1 00:00:03,440 --> 00:00:07,170 Sean Aylmer: Welcome to the Fear and Greed Daily Interview. I'm Sean Aylmer. Empty 2 00:00:07,170 --> 00:00:09,420 Sean Aylmer: shells and long delays for products seem to be a 3 00:00:09,420 --> 00:00:12,840 Sean Aylmer: part of everyday life. Now, staff shortages due to COVID, 4 00:00:13,030 --> 00:00:16,610 Sean Aylmer: shipping bottlenecks and a shortage of components like microchips are 5 00:00:16,610 --> 00:00:19,989 Sean Aylmer: all part of a global supply chain nightmare. I wanted 6 00:00:19,989 --> 00:00:22,590 Sean Aylmer: to look today whether the problems can be solved by 7 00:00:22,590 --> 00:00:25,950 Sean Aylmer: automation and what it means for those whose jobs are 8 00:00:25,950 --> 00:00:29,420 Sean Aylmer: automated. Greg Eyre is the vice president, Australia & New Zealand 9 00:00:29,450 --> 00:00:33,190 Sean Aylmer: of Blue Prism, a UK- listed company that specializes in 10 00:00:33,190 --> 00:00:35,140 Sean Aylmer: automation. Greg, welcome to Fear and Greed. 11 00:00:35,500 --> 00:00:35,890 Greg Eyre: Thank you, Sean. 12 00:00:36,550 --> 00:00:40,560 Sean Aylmer: Okay. Straight up. Does automation have the potential to resolve 13 00:00:40,560 --> 00:00:43,599 Sean Aylmer: at least some of the supply chain issues that we're facing 14 00:00:43,600 --> 00:00:44,160 Sean Aylmer: at the moment? 15 00:00:44,560 --> 00:00:48,199 Greg Eyre: The short answer is yes, Sean. The reality is that 16 00:00:48,200 --> 00:00:50,540 Greg Eyre: the big issue with supply chain right now is the 17 00:00:50,540 --> 00:00:53,000 Greg Eyre: fact that there are people who aren't able to make 18 00:00:53,000 --> 00:00:55,730 Greg Eyre: it to work. And as a consequence, there's a shortage 19 00:00:55,730 --> 00:00:59,040 Greg Eyre: of drivers. There's a shortage of delivery people. There's a shortage 20 00:00:59,040 --> 00:01:03,330 Greg Eyre: of people in the actual logistics elements like in a 21 00:01:03,330 --> 00:01:07,140 Greg Eyre: factory and assembly plant warehouses and dispatchers. The challenge, I 22 00:01:07,140 --> 00:01:11,530 Greg Eyre: guess, is in other areas where due to COVID, there's 23 00:01:11,530 --> 00:01:15,600 Greg Eyre: fundamental back office staff that can be repurposed. And we 24 00:01:15,600 --> 00:01:18,940 Greg Eyre: are seeing that already in industry. And how automation can 25 00:01:18,940 --> 00:01:22,149 Greg Eyre: help is by automating the effort of some of those in the 26 00:01:22,200 --> 00:01:25,840 Greg Eyre: individuals, so that they can be repurposed into filling gaps 27 00:01:25,840 --> 00:01:29,449 Greg Eyre: in the supply chain. There are varieties of different ways 28 00:01:29,450 --> 00:01:31,490 Greg Eyre: that it can be used to help that. For example, 29 00:01:31,490 --> 00:01:35,070 Greg Eyre: we've got a fairly simple and small to some extent 30 00:01:35,520 --> 00:01:39,920 Greg Eyre: manufacturing plant, and they've used our technology to automate their 31 00:01:39,920 --> 00:01:43,459 Greg Eyre: accounts payable division and the accounting area so that the people 32 00:01:43,459 --> 00:01:45,380 Greg Eyre: can support other areas of the business. 33 00:01:45,390 --> 00:01:49,760 Sean Aylmer: Okay. So, when we talk about robotic process automation, we 34 00:01:49,760 --> 00:01:53,620 Sean Aylmer: are often talking about that sort of back office paperwork for one of 35 00:01:53,620 --> 00:01:56,690 Sean Aylmer: a better term, at least in the short- term, that's 36 00:01:56,690 --> 00:01:58,710 Sean Aylmer: where the gains can be made most easily? 37 00:01:59,100 --> 00:02:03,540 Greg Eyre: Yeah. So, robotic process automation is a phrase for a 38 00:02:03,540 --> 00:02:07,090 Greg Eyre: new area of software technology. So we aren't talking about 39 00:02:07,090 --> 00:02:09,680 Greg Eyre: physical robots like you would have in an assembly plant, 40 00:02:09,680 --> 00:02:13,079 Greg Eyre: for example. This is a piece of software that takes 41 00:02:13,080 --> 00:02:17,889 Greg Eyre: over the repetitive, mundane type activities that one would typically 42 00:02:17,889 --> 00:02:21,270 Greg Eyre: do in the back office and allows those individuals to 43 00:02:21,270 --> 00:02:25,360 Greg Eyre: focus on more challenging activities, more rewarding activities, and in 44 00:02:25,360 --> 00:02:29,270 Greg Eyre: doing so support the customer experience, the business better and 45 00:02:29,270 --> 00:02:32,460 Greg Eyre: therefore really just freeing them up to do better things. 46 00:02:32,460 --> 00:02:35,560 Greg Eyre: And that's something that's really coming to the fore with 47 00:02:35,580 --> 00:02:38,299 Greg Eyre: COVID. People are looking for things that are more challenged 48 00:02:38,400 --> 00:02:42,080 Greg Eyre: to do, not necessarily in the logistics space. But pre- 49 00:02:42,080 --> 00:02:46,080 Greg Eyre: COVID, for example, our technology was able take aircraft billings 50 00:02:46,460 --> 00:02:51,299 Greg Eyre: from being say 60% capacities on average to 70% capacity. 51 00:02:51,669 --> 00:02:54,900 Greg Eyre: And simply by automating some of the tasks that a 52 00:02:54,900 --> 00:02:57,889 Greg Eyre: human took too long to do. These airlines are able 53 00:02:57,889 --> 00:03:01,130 Greg Eyre: to increase their billing capacity from that 60 to 70% mark 54 00:03:01,130 --> 00:03:05,510 Greg Eyre: through to 80, 90% mark. And again, allowing logistics to flow a 55 00:03:05,510 --> 00:03:08,110 Greg Eyre: lot quicker, a lot better simply because robots can work 56 00:03:08,110 --> 00:03:10,740 Greg Eyre: a lot faster than humans. And the humans really were 57 00:03:10,740 --> 00:03:14,010 Greg Eyre: challenged with meeting that timeframe. As a consequence, you have 58 00:03:14,010 --> 00:03:16,380 Greg Eyre: people in the logistics space quite happy with the outcome 59 00:03:16,380 --> 00:03:19,310 Greg Eyre: of their effort and they're able to focus more servicing their customers. 60 00:03:19,780 --> 00:03:22,160 Sean Aylmer: So, can you give me an exact example of what 61 00:03:22,160 --> 00:03:25,880 Sean Aylmer: you're talking about? Be it the airplane example or something else? 62 00:03:26,220 --> 00:03:28,510 Greg Eyre: Yeah. Look, there's a number of different areas but in 63 00:03:28,510 --> 00:03:32,320 Greg Eyre: particular, for example with COVID, I think the best and 64 00:03:32,320 --> 00:03:35,050 Greg Eyre: one we are most proud of at Blue Prism is 65 00:03:35,520 --> 00:03:39,720 Greg Eyre: when the Australian economy was struggling. It was absolutely struggling. 66 00:03:39,720 --> 00:03:43,280 Greg Eyre: We needed the government and all of the regulatory authorities 67 00:03:43,280 --> 00:03:46,580 Greg Eyre: needed to pump money back into the economy to help 68 00:03:46,580 --> 00:03:51,190 Greg Eyre: businesses survive, re- engineering loans, giving people mortgage, holidays, et 69 00:03:51,190 --> 00:03:53,810 Greg Eyre: cetera. And the banks just couldn't do that in time. 70 00:03:53,990 --> 00:03:56,630 Greg Eyre: They really couldn't. And as a consequence of that, they 71 00:03:56,630 --> 00:03:59,640 Greg Eyre: used our technology and this is made quite public as 72 00:03:59,640 --> 00:04:04,410 Greg Eyre: well as other intelligent automation- type software to very quickly 73 00:04:04,410 --> 00:04:07,540 Greg Eyre: and very rapidly automate in a few days, quite literally 74 00:04:07,540 --> 00:04:12,580 Greg Eyre: a few days, the bronze processing, the mortgage repurposing for 75 00:04:12,580 --> 00:04:16,270 Greg Eyre: businesses and for individuals, and in doing so pumping in 76 00:04:16,320 --> 00:04:20,420 Greg Eyre: tens of millions of dollars into the Australian economy so 77 00:04:20,420 --> 00:04:23,130 Greg Eyre: that businesses and people could make it through the COVID 78 00:04:23,130 --> 00:04:25,810 Greg Eyre: period. And in support of really our major banks, the 79 00:04:25,810 --> 00:04:28,370 Greg Eyre: reverse is where there are other banks out there who 80 00:04:28,370 --> 00:04:31,560 Greg Eyre: didn't have this capability available to them, and they lost 81 00:04:31,560 --> 00:04:33,930 Greg Eyre: market share because one of the things that happened was 82 00:04:34,350 --> 00:04:37,730 Greg Eyre: the market started shifting very heavily to real estate and 83 00:04:37,730 --> 00:04:41,010 Greg Eyre: people going for a change in lifestyle. And the banks 84 00:04:41,010 --> 00:04:43,550 Greg Eyre: that had this capability available were able to respond to 85 00:04:43,550 --> 00:04:45,909 Greg Eyre: their customers a lot quicker. They didn't get rid of 86 00:04:45,910 --> 00:04:48,400 Greg Eyre: any staff because they still needed them. But at the 87 00:04:48,400 --> 00:04:50,580 Greg Eyre: end of the day, they were able to respond quicker. 88 00:04:50,580 --> 00:04:52,830 Greg Eyre: And those who didn't have this technology, and again, this 89 00:04:52,830 --> 00:04:55,940 Greg Eyre: is published quite widely, actually, the ones who didn't have 90 00:04:56,000 --> 00:04:59,620 Greg Eyre: intelligent automation, ones that did not have robotic process automation 91 00:04:59,620 --> 00:05:02,650 Greg Eyre: like the major banks do suffered. Their mortgage books suffered. 92 00:05:03,040 --> 00:05:05,109 Greg Eyre: And that's really something we're proud of. 93 00:05:05,620 --> 00:05:12,510 Sean Aylmer: Stay with me, Greg. We'll be back in a minute. Yes. This 94 00:05:12,510 --> 00:05:15,789 Sean Aylmer: morning is Greg Eyre, vice president Australia & New Zealand of 95 00:05:15,790 --> 00:05:19,510 Sean Aylmer: Blue Prism. Say, okay, that great discussion last year. And 96 00:05:19,510 --> 00:05:21,900 Sean Aylmer: it sort of still is flying through with a big banks 97 00:05:21,900 --> 00:05:24,830 Sean Aylmer: and the smaller lenders about the time it takes to 98 00:05:24,830 --> 00:05:27,859 Sean Aylmer: get a yes or no from a mortgage. That's the 99 00:05:27,860 --> 00:05:29,460 Sean Aylmer: sort of thing you'd mean, one of the big banks, 100 00:05:29,460 --> 00:05:31,450 Sean Aylmer: for example, it's up to 52 days or something at 101 00:05:31,450 --> 00:05:32,029 Sean Aylmer: one point. 102 00:05:32,250 --> 00:05:32,300 Greg Eyre: Correct. 103 00:05:32,300 --> 00:05:35,020 Sean Aylmer: And that bank lost market share directly as a result. 104 00:05:35,210 --> 00:05:35,239 Greg Eyre: Yes. 105 00:05:35,320 --> 00:05:36,780 Sean Aylmer: That's the sort of thing we're talking about? 106 00:05:37,150 --> 00:05:39,820 Greg Eyre: Correct. And now if you look at the flow of 107 00:05:39,900 --> 00:05:42,840 Greg Eyre: those funds into the economy, and then how that then 108 00:05:42,850 --> 00:05:46,430 Greg Eyre: supports the supply chain and the logistics issue, it allows 109 00:05:46,430 --> 00:05:49,240 Greg Eyre: the economy to flow better. Much, much better in trying 110 00:05:49,240 --> 00:05:49,610 Greg Eyre: times like this. 111 00:05:50,410 --> 00:05:54,060 Sean Aylmer: The mortgage example is a great one because I see exactly what you're talking 112 00:05:54,060 --> 00:05:58,530 Sean Aylmer: about but they are big organizations. What about ordinary businesses 113 00:05:58,910 --> 00:06:05,760 Sean Aylmer: where abounds can automation fit into their businesses and improve outcomes? 114 00:06:06,190 --> 00:06:08,820 Greg Eyre: I'm really glad you asked that question. And the truth 115 00:06:08,820 --> 00:06:12,919 Greg Eyre: is, it stands with the recognition that times have changed. 116 00:06:12,920 --> 00:06:15,880 Greg Eyre: We live in what I describe as a " TikTok world." 117 00:06:15,880 --> 00:06:19,930 Greg Eyre: Now people only have 10 to 15 seconds of space. The world's 118 00:06:19,930 --> 00:06:22,910 Greg Eyre: changing and for you to respond to your customers quicker, 119 00:06:23,440 --> 00:06:27,909 Greg Eyre: digitization through robotic process automation and intelligent automation is one 120 00:06:27,910 --> 00:06:30,599 Greg Eyre: way to do that. So it doesn't necessarily matter how 121 00:06:30,600 --> 00:06:33,620 Greg Eyre: big your organization is. It just starts with one process 122 00:06:33,620 --> 00:06:37,010 Greg Eyre: and one robot. In the case I mentioned earlier, again, 123 00:06:37,010 --> 00:06:39,419 Greg Eyre: this was in response to COVID, you wouldn't think of 124 00:06:39,420 --> 00:06:42,210 Greg Eyre: it, a suit manufacturing company, a very famous one of 125 00:06:42,210 --> 00:06:45,060 Greg Eyre: that, they struggled. They had a factory and they were 126 00:06:45,060 --> 00:06:47,650 Greg Eyre: struggling and all they needed was one robot because they 127 00:06:47,650 --> 00:06:50,589 Greg Eyre: could see that this was their way out and supporting 128 00:06:50,589 --> 00:06:54,020 Greg Eyre: them through COVID, they've come through COVID now, but they're still 129 00:06:54,020 --> 00:06:56,150 Greg Eyre: using that robot. And the next step is to say, " 130 00:06:56,150 --> 00:06:58,060 Greg Eyre: Well, look, how else can we do this?" It's not 131 00:06:58,060 --> 00:07:00,310 Greg Eyre: going to get rid of people's jobs. I can assure 132 00:07:00,310 --> 00:07:04,619 Greg Eyre: you. A software robot can't lift pallets in empty containers, 133 00:07:04,620 --> 00:07:06,200 Greg Eyre: which is where we are having a lot of supply 134 00:07:06,200 --> 00:07:07,790 Greg Eyre: chain issues. You need people to do that. 135 00:07:08,040 --> 00:07:11,010 Sean Aylmer: Yeah. I mean, I think that's the interesting point. For 136 00:07:11,010 --> 00:07:15,090 Sean Aylmer: years people have been worried that automation would lose jobs 137 00:07:15,090 --> 00:07:18,810 Sean Aylmer: but in actual fact automation creates jobs because it expands the economy. 138 00:07:19,280 --> 00:07:23,350 Greg Eyre: Exactly. And when this technology was first introduced into the 139 00:07:23,350 --> 00:07:26,240 Greg Eyre: market and became quite prevalent in Australia, approximately six years 140 00:07:26,480 --> 00:07:29,930 Greg Eyre: ago, that was the business case for doing this. That's 141 00:07:29,930 --> 00:07:33,620 Greg Eyre: no longer the case. The reality is automation just replaces 142 00:07:33,720 --> 00:07:38,070 Greg Eyre: effort. It doesn't replace people. It replaces efforts so they 143 00:07:38,070 --> 00:07:40,370 Greg Eyre: can go off and do other things, live better lives, 144 00:07:40,370 --> 00:07:44,120 Greg Eyre: change their current circumstances to something better. And we certainly 145 00:07:44,120 --> 00:07:44,660 Greg Eyre: encourage that. 146 00:07:44,660 --> 00:07:48,540 Sean Aylmer: Okay. So at the moment, the global supply chain issues 147 00:07:48,630 --> 00:07:50,960 Sean Aylmer: are probably transitory to some extent. And a lot of 148 00:07:50,960 --> 00:07:53,869 Sean Aylmer: things that you are talking about, the mortgages was an example where 149 00:07:53,870 --> 00:07:56,340 Sean Aylmer: you could have a solution for within two or three 150 00:07:56,340 --> 00:07:59,610 Sean Aylmer: days, but mostly for businesses, this is something they should 151 00:07:59,610 --> 00:08:01,850 Sean Aylmer: be thinking about over the next year, 2, 3, 5 152 00:08:01,850 --> 00:08:05,310 Sean Aylmer: years, well, and truly beyond COVID. Where do you think 153 00:08:05,430 --> 00:08:07,910 Sean Aylmer: we get to eventually with this? 154 00:08:08,370 --> 00:08:11,760 Greg Eyre: Interesting, it's something that in my opinion, we move into 155 00:08:11,760 --> 00:08:15,830 Greg Eyre: this domain of artificial intelligence. Again, another scary word for 156 00:08:15,830 --> 00:08:18,160 Greg Eyre: people and it's nothing to be afraid of. We use 157 00:08:18,160 --> 00:08:21,610 Greg Eyre: artificial intelligence in our daily lives today without even realizing 158 00:08:21,950 --> 00:08:27,050 Greg Eyre: it. If you're using, for example, Google Maps or Waze you're 159 00:08:27,050 --> 00:08:30,690 Greg Eyre: using an element of artificial intelligence. What I think is 160 00:08:30,690 --> 00:08:33,460 Greg Eyre: going to happen is that as more and more of 161 00:08:33,460 --> 00:08:38,050 Greg Eyre: these capabilities become available to the market and we adopt 162 00:08:38,130 --> 00:08:42,090 Greg Eyre: and actually adapt our lifestyles to that without realizing it, 163 00:08:42,160 --> 00:08:45,160 Greg Eyre: we'll be using this type of technology more and more 164 00:08:45,179 --> 00:08:47,740 Greg Eyre: making our lives a lot easier, a lot more convenient 165 00:08:48,110 --> 00:08:50,910 Greg Eyre: and giving us better and more challenging things to do. 166 00:08:51,350 --> 00:08:54,410 Greg Eyre: Every time you use Google Maps, if you think about 167 00:08:54,410 --> 00:08:57,980 Greg Eyre: it, you're actually using an element of artificial intelligence. 168 00:08:58,480 --> 00:08:59,970 Sean Aylmer: Explain that to me. Why am I doing that? 169 00:09:00,440 --> 00:09:04,079 Greg Eyre: Quite simply, if you were looking for the directions within 170 00:09:04,429 --> 00:09:07,080 Greg Eyre: a software navigator type tool, like Google Maps. And you said, "I want to 171 00:09:07,520 --> 00:09:10,809 Greg Eyre: go from A to B," the technology can quickly say, "Hey, 172 00:09:10,809 --> 00:09:13,910 Greg Eyre: look, here's where all the traffic is the busiest. Here's 173 00:09:13,910 --> 00:09:16,460 Greg Eyre: where all the tolls are." And give you options on 174 00:09:16,460 --> 00:09:19,190 Greg Eyre: which is the quickest and most convenient route to take 175 00:09:19,590 --> 00:09:22,130 Greg Eyre: as well as what time would be best to leave 176 00:09:22,440 --> 00:09:24,500 Greg Eyre: if you've got a timeframe to get to. And then 177 00:09:24,500 --> 00:09:26,640 Greg Eyre: you pick the option and off you go. And as 178 00:09:26,640 --> 00:09:29,790 Greg Eyre: you're driving, something that really came out a little while 179 00:09:29,790 --> 00:09:32,660 Greg Eyre: ago but it's still relatively new, it can adapt your 180 00:09:32,660 --> 00:09:34,830 Greg Eyre: route and give you options as to say, " Look, there 181 00:09:34,830 --> 00:09:36,870 Greg Eyre: was a traffic jam here. So why don't you take 182 00:09:36,880 --> 00:09:39,929 Greg Eyre: the left and take an alternative route to get to your destination?" 183 00:09:40,290 --> 00:09:42,760 Greg Eyre: It's very simple. But think about it. We are using this in 184 00:09:43,080 --> 00:09:46,120 Greg Eyre: every day and we don't realize it. 185 00:09:46,679 --> 00:09:49,420 Sean Aylmer: How do you think this will play out eventually in the mobility 186 00:09:49,420 --> 00:09:53,699 Sean Aylmer: of the workforce? So, these things that you are talking about, they 187 00:09:53,700 --> 00:09:57,569 Sean Aylmer: won't replace people but the people may have to do different 188 00:09:57,660 --> 00:10:00,980 Sean Aylmer: jobs. Do you think that creates more mobile workforce? I 189 00:10:00,980 --> 00:10:03,970 Sean Aylmer: mean, we've had the great resignation in the US. I 190 00:10:03,970 --> 00:10:06,809 Sean Aylmer: think Josh Frydenberg in Australia called the Great Reshuffle and 191 00:10:06,809 --> 00:10:09,260 Sean Aylmer: the Reserve Bank basically said, " We don't believe it." But 192 00:10:09,330 --> 00:10:12,510 Sean Aylmer: what about mobility of workers? Do you think AI actually 193 00:10:12,530 --> 00:10:14,860 Sean Aylmer: provides for greater mobility of workers or how does that 194 00:10:14,860 --> 00:10:15,280 Sean Aylmer: play out? 195 00:10:16,000 --> 00:10:20,020 Greg Eyre: In short, yes. Confess I'm struggling to see, because in 196 00:10:20,020 --> 00:10:23,140 Greg Eyre: my opinion and from my observations, it's more a lifestyle 197 00:10:23,140 --> 00:10:27,770 Greg Eyre: choice as opposed to mobility per se. And so people 198 00:10:27,770 --> 00:10:30,440 Greg Eyre: moving from A to B and finding other means of 199 00:10:30,450 --> 00:10:35,170 Greg Eyre: communicating far more convenient. It allows effort to some degree 200 00:10:35,200 --> 00:10:40,479 Greg Eyre: to be automated by a machine or software. But at 201 00:10:40,480 --> 00:10:43,309 Greg Eyre: this point I'm struggling to see or envision what that 202 00:10:43,309 --> 00:10:45,300 Greg Eyre: could look like in the future and how it can 203 00:10:45,300 --> 00:10:48,760 Greg Eyre: support how we change. In my opinion, there's still nothing 204 00:10:48,760 --> 00:10:52,210 Greg Eyre: that beats a face- to- face discussion. Meeting someone in 205 00:10:52,210 --> 00:10:55,840 Greg Eyre: person with over coffee and just socializing with your colleagues. 206 00:10:55,840 --> 00:10:58,590 Greg Eyre: It's very, very different. And as humans that's something we 207 00:10:58,590 --> 00:10:59,910 Greg Eyre: can't replace with software. 208 00:11:00,360 --> 00:11:02,840 Sean Aylmer: Yeah. Greg, thank you for talking to Fear and Greed. 209 00:11:03,520 --> 00:11:04,390 Greg Eyre: Thank you for having me. 210 00:11:04,730 --> 00:11:07,610 Sean Aylmer: That was Greg Eyre, vice president Australia & New Zealand of 211 00:11:07,610 --> 00:11:10,449 Sean Aylmer: Blue Prism. This is a Fear and Greed Daily Interview. 212 00:11:10,450 --> 00:11:12,440 Sean Aylmer: Join me every morning for the full Fear and Greed 213 00:11:12,440 --> 00:11:14,770 Sean Aylmer: Podcast with all the business news you need to know. 214 00:11:15,059 --> 00:11:16,809 Sean Aylmer: I'm Sean Aylmer. Enjoy your day.