1 00:00:00,080 --> 00:00:03,280 Speaker 1: Fun fact, we spent twenty two million hours on hold 2 00:00:03,400 --> 00:00:05,240 Speaker 1: last year. Now the good news is that's down from 3 00:00:05,240 --> 00:00:07,560 Speaker 1: twenty four million the year before. Banks and retailers the 4 00:00:07,600 --> 00:00:09,760 Speaker 1: best performers, resolving issues in two and a half days 5 00:00:09,760 --> 00:00:13,040 Speaker 1: on average. Government five point eight days, Manufacturer six point five. 6 00:00:13,160 --> 00:00:16,239 Speaker 1: Kate Tulp is the Service Now's New Zealand country manager 7 00:00:16,280 --> 00:00:19,279 Speaker 1: and as well as Kate morning, good morning, jeez that 8 00:00:19,440 --> 00:00:22,040 Speaker 1: that report you put out. It's a big, thick, juicy number. 9 00:00:22,239 --> 00:00:23,920 Speaker 1: I mean, for goodness sake, how many pages you want 10 00:00:23,920 --> 00:00:25,360 Speaker 1: to put on that one? This morning? Greets me at 11 00:00:25,360 --> 00:00:26,880 Speaker 1: three o'clock in the morning that what the hell's going 12 00:00:26,920 --> 00:00:28,120 Speaker 1: on here? Well? 13 00:00:28,120 --> 00:00:29,840 Speaker 2: What better way to wake up than good news that 14 00:00:29,960 --> 00:00:32,800 Speaker 2: wait times are down? Also good news is that Kiwi's 15 00:00:32,840 --> 00:00:35,160 Speaker 2: trust and technology is up. This is the fourth year 16 00:00:35,159 --> 00:00:37,519 Speaker 2: of our report that we produce or the research and 17 00:00:37,840 --> 00:00:41,360 Speaker 2: technology trust is on the way up. Unfortunately, expectations of 18 00:00:41,720 --> 00:00:43,560 Speaker 2: Kiwi customers is also on the way up as well, 19 00:00:43,600 --> 00:00:45,400 Speaker 2: so it's a bit of a race for Kiwi businesses 20 00:00:45,440 --> 00:00:46,000 Speaker 2: to keep up. 21 00:00:46,159 --> 00:00:48,640 Speaker 1: Have you dealt with tulip? Do you know what tulip is? 22 00:00:50,840 --> 00:00:52,159 Speaker 2: No? As in the flower? 23 00:00:52,520 --> 00:00:55,960 Speaker 1: Well no, it's an AI generated voice I think vector 24 00:00:56,040 --> 00:00:58,760 Speaker 1: run it and they go, hey, I'm tulip, I can 25 00:00:58,880 --> 00:01:01,680 Speaker 1: understand you, And I'm thinking, I don't know what I 26 00:01:01,680 --> 00:01:04,800 Speaker 1: wanted to whether I want to kill it or whether 27 00:01:04,840 --> 00:01:06,759 Speaker 1: it's apt shit. I mean, it seems to me we're 28 00:01:06,800 --> 00:01:08,840 Speaker 1: a mile off in terms of AI have actually been 29 00:01:08,959 --> 00:01:10,360 Speaker 1: useful or am I just impatient? 30 00:01:11,360 --> 00:01:14,520 Speaker 2: Well, far bait from me to say that you're impatient, like, 31 00:01:14,600 --> 00:01:16,480 Speaker 2: But what I would say is that if you've got 32 00:01:16,560 --> 00:01:19,600 Speaker 2: AI that's masquerading as AI, and I don't know anything 33 00:01:19,600 --> 00:01:21,400 Speaker 2: about you lips, so I can't discuss on that one. 34 00:01:21,400 --> 00:01:24,280 Speaker 2: But if you've got AI that's not clever and doesn't 35 00:01:24,280 --> 00:01:26,560 Speaker 2: feel like a great experience for you, then that probably 36 00:01:26,600 --> 00:01:29,640 Speaker 2: means that you've got AI that is just for show. 37 00:01:29,720 --> 00:01:31,480 Speaker 2: It's not being connected up to all of the data 38 00:01:31,520 --> 00:01:34,120 Speaker 2: and the systems and the information that an organization holds 39 00:01:34,160 --> 00:01:36,120 Speaker 2: about you, because it's not really able to help you 40 00:01:36,200 --> 00:01:38,480 Speaker 2: unless it's got the real information about you as a 41 00:01:38,520 --> 00:01:39,319 Speaker 2: customer exactly. 42 00:01:39,319 --> 00:01:42,440 Speaker 1: That's asking for good answer because they don't on shore 43 00:01:42,560 --> 00:01:44,720 Speaker 1: or offshore. Where are we at with the onshore offshore 44 00:01:44,720 --> 00:01:46,600 Speaker 1: thing in terms of called centers and help and wait 45 00:01:46,640 --> 00:01:48,400 Speaker 1: times and stuff, Well. 46 00:01:48,480 --> 00:01:50,680 Speaker 2: I don't think that that's necessarily the issue anymore about 47 00:01:50,680 --> 00:01:53,520 Speaker 2: where this thing is located, because where we're seeing organizations 48 00:01:53,560 --> 00:01:56,160 Speaker 2: get massive leaps and customer service, it's actually because they're 49 00:01:56,240 --> 00:01:59,880 Speaker 2: using AI and that technology investment is definitely starting to 50 00:01:59,880 --> 00:02:01,480 Speaker 2: pay off for them. You know, you talked about the 51 00:02:01,520 --> 00:02:05,160 Speaker 2: banks having a pretty good turn on the response times 52 00:02:05,160 --> 00:02:07,320 Speaker 2: and customer weights, et cetera, and that's because they've been 53 00:02:07,320 --> 00:02:10,040 Speaker 2: able to invest over the period of time. We're definitely 54 00:02:10,040 --> 00:02:13,120 Speaker 2: seeing customers say that they want to engage with AI though, 55 00:02:13,200 --> 00:02:15,320 Speaker 2: So seventy two percent of New Zealanders say that they 56 00:02:15,320 --> 00:02:17,639 Speaker 2: are willing to embrace AI and they actually want to 57 00:02:17,720 --> 00:02:20,160 Speaker 2: use self service. Fest that's a tipping point. There is 58 00:02:20,200 --> 00:02:22,640 Speaker 2: no going back on that, So that is the expectation. 59 00:02:23,080 --> 00:02:25,400 Speaker 2: They think that fifty five percent of kiwis say that 60 00:02:25,440 --> 00:02:27,720 Speaker 2: they'll get a benefit out of twenty four x seven 61 00:02:27,919 --> 00:02:31,119 Speaker 2: from AI and that they'll get an improved feeding efficiency. 62 00:02:31,240 --> 00:02:33,680 Speaker 2: So kiwis are saying, Hey, I'm up for this and hually, 63 00:02:33,680 --> 00:02:36,120 Speaker 2: i'd like you to use this technology. They're also saying 64 00:02:36,520 --> 00:02:38,639 Speaker 2: that wait times are a massive deal breaker for them. 65 00:02:38,760 --> 00:02:41,760 Speaker 2: So twenty percent of people will make a complaint, twenty 66 00:02:41,760 --> 00:02:44,280 Speaker 2: percent of people will request a refund and half of 67 00:02:44,320 --> 00:02:46,440 Speaker 2: them will leave you after a bad customer experience. So 68 00:02:46,440 --> 00:02:47,359 Speaker 2: we've got to get this right. 69 00:02:47,520 --> 00:02:49,480 Speaker 1: Well done, Kate nice and Sight appreciate it very much. 70 00:02:49,560 --> 00:02:51,359 Speaker 1: Kate Top she's such a nice person. They will most 71 00:02:51,400 --> 00:02:54,960 Speaker 1: forgiven for them producing such a large report. Jack Dorsey 72 00:02:55,080 --> 00:02:57,520 Speaker 1: when she says AI, Jack Dawsy, what was the what's 73 00:02:57,560 --> 00:03:00,400 Speaker 1: his thing? The other day it's the art who block 74 00:03:00,639 --> 00:03:03,119 Speaker 1: was to read a very good piece in the Australian media. 75 00:03:03,160 --> 00:03:06,040 Speaker 1: They said this real AI is real in terms of jobs, 76 00:03:06,080 --> 00:03:08,760 Speaker 1: those sort of jobs you talk about call centers. The 77 00:03:08,800 --> 00:03:12,120 Speaker 1: AI piece quote unquote is very real because there's an 78 00:03:12,160 --> 00:03:14,640 Speaker 1: argument around saying, are they sort of AI washing at 79 00:03:14,639 --> 00:03:16,760 Speaker 1: the moment, just pretending it's going to lose it? It's real. 80 00:03:17,320 --> 00:03:19,000 Speaker 1: There are going to be there's going to be a 81 00:03:19,240 --> 00:03:22,760 Speaker 1: swathe of jobs cleaned out because of AI and that 82 00:03:22,960 --> 00:03:24,799 Speaker 1: what could it be argued that jobs to do with 83 00:03:24,880 --> 00:03:26,799 Speaker 1: social media weren't real jobs in the first one, I 84 00:03:26,840 --> 00:03:29,800 Speaker 1: could argue that for more from the mic Asking Breakfast 85 00:03:29,960 --> 00:03:33,280 Speaker 1: listen live to news talks it'd be from six am weekdays, 86 00:03:33,520 --> 00:03:35,560 Speaker 1: or follow the podcast on iHeartRadio.