1 00:00:05,960 --> 00:00:07,680 Speaker 1: Welcome to Fear and Greed Q and A, where we 2 00:00:07,760 --> 00:00:11,600 Speaker 1: ask and answer questions about business, investing, economics, politics and more. 3 00:00:11,640 --> 00:00:12,400 Speaker 2: I'm suan Alma. 4 00:00:12,760 --> 00:00:16,360 Speaker 1: Australia has a real productivity challenge and AI might just 5 00:00:16,440 --> 00:00:19,840 Speaker 1: be the answer to lifting output. But while AI capability 6 00:00:19,960 --> 00:00:23,479 Speaker 1: is advancing quickly, many businesses are stuck at the final 7 00:00:23,560 --> 00:00:27,840 Speaker 1: point of implementation, fully embedding the technology so it becomes 8 00:00:27,880 --> 00:00:31,160 Speaker 1: part of daily workflows. It's one of the topics being 9 00:00:31,200 --> 00:00:35,360 Speaker 1: explored at today's Agent Force World Tour Sydney. Fear and 10 00:00:35,400 --> 00:00:38,600 Speaker 1: Greed is partnering with Salesforce for this event. Frank Filman 11 00:00:38,800 --> 00:00:43,520 Speaker 1: is executive vice president and General manager of Salesforce a Zed. Frank, 12 00:00:43,640 --> 00:00:44,120 Speaker 1: Welcome to. 13 00:00:44,080 --> 00:00:44,600 Speaker 2: Fear and Greed. 14 00:00:44,880 --> 00:00:46,520 Speaker 3: Thank you so much, John, great to be here. 15 00:00:46,880 --> 00:00:48,160 Speaker 2: Let's go big picture first. 16 00:00:48,240 --> 00:00:50,960 Speaker 1: How can AI help Australia's productivity dilemma? 17 00:00:51,520 --> 00:00:53,760 Speaker 3: Well, if you kind of zoom out, we've got a 18 00:00:53,800 --> 00:00:55,960 Speaker 3: productivity challenge. But if you look at the full picture, 19 00:00:56,360 --> 00:01:00,560 Speaker 3: there's more competition. Global companies are both out piece Australia, 20 00:01:00,600 --> 00:01:04,080 Speaker 3: but there's also more global competitors coming in locally. We've 21 00:01:04,120 --> 00:01:07,959 Speaker 3: also got more local competition, especially as industries begin to converge, 22 00:01:08,360 --> 00:01:12,119 Speaker 3: which just means it's a less protected environment. So that's 23 00:01:12,120 --> 00:01:14,400 Speaker 3: sort of the backdrop. And then you've got the people equations, 24 00:01:14,400 --> 00:01:18,000 Speaker 3: so you've got customers with even higher expectations, which is 25 00:01:18,040 --> 00:01:21,520 Speaker 3: no surprise. You and I have personal AI on our phone, 26 00:01:21,640 --> 00:01:25,400 Speaker 3: so whether that's like a restaurant reservation or travel, we're 27 00:01:25,440 --> 00:01:28,520 Speaker 3: getting used to a better experience. What's interesting is now 28 00:01:28,520 --> 00:01:31,399 Speaker 3: we're seeing this show up from the employee side, not 29 00:01:31,520 --> 00:01:36,119 Speaker 3: terribly surprising because they're also consumers, but they're expecting much more. 30 00:01:36,240 --> 00:01:38,000 Speaker 3: And so like in a tight labor market where you 31 00:01:38,040 --> 00:01:40,399 Speaker 3: want to get and retain the best people, they're expecting 32 00:01:41,120 --> 00:01:43,600 Speaker 3: modern tools, which makes it a bit harder. And then 33 00:01:43,640 --> 00:01:46,240 Speaker 3: when I talk to local CEOs, what they're demanding is 34 00:01:46,600 --> 00:01:50,160 Speaker 3: speed of transformation impact, like that's going to move the 35 00:01:50,160 --> 00:01:54,080 Speaker 3: P and L forward, so less experimentation and more output, 36 00:01:54,640 --> 00:01:56,520 Speaker 3: but also bringing in the people along, making sure they're 37 00:01:56,520 --> 00:01:58,960 Speaker 3: getting trained they know how to use the tool. I 38 00:01:58,960 --> 00:02:01,440 Speaker 3: think that's all vital to make sure that companies can 39 00:02:01,520 --> 00:02:04,920 Speaker 3: keep up with the demands on them internally and externally. 40 00:02:05,600 --> 00:02:08,800 Speaker 1: Okay, so we'll come down to kind of the micro 41 00:02:08,960 --> 00:02:11,040 Speaker 1: level in the moment, what businesses should be doing but 42 00:02:11,160 --> 00:02:15,079 Speaker 1: just staying at that bigger picture, what areas will AI 43 00:02:15,520 --> 00:02:18,640 Speaker 1: make the most difference when it comes to productivity. 44 00:02:19,360 --> 00:02:21,720 Speaker 3: If you if you kind of look at the macro 45 00:02:21,800 --> 00:02:24,359 Speaker 3: challenge here, what is a business trying to do. They're 46 00:02:24,360 --> 00:02:26,799 Speaker 3: trying to move the top line or move the bottom line. 47 00:02:27,240 --> 00:02:30,760 Speaker 3: Bottom line is productivity and efficiency. Top line is growth 48 00:02:30,760 --> 00:02:34,000 Speaker 3: in customer intimacy. I think if you look globally, Australia 49 00:02:34,040 --> 00:02:36,560 Speaker 3: has over index on productivity and efficiency, which is good 50 00:02:36,880 --> 00:02:39,760 Speaker 3: because that cost justifies the investment. The reality is we 51 00:02:39,800 --> 00:02:43,280 Speaker 3: can find use cases with like ANZI bank or zero 52 00:02:43,840 --> 00:02:47,880 Speaker 3: with one New Zealand Telcom where they're finding ways to 53 00:02:47,880 --> 00:02:51,320 Speaker 3: actually move the needle with customers and they're driving efficiency 54 00:02:51,560 --> 00:02:55,520 Speaker 3: and scalability inside the organization, but they're having top line benefit. 55 00:02:55,639 --> 00:02:58,680 Speaker 3: And so we want to find that balance harmony where 56 00:02:58,680 --> 00:03:00,840 Speaker 3: there's use cases that unlocked all of it. And there's 57 00:03:00,880 --> 00:03:03,320 Speaker 3: this sort of established myth that I that I hear 58 00:03:03,360 --> 00:03:06,320 Speaker 3: in the marketplace, which is let's do efficiency and productivity 59 00:03:06,320 --> 00:03:08,840 Speaker 3: first and then we'll move up. But with more competition 60 00:03:08,919 --> 00:03:12,320 Speaker 3: coming in, if you sequence that out and you only 61 00:03:12,400 --> 00:03:16,160 Speaker 3: handle productivity and then go back to manage the top line, 62 00:03:16,200 --> 00:03:18,359 Speaker 3: it's going to be really difficult to be more competitive 63 00:03:18,360 --> 00:03:21,320 Speaker 3: in the marketplace. And so yeah, I think there's there's 64 00:03:21,320 --> 00:03:24,360 Speaker 3: a much broader story beyond just efficiency and driving more 65 00:03:24,360 --> 00:03:25,640 Speaker 3: productivity at the worker level. 66 00:03:26,320 --> 00:03:27,920 Speaker 2: Okay, so let's bring it to the micro. 67 00:03:28,880 --> 00:03:32,639 Speaker 1: What is it that's causing so many problems for companies 68 00:03:32,960 --> 00:03:35,320 Speaker 1: in that last mile when it comes to AI. 69 00:03:36,120 --> 00:03:38,120 Speaker 3: It's hard, it's hard. I think there's really good news. 70 00:03:38,200 --> 00:03:41,440 Speaker 3: Good news is companies are leaning in on AGENTICKI to say, 71 00:03:41,480 --> 00:03:43,720 Speaker 3: how can I use this to move the needle forward, 72 00:03:43,720 --> 00:03:46,440 Speaker 3: whether that's growing the business or driving more efficiency. The 73 00:03:46,560 --> 00:03:49,720 Speaker 3: challenge is ninety five percent of AI pilots don't work. 74 00:03:50,520 --> 00:03:52,000 Speaker 3: And so you think about all the time and the 75 00:03:52,040 --> 00:03:54,920 Speaker 3: resource and the energy and the dollars required to power 76 00:03:54,960 --> 00:03:57,800 Speaker 3: all these pilots, and only five percent make it into production. 77 00:03:58,080 --> 00:04:00,480 Speaker 3: There's a lot of wasted resources, which is drive some 78 00:04:00,560 --> 00:04:03,120 Speaker 3: of this scrutiny. So the pilots are everywhere and they're 79 00:04:03,120 --> 00:04:07,800 Speaker 3: showing promise, but they don't go into enterprise production. And 80 00:04:07,840 --> 00:04:09,760 Speaker 3: there's a lot of reasons why. But the big answers 81 00:04:09,840 --> 00:04:13,119 Speaker 3: is totally disconnected from their system, right, They're on their own. 82 00:04:13,240 --> 00:04:18,040 Speaker 3: They're disconnected from the data, the customer, the product, and 83 00:04:18,120 --> 00:04:19,839 Speaker 3: so like, if you're a customer on the other end, 84 00:04:19,920 --> 00:04:22,960 Speaker 3: what this means is what you're seeing show up is 85 00:04:22,960 --> 00:04:27,080 Speaker 3: an accurate, relevant, trusted, or compliant, which is which is 86 00:04:27,160 --> 00:04:30,200 Speaker 3: risky for an organization, especially because it's your brand and 87 00:04:30,240 --> 00:04:32,560 Speaker 3: you have to protect it. And everyone's trying to solve 88 00:04:32,560 --> 00:04:34,440 Speaker 3: this problem, right, Everyone wants to solve it. And there's 89 00:04:34,480 --> 00:04:34,960 Speaker 3: some new tech. 90 00:04:35,040 --> 00:04:35,240 Speaker 1: Right. 91 00:04:35,240 --> 00:04:37,960 Speaker 3: We have UI prototypes that can be spun up on 92 00:04:37,960 --> 00:04:40,400 Speaker 3: a weekend. They look really great. We've got vibe coding 93 00:04:41,000 --> 00:04:44,120 Speaker 3: and AI models, but it's like the iceberg underneath is 94 00:04:44,200 --> 00:04:46,760 Speaker 3: all the hard work, right, So part of it's like 95 00:04:46,760 --> 00:04:49,240 Speaker 3: the data, the metadata, the connectors. The other part is 96 00:04:49,240 --> 00:04:52,840 Speaker 3: the logic, the process, the workflow. And so we hear 97 00:04:52,920 --> 00:04:55,680 Speaker 3: like people love the vibe code, but nobody wants to 98 00:04:55,760 --> 00:04:56,840 Speaker 3: vibe operate. 99 00:04:57,800 --> 00:05:00,600 Speaker 2: To just explain the vibe, kay, just explain what that is. 100 00:05:01,120 --> 00:05:03,720 Speaker 3: Vibe coding and the ability to have a conversational interface 101 00:05:03,880 --> 00:05:07,839 Speaker 3: to build applications, which is great in a pilot environment. 102 00:05:07,880 --> 00:05:10,800 Speaker 3: The challenge is when you actually try to plummet in 103 00:05:10,800 --> 00:05:14,600 Speaker 3: in a trusted environment that passes regulatory and compliance scrutiny, 104 00:05:14,800 --> 00:05:17,640 Speaker 3: where you can actually scale it across the enterprise. Everything 105 00:05:17,680 --> 00:05:20,040 Speaker 3: under the water line isn't there. So it's a really 106 00:05:20,080 --> 00:05:22,680 Speaker 3: slick YUI but it doesn't have the depth to be 107 00:05:22,720 --> 00:05:25,640 Speaker 3: able to scale. So it looks great, but it stays there. 108 00:05:26,080 --> 00:05:28,080 Speaker 1: So how does business handle this what you've just been 109 00:05:28,080 --> 00:05:30,919 Speaker 1: discussing in the last two or three minutes, even you know, 110 00:05:30,960 --> 00:05:33,520 Speaker 1: what are the early steps they need to do or 111 00:05:33,680 --> 00:05:35,160 Speaker 1: to progress where they're going. 112 00:05:35,920 --> 00:05:40,000 Speaker 3: So, like if when I speak to leaders and they say, hey, 113 00:05:40,040 --> 00:05:43,400 Speaker 3: you know, actually with a Sean like pick an industry, 114 00:05:43,839 --> 00:05:44,600 Speaker 3: any industry, you. 115 00:05:44,520 --> 00:05:46,960 Speaker 2: Want me manufacturing manufacturing. 116 00:05:46,960 --> 00:05:49,800 Speaker 3: So you're a CEO of a manufacturing firm and you're 117 00:05:49,800 --> 00:05:53,680 Speaker 3: building something to benefit your customers, and you're the CEO. 118 00:05:54,000 --> 00:05:58,760 Speaker 3: Do you want your team building tighter manufacturing supply chain 119 00:05:58,800 --> 00:06:01,560 Speaker 3: and capabilities to benefit customer or do you want them 120 00:06:01,600 --> 00:06:05,280 Speaker 3: to be building apps and CRM? And so the challenge 121 00:06:05,279 --> 00:06:07,719 Speaker 3: is if you look at like the capability the data 122 00:06:07,800 --> 00:06:10,599 Speaker 3: link provides, they're incredibly valuable. They have like all of 123 00:06:10,600 --> 00:06:13,320 Speaker 3: this rich intelligence about your customer. Sometimes it's under lock 124 00:06:13,360 --> 00:06:15,400 Speaker 3: and key and it doesn't touch the front office. 125 00:06:15,480 --> 00:06:16,240 Speaker 2: That's a challenge. 126 00:06:16,320 --> 00:06:20,520 Speaker 3: The models provide incredible capability, they look really great, and 127 00:06:20,560 --> 00:06:22,080 Speaker 3: they can provide a lot of value, but they're not 128 00:06:22,120 --> 00:06:25,600 Speaker 3: plumbed in and so there is better together formula. When 129 00:06:25,600 --> 00:06:27,359 Speaker 3: you've got the power of a data lake, the power 130 00:06:27,360 --> 00:06:28,920 Speaker 3: of a model, and you can plug it into an 131 00:06:28,920 --> 00:06:32,240 Speaker 3: open arc architecture like ours, then you get the benefit 132 00:06:32,400 --> 00:06:35,280 Speaker 3: of all of it together and then your customer benefits 133 00:06:35,320 --> 00:06:36,880 Speaker 3: instead of bringing risk to your customer. 134 00:06:37,520 --> 00:06:39,280 Speaker 1: Okay, so hey, he's doing it, will Frank give me 135 00:06:39,320 --> 00:06:40,440 Speaker 1: a couple of examples. 136 00:06:41,240 --> 00:06:43,240 Speaker 3: So if you look globally and then I'll come right 137 00:06:43,279 --> 00:06:45,679 Speaker 3: down to Australia, New Zealand. So globally we've were seeing 138 00:06:46,400 --> 00:06:48,680 Speaker 3: an increase quarter on quarter of seventy percent of our 139 00:06:48,720 --> 00:06:52,400 Speaker 3: customers going from pilot to production with Agent Force, which 140 00:06:52,400 --> 00:06:55,679 Speaker 3: is super exciting. So all in we've got over thirty 141 00:06:55,680 --> 00:06:59,160 Speaker 3: three hundred customers globally live in production getting value from 142 00:06:59,200 --> 00:07:01,880 Speaker 3: Agent Force. If you zoom down here to Ashraw you 143 00:07:01,920 --> 00:07:04,680 Speaker 3: in New Zealand, it's over one hundred. But it's really 144 00:07:04,680 --> 00:07:07,320 Speaker 3: interesting when you see who's doing it. It's like big 145 00:07:07,360 --> 00:07:11,000 Speaker 3: and small companies across every industry across our region. I'll 146 00:07:11,000 --> 00:07:14,600 Speaker 3: give you a couple So ANZ Bank they're saving their 147 00:07:14,640 --> 00:07:18,520 Speaker 3: commercial bankers a month per year per banker, so like 148 00:07:19,040 --> 00:07:21,840 Speaker 3: less time doing admin more time with customers and everyone 149 00:07:21,880 --> 00:07:26,920 Speaker 3: in the equation wins zero. Yeah, that's an incredible company 150 00:07:26,960 --> 00:07:31,000 Speaker 3: with big global ambitions. Their customer experience team is averaging 151 00:07:31,040 --> 00:07:35,360 Speaker 3: an increase of twenty percent higher speed to answer and 152 00:07:35,440 --> 00:07:38,600 Speaker 3: twenty percent higher and customer happiness. So like they're seeing 153 00:07:38,600 --> 00:07:42,280 Speaker 3: efficiency inside their business, but they have happy customers and 154 00:07:42,280 --> 00:07:45,680 Speaker 3: happy employees along the way, which is great. And one 155 00:07:45,680 --> 00:07:47,920 Speaker 3: of my favorites is the grout guy like Brad, such 156 00:07:47,960 --> 00:07:51,640 Speaker 3: an innovator. We're seeing his time to quote dropping from 157 00:07:51,720 --> 00:07:55,000 Speaker 3: days to minutes and the conversion on those opportunities go 158 00:07:55,040 --> 00:07:58,680 Speaker 3: from about a third to over fifty percent. We also 159 00:07:58,720 --> 00:08:00,480 Speaker 3: like to talk about what we're doing in turn. We've 160 00:08:00,520 --> 00:08:03,680 Speaker 3: got dozens of agents live across our business. We've got 161 00:08:03,680 --> 00:08:07,480 Speaker 3: an external agent for customers called help. We've got million 162 00:08:07,560 --> 00:08:09,800 Speaker 3: and a half per year using this tool, Like no 163 00:08:09,880 --> 00:08:12,360 Speaker 3: one if they want a password reset, No one wants 164 00:08:12,400 --> 00:08:14,280 Speaker 3: to talk to a person. They just want to get 165 00:08:14,320 --> 00:08:17,480 Speaker 3: a password reset, move on. So that's a simple use case, 166 00:08:17,520 --> 00:08:21,120 Speaker 3: but it's got scaled success. And then we have an 167 00:08:21,120 --> 00:08:23,480 Speaker 3: employee agent called slack Bot. I use it every day 168 00:08:23,520 --> 00:08:26,400 Speaker 3: on my phone before every customer meeting, and it scrapes 169 00:08:26,480 --> 00:08:29,840 Speaker 3: Google Drive my email and Salesforce and it gives me 170 00:08:29,880 --> 00:08:32,280 Speaker 3: the contextual intelligence to save me time. If I don't 171 00:08:32,520 --> 00:08:34,480 Speaker 3: get a briefing before a customer, I can walk in 172 00:08:34,520 --> 00:08:37,640 Speaker 3: and know more about them and know what questions to ask. 173 00:08:38,280 --> 00:08:40,959 Speaker 2: Frank, thanks for talking to Fear and Greed. Thanks so much. 174 00:08:40,960 --> 00:08:43,080 Speaker 3: I'm so excited to be joining you. Thanks for hosting me. 175 00:08:43,480 --> 00:08:46,640 Speaker 1: That was Frank Filman, executive vice president and general manager 176 00:08:46,720 --> 00:08:49,160 Speaker 1: of Salesforce, A and Zen and who'll be appearing today 177 00:08:49,200 --> 00:08:52,400 Speaker 1: at Agent Force World Tour Sydney. Fearing Greed is proud 178 00:08:52,440 --> 00:08:54,920 Speaker 1: to partner with Salesforce for this event at the ICC 179 00:08:55,360 --> 00:08:56,680 Speaker 1: and over in the next couple of weeks, but we 180 00:08:56,679 --> 00:08:59,520 Speaker 1: will bring you a series of interviews exploring how a 181 00:08:59,640 --> 00:09:05,280 Speaker 1: gent ai is transforming ossie businesses. Head to www dot 182 00:09:05,360 --> 00:09:09,760 Speaker 1: salesforce dot com, slash au slash news for more information. 183 00:09:10,080 --> 00:09:12,280 Speaker 2: I'm Seanielmer and this is here and Greed Q and 184 00:09:12,320 --> 00:09:12,360 Speaker 2: A