1
00:00:00,160 --> 00:00:04,880
Speaker 1: This episode is sponsored by Elmo the Complete AI Workforce Platform.

2
00:00:05,240 --> 00:00:09,720
Speaker 1: It unifies HR, payroll and rostering on one platform with

3
00:00:09,920 --> 00:00:14,040
Speaker 1: native AI that turns connected data into trusted answers. You

4
00:00:14,080 --> 00:00:18,880
Speaker 1: can act on the best prepared person in the room

5
00:00:19,120 --> 00:00:23,560
Speaker 1: wins right. But when your day is back to back meetings,

6
00:00:23,720 --> 00:00:27,320
Speaker 1: unread emails, and conversations you haven't had time to think through,

7
00:00:27,760 --> 00:00:32,600
Speaker 1: preparation feels like a luxury you can't afford. But Joseph

8
00:00:32,680 --> 00:00:35,159
Speaker 1: Lyons has a better answer, and it comes down to

9
00:00:35,240 --> 00:00:40,120
Speaker 1: how he uses AI. Joseph is the president of Elmo Software,

10
00:00:40,520 --> 00:00:43,120
Speaker 1: and I love this conversation because he isn't just talking

11
00:00:43,159 --> 00:00:46,240
Speaker 1: about how he uses AI in theory. In this chat,

12
00:00:46,400 --> 00:00:49,640
Speaker 1: he pulls back the curtain to share how he's using

13
00:00:49,680 --> 00:00:54,600
Speaker 1: AI every single day in super practical ways. We get

14
00:00:54,600 --> 00:00:59,280
Speaker 1: into the personalized daily intelligence brief his entire exec team

15
00:00:59,400 --> 00:01:02,320
Speaker 1: wakes up to every morning. We talk about how he

16
00:01:02,360 --> 00:01:05,280
Speaker 1: turned a six hour strategy workshop transcript into something that

17
00:01:05,400 --> 00:01:08,920
Speaker 1: was actually usable and actionable, and the way he now

18
00:01:09,120 --> 00:01:14,319
Speaker 1: roleplays tough negotiations and difficult performance conversations with AI, including

19
00:01:14,440 --> 00:01:23,160
Speaker 1: on his drive to work. Welcome to How I Work,

20
00:01:23,360 --> 00:01:27,520
Speaker 1: a show about habits rituals and strategies for optimizing your day.

21
00:01:28,120 --> 00:01:34,959
Speaker 1: I'm your host, doctor Amantha imber Joe. I want to

22
00:01:35,040 --> 00:01:39,319
Speaker 1: start with this ELT productivity agent that you have built.

23
00:01:39,400 --> 00:01:42,319
Speaker 1: Can you tell me what this is and what it does?

24
00:01:42,680 --> 00:01:45,720
Speaker 2: Yeah, Look, I think we have been fortunate in that

25
00:01:45,920 --> 00:01:48,919
Speaker 2: at Elmo, we've been able to deploy a pretty amazing

26
00:01:48,960 --> 00:01:52,200
Speaker 2: tool called Glean and that's like an enterprise AI layer

27
00:01:52,240 --> 00:01:54,880
Speaker 2: that we've got and it integrates with all of the

28
00:01:55,000 --> 00:01:57,240
Speaker 2: various systems that we have. So we've got, you know,

29
00:01:57,320 --> 00:01:59,800
Speaker 2: obviously our g suite, We've got Slack, which is used

30
00:02:00,000 --> 00:02:05,240
Speaker 2: extensively Salesforce, a range of different tools. And then what's

31
00:02:05,280 --> 00:02:08,240
Speaker 2: exciting about that platform is that it's actually allowed us

32
00:02:08,240 --> 00:02:11,440
Speaker 2: to build agents in natural language. It doesn't mean that

33
00:02:11,480 --> 00:02:13,600
Speaker 2: you need to have an engineering background, you don't need

34
00:02:13,680 --> 00:02:16,680
Speaker 2: to understand how to code. You're pretty much able to

35
00:02:16,680 --> 00:02:19,120
Speaker 2: describe what you want the agent to do, and then

36
00:02:19,160 --> 00:02:22,160
Speaker 2: it connects to the data sources and runs. So what

37
00:02:22,240 --> 00:02:24,520
Speaker 2: we've been able to do for our ELT is actually

38
00:02:24,520 --> 00:02:27,600
Speaker 2: build a productivity agent and then it allows us to

39
00:02:27,800 --> 00:02:29,040
Speaker 2: pretty much run.

40
00:02:29,320 --> 00:02:31,519
Speaker 3: For daily for each of our exec team.

41
00:02:31,560 --> 00:02:34,320
Speaker 2: It connects all of the applications that they've got and

42
00:02:34,360 --> 00:02:37,959
Speaker 2: it pulls together a prioritized daily brief, summarizes all the

43
00:02:38,000 --> 00:02:40,400
Speaker 2: key emails that we need to do, outlines the next

44
00:02:40,440 --> 00:02:43,000
Speaker 2: seven days of meetings, any sort of open action items.

45
00:02:43,680 --> 00:02:45,760
Speaker 3: It surfaces and flags.

46
00:02:45,320 --> 00:02:47,799
Speaker 2: What's high importance, what might need to go to the board,

47
00:02:48,320 --> 00:02:51,680
Speaker 2: any key customer meetings, or any major risks or escalations.

48
00:02:51,720 --> 00:02:54,919
Speaker 2: So it's been a massive timesaver for all of us

49
00:02:55,040 --> 00:02:58,120
Speaker 2: in the exec and it makes sure that we're acting

50
00:02:58,160 --> 00:02:59,359
Speaker 2: on the things that we need to do, and it

51
00:02:59,440 --> 00:03:02,480
Speaker 2: is delivered beautifully into Slack every morning for us.

52
00:03:02,560 --> 00:03:05,880
Speaker 1: That's amazing. And so does every member of the ELT

53
00:03:06,000 --> 00:03:09,400
Speaker 1: get their own personalized version of that report based on

54
00:03:09,440 --> 00:03:10,800
Speaker 1: what they need to know?

55
00:03:11,280 --> 00:03:12,399
Speaker 3: Yes, exactly.

56
00:03:12,560 --> 00:03:15,440
Speaker 1: Wow, that is absolutely amazing. How much time do you

57
00:03:15,440 --> 00:03:18,080
Speaker 1: reckon that has saved you in terms of all the

58
00:03:18,080 --> 00:03:20,600
Speaker 1: things that you need to be across Joe.

59
00:03:20,360 --> 00:03:23,320
Speaker 2: I think it's not just saving time about planning for

60
00:03:23,440 --> 00:03:26,960
Speaker 2: that day, but it's also saving time in between a

61
00:03:27,080 --> 00:03:29,360
Speaker 2: very full meeting schedule because it allows you to have

62
00:03:29,400 --> 00:03:32,440
Speaker 2: a reference guide and a layer to move through the day.

63
00:03:32,480 --> 00:03:34,040
Speaker 3: So I reckon it's saving at least than an hour

64
00:03:34,120 --> 00:03:34,720
Speaker 3: or two a day.

65
00:03:34,920 --> 00:03:38,800
Speaker 1: Now. I know that you know using AI to improve

66
00:03:38,960 --> 00:03:42,000
Speaker 1: how effective we are with meetings is pretty common. But

67
00:03:42,280 --> 00:03:46,119
Speaker 1: tell me about a six hour workshop that I think

68
00:03:46,200 --> 00:03:49,920
Speaker 1: you attended this week, and tell me how you used

69
00:03:49,960 --> 00:03:52,320
Speaker 1: AI for that, because that is a very long workshop.

70
00:03:52,560 --> 00:03:55,040
Speaker 3: Yeah, it is. I mean it's a big time commitment.

71
00:03:55,040 --> 00:03:57,520
Speaker 2: It was an important strategy workshop that we were running,

72
00:03:57,560 --> 00:03:59,880
Speaker 2: and we had a number of senior execs that were

73
00:04:00,080 --> 00:04:03,360
Speaker 2: in there, and there were some important strategic conversations that

74
00:04:03,400 --> 00:04:06,040
Speaker 2: we needed to get through, and I found that Zoom

75
00:04:06,200 --> 00:04:08,840
Speaker 2: AI companion it really only gives you kind of a

76
00:04:08,880 --> 00:04:12,320
Speaker 2: surface level, bullet point recap of what was discussed then,

77
00:04:12,360 --> 00:04:15,640
Speaker 2: particularly when you're talking about a six hour conversation that

78
00:04:15,720 --> 00:04:19,240
Speaker 2: could cover a range of different topics. We did get

79
00:04:19,240 --> 00:04:21,360
Speaker 2: a zoom summary of it, but I found it didn't

80
00:04:21,400 --> 00:04:24,520
Speaker 2: provide us the full detail and get to us a

81
00:04:24,600 --> 00:04:28,159
Speaker 2: clear set of outcome. So I went back into Zoom

82
00:04:28,240 --> 00:04:32,000
Speaker 2: and that actually pulled out the full detailed transcript of

83
00:04:32,040 --> 00:04:35,520
Speaker 2: the day and I uploaded that into Claude.

84
00:04:35,880 --> 00:04:36,799
Speaker 3: What came back.

85
00:04:36,680 --> 00:04:39,360
Speaker 2: Genuinely kind of blew me away, because what it did

86
00:04:39,480 --> 00:04:42,000
Speaker 2: was it captured all of the wisdom and intelligence of

87
00:04:42,040 --> 00:04:45,679
Speaker 2: everything that we discussed, the strategic decisions that were made.

88
00:04:46,000 --> 00:04:49,000
Speaker 2: Also the ones that were still opened. It pulled together

89
00:04:49,040 --> 00:04:51,000
Speaker 2: all of the key themes and the changes that the

90
00:04:51,080 --> 00:04:54,480
Speaker 2: room agreed on. It then tabled for me the next steps.

91
00:04:54,839 --> 00:04:57,320
Speaker 2: It summarized who were the key owners, and then it

92
00:04:57,400 --> 00:05:01,960
Speaker 2: reorganized everything by topic and sort of the chronological order.

93
00:05:01,800 --> 00:05:02,240
Speaker 3: Of the day.

94
00:05:02,720 --> 00:05:05,920
Speaker 2: I then prompted it again to pull together an artifact

95
00:05:05,920 --> 00:05:08,920
Speaker 2: which I could use to share and cascade with the

96
00:05:09,200 --> 00:05:12,160
Speaker 2: team members that weren't in there, and then a full

97
00:05:12,279 --> 00:05:16,400
Speaker 2: email and common structure that I was allowed enabled me to.

98
00:05:15,880 --> 00:05:18,240
Speaker 3: Use that to cascade to the rest of the team

99
00:05:18,279 --> 00:05:18,560
Speaker 3: as well.

100
00:05:18,680 --> 00:05:21,320
Speaker 2: So it was pretty mind blowing and kind of went

101
00:05:21,839 --> 00:05:24,679
Speaker 2: a much larger step further than what you would typically

102
00:05:24,720 --> 00:05:25,560
Speaker 2: get with Zoom.

103
00:05:25,560 --> 00:05:28,560
Speaker 1: That's so interesting and just the value that you got

104
00:05:28,600 --> 00:05:34,520
Speaker 1: from taking that out of Zoom's AI companion and into Claude.

105
00:05:34,760 --> 00:05:37,920
Speaker 1: Can you tell me some of the things that perhaps

106
00:05:37,920 --> 00:05:41,240
Speaker 1: you were thinking about when you were prompting Claude to

107
00:05:41,400 --> 00:05:44,599
Speaker 1: get the best possible output from it, and whether you

108
00:05:44,640 --> 00:05:48,039
Speaker 1: know you used the thinking model or just on AUDO,

109
00:05:48,120 --> 00:05:49,120
Speaker 1: tell me a little bit about that.

110
00:05:49,279 --> 00:05:52,560
Speaker 2: Yeah, so I'd already used Claude to prepare, help me

111
00:05:52,600 --> 00:05:55,920
Speaker 2: prepare the agenda, help me prepare and consolidate some of

112
00:05:55,960 --> 00:06:00,039
Speaker 2: the pre context and materials, and then you know the

113
00:06:00,080 --> 00:06:03,240
Speaker 2: outputs in the objectives of what we had for the day.

114
00:06:03,279 --> 00:06:06,200
Speaker 2: So Claude already had the context of what I was

115
00:06:06,800 --> 00:06:11,120
Speaker 2: hoping to achieve for the entire workshop, and then having

116
00:06:11,440 --> 00:06:14,880
Speaker 2: the full transcript and conversation that was following that it

117
00:06:14,960 --> 00:06:17,960
Speaker 2: already had connected the dots between the conversation and what

118
00:06:18,279 --> 00:06:20,839
Speaker 2: the goals were. So it was then simple to be

119
00:06:20,920 --> 00:06:24,000
Speaker 2: able to prompt it in saying based on everything that

120
00:06:24,080 --> 00:06:26,839
Speaker 2: you knew of what we wanted to achieve the entire

121
00:06:27,160 --> 00:06:30,359
Speaker 2: span of the conversation. And then we were really conscious

122
00:06:30,400 --> 00:06:32,360
Speaker 2: while we were in the meeting, like no one needing

123
00:06:32,400 --> 00:06:36,400
Speaker 2: to take notes as you typically would. We would actively

124
00:06:37,000 --> 00:06:40,040
Speaker 2: talk to the AI and make sure that we were

125
00:06:40,040 --> 00:06:43,320
Speaker 2: clear on capturing actions that needed to be done or

126
00:06:43,360 --> 00:06:46,680
Speaker 2: owners or timeframes. So it did make the summarizing of

127
00:06:46,720 --> 00:06:50,720
Speaker 2: the full day far easier because it did capture everything

128
00:06:50,760 --> 00:06:51,640
Speaker 2: that was spoken.

129
00:06:51,360 --> 00:06:52,000
Speaker 3: Throughout the day.

130
00:06:52,120 --> 00:06:54,960
Speaker 1: I love the idea and something we do at Inventium

131
00:06:55,200 --> 00:06:58,600
Speaker 1: is that if we are gathering in person for an

132
00:06:58,600 --> 00:07:01,800
Speaker 1: off site or something like that, generally Granola is our

133
00:07:01,839 --> 00:07:04,560
Speaker 1: meeting transcription tool of choice and we will just pop

134
00:07:04,560 --> 00:07:07,240
Speaker 1: a phone with granola running in the middle of the

135
00:07:07,240 --> 00:07:10,240
Speaker 1: workshop room, and it just allows us all to be

136
00:07:10,280 --> 00:07:12,520
Speaker 1: so much more present because no one is needing to

137
00:07:12,560 --> 00:07:15,120
Speaker 1: take notes, no one is needing to capture things on

138
00:07:15,160 --> 00:07:18,920
Speaker 1: a whiteboard. And then because we've got that full transcript,

139
00:07:18,920 --> 00:07:20,680
Speaker 1: we can put it into any AI tool that we

140
00:07:20,720 --> 00:07:22,800
Speaker 1: want prompted in ways that are going to get the

141
00:07:22,840 --> 00:07:26,920
Speaker 1: best output. So I love that example, Joe. Now, something

142
00:07:26,960 --> 00:07:30,200
Speaker 1: that I would imagine your job as president of Elmo

143
00:07:30,360 --> 00:07:33,920
Speaker 1: involves a lot is negotiating. Can you tell me how

144
00:07:34,000 --> 00:07:36,960
Speaker 1: you've used AI to help with negotiations?

145
00:07:37,240 --> 00:07:39,960
Speaker 2: Yeah, I mean, we're also fortunate that we've deployed a

146
00:07:40,000 --> 00:07:43,320
Speaker 2: tool called Gong, which is also a pretty mind blowing

147
00:07:43,880 --> 00:07:47,520
Speaker 2: platform because what it does is it captures every single

148
00:07:47,800 --> 00:07:50,320
Speaker 2: interaction that we have with our customers, so every call,

149
00:07:50,400 --> 00:07:53,240
Speaker 2: every meeting, any piece of content that they've engaged with,

150
00:07:53,360 --> 00:07:56,320
Speaker 2: any presentation or even voice messages that may be left.

151
00:07:56,520 --> 00:07:59,520
Speaker 2: So it gives us a complete history of the relationship

152
00:07:59,560 --> 00:08:02,040
Speaker 2: with had with either a new customer that we're looking

153
00:08:02,080 --> 00:08:05,040
Speaker 2: to engage with or an existing customer as well. And

154
00:08:05,080 --> 00:08:07,600
Speaker 2: then I guess where it gets really powerful is when

155
00:08:07,600 --> 00:08:11,239
Speaker 2: you feed that history into AI. Before I walk into

156
00:08:11,280 --> 00:08:14,920
Speaker 2: a conversation or a negotiation. Because what I've been able

157
00:08:14,960 --> 00:08:17,680
Speaker 2: to do is extract all of the intelligence from any

158
00:08:17,720 --> 00:08:19,680
Speaker 2: of the pre meetings that have occurred with some of

159
00:08:19,680 --> 00:08:24,320
Speaker 2: our team. I can upload the detail of the partnership

160
00:08:24,320 --> 00:08:26,880
Speaker 2: contract that we're trying to work through. What I can

161
00:08:26,920 --> 00:08:29,800
Speaker 2: then do is prompt and explain kind of what my

162
00:08:29,840 --> 00:08:34,079
Speaker 2: commercial position is, what theirs may be, Prompt and understand

163
00:08:34,120 --> 00:08:36,400
Speaker 2: what is some of the likely objections that might come

164
00:08:36,440 --> 00:08:40,400
Speaker 2: out of the discussion, and then help define pretty much

165
00:08:40,440 --> 00:08:42,599
Speaker 2: a cheat sheet that I can keep hoping during my

166
00:08:42,679 --> 00:08:45,920
Speaker 2: laptop in the call, and effectively it's kind of mapped

167
00:08:45,920 --> 00:08:48,240
Speaker 2: out what the game theory of that negotiation might be.

168
00:08:48,720 --> 00:08:49,720
Speaker 3: I don't just rely.

169
00:08:49,600 --> 00:08:52,320
Speaker 2: On the intelligence though what we've got in Gong obviously,

170
00:08:52,720 --> 00:08:54,760
Speaker 2: make sure that I'm always speaking to the humans that

171
00:08:54,800 --> 00:08:58,080
Speaker 2: have engaged with customers or prospects as well, because it

172
00:08:58,120 --> 00:08:59,960
Speaker 2: is important to get that human overlay as well.

173
00:09:00,160 --> 00:09:02,800
Speaker 1: I'm wondering, Joe, is there an example of a meeting

174
00:09:02,800 --> 00:09:04,640
Speaker 1: you know that you've had in the last few weeks

175
00:09:04,679 --> 00:09:08,040
Speaker 1: that you know maybe has required some tough negotiation and

176
00:09:08,400 --> 00:09:11,760
Speaker 1: just some specific examples of how that has helped you.

177
00:09:11,800 --> 00:09:16,319
Speaker 1: Because it sounds incredibly powerful. I'm very aware of gong.

178
00:09:16,720 --> 00:09:19,160
Speaker 1: We don't personally use it an inventium, but yeah, a

179
00:09:19,240 --> 00:09:22,400
Speaker 1: super cool tool. Yes, I'd love to hear an example

180
00:09:22,440 --> 00:09:25,920
Speaker 1: maybe of where in this AI world do you approach

181
00:09:26,000 --> 00:09:28,319
Speaker 1: the negotiation differently and got to a better result than

182
00:09:28,559 --> 00:09:29,840
Speaker 1: you know three or four years ago.

183
00:09:30,080 --> 00:09:32,480
Speaker 2: Yeah, we had a partnership agreement that we were looking

184
00:09:32,520 --> 00:09:34,640
Speaker 2: to strike and this has only happened just over the

185
00:09:34,720 --> 00:09:37,480
Speaker 2: last couple of weeks. We had what their position was

186
00:09:37,520 --> 00:09:40,880
Speaker 2: on the commercial terms, what mine and the businesses were

187
00:09:41,360 --> 00:09:45,080
Speaker 2: both on pricing, on contract duration, and on some of

188
00:09:45,120 --> 00:09:49,080
Speaker 2: the sort of technical components. So it allowed me to

189
00:09:49,800 --> 00:09:52,840
Speaker 2: prompt it to say, well, where were the points of

190
00:09:52,880 --> 00:09:57,679
Speaker 2: contention and help empathize what the partner's position was, but

191
00:09:57,840 --> 00:10:00,640
Speaker 2: most importantly what ours was, and then what would be

192
00:10:00,720 --> 00:10:03,880
Speaker 2: some recommended solutions that we could come to terms on,

193
00:10:04,320 --> 00:10:06,840
Speaker 2: and most importantly, how to kind of build a talk

194
00:10:06,920 --> 00:10:09,360
Speaker 2: track and an empathy layer so that we could actually

195
00:10:09,440 --> 00:10:13,000
Speaker 2: reach a conclusion. And fortunately we've just had sign off

196
00:10:13,040 --> 00:10:15,640
Speaker 2: and we're moving forward with getting a contract stood up,

197
00:10:15,640 --> 00:10:16,800
Speaker 2: so it did actually work.

198
00:10:16,960 --> 00:10:20,440
Speaker 1: Yeah, awesome. I would imagine that that would also change

199
00:10:20,679 --> 00:10:24,240
Speaker 1: your kind of mental and emotional state going into a negotiation,

200
00:10:24,280 --> 00:10:26,719
Speaker 1: which can obviously be quite stressful situations.

201
00:10:26,960 --> 00:10:27,160
Speaker 3: Yeah.

202
00:10:27,200 --> 00:10:30,400
Speaker 2: Absolutely, I mean I think it allows me to feel

203
00:10:30,480 --> 00:10:34,760
Speaker 2: more confident knowing all of the variables, feeling really prepared,

204
00:10:35,120 --> 00:10:39,120
Speaker 2: knowing what's transpired before, and then knowing what the points

205
00:10:39,120 --> 00:10:41,760
Speaker 2: of contention are and then you know what option AB

206
00:10:41,920 --> 00:10:43,600
Speaker 2: or C might be on how to respond to that.

207
00:10:43,840 --> 00:10:45,679
Speaker 1: Now, something that I know a lot of people use

208
00:10:45,720 --> 00:10:49,800
Speaker 1: AI for is role playing conversations. And I would love

209
00:10:50,120 --> 00:10:53,120
Speaker 1: to know for you as the president of an organization,

210
00:10:53,440 --> 00:10:56,160
Speaker 1: and I feel like people think of, well, you're running

211
00:10:56,200 --> 00:10:59,000
Speaker 1: an organization, like surely you're confident, you don't need to

212
00:10:59,080 --> 00:11:01,800
Speaker 1: like you know, PA for meetings, like you know you've

213
00:11:01,800 --> 00:11:03,800
Speaker 1: got it. But I know that you do use AI

214
00:11:03,920 --> 00:11:05,800
Speaker 1: in this way, Jo, And i'd love to hear like

215
00:11:05,880 --> 00:11:09,520
Speaker 1: a couple of examples of where and how you've used

216
00:11:09,559 --> 00:11:12,120
Speaker 1: AI to prepare for tough conversations.

217
00:11:12,640 --> 00:11:14,480
Speaker 3: Yeah. So it's a good question, Amantha.

218
00:11:14,520 --> 00:11:18,800
Speaker 2: I recently had a pretty difficult performance conversation with a

219
00:11:18,840 --> 00:11:21,839
Speaker 2: team member that I needed to have and wanted to

220
00:11:21,880 --> 00:11:23,840
Speaker 2: make sure that I was really well prepared and that

221
00:11:23,880 --> 00:11:26,720
Speaker 2: I'd thought through how I was going to frame that

222
00:11:26,760 --> 00:11:31,319
Speaker 2: feedback the specific examples, and I quite often use the

223
00:11:31,440 --> 00:11:34,400
Speaker 2: drive into work as time to think. And I have

224
00:11:34,559 --> 00:11:38,800
Speaker 2: connected Clawed Voice directly through so I can actually speak

225
00:11:38,800 --> 00:11:40,880
Speaker 2: to it in my car. So I was able to

226
00:11:40,920 --> 00:11:44,880
Speaker 2: just in a conversational way explain the situation, explain the

227
00:11:44,920 --> 00:11:48,200
Speaker 2: person that I was dealing with, the specific challenge that

228
00:11:48,240 --> 00:11:51,720
Speaker 2: we were having to face into the feedback that I

229
00:11:51,920 --> 00:11:54,520
Speaker 2: needed to provide. And it was amazing what it came

230
00:11:54,559 --> 00:11:58,040
Speaker 2: back with in terms of giving me a pretty cohesive talk.

231
00:11:57,960 --> 00:11:59,400
Speaker 3: Track and how to approach it.

232
00:12:00,040 --> 00:12:03,640
Speaker 2: And I could then refine it based on further prompts

233
00:12:03,640 --> 00:12:06,640
Speaker 2: to make sure that it was engaging, that we got

234
00:12:06,679 --> 00:12:09,920
Speaker 2: to a clear outcome, and that it was a mutually

235
00:12:09,920 --> 00:12:11,040
Speaker 2: beneficial conversation.

236
00:12:11,360 --> 00:12:15,000
Speaker 1: And how are you prompting the AI in that kind

237
00:12:15,040 --> 00:12:18,760
Speaker 1: of an instance, Like how much or how little information

238
00:12:18,960 --> 00:12:22,679
Speaker 1: are you feeding at the start of that conversation if

239
00:12:22,679 --> 00:12:24,079
Speaker 1: you like, in the car, I think.

240
00:12:23,880 --> 00:12:24,640
Speaker 3: The more the better.

241
00:12:24,800 --> 00:12:28,400
Speaker 2: So it's explaining who the person is on the other side,

242
00:12:28,520 --> 00:12:32,520
Speaker 2: what role they're in their history, either working with us

243
00:12:32,559 --> 00:12:34,959
Speaker 2: or it might be a customer for example. You know,

244
00:12:35,040 --> 00:12:37,360
Speaker 2: what are the key challenges or the opportunities that we're

245
00:12:37,360 --> 00:12:40,120
Speaker 2: needing to work through being as specific as you possibly can,

246
00:12:40,679 --> 00:12:43,440
Speaker 2: so that the AI has full context of the environment

247
00:12:43,760 --> 00:12:45,280
Speaker 2: of what we're chatting about.

248
00:12:45,360 --> 00:12:50,079
Speaker 1: Now. Obviously, as a leader, like you're getting so much

249
00:12:50,400 --> 00:12:54,240
Speaker 1: input from so many sources, you're getting I would imagine

250
00:12:54,400 --> 00:12:58,600
Speaker 1: weekly reports from different teams, different functions. Can you tell

251
00:12:58,640 --> 00:13:03,040
Speaker 1: me how you're using AI to synthesize all that information

252
00:13:03,200 --> 00:13:05,080
Speaker 1: that must be incoming in your world.

253
00:13:05,240 --> 00:13:07,760
Speaker 2: That's probably been the biggest game changer for me, and

254
00:13:07,800 --> 00:13:10,720
Speaker 2: particularly in the last few months. I'm sure many of

255
00:13:10,720 --> 00:13:13,400
Speaker 2: your listeners and most leaders would experience this. You find

256
00:13:13,400 --> 00:13:18,040
Speaker 2: yourself getting different reports in different formats from five or

257
00:13:18,080 --> 00:13:22,840
Speaker 2: six different functions, and each presenting their own complex data sets.

258
00:13:22,920 --> 00:13:25,960
Speaker 2: They might have their own perspective, and in many cases

259
00:13:26,040 --> 00:13:28,760
Speaker 2: there's not really any single person that's holding the full picture.

260
00:13:29,679 --> 00:13:32,920
Speaker 2: So what I've experienced particularly lately is that now I

261
00:13:32,960 --> 00:13:35,680
Speaker 2: can ingest the range of different information I can pull.

262
00:13:35,760 --> 00:13:38,840
Speaker 2: Sales data in might be marketing metrics, it might be

263
00:13:39,400 --> 00:13:44,640
Speaker 2: customer insights, it might be performance cross border with different currencies.

264
00:13:44,720 --> 00:13:47,520
Speaker 2: It might be product and engineering updates as well. So

265
00:13:47,559 --> 00:13:51,199
Speaker 2: I can ingest a lot of different formats and data sets,

266
00:13:51,240 --> 00:13:53,480
Speaker 2: and what I'm finding is that the AI can now

267
00:13:53,559 --> 00:13:57,480
Speaker 2: produce really cohesive and consistent and useful reporting, which I

268
00:13:57,520 --> 00:14:00,640
Speaker 2: think previously would have taken hours and hours and hours

269
00:14:00,640 --> 00:14:03,600
Speaker 2: to consolidate. And what I'm seeing is that the quality

270
00:14:03,640 --> 00:14:07,000
Speaker 2: is incredibly high because it's spotting patterns across the data

271
00:14:07,440 --> 00:14:09,160
Speaker 2: and things that I may have missed if I've just

272
00:14:09,200 --> 00:14:10,680
Speaker 2: read each report individually.

273
00:14:11,760 --> 00:14:14,360
Speaker 1: And how is that set up behind the scenes? Is

274
00:14:14,360 --> 00:14:18,079
Speaker 1: that an automated process now is that you're manually uploading

275
00:14:18,120 --> 00:14:20,840
Speaker 1: documents to Glean or to claude, Like, what does that

276
00:14:20,880 --> 00:14:21,960
Speaker 1: look like behind the scenes.

277
00:14:22,200 --> 00:14:24,840
Speaker 2: So initially it had been using claw to be able

278
00:14:24,880 --> 00:14:27,240
Speaker 2: to pull that together in prompting it, in defining a

279
00:14:27,240 --> 00:14:29,880
Speaker 2: well formatted set of materials that are structured with clear

280
00:14:29,920 --> 00:14:30,800
Speaker 2: recommendations for.

281
00:14:30,760 --> 00:14:31,800
Speaker 3: The week ahead.

282
00:14:32,320 --> 00:14:34,400
Speaker 2: And now we're in the process of being able to

283
00:14:34,400 --> 00:14:38,480
Speaker 2: automate that utilizing Glean with an agent, which means that

284
00:14:38,520 --> 00:14:40,840
Speaker 2: on a weekly basis, I've got all of the inputs

285
00:14:40,880 --> 00:14:43,840
Speaker 2: of the reports coming from different teams and functions, and

286
00:14:43,960 --> 00:14:47,080
Speaker 2: then producing a format and an output that I've landed

287
00:14:47,120 --> 00:14:51,760
Speaker 2: on as being the format that's relevant, that provides cohesion

288
00:14:51,760 --> 00:14:55,120
Speaker 2: across the metrics, but then more importantly, recommendations and insights

289
00:14:55,160 --> 00:14:56,200
Speaker 2: for me to act on that.

290
00:14:56,240 --> 00:15:00,480
Speaker 1: Sounds absolutely amazing and such a timesaver as well, to know,

291
00:15:01,320 --> 00:15:03,960
Speaker 1: Joe clevisely like a large part of a leader's role

292
00:15:04,280 --> 00:15:07,480
Speaker 1: is strategic thinking. And you know, I think in the

293
00:15:07,520 --> 00:15:11,880
Speaker 1: AOL conversation we hear so much around all the productivity

294
00:15:12,080 --> 00:15:15,360
Speaker 1: benefits and the time savings, but I feel like people

295
00:15:15,400 --> 00:15:19,080
Speaker 1: talk less around how it is augmenting the quality of

296
00:15:19,160 --> 00:15:22,320
Speaker 1: their thinking. Like I'd love to know, Joe, you know

297
00:15:22,360 --> 00:15:26,080
Speaker 1: some other ways that you use AI to stress test

298
00:15:26,120 --> 00:15:29,560
Speaker 1: your thinking, to bounce ideas around, you know, anything that's

299
00:15:29,600 --> 00:15:31,720
Speaker 1: improving the quality of your thinking. How are you using

300
00:15:31,760 --> 00:15:32,360
Speaker 1: it in that way?

301
00:15:32,560 --> 00:15:33,320
Speaker 3: Yeah? Pretty much.

302
00:15:33,320 --> 00:15:38,200
Speaker 2: Any input that I'm getting where my instinct to respond

303
00:15:38,240 --> 00:15:40,600
Speaker 2: to it may not be as quick and I need

304
00:15:40,640 --> 00:15:42,800
Speaker 2: some time to think about it. So it might be

305
00:15:42,880 --> 00:15:45,880
Speaker 2: an email, it might be a Slack message, it might

306
00:15:45,960 --> 00:15:48,320
Speaker 2: be you know, just the thought that's come to me

307
00:15:48,400 --> 00:15:52,000
Speaker 2: after a meeting. I'm very open and willing to just

308
00:15:52,600 --> 00:15:55,840
Speaker 2: feed that into a claud or whatever model that I'm using,

309
00:15:56,400 --> 00:15:59,840
Speaker 2: and then prompted to help me evaluate what the issue

310
00:15:59,880 --> 00:16:02,440
Speaker 2: with is, what the challenges are, how am I consider

311
00:16:02,440 --> 00:16:04,600
Speaker 2: it and give me two or three options about how

312
00:16:04,640 --> 00:16:07,840
Speaker 2: to move forward with it? So it's increasingly becoming my

313
00:16:08,000 --> 00:16:11,080
Speaker 2: system in pretty much everything that I'm doing, which is

314
00:16:11,080 --> 00:16:13,480
Speaker 2: allowing me to be far more productive and more effective

315
00:16:13,480 --> 00:16:15,880
Speaker 2: in the moment for what that issue is that's coming

316
00:16:15,960 --> 00:16:16,360
Speaker 2: my way.

317
00:16:17,040 --> 00:16:20,440
Speaker 1: Amazing, Jo, it has been so great having you on.

318
00:16:20,800 --> 00:16:22,520
Speaker 1: I feel like it's not every day you get to

319
00:16:22,920 --> 00:16:25,400
Speaker 1: speak to the head of a company just really transparently

320
00:16:25,440 --> 00:16:28,680
Speaker 1: around how they are using AI and how it's really transformed,

321
00:16:29,080 --> 00:16:31,600
Speaker 1: like really a lot of what you do in your

322
00:16:31,840 --> 00:16:34,520
Speaker 1: role as president of Elmo. So thank you so much

323
00:16:34,560 --> 00:16:37,480
Speaker 1: for sharing and for being so practical with all those

324
00:16:37,520 --> 00:16:38,880
Speaker 1: examples that you have given.

325
00:16:39,080 --> 00:16:40,840
Speaker 3: Thanks Amantha, it's been great talking with you.

326
00:16:41,960 --> 00:16:45,160
Speaker 1: If you want to explore how Elmo is building AI

327
00:16:45,400 --> 00:16:49,000
Speaker 1: into how Australian organizations manage and develop their people, head

328
00:16:49,000 --> 00:16:52,640
Speaker 1: to Elmosoftware dot com dot au and of course to

329
00:16:52,720 --> 00:16:55,640
Speaker 1: follow How I Work wherever you listen to your podcasts

330
00:16:55,880 --> 00:16:59,000
Speaker 1: so you don't miss what's coming up next. If you

331
00:16:59,200 --> 00:17:02,040
Speaker 1: like today's show, make sure you hit follow on your

332
00:17:02,080 --> 00:17:05,840
Speaker 1: podcast app to be alerted when new episodes drop. How

333
00:17:05,920 --> 00:17:08,280
Speaker 1: I Work was recorded on the traditional land of the

334
00:17:08,320 --> 00:17:10,400
Speaker 1: Warrangery people, part of the Coulan nation.