366
00:18:56,940 --> 00:18:58,580
it might be the best

367
00:18:58,840 --> 00:19:00,980
but it might mean not the most effective

368
00:19:01,040 --> 00:19:04,340
so if I think about Bloomberg when they did the LLM

369
00:19:04,480 --> 00:19:07,460
they were trying to protect their data

370
00:19:07,560 --> 00:19:10,580
and I applaud them because
they were trying to do that very early

371
00:19:10,660 --> 00:19:12,040
and they spent $10 million dollars.

372
00:19:12,320 --> 00:19:14,940
Nowadays you're going to chatGPT,
you get a better answer.

373
00:19:15,140 --> 00:19:17,400
I think a lot of the decisions we need to make is

374
00:19:17,520 --> 00:19:19,040
exactly what we buy

375
00:19:19,340 --> 00:19:22,700
like what we use, chatGPT, Co-pilot

376
00:19:22,940 --> 00:19:26,540
what we build all on our own but then how we partner

377
00:19:26,640 --> 00:19:28,240
how we get the best of

378
00:19:28,880 --> 00:19:31,740
industry specific application, CRM for instance

379
00:19:31,840 --> 00:19:33,160
and get our data

380
00:19:33,260 --> 00:19:37,040
and I think the decisions to do one
or the other or the other

381
00:19:37,280 --> 00:19:39,340
will actually make or break

382
00:19:39,620 --> 00:19:43,600
our companies in terms of going to market,
so i just wanted to add that.

383
00:19:44,720 --> 00:19:46,200
Yeah, thank you.

384
00:19:46,280 --> 00:19:49,160
And another thing to consider just to close the loop also

385
00:19:49,260 --> 00:19:52,080
is the fact that we can also use models

386
00:19:52,080 --> 00:19:54,500
in combination, in parallel

387
00:19:54,620 --> 00:19:58,900
and that's something that we can consider to..

388
00:19:59,840 --> 00:20:02,280
Ok, we have another comment

389
00:20:02,440 --> 00:20:06,280
from Jennifer, she says: Marco, good to see you.

390
00:20:06,400 --> 00:20:09,780
haha I'm going to overlay the chat

391
00:20:09,910 --> 00:20:12,840
so it's easier for us to see everything.

392
00:20:12,880 --> 00:20:15,300
I don't know why... Oh there you go!

393
00:20:15,500 --> 00:20:18,840
It says: Great to join this webinar today.

394
00:20:18,920 --> 00:20:25,080
I would love to hear how the panel is seeing or planning for

395
00:20:25,080 --> 00:20:29,040
use of gen AI in the context of

396
00:20:29,140 --> 00:20:33,360
of Precision, Health. Oh sorry Precision Health

397
00:20:33,560 --> 00:20:38,700
How we can identify patients with disease
or with uncontrolled disease?

398
00:20:38,900 --> 00:20:42,320
with the goal of getting optimal medicine to them

399
00:20:42,380 --> 00:20:44,040
in time or earlier

400
00:20:44,140 --> 00:20:45,940
and reducing healthcare cost?

401
00:20:49,600 --> 00:20:52,580
So maybe that's a little bit

402
00:20:52,680 --> 00:20:56,220
what I was alluding to when I was explaining

403
00:20:56,420 --> 00:20:59,540
how we chose which physicians we go to
based on the patient that they have

404
00:20:59,820 --> 00:21:01,000
what you can do

405
00:21:01,030 --> 00:21:03,620
and that's US only unfortunately,

406
00:21:03,720 --> 00:21:06,420
we don't have that level of data yet in Europe.

407
00:21:06,480 --> 00:21:08,620
So we need to find a way to get to the proxy level

408
00:21:08,740 --> 00:21:09,960
but it's really literally

409
00:21:10,040 --> 00:21:11,320
to understand okay

410
00:21:11,440 --> 00:21:13,060
I've got my treatment

411
00:21:13,300 --> 00:21:17,000
the patient who is probably going to be the most responsive

412
00:21:17,120 --> 00:21:19,940
to that treatment has those characteristics

413
00:21:20,100 --> 00:21:22,840
has been seeing those specialist,

414
00:21:23,060 --> 00:21:26,480
has been seeing 10 doctors in the last five years,

415
00:21:26,790 --> 00:21:29,240
has been on those type of treatments,

416
00:21:29,480 --> 00:21:31,880
and has those you know characteristics

417
00:21:31,880 --> 00:21:34,240
in terms of social demographics

418
00:21:34,880 --> 00:21:37,300
with that you can really target

419
00:21:37,360 --> 00:21:39,700
the right patients through the right HCP.

420
00:21:40,440 --> 00:21:42,320
How do we get to that in Europe?

421
00:21:42,600 --> 00:21:44,240
I don't know yet honestly.

422
00:21:44,360 --> 00:21:46,720
It's a very complicated data set

423
00:21:46,840 --> 00:21:48,620
that needs to be of high quality

424
00:21:48,740 --> 00:21:52,640
what we can tell though is to give the doctors

425
00:21:53,100 --> 00:21:56,340
descriptive of who those ideal patients are

426
00:21:56,660 --> 00:22:00,440
so that they can detect them in their own patient pool.

427
00:22:00,640 --> 00:22:02,460
But it has to be through a proxy

428
00:22:02,520 --> 00:22:04,060
through the HCP.

429
00:22:06,540 --> 00:22:07,740
Ok.

430
00:22:08,120 --> 00:22:10,900
Just to add to that quickly

431
00:22:11,760 --> 00:22:13,720
A lot of what we think

432
00:22:13,780 --> 00:22:17,860
generative AI broad we think is
only new tech applied to new data.

433
00:22:18,620 --> 00:22:21,540
It's also new tech applied to old data.

434
00:22:21,680 --> 00:22:23,980
And I think that's why we've been seing

435
00:22:24,080 --> 00:22:25,140
incredible progress.

436
00:22:25,180 --> 00:22:27,980
I think Precision medicine is one of the most

437
00:22:28,080 --> 00:22:32,280
incredible areas where we're going to have more impact because

438
00:22:32,640 --> 00:22:36,220
the new tech applied to old data is

439
00:22:36,420 --> 00:22:39,900
not every big company has an army of data scientists

440
00:22:40,140 --> 00:22:42,640
and now you can actually go

441
00:22:42,660 --> 00:22:45,360
human what would be humanly impossible

442
00:22:45,460 --> 00:22:47,700
you can apply the data and derive insights

443
00:22:48,080 --> 00:22:50,900
from something that you've been having right now

444
00:22:51,040 --> 00:22:53,120
So again, as Manuel you were saying,

445
00:22:53,220 --> 00:22:54,520
theme of speed

446
00:22:54,640 --> 00:22:56,720
and what you can identify in those things

447
00:22:56,880 --> 00:23:00,140
it's something that generative AI enables.

448
00:23:01,620 --> 00:23:05,780
Thank you for that. We have another question from Claude.

449
00:23:05,860 --> 00:23:08,680
Jennifer, brilliant question.

450
00:23:08,760 --> 00:23:09,980
I'll be curious to learn

451
00:23:10,040 --> 00:23:12,060
how Novartis is leveraging data42

452
00:23:12,140 --> 00:23:15,640
in the aspect to train AI models for

453
00:23:15,720 --> 00:23:17,020
enhanced commercial excellence.

454
00:23:17,200 --> 00:23:20,980
I think, Marco, that goes to you.

455
00:23:21,620 --> 00:23:25,740
It's like Senate hearing of Novartis.

456
00:23:25,930 --> 00:23:27,900
ha ha ha

457
00:23:28,100 --> 00:23:30,780
I'm going to have to give you the same

458
00:23:30,900 --> 00:23:32,500
the same answer, Claude.

459
00:23:32,600 --> 00:23:36,300
There are some things I can't,
I can't obviously answer.

460
00:23:36,460 --> 00:23:39,360
to you or the public, or in private. I'm sorry.

461
00:23:39,660 --> 00:23:42,300
Haha! No, it's perfectly fine.

462
00:23:42,580 --> 00:23:43,920
Ok...

463
00:23:44,000 --> 00:23:46,580
Which amendment is that, Marco?

464
00:23:46,640 --> 00:23:50,620
Is it second amendment that you cannot incriminate yourself?

465
00:23:50,720 --> 00:23:53,740
I'm not in US! I'm not in US!

466
00:23:53,900 --> 00:23:55,180
ha ha

467
00:23:55,800 --> 00:23:56,720
Yeah

468
00:23:59,080 --> 00:24:02,960
So, now, Stef. Do we have a followup?

469
00:24:03,100 --> 00:24:09,940
We shared the link that Manuel was talking about.

470
00:24:10,400 --> 00:24:12,400
Stef was correct

471
00:24:12,480 --> 00:24:17,460
that was Manuel for the Stanford University.

472
00:24:17,540 --> 00:24:18,500
But that's okay.

473
00:24:18,620 --> 00:24:20,260
Stef, can you?

474
00:24:20,420 --> 00:24:23,180
do you have another question or another comment?

475
00:24:23,180 --> 00:24:24,220
Yeah, so

476
00:24:25,150 --> 00:24:27,480
in fact we do have a question from

477
00:24:27,880 --> 00:24:31,380
Mr Adeel Siddiqui and his question is

478
00:24:31,480 --> 00:24:32,700
how can AI...?

479
00:24:33,080 --> 00:24:35,260
how AI can be implemented to do

480
00:24:35,300 --> 00:24:38,220
to do segmentation and targeting for HCPs?

481
00:24:38,390 --> 00:24:40,880
What is the role of commercial excellence

482
00:24:40,960 --> 00:24:43,300
in segmentation targeting of HCP?

483
00:24:43,460 --> 00:24:45,560
Again, how AI can Implement

484
00:24:45,800 --> 00:24:48,380
to do segmentation and targeting of HCPs?

485
00:24:48,440 --> 00:24:50,260
What is the role of commercial excellence

486
00:24:50,340 --> 00:24:52,220
in segmentation and targeting of HCPs?

487
00:24:54,780 --> 00:24:57,660
So well, for me, I mean

488
00:24:57,740 --> 00:24:59,840
that's personal right?

489
00:24:59,940 --> 00:25:01,580
Commercial Excellence

490
00:25:01,680 --> 00:25:05,300
is there to give the rule in terms of

491
00:25:05,360 --> 00:25:07,020
segmentation and targeting.

492
00:25:07,560 --> 00:25:13,020
I mean if you want a neutral ground between medical, marketing, sales,

493
00:25:13,120 --> 00:25:16,680
market access... That's in commercial excellence's land.

494
00:25:17,340 --> 00:25:18,480
So that's the first thing.

495
00:25:18,580 --> 00:25:21,360
The second thing is you need someone to process all that data

496
00:25:21,420 --> 00:25:26,860
in a fair way and to decide
"okay we need to apply this type of things"

497
00:25:26,960 --> 00:25:29,460
and we need to get it implemented into the system.

498
00:25:30,220 --> 00:25:31,680
Again you are now coming back to

499
00:25:31,740 --> 00:25:35,320
the two dimensions before,
and the more than 200 dimensions that we have

500
00:25:35,420 --> 00:25:38,260
in the salesforce customer data platform today

501
00:25:38,720 --> 00:25:40,740
it takes a lot of

502
00:25:40,820 --> 00:25:41,720
steering

503
00:25:42,160 --> 00:25:45,080
and that's an understatement

504
00:25:45,340 --> 00:25:47,760
it takes a lot of steering to get

505
00:25:47,980 --> 00:25:50,340
to each customer being profiled

506
00:25:51,240 --> 00:25:53,980
willingly on 200 Dimensions

507
00:25:54,080 --> 00:25:55,660
with high quality of data

508
00:25:55,780 --> 00:25:59,320
and to maintain the data quality over time

509
00:25:59,420 --> 00:26:02,540
so that your segmentation and targeting remains valid

510
00:26:02,800 --> 00:26:04,420
so I really think it's on us.

511
00:26:04,540 --> 00:26:08,700
For this one for me it's really in the commercial excellence and

512
00:26:09,030 --> 00:26:10,640
my personal advice is

513
00:26:10,760 --> 00:26:13,020
if someone comes to you saying

514
00:26:13,140 --> 00:26:15,760
oh I got that beautiful algorithm that is a blackbox

515
00:26:15,960 --> 00:26:19,440
and that's going to speed out a segmentation and targeting,

516
00:26:19,520 --> 00:26:21,660
you press delete

517
00:26:21,840 --> 00:26:23,720
and you send them to hell

518
00:26:23,800 --> 00:26:27,680
because you need to own your segmentation and targeting.

519
00:26:27,790 --> 00:26:28,660
Otherwise

520
00:26:28,790 --> 00:26:30,100
you don't know your customers

521
00:26:30,160 --> 00:26:32,700
and you don't know what you're doing,
and you're just wasting your money.

522
00:26:34,320 --> 00:26:37,600
I think one thing if I can add, Florent, just on that

523
00:26:37,900 --> 00:26:39,740
I think it will be increasing...

524
00:26:39,820 --> 00:26:42,120
again going back to the behaviours,

525
00:26:44,320 --> 00:26:48,540
so if doctors as an example physicians are using

526
00:26:48,600 --> 00:26:50,660
declaring their intentions

527
00:26:50,900 --> 00:26:53,200
and for instance a GPT

528
00:26:53,340 --> 00:26:56,540
so for instance there's this tonic app that I saw a presentation of

529
00:26:56,700 --> 00:26:59,120
and they're actually almost a companion

530
00:26:59,440 --> 00:27:03,780
and if those companies are not
at certain point making that data available

531
00:27:04,200 --> 00:27:06,860
how are we going to know how to segment them

532
00:27:06,910 --> 00:27:08,500
if we don't know their intentions

533
00:27:08,640 --> 00:27:10,660
so I think for us to your point is

534
00:27:11,080 --> 00:27:13,180
what we do with the data but

535
00:27:13,360 --> 00:27:16,440
how are we going to have access to that data

536
00:27:16,560 --> 00:27:18,920
to know what are their behaviors and their intentions

537
00:27:19,000 --> 00:27:20,640
that's going to be a challenge for us.

538
00:27:23,020 --> 00:27:26,680
but I think a little bit to what, sorry,

539
00:27:26,820 --> 00:27:28,200
I think it was Marcus

540
00:27:28,520 --> 00:27:32,940
I think Marcus was saying we can only control

541
00:27:33,560 --> 00:27:35,160
thanks to GDPR

542
00:27:35,340 --> 00:27:38,620
only the customer that interacts willingly with us

543
00:27:39,020 --> 00:27:42,640
and their data, that that's the only thing we can really control.

544
00:27:42,820 --> 00:27:46,400
We have to make sure we do a good management of that

545
00:27:46,520 --> 00:27:48,240
but in this case we can know

546
00:27:48,320 --> 00:27:49,880
for the other ones I agree with you

547
00:27:49,960 --> 00:27:52,760
there's no way we will know what's happening on GPT.

548
00:27:54,480 --> 00:27:57,300
And from my side I fully agree with you

549
00:27:57,380 --> 00:28:00,320
and I mean you have to own

550
00:28:00,440 --> 00:28:03,660
let's say your segmentation and targeting, absolutely.

551
00:28:03,700 --> 00:28:06,640
Maybe one thing that AI can

552
00:28:06,780 --> 00:28:08,420
can help there

553
00:28:08,500 --> 00:28:10,580
it's like it's an additional tool

554
00:28:10,780 --> 00:28:12,820
and I think that

555
00:28:12,940 --> 00:28:15,540
when it comes to segmentation and targeting

556
00:28:15,640 --> 00:28:19,040
first of all it will be one let's say building block

557
00:28:19,120 --> 00:28:21,720
for building the hyper personalization

558
00:28:21,820 --> 00:28:25,000
also not only in terms of content creation but also

559
00:28:25,100 --> 00:28:29,240
in terms of touch point flow generation

560
00:28:29,550 --> 00:28:34,200
and another thing is that AI is very good in

561
00:28:34,220 --> 00:28:37,460
finding let's say pattern, trends

562
00:28:37,560 --> 00:28:40,100
that maybe from a human analyst standpoint

563
00:28:40,200 --> 00:28:43,720
we are not able to see at first glance so

564
00:28:43,840 --> 00:28:47,940
I see it is an additional tool

565
00:28:48,040 --> 00:28:51,360
for the people working in Commercial Excellence.

566
00:28:51,790 --> 00:28:54,600
Absolutely fine, I mean, I absolutely agree with you.

567
00:28:54,640 --> 00:28:55,860
I would never say

568
00:28:55,940 --> 00:28:57,860
Okay, push the button and

569
00:28:57,980 --> 00:29:00,360
that's your segmentation and targeting

570
00:29:00,480 --> 00:29:02,760
done by the algorithm.

571
00:29:02,860 --> 00:29:05,200
I mean, if you come back historically

572
00:29:05,220 --> 00:29:08,500
you know, why did we do it in two dimensions before?

573
00:29:08,580 --> 00:29:10,860
because most people

574
00:29:10,920 --> 00:29:12,740
live in two dimensions, you know.

575
00:29:12,840 --> 00:29:15,280
Very few people like Stefan can live in three dimensions

576
00:29:15,380 --> 00:29:17,340
with All Star Wars behind them, right?

577
00:29:17,460 --> 00:29:20,580
I mean we usually live in two dimensions.

578
00:29:20,660 --> 00:29:22,660
And that's why we simplified it this way

579
00:29:22,720 --> 00:29:24,560
but now with the tools, the system

580
00:29:24,680 --> 00:29:27,980
can create 300 500 600 segments

581
00:29:28,320 --> 00:29:30,300
and manipulate them at the same time

582
00:29:30,400 --> 00:29:32,660
doing differentiated campaigns to all those segments

583
00:29:32,780 --> 00:29:35,040
and no one has to know exactly

584
00:29:35,140 --> 00:29:38,100
which customers in which segment doesn't matter anymore.

585
00:29:38,900 --> 00:29:39,680
Yeah.

586
00:29:40,920 --> 00:29:43,140
Guys, we have another question.

587
00:29:43,240 --> 00:29:45,360
Hi, everyone. I got one question.

588
00:29:45,420 --> 00:29:50,400
Beyond lead scoring can AI predict...?
Let me show the question.

589
00:29:50,680 --> 00:29:54,020
Can AI predict the closability of a lead

590
00:29:54,240 --> 00:29:58,560
allowing sales rep to prioritize their efforts?

591
00:29:59,700 --> 00:30:01,800
I can take that one.

592
00:30:01,880 --> 00:30:03,660
So one of the industries I always admired

593
00:30:03,740 --> 00:30:06,120
and as a reference from in Pharma is the B2B

594
00:30:06,240 --> 00:30:10,180
software as a service and I think they perfected this.

595
00:30:11,060 --> 00:30:12,400
Three years ago,

596
00:30:12,440 --> 00:30:15,140
Claude is going to be happy because this I can disclose.

597
00:30:15,160 --> 00:30:18,420
Three years ago when I was in Sandos,

598
00:30:18,540 --> 00:30:21,340
we started doing even before AI was a thing

599
00:30:21,540 --> 00:30:24,500
a pilot project with the consent of HCPs

600
00:30:24,820 --> 00:30:27,580
to record their conversations

601
00:30:28,150 --> 00:30:30,660
and it was actually incredible because

602
00:30:30,760 --> 00:30:32,900
the amount of data you could get from that,

603
00:30:32,980 --> 00:30:35,160
the training that you could do,

604
00:30:35,300 --> 00:30:39,040
the prediction of Adverse Events for instance

605
00:30:39,120 --> 00:30:43,200
I mean Adverse Events is something quite serious, quite serious

606
00:30:43,500 --> 00:30:46,040
that sometimes it doesn't even get

607
00:30:46,320 --> 00:30:49,040
raised because it gets lost, right?

608
00:30:49,220 --> 00:30:51,780
So all of the possibility that you can do that

609
00:30:51,960 --> 00:30:53,800
what are the most asked questions?

610
00:30:53,920 --> 00:30:57,100
how good it would be, we were testing some things at the time,

611
00:30:57,200 --> 00:31:01,600
to go from... get the top 10 asked questions about a certain drug

612
00:31:01,720 --> 00:31:03,040
in a certain market

613
00:31:03,150 --> 00:31:05,280
and have it updated on a daily basis so

614
00:31:05,480 --> 00:31:08,600
I think that is something that B2B has been doing it.

615
00:31:08,760 --> 00:31:11,520
for a lot of time.

616
00:31:11,600 --> 00:31:14,140
Obviously there's a lot of compliance, legal,

617
00:31:14,320 --> 00:31:16,000
consents that we need to iron out

618
00:31:16,140 --> 00:31:19,000
but I think we will all need to get used to

619
00:31:19,120 --> 00:31:23,960
that having our calls recorded internally and externally

620
00:31:24,200 --> 00:31:26,300
will just become the default

621
00:31:26,360 --> 00:31:28,820
and from that I think there is incredible possibility

622
00:31:29,030 --> 00:31:31,560
So I'm a big big fan of that.

623
00:31:33,480 --> 00:31:37,020
Guys, anything? Someone else wants to add something?

624
00:31:37,160 --> 00:31:40,400
Or we move to the next question? Manuel? Florent?

625
00:31:42,080 --> 00:31:43,900
Yeah yeah. I totally align with Marco.

626
00:31:43,980 --> 00:31:44,740
Yeah!

627
00:31:44,740 --> 00:31:46,440
I would like to...

628
00:31:46,440 --> 00:31:48,440
I think Marco is right when it comes to

629
00:31:48,500 --> 00:31:51,300
AI and SaaS, in SaaS you really have to

630
00:31:51,340 --> 00:31:52,840
use lead scoring models but

631
00:31:53,580 --> 00:31:55,480
there is a very big problem with SaaS that

632
00:31:55,560 --> 00:31:59,020
these lead score scoring models do not work.

633
00:31:59,150 --> 00:32:01,400
And they do not work in any industry.

634
00:32:01,630 --> 00:32:03,060
because they give you

635
00:32:03,140 --> 00:32:05,540
they give you some predictions based on

636
00:32:06,000 --> 00:32:08,360
let's say I'm watching this webinar with yo

637
00:32:08,760 --> 00:32:11,460
and it says "okay I'll give you a score"

638
00:32:11,720 --> 00:32:13,500
I don't know 15, right,

639
00:32:13,660 --> 00:32:16,140
and the score of 15 means then

640
00:32:16,240 --> 00:32:21,100
maybe I'll click on your website,
on novartis.com, right? I'll go there.

641
00:32:21,360 --> 00:32:23,160
And that will give me another five points.

642
00:32:23,240 --> 00:32:24,660
And 20 points

643
00:32:24,800 --> 00:32:28,300
I'm supposed to talk to a Medical Rep or sales rep

644
00:32:28,320 --> 00:32:29,360
if you talk about business

645
00:32:29,760 --> 00:32:34,060
and this is, you know, I might not be in the market to but this

646
00:32:35,020 --> 00:32:37,900
The same comes for phama, like HCP can...

647
00:32:38,200 --> 00:32:41,080
You have a personalized journey you know

648
00:32:41,460 --> 00:32:44,000
Now I have a meeting with a medrep

649
00:32:44,100 --> 00:32:45,500
and I'm going to go and research

650
00:32:45,600 --> 00:32:48,040
through perplexity AI which we mentioned today

651
00:32:48,320 --> 00:32:50,380
a certain drug for example

652
00:32:50,480 --> 00:32:52,080
or for maybe a rare disease

653
00:32:52,440 --> 00:32:55,180
that I'm encountering with my patiens but

654
00:32:55,520 --> 00:32:57,720
the AI scoring model means that

655
00:32:57,940 --> 00:32:59,460
by doing these actions which

656
00:32:59,660 --> 00:33:02,380
basically me as a company I can score you know

657
00:33:02,420 --> 00:33:04,060
I don't know what's happening back there.

658
00:33:04,480 --> 00:33:06,180
It's a complete Black Box

659
00:33:06,300 --> 00:33:08,920
but I know that you interacted with these points

660
00:33:08,960 --> 00:33:11,340
and then I'm going to give you...

661
00:33:11,440 --> 00:33:13,880
You have this touch points, I'm going to
give you a score, and based on that

662
00:33:14,000 --> 00:33:17,540
I'm going to presume that you are in the market for

663
00:33:17,660 --> 00:33:20,560
for the drug or you're ready to

664
00:33:20,640 --> 00:33:23,100
start getting samples and tests.

665
00:33:23,360 --> 00:33:25,620
I think it's... We're not yet there

666
00:33:25,880 --> 00:33:27,960
because a lot of it is

667
00:33:28,120 --> 00:33:30,260
in the dark funnel which we cannot track

668
00:33:30,560 --> 00:33:32,580
and...

669
00:33:32,840 --> 00:33:35,180
I think Omni Channel solves that issue

670
00:33:35,260 --> 00:33:37,880
where you don't have maybe in particular

671
00:33:37,960 --> 00:33:40,880
that much pressure on the HCP to take a decision

672
00:33:40,940 --> 00:33:42,120
in your favor because

673
00:33:42,280 --> 00:33:43,920
your goal is to influence them

674
00:33:44,060 --> 00:33:47,580
and not to score a sale right away, right?

675
00:33:47,840 --> 00:33:50,520
so your goal is more to basically be

676
00:33:50,600 --> 00:33:54,180
interface and help them out, make a

677
00:33:54,260 --> 00:33:57,200
a good decision, make an informed decision

678
00:33:57,320 --> 00:34:01,420
and not you know prescribe your drug for

679
00:34:01,520 --> 00:34:04,180
for, you know, no matter if it's good for the patient or not.

680
00:34:04,380 --> 00:34:06,200
I mean that's my 5 cents.

681
00:34:07,580 --> 00:34:10,140
Well so

682
00:34:10,320 --> 00:34:13,500
I think we are in, I agree with you,

683
00:34:13,600 --> 00:34:16,240
not to score a sale, but we are in to make sure

684
00:34:16,300 --> 00:34:19,120
that a patient that can benefit from our medicine

685
00:34:19,440 --> 00:34:22,060
will have a chance to get onto that medicine

686
00:34:22,390 --> 00:34:24,680
if he's the appropriate patient, right?

687
00:34:24,940 --> 00:34:27,140
so that's something that you can know

688
00:34:27,200 --> 00:34:30,160
like, you know, I used to work in respiratory, right?

689
00:34:30,200 --> 00:34:32,080
When you launch your respiratory biologics

690
00:34:32,220 --> 00:34:33,560
you know that only

691
00:34:33,620 --> 00:34:36,800
a certain number of respiratory specialists

692
00:34:36,980 --> 00:34:39,520
are going to adopt those biologics

693
00:34:39,780 --> 00:34:41,500
the other ones will adopt them

694
00:34:41,640 --> 00:34:43,480
maybe in seven years later

695
00:34:43,640 --> 00:34:45,840
when it's going to be very mainstream on the market

696
00:34:46,060 --> 00:34:47,360
with a lot of experience.

697
00:34:47,520 --> 00:34:49,740
By detecting those early adopters

698
00:34:49,960 --> 00:34:52,140
being able to train them on the product,

699
00:34:52,200 --> 00:34:54,480
making them understand the science

700
00:34:54,580 --> 00:34:57,260
and giving them all the element that they need

701
00:34:57,320 --> 00:34:58,680
to detect the right patient

702
00:34:59,000 --> 00:35:00,900
we allow that patient

703
00:35:01,080 --> 00:35:02,600
that patient cohort

704
00:35:02,780 --> 00:35:04,600
to go on the product

705
00:35:04,620 --> 00:35:09,120
so that the physician can realize
if that's a good therapeutic option

706
00:35:09,400 --> 00:35:11,440
and that's what I would call

707
00:35:11,580 --> 00:35:12,840
"closing the deal"

708
00:35:13,100 --> 00:35:16,060
That's when we have done our job correctly.

709
00:35:17,320 --> 00:35:20,200
Yeah, I like that because it doesn't need to be linear.

710
00:35:20,300 --> 00:35:22,760
That's the point. It doesn't mean that lead scoring

711
00:35:22,880 --> 00:35:24,460
is for you to make a sale

712
00:35:24,660 --> 00:35:26,040
but lead scoring is for instance

713
00:35:26,120 --> 00:35:29,280
if you get the top asked question, if certainly a client

714
00:35:29,280 --> 00:35:31,680
is asking a lot of questions about your product,

715
00:35:31,760 --> 00:35:33,120
let's take an example of B2B

716
00:35:33,180 --> 00:35:34,860
is one either because he doesn't know

717
00:35:34,920 --> 00:35:36,180
second because he's interested

718
00:35:36,400 --> 00:35:40,520
So your job is not to just make a sale
of something he doesn't have information on

719
00:35:40,640 --> 00:35:43,580
is to get him or her the information that he needs.

720
00:35:43,700 --> 00:35:45,220
So that he can do a better job

721
00:35:45,280 --> 00:35:46,840
and then just to add on Florent's point

722
00:35:47,300 --> 00:35:49,600
there's an incredible potential

723
00:35:49,700 --> 00:35:52,560
for AI with patient endurance

724
00:35:53,200 --> 00:35:55,740
incredible if you think about some of these things

725
00:35:55,880 --> 00:35:57,960
a lot of these treatments don't work

726
00:35:58,040 --> 00:36:00,060
because of patient endurance

727
00:36:00,220 --> 00:36:03,220
all the things that you see in Nvidia nurse

728
00:36:03,400 --> 00:36:06,660
if I could have someone remind me every day

729
00:36:06,700 --> 00:36:08,140
some form of GPT

730
00:36:08,200 --> 00:36:11,200
of how am I doing, monitoring with my Oura ring

731
00:36:11,300 --> 00:36:12,560
how am I doing with...

732
00:36:12,640 --> 00:36:14,960
imagine if it's a condition related to that

733
00:36:15,020 --> 00:36:17,420
and reminding me I need to take my meds

734
00:36:17,720 --> 00:36:20,200
I mean that is incredible for endurance so

735
00:36:20,280 --> 00:36:22,540
good point on raising it, Florent,

736
00:36:22,600 --> 00:36:24,260
because we shouldn't forget that part.

737
00:36:25,820 --> 00:36:29,400
Thank you. We have a one question that was on the chat.

738
00:36:29,440 --> 00:36:30,660
but it got...

739
00:36:30,740 --> 00:36:32,880
We have a lot of questions actually, not one.

740
00:36:32,960 --> 00:36:35,560
But the first question that we have is from Jennifer.

741
00:36:35,820 --> 00:36:38,140
She said: one more question

742
00:36:38,240 --> 00:36:40,560
or perhaps controversial topic

743
00:36:40,720 --> 00:36:43,840
at the end of the day we need health decision makers

744
00:36:43,920 --> 00:36:46,540
to accept and changed behavior

745
00:36:46,640 --> 00:36:49,660
hcps, patients and policy markers,

746
00:36:49,740 --> 00:36:52,120
Health Systems, governments, payers

747
00:36:52,340 --> 00:36:54,900
how does industry

748
00:36:54,960 --> 00:36:58,200
Pharma, data, aggregators, developers, etc

749
00:36:58,300 --> 00:37:00,860
most efficiently support

750
00:37:00,960 --> 00:37:04,040
fund and influence stakeholders?

751
00:37:05,880 --> 00:37:11,360
Maybe I can start with this one because I was reading it

752
00:37:11,440 --> 00:37:15,320
in the chat before and I think it is something really

753
00:37:15,440 --> 00:37:19,980
interesting, it reminded me, let's say

754
00:37:20,080 --> 00:37:23,140
some years ago when we were approaching

755
00:37:23,240 --> 00:37:25,600
the digital channels let's say

756
00:37:25,680 --> 00:37:29,840
first sales call, virtual sales calls,

757
00:37:29,900 --> 00:37:32,100
emails, and so on

758
00:37:32,180 --> 00:37:34,260
and I think when it comes to this

759
00:37:34,540 --> 00:37:38,600
adoption of Technology, of course you have

760
00:37:38,700 --> 00:37:40,320
have a curve

761
00:37:40,580 --> 00:37:43,120
with the the early adopter and

762
00:37:43,300 --> 00:37:46,940
from the early adopter to those who are let's say

763
00:37:47,020 --> 00:37:50,680
very late into the adoption

764
00:37:51,020 --> 00:37:52,380
from my perspective

765
00:37:52,500 --> 00:37:55,880
and that's my personal opinion

766
00:37:56,040 --> 00:37:58,700
I think that the

767
00:37:58,840 --> 00:38:01,320
let's say, the AI Revolution will

768
00:38:01,400 --> 00:38:05,660
will take place will happen and it will be not led

769
00:38:05,900 --> 00:38:11,640
only by the Pharma industry so the HCP will be in the middle of

770
00:38:11,780 --> 00:38:17,360
a product that he or she use into the clinical practice that maybe

771
00:38:17,420 --> 00:38:21,880
are AI augmented so more and more people

772
00:38:22,100 --> 00:38:25,480
from let's say consumer to professional will be

773
00:38:25,760 --> 00:38:29,700
more and more used to work with the AI

774
00:38:29,740 --> 00:38:34,120
and I think the key point for us as pharma industry

775
00:38:34,200 --> 00:38:36,900
is to be ready

776
00:38:36,980 --> 00:38:40,540
and to, let's say, be ready very very soon

777
00:38:40,620 --> 00:38:42,580
in order to let's say

778
00:38:42,620 --> 00:38:46,320
do not frustrate the early adopter that are of course

779
00:38:46,360 --> 00:38:49,300
those who are accelerating an adoption of

780
00:38:49,380 --> 00:38:52,000
a technology so I think that's

781
00:38:52,080 --> 00:38:53,660
something important

782
00:38:53,760 --> 00:38:56,280
of course there are all the other stakeholders

783
00:38:56,360 --> 00:39:00,020
all around I think some of them will be faster,

784
00:39:00,260 --> 00:39:02,140
some of the other,

785
00:39:02,160 --> 00:39:04,520
especially I think about, for example,

786
00:39:04,600 --> 00:39:08,420
the policy makers maybe they will be a a little bit slower

787
00:39:08,480 --> 00:39:11,240
but I think it really...

788
00:39:11,280 --> 00:39:14,020
It really depends stakeholder by stakeholder

789
00:39:14,100 --> 00:39:16,940
in Europe I think we are in a good shape

790
00:39:17,060 --> 00:39:20,140
let's say with the AI act and so on

791
00:39:20,560 --> 00:39:24,780
So if I compare our environment to other countries

792
00:39:24,800 --> 00:39:27,760
I think at least there is some

793
00:39:28,240 --> 00:39:31,300
effort, some attention that is going

794
00:39:31,360 --> 00:39:35,080
into the direction of giving a framework, giving a structure

795
00:39:35,240 --> 00:39:38,420
but yeah that's what I...

796
00:39:38,520 --> 00:39:40,780
what I think about the question, Jennifer.

797
00:39:40,840 --> 00:39:42,000
Thank you for reasoning

798
00:39:42,080 --> 00:39:44,120
because it's a very good topic.

799
00:39:46,280 --> 00:39:49,400
Yeah, I think just on this topic I believe

800
00:39:49,620 --> 00:39:52,400
science-based transparency

801
00:39:52,580 --> 00:39:55,100
in phrma, that this is

802
00:39:55,200 --> 00:39:57,400
what will make the real difference

803
00:39:57,540 --> 00:40:00,420
I think the era of

804
00:40:00,480 --> 00:40:02,400
the marketing and the bullshit

805
00:40:02,560 --> 00:40:06,620
and the sales rep carpet bombing is over

806
00:40:07,100 --> 00:40:12,680
and right now because the patients and the hcps will have access

807
00:40:12,820 --> 00:40:15,440
to neutral AI models

808
00:40:15,560 --> 00:40:19,000
that will give them briefings on the products,

809
00:40:19,310 --> 00:40:21,240
on the disease, on the symptoms,

810
00:40:21,320 --> 00:40:23,280
on absolutely everything

811
00:40:23,540 --> 00:40:27,380
If you are not transparent, if you are not honest,

812
00:40:27,960 --> 00:40:31,680
you will not be heard and that's a huge difference

813
00:40:31,780 --> 00:40:33,800
versus the past and

814
00:40:33,980 --> 00:40:35,680
products that are not actually

815
00:40:35,720 --> 00:40:37,940
differentiated based on science

816
00:40:38,100 --> 00:40:40,940
and make it transparent, those ones will fail.

817
00:40:41,260 --> 00:40:44,120
You can launch it, you can spend billions

818
00:40:44,240 --> 00:40:47,480
on marketing that stuff will not take off.

819
00:40:47,760 --> 00:40:50,580
And that's a game changer for pharma.

820
00:40:52,960 --> 00:40:56,740
We have another question for Florent.

821
00:40:56,860 --> 00:40:59,020
Let me put it here.

822
00:40:59,100 --> 00:41:02,940
It says: on the topic of unstructured data

823
00:41:03,100 --> 00:41:06,380
how do you handle unstructured data enhancement

824
00:41:06,440 --> 00:41:08,560
to enable quality recommendations

825
00:41:08,830 --> 00:41:11,180
on whatever data sets you may have?

826
00:41:11,260 --> 00:41:17,160
clinical, KOL/DOL, portal data or data that you may buy?

827
00:41:18,560 --> 00:41:19,920
Wow! So... hahaha

828
00:41:21,700 --> 00:41:24,180
In terms of unstructured data,

829
00:41:24,280 --> 00:41:26,780
we've got multiple types of unstructured data

830
00:41:27,040 --> 00:41:30,180
so we have unstructured data

831
00:41:30,200 --> 00:41:32,460
like the historical data of the company

832
00:41:32,680 --> 00:41:36,280
I would call them unstructured
because they are all types and formats.

833
00:41:36,400 --> 00:41:38,740
so those ones we are going to try to clean

834
00:41:38,880 --> 00:41:42,060
document we are trying funnily enough

835
00:41:42,350 --> 00:41:46,520
to use AI to be able to analyze them and tag them

836
00:41:46,640 --> 00:41:49,240
using a very broad taxonomy

837
00:41:49,480 --> 00:41:52,360
so that we can at least know what we have in house.

838
00:41:52,540 --> 00:41:54,300
Then we've got, you know,

839
00:41:54,440 --> 00:41:57,300
all the unstructured data that is coming back to our systems,

840
00:41:57,420 --> 00:41:59,800
like you know the physician voice

841
00:41:59,860 --> 00:42:02,280
voice of the customer, voice of the employee,

842
00:42:02,380 --> 00:42:03,320
you know all that,

843
00:42:03,420 --> 00:42:06,020
so for those ones as I said we don't have the means

844
00:42:06,080 --> 00:42:07,760
and the capabilities to deploy

845
00:42:07,820 --> 00:42:10,140
and create our own models so we're going to use

846
00:42:10,240 --> 00:42:14,260
Salesforce, sentiment analysis, next best action,

847
00:42:14,360 --> 00:42:15,580
and tools like that

848
00:42:15,640 --> 00:42:18,200
to be able to analyze all that unstructured data

849
00:42:18,300 --> 00:42:20,720
and get insights coming out of it

850
00:42:21,180 --> 00:42:23,720
and then there is the unstructured data

851
00:42:23,900 --> 00:42:25,840
for me the best example

852
00:42:25,940 --> 00:42:29,280
is Twitter or X or you know

853
00:42:29,600 --> 00:42:32,120
and those ones right now we don't touch

854
00:42:32,280 --> 00:42:34,840
because otherwise we would be totally overloaded

855
00:42:34,960 --> 00:42:39,100
you need I think to bite one size you know

856
00:42:39,220 --> 00:42:42,680
after the other, just like not everything at the same time.

857
00:42:42,780 --> 00:42:44,260
So we know it's suboptimal

858
00:42:44,340 --> 00:42:46,580
but right now we're ready to try to

859
00:42:46,700 --> 00:42:48,940
control and structure data internal

860
00:42:49,080 --> 00:42:52,080
the external that is generated in our channels

861
00:42:52,280 --> 00:42:55,180
and then we go to the external unstructured.

862
00:42:55,960 --> 00:42:56,920
Okay.

863
00:42:57,380 --> 00:42:59,500
Then we have another question.

864
00:42:59,580 --> 00:43:02,900
I told you guys we had quite a few questions.

865
00:43:03,060 --> 00:43:06,480
It says: how do you describe the implementation?

866
00:43:06,540 --> 00:43:08,700
And do Pharma usually relies on

867
00:43:08,760 --> 00:43:12,160
external agencies or it can be implemented inhouse?

868
00:43:13,800 --> 00:43:16,460
I can take that.

869
00:43:16,550 --> 00:43:17,460
Yeah, please!

870
00:43:17,540 --> 00:43:21,480
I don't think there's a one size fits all in this case.

871
00:43:21,600 --> 00:43:23,200
I think it needs to be

872
00:43:23,640 --> 00:43:25,840
not only case by case but the situation

873
00:43:25,920 --> 00:43:28,760
I think there there's three things that are really important is

874
00:43:28,960 --> 00:43:32,340
decisions you need to make is right now

875
00:43:32,440 --> 00:43:35,860
you need to rescale your entire organization,

876
00:43:36,040 --> 00:43:37,620
like absolutely

877
00:43:37,700 --> 00:43:40,240
in Pharma and outside of Pharma

878
00:43:40,660 --> 00:43:42,960
so how are you going to rescale and going about it?

879
00:43:43,220 --> 00:43:44,760
Some people may decide

880
00:43:44,880 --> 00:43:47,480
we go every... We put everything on the agencies.

881
00:43:48,000 --> 00:43:49,320
Right, okay.

882
00:43:49,540 --> 00:43:51,600
Some others we bring everything in house

883
00:43:51,940 --> 00:43:53,580
but if you bring everything in house

884
00:43:53,680 --> 00:43:55,640
you need to make sure you have the right tools,

885
00:43:55,780 --> 00:43:58,020
you need to build them, or buy them, or borrow them

886
00:43:58,280 --> 00:44:00,020
and you need to train your people

887
00:44:00,440 --> 00:44:02,460
and I think the training piece is

888
00:44:02,540 --> 00:44:04,720
even before we start thinking about these things,

889
00:44:05,200 --> 00:44:07,960
if we, and I put myself in that category,

890
00:44:08,040 --> 00:44:11,600
if we don't really understand the impact
that AI can have in the value chain

891
00:44:11,880 --> 00:44:13,680
we can't be making those decisions.

892
00:44:13,760 --> 00:44:17,380
So Florent knows I'm a big fan of
the work he's been doing in Grünenthal,

893
00:44:17,580 --> 00:44:19,780
because I feel as

894
00:44:20,340 --> 00:44:22,020
it's not even as an industry,

895
00:44:22,140 --> 00:44:24,500
we started focusing on the tech, on the data

896
00:44:24,600 --> 00:44:25,880
in the use cases.

897
00:44:26,380 --> 00:44:27,620
Guess what?

898
00:44:27,980 --> 00:44:30,980
They're saying that generative is a flop

899
00:44:31,120 --> 00:44:36,600
And it's not! It's like we didn't give the proper time

900
00:44:36,940 --> 00:44:38,240
to train people.

901
00:44:38,240 --> 00:44:41,160
When we moved from a calculator to excel,

902
00:44:41,350 --> 00:44:44,380
we had to be trained and I think now is the same.

903
00:44:44,420 --> 00:44:45,760
It's just a tool

904
00:44:45,880 --> 00:44:48,760
so that's the thing, even before making those decisions,

905
00:44:48,880 --> 00:44:50,560
we need to make people aware

906
00:44:50,660 --> 00:44:54,840
of the implications of those decisions based on the tech that is used.

907
00:44:54,920 --> 00:44:56,240
Those are my two cents.

908
00:44:57,380 --> 00:45:00,280
And I fully agree, Marco,

909
00:45:00,340 --> 00:45:02,960
I think you touched a very very important topic

910
00:45:03,040 --> 00:45:06,860
that is the training and the re-skilling part

911
00:45:07,220 --> 00:45:10,260
personally let's say I don't think

912
00:45:10,340 --> 00:45:13,600
it would be let's say

913
00:45:13,830 --> 00:45:15,520
for a pharma company

914
00:45:15,560 --> 00:45:18,900
it would be the right move maybe from the very beginning to

915
00:45:19,280 --> 00:45:22,700
do everything in house because of course

916
00:45:22,820 --> 00:45:25,240
it is a fast-paced environment

917
00:45:25,320 --> 00:45:27,100
so I think there are a lot of

918
00:45:27,380 --> 00:45:30,020
players outside that are doing just the thing

919
00:45:30,080 --> 00:45:32,640
and they are doing it very good so I think

920
00:45:32,780 --> 00:45:36,520
a mix could be as usual

921
00:45:37,000 --> 00:45:40,540
a good, let's say, a good solution

922
00:45:40,700 --> 00:45:43,740
but pharma companies cannot

923
00:45:43,820 --> 00:45:46,740
ignore the reskill need

924
00:45:46,800 --> 00:45:50,340
because otherwise, and then starting from the business

925
00:45:50,480 --> 00:45:54,920
because if we say "okay, AI is a an IT thing"

926
00:45:56,040 --> 00:45:57,800
That's something that is

927
00:45:57,900 --> 00:45:59,820
becoming a flop because people i

928
00:45:59,900 --> 00:46:03,360
not able to use it, so you give an Excel

929
00:46:03,500 --> 00:46:06,340
a software to a child

930
00:46:06,760 --> 00:46:09,020
and of course it's not working.

931
00:46:09,440 --> 00:46:11,900
It's something that is important.

932
00:46:12,800 --> 00:46:16,580
To come back on the first part of the question,

933
00:46:16,640 --> 00:46:19,120
even that's going to make me look like a Maverick,

934
00:46:19,280 --> 00:46:23,140
I want to say that the implementation is

935
00:46:23,200 --> 00:46:24,780
a fantastic moment.

936
00:46:24,880 --> 00:46:26,680
It's super exciting.

937
00:46:26,740 --> 00:46:29,220
It's not that often in my career

938
00:46:29,300 --> 00:46:31,260
that I could go to a group and tell them

939
00:46:31,590 --> 00:46:36,600
"there's a wave, 100 ft size wave,

940
00:46:36,800 --> 00:46:38,680
that is going to land on us

941
00:46:38,860 --> 00:46:42,180
and I need 20,30 people

942
00:46:42,300 --> 00:46:44,860
who are going to be crazy enough to

943
00:46:45,040 --> 00:46:48,880
with zero budget and no external support

944
00:46:49,040 --> 00:46:51,480
are going to surf this wave together

945
00:46:51,840 --> 00:46:55,480
and I had 20 to 30 ends raised

946
00:46:55,620 --> 00:46:59,020
in the room, because it's super exciting

947
00:46:59,120 --> 00:47:02,280
and if you take it, you know, with the learning eyes

948
00:47:02,380 --> 00:47:04,180
and let's try and let's fail so

949
00:47:04,220 --> 00:47:06,460
we have decided for instance that at the same time

950
00:47:06,520 --> 00:47:08,600
we are going to implement agile at scale

951
00:47:08,820 --> 00:47:11,060
so we are launching 15 Pilots

952
00:47:11,140 --> 00:47:13,680
that we are running in an agile mode

953
00:47:13,780 --> 00:47:17,100
so that we can kill very fast what should not survive

954
00:47:17,200 --> 00:47:19,020
and we can move to the next pilot

955
00:47:19,280 --> 00:47:22,500
but the people in it are doing it on top of their time

956
00:47:22,760 --> 00:47:25,560
They are strapping the heart to find money

957
00:47:25,680 --> 00:47:26,780
to fund the pilots

958
00:47:26,860 --> 00:47:29,180
and they do it because they think

959
00:47:29,280 --> 00:47:31,980
it's a game changer, so I would say

960
00:47:32,540 --> 00:47:35,420
it's complicated but it's so fun

961
00:47:35,500 --> 00:47:37,840
I would encourage everyone to try it.

962
00:47:38,660 --> 00:47:41,340
Well, Carolina Quezada is saying

963
00:47:41,440 --> 00:47:44,120
so she was responding to what Marco said

964
00:47:44,240 --> 00:47:47,240
and she said: it's important to have fun

965
00:47:47,340 --> 00:47:50,240
So, there you go, Florent haha

966
00:47:50,480 --> 00:47:52,480
but I know Carolina

967
00:47:52,540 --> 00:47:54,320
and yeah, it's true I mean

968
00:47:54,440 --> 00:47:56,160
if you don't, I mean seriously,

969
00:47:56,240 --> 00:47:58,580
we are doing a fantastic job.

970
00:47:58,660 --> 00:48:01,640
We try to bring back the health to people and stuff like that.

971
00:48:01,760 --> 00:48:03,240
That's a great mission.

972
00:48:03,400 --> 00:48:05,660
But we are also human, we need to have fun!

973
00:48:05,700 --> 00:48:08,180
If we don't have fun, we need to change

974
00:48:08,300 --> 00:48:11,000
We go to the entertainment industry,
or something like that.

975
00:48:11,080 --> 00:48:14,340
You know? If it has to be boring from Monday to Friday,

976
00:48:14,420 --> 00:48:15,560
then just don't do it.

977
00:48:15,680 --> 00:48:18,560
And this is a way to create fun for all.

978
00:48:19,920 --> 00:48:21,400
And if I can just

979
00:48:21,480 --> 00:48:23,360
I've asked to put on the chat

980
00:48:23,520 --> 00:48:26,400
there's actually an article from last week

981
00:48:26,440 --> 00:48:28,280
which exactly says that

982
00:48:28,540 --> 00:48:31,360
so we might sound a bit crazy yeah you know

983
00:48:31,460 --> 00:48:33,400
have fun and play and everything

984
00:48:33,480 --> 00:48:36,520
but actually you can see that managers

985
00:48:36,660 --> 00:48:39,880
that have actually played with generative AI

986
00:48:40,000 --> 00:48:43,940
and show people like they show examples to their people

987
00:48:44,120 --> 00:48:46,220
the engagement and adoption

988
00:48:46,300 --> 00:48:48,180
is 350% higher

989
00:48:48,380 --> 00:48:51,780
so take a look at that because now
there's data that actually proves

990
00:48:52,060 --> 00:48:55,280
that the playful thing is not having fun

991
00:48:55,340 --> 00:48:56,980
is actually way more effective.

992
00:48:57,300 --> 00:48:58,660
Nice.

993
00:48:58,880 --> 00:49:01,140
We have another question.

994
00:49:01,240 --> 00:49:04,620
It says: so we're talking about commercial setting

995
00:49:04,680 --> 00:49:09,500
do we need to be looking at proof of concept to use in trials 1st?

996
00:49:12,100 --> 00:49:13,880
So first I don't know

997
00:49:13,880 --> 00:49:16,480
but that's, so it's really interesting, you know.

998
00:49:16,600 --> 00:49:18,120
When you go, so,

999
00:49:18,280 --> 00:49:21,340
we are normally very siloed between R&D

1000
00:49:21,550 --> 00:49:24,280
and the people who are doing the clinical trials

1001
00:49:24,320 --> 00:49:27,060
and the people that are in commercial.

1002
00:49:27,550 --> 00:49:29,480
Like, you know, we are not supposed to be

1003
00:49:29,640 --> 00:49:31,280
working too much together

1004
00:49:31,360 --> 00:49:32,980
and in fact what we see

1005
00:49:33,040 --> 00:49:36,580
that those guys in R&D are moving very very fast

1006
00:49:36,660 --> 00:49:38,020
and they have been using

1007
00:49:38,120 --> 00:49:42,440
without anyone knowing it AI for years

1008
00:49:42,520 --> 00:49:44,880
to select and create molecules,

1009
00:49:44,980 --> 00:49:48,440
to select patients for the clinical trials,
and to do that type of stuff

1010
00:49:48,620 --> 00:49:50,560
they are actually doing it

1011
00:49:50,720 --> 00:49:53,400
but because they are R&D people

1012
00:49:53,580 --> 00:49:56,840
and not commercial people, they don't make a big fuss about it

1013
00:49:57,070 --> 00:50:00,500
that's a big difference so we can be their first maker.

1014
00:50:02,660 --> 00:50:03,780
Interesting.

1015
00:50:05,840 --> 00:50:10,120
Do you guys, Manuel, Marco, do you have anything else to add?

1016
00:50:10,830 --> 00:50:13,840
No, from my side, I mean I fully agree with Florent

1017
00:50:13,920 --> 00:50:17,400
I do not have big experience on R&D.

1018
00:50:18,680 --> 00:50:19,800
Oh, please!

1019
00:50:19,920 --> 00:50:21,980
I got a question, it's not related to this,

1020
00:50:22,060 --> 00:50:24,480
I'd like to dwindle the topic a little bit so

1021
00:50:24,940 --> 00:50:26,420
we have

1022
00:50:27,640 --> 00:50:30,140
so, you know, we talk a lot about AI,

1023
00:50:30,220 --> 00:50:32,180
and how it helps us with a go-to-market strategies

1024
00:50:32,220 --> 00:50:34,520
so go-to-market strategy you have

1025
00:50:34,980 --> 00:50:39,040
part one is targeting the right doctors

1026
00:50:39,220 --> 00:50:41,400
and the patients

1027
00:50:41,480 --> 00:50:44,020
and the other part is like field enablement you know

1028
00:50:44,400 --> 00:50:49,140
so the question is how can AI help us with field enablement?

1029
00:50:49,240 --> 00:50:51,900
I'll let anyone start with this.

1030
00:50:55,080 --> 00:50:58,020
I can take a pass at it.

1031
00:50:58,200 --> 00:51:00,060
It is related with the question we were

1032
00:51:00,480 --> 00:51:02,660
having earlier on...

1033
00:51:02,760 --> 00:51:04,700
on lead scoring.

1034
00:51:06,200 --> 00:51:10,660
We will all, regardless of our function, need to up our game

1035
00:51:10,880 --> 00:51:12,560
because access to information

1036
00:51:12,620 --> 00:51:14,280
the barries to enter are super low

1037
00:51:14,760 --> 00:51:18,440
so there's this story I always tell of this of this lady

1038
00:51:18,540 --> 00:51:20,800
she went through 17 doctors

1039
00:51:20,920 --> 00:51:24,860
three years trying to diagnose her son

1040
00:51:25,320 --> 00:51:27,140
after she was getting desperate

1041
00:51:27,200 --> 00:51:31,260
she took all of the notes, the last exam, and in half an hour ChatGPT

1042
00:51:31,320 --> 00:51:32,560
she got diagnosed

1043
00:51:32,680 --> 00:51:34,700
so patients are going to become super patients,

1044
00:51:34,820 --> 00:51:37,220
HCPs are going to become super HCPs

1045
00:51:37,380 --> 00:51:40,320
which means in terms of field

1046
00:51:40,830 --> 00:51:42,580
will need to become super reps

1047
00:51:42,700 --> 00:51:45,360
we will need to know what they are looking for

1048
00:51:45,640 --> 00:51:47,140
what are their questions,

1049
00:51:47,200 --> 00:51:49,560
they're going to come way more informed so

1050
00:51:49,680 --> 00:51:51,920
what we were discussing earlier again

1051
00:51:52,040 --> 00:51:53,800
to your point, Stefan, is about

1052
00:51:53,900 --> 00:51:56,980
about the lead scoring, is not so much as we will need to be

1053
00:51:57,120 --> 00:51:59,300
ready to have interactions with them

1054
00:51:59,720 --> 00:52:01,700
because they will know way more

1055
00:52:01,780 --> 00:52:03,380
than they could know before

1056
00:52:03,780 --> 00:52:05,780
and AI will help us

1057
00:52:05,940 --> 00:52:08,980
not only generate documentation but gather insights,

1058
00:52:09,020 --> 00:52:12,160
gather notes, allow us to be more present,
we always forget that.

1059
00:52:12,680 --> 00:52:16,460
People think I'm a romantic saying
that AI will make us more human.

1060
00:52:16,580 --> 00:52:19,320
but if I'm in a conversation with Manuel

1061
00:52:19,460 --> 00:52:21,860
and if I'm focusing on taking notes and

1062
00:52:21,940 --> 00:52:25,660
getting action items, everything,
I'm not fully present with him.

1063
00:52:25,780 --> 00:52:27,600
and I think that's how AI can help

1064
00:52:27,700 --> 00:52:29,480
tremendously on sales engagement.

1065
00:52:32,100 --> 00:52:35,940
Another thing to build on what you were saying, Marco,

1066
00:52:36,020 --> 00:52:40,040
I'm thinking of a practical example of

1067
00:52:40,100 --> 00:52:41,980
when it comes to field

1068
00:52:42,220 --> 00:52:45,780
so if you are for example entering

1069
00:52:45,830 --> 00:52:48,620
a new therapeutic area with a new compound

1070
00:52:48,640 --> 00:52:51,060
in an area that it is unknown for you

1071
00:52:51,260 --> 00:52:52,840
for the company of course

1072
00:52:52,940 --> 00:52:56,040
I think that AI can

1073
00:52:56,180 --> 00:52:59,160
play a crucial role in

1074
00:52:59,240 --> 00:53:01,540
let's say taking the data we all know

1075
00:53:01,640 --> 00:53:04,640
we can take the data from IQVIA for example

1076
00:53:04,720 --> 00:53:06,680
or from other service provide

1077
00:53:06,820 --> 00:53:08,300
like that

1078
00:53:08,440 --> 00:53:13,820
but I think the competitive advantage
will be also the usage that we do

1079
00:53:13,920 --> 00:53:18,480
of the AI to do for example a field sizing,

1080
00:53:18,580 --> 00:53:20,860
a field force planning

1081
00:53:20,920 --> 00:53:24,460
done in a very laser focused way

1082
00:53:24,560 --> 00:53:27,240
because we all know that of course

1083
00:53:27,740 --> 00:53:29,860
let's say unfortunately or not

1084
00:53:29,960 --> 00:53:35,220
the access of the sales rep to the Physician
is decreasing every year

1085
00:53:35,420 --> 00:53:37,080
so of course we have to

1086
00:53:37,180 --> 00:53:39,260
maximize as much as possible

1087
00:53:39,400 --> 00:53:43,120
the sales force if we want to leverage on this channel

1088
00:53:43,220 --> 00:53:45,540
and I think AI can really

1089
00:53:45,640 --> 00:53:47,960
help a lot as a tool.

1090
00:53:50,600 --> 00:53:55,700
It's just... I'm reacting also on
the message from Chiara in the chat

1091
00:53:57,310 --> 00:53:58,980
The other day I was watching a movie

1092
00:53:59,040 --> 00:54:00,960
I can't recall unfortunately the movie

1093
00:54:01,080 --> 00:54:04,300
but there was someone, one of the people

1094
00:54:04,590 --> 00:54:09,060
people in the movie that was describing symptoms

1095
00:54:09,300 --> 00:54:10,600
and I was hearing that

1096
00:54:10,680 --> 00:54:12,160
I went on to chat GPT

1097
00:54:12,200 --> 00:54:15,100
and I was... you know what? okay those are the symptoms

1098
00:54:15,200 --> 00:54:16,300
what is a disease?

1099
00:54:16,500 --> 00:54:20,300
and chatgpt gave me the right disease,
like same in the movie

1100
00:54:20,420 --> 00:54:23,080
So it's not a real life setting and stuff like that

1101
00:54:23,240 --> 00:54:26,320
but it shows how powerful those systems can be

1102
00:54:26,460 --> 00:54:29,240
and how much they are going to influence the patient

1103
00:54:29,720 --> 00:54:31,100
because then

1104
00:54:31,260 --> 00:54:34,160
before it was "okay, Dr Google is telling me that"

1105
00:54:34,280 --> 00:54:37,180
the doctors rapidly said "Dr Google is a donkey"

1106
00:54:37,480 --> 00:54:40,080
"so don't believe what Dr donkey says"

1107
00:54:40,280 --> 00:54:43,620
but tomorrow it's going to be Dr ChatGPT and by the way

1108
00:54:43,660 --> 00:54:46,100
Dr ChatGPT graduated as

1109
00:54:46,200 --> 00:54:47,840
a generalist in the UK

1110
00:54:47,920 --> 00:54:49,740
so don't believe it's a donkey

1111
00:54:49,840 --> 00:54:52,340
or you know UK donkey haha

1112
00:54:52,440 --> 00:54:55,720
but I think it's really really important we take that into account

1113
00:54:55,760 --> 00:54:57,260
in our go to market strategies

1114
00:54:57,360 --> 00:55:00,360
the patients will be educated by

1115
00:55:00,520 --> 00:55:01,780
the AIs tomorrow.

1116
00:55:01,940 --> 00:55:04,260
I definitely believe that.

1117
00:55:04,420 --> 00:55:08,980
We were having a discussion with my husband
about something, I don't remember what it was

1118
00:55:09,080 --> 00:55:11,200
but I was feeling a little bit sick

1119
00:55:11,280 --> 00:55:14,080
and the first thing that he told me was "Don't Google it"

1120
00:55:14,180 --> 00:55:17,200
because, you know, usually you describe your symptoms

1121
00:55:17,260 --> 00:55:18,820
and there you go haha

1122
00:55:18,920 --> 00:55:21,760
but now that I know that chatgpt can actually

1123
00:55:21,820 --> 00:55:24,640
gave me what I have, then I have a new doctor haha

1124
00:55:24,680 --> 00:55:26,580
No, I'm kidding haha

1125
00:55:26,660 --> 00:55:28,960
I know that, and that's scary because

1126
00:55:29,100 --> 00:55:30,600
then you're wondering

1127
00:55:30,640 --> 00:55:34,620
okay those systems have been educated using data sets

1128
00:55:34,860 --> 00:55:37,400
what was it that data set?

1129
00:55:37,920 --> 00:55:40,640
if you're starting to touch the health of the people

1130
00:55:40,800 --> 00:55:43,140
did someone pollute

1131
00:55:43,200 --> 00:55:44,980
the data set intentionally?

1132
00:55:45,180 --> 00:55:49,520
so that the system is going to give
the wrong answer to the patient

1133
00:55:49,720 --> 00:55:50,740
I mean, it can.

1134
00:55:50,800 --> 00:55:53,120
when you think the manipulation
that can happen in that field

1135
00:55:53,220 --> 00:55:55,340
it's like literally very scary

1136
00:55:55,400 --> 00:55:58,680
so we need to be very careful and
we need to educate our role I think

1137
00:55:58,880 --> 00:56:02,080
as trusted HealthCare Partners

1138
00:56:02,440 --> 00:56:04,580
is to educate the people on

1139
00:56:04,740 --> 00:56:07,840
how to use the system and
what are the limits of those systems.

1140
00:56:08,400 --> 00:56:10,460
Well, yeah...

1141
00:56:10,560 --> 00:56:11,080
Please.

1142
00:56:11,200 --> 00:56:13,200
No, no, sorry, good ahead.

1143
00:56:13,540 --> 00:56:17,980
I know, like Chiara said: Indeed, Florent.
Medical protocols will also improve

1144
00:56:18,020 --> 00:56:23,220
as well thanks to AI inquiring power and all patience insights gathered.

1145
00:56:24,800 --> 00:56:26,180
Yeah, totally.

1146
00:56:26,260 --> 00:56:30,060
And, Florent, you made me think about a question

1147
00:56:30,100 --> 00:56:33,040
that I received during another webinar

1148
00:56:33,120 --> 00:56:36,060
and I will not make any names

1149
00:56:36,120 --> 00:56:39,220
because of the question,
but basically the question I received

1150
00:56:39,440 --> 00:56:44,580
was that: is there a way to make sure that the custom GPT

1151
00:56:44,720 --> 00:56:48,440
is going to recommend our drug
instead of the competitor's one

1152
00:56:48,480 --> 00:56:50,920
after the patient asked for the symptoms?

1153
00:56:51,060 --> 00:56:52,340
I receive this question haha

1154
00:56:52,620 --> 00:56:54,260
ha ha yeah

1155
00:56:54,440 --> 00:56:57,040
Even that, but that's a reality, I mean a lot of people are saying

1156
00:56:57,060 --> 00:56:58,720
"You have SEO, right?"

1157
00:56:59,310 --> 00:57:03,320
You're going to start having some model
of large language model optimization.

1158
00:57:03,400 --> 00:57:05,180
That's going to be a whole new...

1159
00:57:05,400 --> 00:57:07,440
So we also need to be ready for those disciplines,

1160
00:57:07,520 --> 00:57:10,140
new disciplines to emerge, because, yes, of course...

1161
00:57:11,260 --> 00:57:14,880
I just want to quickly comment if I can just on Jennifer's

1162
00:57:14,940 --> 00:57:16,960
how do we bridge R&D and commercial

1163
00:57:17,040 --> 00:57:18,680
and I would love to hear it

1164
00:57:19,660 --> 00:57:24,340
the biggest concern I have right now
for the world and for the industry

1165
00:57:24,720 --> 00:57:26,320
is tech people

1166
00:57:26,620 --> 00:57:28,540
are not speaking enough to business

1167
00:57:28,580 --> 00:57:30,680
and business people are not being tech enough

1168
00:57:30,780 --> 00:57:33,040
I think we are in a very polarized world

1169
00:57:33,120 --> 00:57:36,060
I'm not going to lecture us on that,
we know different reasons for that

1170
00:57:36,100 --> 00:57:37,600
but my biggest concern is that

1171
00:57:37,800 --> 00:57:41,380
if we in business don't try to become a bit more techy

1172
00:57:41,460 --> 00:57:43,640
if the techies don't start become a bit more business

1173
00:57:43,820 --> 00:57:46,700
if R&D doesn't go closer to commercial

1174
00:57:46,800 --> 00:57:49,200
if medical affairs doesn't go closer

1175
00:57:49,440 --> 00:57:52,820
to any other function

1176
00:57:53,000 --> 00:57:56,060
I think we're going to fail on bridging this.

1177
00:57:56,120 --> 00:57:58,200
So that bridging word

1178
00:57:58,260 --> 00:57:59,820
triggered a lot for me

1179
00:57:59,920 --> 00:58:02,860
and that's what we need to do,
because I think if we do that

1180
00:58:03,000 --> 00:58:04,340
everyone is going to benefit

1181
00:58:04,380 --> 00:58:07,280
Pick the most skeptical person in your organization

1182
00:58:07,460 --> 00:58:09,140
go and have a coffee with them,

1183
00:58:09,240 --> 00:58:11,020
then we'll have, in complete denial of AI,

1184
00:58:11,080 --> 00:58:12,400
just have a conversation with them.

1185
00:58:14,580 --> 00:58:19,760
Thank you. We are two minutes away from ending this webinar.

1186
00:58:19,860 --> 00:58:21,980
So, guys,

1187
00:58:22,080 --> 00:58:24,640
do you have any closing comments for our audience?

1188
00:58:24,720 --> 00:58:28,340
I know you guys have a really really packed agenda today so

1189
00:58:28,520 --> 00:58:31,040
I want to be respectful of your time.

1190
00:58:31,180 --> 00:58:33,140
So, who wants to go first?

1191
00:58:33,320 --> 00:58:34,280
Manuel?

1192
00:58:34,740 --> 00:58:36,360
I can just...

1193
00:58:36,360 --> 00:58:37,140
Florent?

1194
00:58:37,140 --> 00:58:38,600
Sorry, maybe I will just...

1195
00:58:38,700 --> 00:58:42,380
because that's a link, I will just build on Marco's comment there.

1196
00:58:43,140 --> 00:58:45,680
Totally, our biggest enemy

1197
00:58:46,020 --> 00:58:48,260
is not external, it's not our competition,

1198
00:58:48,280 --> 00:58:50,760
it's not our biggest enemy is

1199
00:58:50,840 --> 00:58:53,880
our internal Silo mentality

1200
00:58:54,240 --> 00:58:57,820
where I take care of my stuff and
I end it over to someone else.

1201
00:58:58,120 --> 00:59:01,500
Those are the walls we need to break

1202
00:59:01,700 --> 00:59:04,260
to create a united data platform

1203
00:59:04,380 --> 00:59:06,860
on which we can build our models

1204
00:59:07,000 --> 00:59:10,080
across function for the benefits of patients and HCPs

1205
00:59:10,360 --> 00:59:12,540
and that means, to Marco's point,

1206
00:59:12,720 --> 00:59:17,240
business , IT, everyone talking and working together.

1207
00:59:17,550 --> 00:59:19,220
Otherwise, we won't be successful

1208
00:59:19,310 --> 00:59:22,300
and that expands in my mind also to partners

1209
00:59:22,480 --> 00:59:26,700
like openAI, like Veeva, like IQVIA, like all others,

1210
00:59:26,680 --> 00:59:28,760
because they play a role in our ecosystem.

1211
00:59:28,880 --> 00:59:30,860
and that's for me the only way

1212
00:59:30,960 --> 00:59:34,600
we're going to maximize the positive impact of the AI wave.

1213
00:59:34,800 --> 00:59:36,600
So sorry, Manuel,

1214
00:59:36,680 --> 00:59:37,780
I just jumped in because

1215
00:59:37,820 --> 00:59:40,040
that was connected to what Marco was saying.

1216
00:59:40,220 --> 00:59:42,820
No, no problem! I mean

1217
00:59:43,080 --> 00:59:45,700
From my side, let's say, I think

1218
00:59:45,760 --> 00:59:48,880
first of all I fully agree with you both

1219
00:59:49,000 --> 00:59:51,940
for both points, from my side

1220
00:59:52,100 --> 00:59:54,520
I would like to close with

1221
00:59:54,660 --> 00:59:57,520
something that I'm also doing personally

1222
00:59:57,600 --> 01:00:01,140
that it is to learn also from other Industries

1223
01:00:01,280 --> 01:00:04,740
because of course our customers are also customers

1224
01:00:04,860 --> 01:00:07,040
from other kind of products

1225
01:00:07,160 --> 01:00:10,120
in way less regulated environment

1226
01:00:10,240 --> 01:00:13,680
meaning that they can have more speed, more agility

1227
01:00:13,760 --> 01:00:15,100
and so on, so from our side,

1228
01:00:15,200 --> 01:00:16,860
from a pharma standpoint

1229
01:00:16,920 --> 01:00:19,420
we could have the luxury let's say to learn

1230
01:00:19,500 --> 01:00:23,440
from those who already tested in a safe environment

1231
01:00:23,480 --> 01:00:26,520
so, learn also from the other industry.

1232
01:00:27,260 --> 01:00:30,200
Thank you! Marco, something to leave or...?

1233
01:00:30,340 --> 01:00:33,840
No, the last thing is just, as as Florent was saying,

1234
01:00:34,100 --> 01:00:36,320
we're all different. we're all from competitors

1235
01:00:36,380 --> 01:00:37,900
and we're all here being very open

1236
01:00:37,920 --> 01:00:39,660
with the fact that we all have the same challenges

1237
01:00:39,780 --> 01:00:43,220
and I think we need to do that way more
if we want the industry to go forward.

1238
01:00:43,300 --> 01:00:44,840
So, thank you for inviting us.

1239
01:00:44,960 --> 01:00:47,380
And I really appreciate the open and candid conversation.

1240
01:00:48,000 --> 01:00:50,660
Thank you. Thank you, everyone. Thank you, audience.

1241
01:00:50,760 --> 01:00:54,540
And just to let you guys be

1242
01:00:54,740 --> 01:00:57,460
very calm and don't worry about me

1243
01:00:57,520 --> 01:01:00,220
I'm not going to use ChatGPT as my doctor

1244
01:01:00,360 --> 01:01:03,380
I will go to an actual doctor ha ha

1245
01:01:03,740 --> 01:01:06,720
So thank you all for being here.

1246
01:01:06,820 --> 01:01:10,940
Thank you to our audience.
We hope you have a great rest of the week.

1247
01:01:11,080 --> 01:01:15,240
And I will see you all on our next "Pharma Insights"

1248
01:01:15,380 --> 01:01:19,180
So it's gonna be on the 27th of June.

1249
01:01:19,280 --> 01:01:20,680
27th of June, guys,

1250
01:01:20,700 --> 01:01:25,420
write it up, and very soon
we're going to be starting a community

1251
01:01:25,940 --> 01:01:29,440
We're going to be giving you more information about that.

1252
01:01:29,560 --> 01:01:32,020
Thank you so much for being with us today.

1253
01:01:32,120 --> 01:01:33,120
I know it's hard.

1254
01:01:33,180 --> 01:01:35,360
The European Championship is starting.

1255
01:01:35,460 --> 01:01:36,760
A lot of games, you know.

1256
01:01:38,620 --> 01:01:41,000
Good luck to everyone!

1257
01:01:41,120 --> 01:01:43,660
Thank you very much! Thanks!

1258
01:01:43,800 --> 01:01:46,920
If you want good food and good weather, go to Italy.

1259
01:01:47,000 --> 01:01:49,320
I will see you all in our next "Pharma Insights".

1260
01:01:49,320 --> 01:01:51,320
Bye bye!