WEBVTT - GenAI for SMPs (Part 1) 0:00:07.620 --> 0:00:11.880 Hello, my name is Gillian Bowen and this is Small Firm, 0:00:11.880 --> 0:00:12.990 Big Impact. 0:00:20.120 --> 0:00:23.119 If we take that down to what we do as 0:00:23.120 --> 0:00:27.590 chartered accountants and the way that we help organisations, I 0:00:27.590 --> 0:00:30.139 think the biggest impact that we need to be thinking 0:00:30.140 --> 0:00:34.880 about is all around productivity. Humans with AI will replace 0:00:34.880 --> 0:00:38.510 humans without it. Now, that's not going to happen tomorrow. Um, 0:00:38.510 --> 0:00:41.270 but I think that's probably the next horizon that we're 0:00:41.270 --> 0:00:44.239 looking at. And so I think it's extremely important to 0:00:44.240 --> 0:00:45.230 get across this. 0:00:49.680 --> 0:00:52.650 It's the podcast giving Chartered Accountants the up to date 0:00:52.650 --> 0:00:56.220 information they need to do their jobs. Each episode, I 0:00:56.220 --> 0:01:00.540 share resources, tools and expert advice provided by Chartered Accountants 0:01:00.540 --> 0:01:03.870 Australia and New Zealand and a range of people across 0:01:03.870 --> 0:01:07.470 our profession. So get following the pod in your favourite 0:01:07.470 --> 0:01:19.280 podcast app. Let's start a conversation. Today, we have Kayur Patel CA. 0:01:19.310 --> 0:01:25.369 The topic, Artificial intelligence, but specifically tailored to accounting; accounting right 0:01:25.370 --> 0:01:29.690 now in 2024. But also what's just around the corner. 0:01:29.720 --> 0:01:32.930 Kayur welcome to Small Firm, Big Impact. 0:01:33.350 --> 0:01:34.340 Thanks for having me Gill. 0:01:34.970 --> 0:01:37.910 So regular listeners of the podcast will know I like 0:01:37.910 --> 0:01:40.610 to get my guests to take me through their expertise 0:01:40.610 --> 0:01:44.000 and why it's relevant here. Why you're on the podcast 0:01:44.000 --> 0:01:45.589 with us. Take it away. 0:01:46.100 --> 0:01:48.740 Yeah. Thanks, Gill. Um, so I've been in this space 0:01:48.740 --> 0:01:50.840 for quite a while, and I've been very lucky with 0:01:50.840 --> 0:01:53.690 my career to have roles that have allowed me to 0:01:53.690 --> 0:01:56.690 explore the cutting edge of technology. Um, so for a 0:01:56.690 --> 0:01:59.660 day job, I spend my time, um, immersed in the 0:01:59.660 --> 0:02:03.290 AI space at PwC and helping ourselves and our clients 0:02:03.290 --> 0:02:05.840 get across AI and what it means for their business. Um, 0:02:06.140 --> 0:02:08.600 I also lecture in this space at at the University 0:02:08.600 --> 0:02:11.180 of Auckland as well. Um, and that's really cool because 0:02:11.180 --> 0:02:13.220 I get to start thinking around how this is going 0:02:13.220 --> 0:02:18.260 to impact us as a society, our industry and in our future. Um, 0:02:18.260 --> 0:02:21.980 and also as, as I am relatively involved in the, 0:02:22.010 --> 0:02:25.340 in the CA space on some local governance committees. And 0:02:25.340 --> 0:02:29.929 so I get to have conversations with CAs around what 0:02:29.930 --> 0:02:32.540 in the AI space is really of interest, what the 0:02:32.540 --> 0:02:36.440 concerns are, why people are excited and how this could 0:02:36.710 --> 0:02:39.230 impact them. So it's something I'm immersed in a lot of, 0:02:39.230 --> 0:02:40.790 and it's something I really enjoy doing. 0:02:41.090 --> 0:02:43.190 Mmm, okay, this is great. I feel like we've got 0:02:43.190 --> 0:02:46.190 the right person for the job here, which is fantastic. Now, 0:02:46.190 --> 0:02:50.030 we have talked about AI on this podcast before. You're right, it's 0:02:50.030 --> 0:02:52.669 a topic that our members come to us regularly about, 0:02:52.669 --> 0:02:55.190 and it was an episode back in season three, and 0:02:55.190 --> 0:02:56.810 I just kind of sat down with a member in 0:02:56.810 --> 0:03:00.830 a smaller practice and asked how she was using generative AI. Right? 0:03:00.830 --> 0:03:03.380 So what she discovered it could do what she thought 0:03:03.380 --> 0:03:05.510 might be helpful to practices going forward. It was all 0:03:05.510 --> 0:03:08.990 kind of new-ish at this stage and was evolving. And 0:03:08.990 --> 0:03:10.730 I mean, gosh, it's still evolving, isn't it? We're all 0:03:10.730 --> 0:03:14.090 just kind of testing the waters and it's moving so quickly. 0:03:14.090 --> 0:03:18.260 So I thought it was time to again discuss this topic. 0:03:18.260 --> 0:03:21.680 And it is something that is impacting accountants and we 0:03:21.680 --> 0:03:24.890 can't ignore. And it is complex and an important one, 0:03:24.889 --> 0:03:28.220 as you would know. So Kayur and I have decided 0:03:28.220 --> 0:03:30.290 that it would benefit our members and our listeners if 0:03:30.290 --> 0:03:32.810 we did this in two parts. Right. So this episode 0:03:32.810 --> 0:03:35.570 is part one, and the episode straight after this will 0:03:35.570 --> 0:03:39.020 be part two. Part one we're going to focus on 0:03:39.020 --> 0:03:42.470 what I'm thinking is the what, the why, how, when, 0:03:42.470 --> 0:03:45.680 what if sort of discussion, the landscape in 2024 and 0:03:45.680 --> 0:03:50.420 then part two will focus on accounting specific use cases 0:03:50.420 --> 0:03:55.010 relevant to right now. So make sure you're ready for 0:03:55.280 --> 0:03:57.200 the episode that'll follow this, so part two as well. 0:03:57.200 --> 0:04:03.380 But let's get cracking. So, Kayur what does AI mean, essentially? 0:04:03.380 --> 0:04:08.720 Why does it matter to New Zealand and Australia, to accountants? 0:04:09.480 --> 0:04:11.910 Yeah. It's, um, it's a good way to start. Maybe we. 0:04:11.910 --> 0:04:13.650 Maybe we start with why it matters, and then we 0:04:13.650 --> 0:04:16.380 can go through what it is. So, I mean, it 0:04:16.380 --> 0:04:19.020 matters for a lot of reasons like this technology could 0:04:19.020 --> 0:04:24.540 really change the way that society, um, thinks about itself, 0:04:24.540 --> 0:04:27.330 the way it organizes itself. There's some really high level 0:04:27.330 --> 0:04:29.940 existential things that this could have an impact on. But 0:04:29.940 --> 0:04:33.359 if we take that down to what we do as chartered 0:04:33.360 --> 0:04:37.740 accountants and the way that we help organizations, I think 0:04:37.740 --> 0:04:40.500 the biggest impact that we need to be thinking about 0:04:40.500 --> 0:04:45.720 is all around productivity. So, um, I live in New Zealand. 0:04:45.750 --> 0:04:50.279 New Zealand, we're not a very productive country at all. And 0:04:50.279 --> 0:04:52.440 and in fact, we're way down at the bottom of 0:04:52.440 --> 0:04:55.230 the end of that spectrum. And I think Xero, which 0:04:55.230 --> 0:04:57.210 you'll be aware of in Australia as well, they came 0:04:57.210 --> 0:05:00.000 out with a report last year to say that if 0:05:00.000 --> 0:05:02.849 New Zealand was to match the same output as a 0:05:02.850 --> 0:05:07.140 country like Ireland, similar, similar factors going on in Ireland. Um, 0:05:07.140 --> 0:05:10.870 every New Zealander would have to work on average 11 0:05:10.870 --> 0:05:14.669 hours a day more than what they already work. So 0:05:14.670 --> 0:05:17.640 on top of what they already work. Wow. Um and you 0:05:17.640 --> 0:05:20.010 guys in Australia are doing better than us, but still 0:05:20.010 --> 0:05:22.049 not at the top end of the spectrum. You're still 0:05:22.050 --> 0:05:24.810 kind of in that middle bucket, you know. AI has 0:05:24.810 --> 0:05:27.750 the ability to change the game when it comes to productivity. 0:05:27.750 --> 0:05:30.930 And if we make the investment and take the initiative 0:05:30.930 --> 0:05:34.050 to harness the use cases in the ability for AI to 0:05:34.050 --> 0:05:37.200 make us far more productive, far more efficient, we can 0:05:37.200 --> 0:05:40.560 have both of our countries really skyrocketing up that curve. 0:05:40.589 --> 0:05:43.650 If we don't, we're going to fall further and further behind, 0:05:43.650 --> 0:05:46.169 because you can bet that the other countries at the 0:05:46.170 --> 0:05:47.940 top end are going to be doing that as well. 0:05:48.029 --> 0:05:50.849 And so when I think about what accountants do, we 0:05:51.120 --> 0:05:54.120 help the backbone of business in Australia and New Zealand, 0:05:54.120 --> 0:05:58.140 small medium sized business. And I think as advisors it's 0:05:58.140 --> 0:06:01.830 our job to help our clients, these businesses get across 0:06:01.830 --> 0:06:04.770 this new technology and understand how to implement in their 0:06:04.770 --> 0:06:07.320 business in a safe, reliable way to really get up 0:06:07.320 --> 0:06:10.890 the productivity curve. That'll help them. It'll help us, and 0:06:10.890 --> 0:06:13.109 then it will help our economies in New Zealand and 0:06:13.110 --> 0:06:13.950 Australia as well. 0:06:14.220 --> 0:06:16.950 So there's two parts there to that for me. So 0:06:16.950 --> 0:06:20.610 it's not only you understanding it as a member, as 0:06:20.610 --> 0:06:23.250 an accountant to how it can help your individual business, 0:06:23.250 --> 0:06:26.040 but then also being able to understand it enough to 0:06:26.040 --> 0:06:29.310 do that advisor role to show your clients as to 0:06:29.310 --> 0:06:30.510 how it can help them. 0:06:31.020 --> 0:06:34.980 Yeah, I really think we're quite lucky and privileged in our 0:06:34.980 --> 0:06:38.490 types of roles we get to do it from both perspectives. 0:06:38.490 --> 0:06:40.589 And even if I think about the journey that we're 0:06:40.589 --> 0:06:42.810 going on in our own firm, you know, we are 0:06:42.810 --> 0:06:44.789 very much of the opinion that we got to do 0:06:44.790 --> 0:06:47.220 this to ourselves. And we've we've got to go through 0:06:47.220 --> 0:06:50.310 this journey ourselves. And then we have the ability and 0:06:50.310 --> 0:06:53.220 the experience to help our clients do that as well. 0:06:53.220 --> 0:06:55.920 So I think for the listeners of this podcast getting 0:06:55.920 --> 0:06:57.390 on to some of these AI tools, and we could 0:06:57.390 --> 0:07:00.060 talk about some examples, but and then we'll, we'll do 0:07:00.060 --> 0:07:02.880 some specific ones in the next, um, in the next episode, 0:07:03.000 --> 0:07:06.300 that's going to really help your listeners get across some 0:07:06.300 --> 0:07:08.700 of these productivity gains for themselves. But I think there's 0:07:08.700 --> 0:07:11.490 also another piece around here. You know, we've talked about 0:07:11.490 --> 0:07:15.000 accountants moving from just focusing on compliance to also being 0:07:15.000 --> 0:07:18.000 those well rounded business advisors. I think this is another 0:07:18.000 --> 0:07:20.310 arm of that well-rounded business advisor that we should be 0:07:20.310 --> 0:07:20.880 looking at. 0:07:22.020 --> 0:07:25.200 I might tie that into the next question I want to ask 0:07:25.200 --> 0:07:29.280 about what exactly generative AI is. And I love the 0:07:29.280 --> 0:07:31.950 way now it's known as Gen AI. So spelling that 0:07:31.950 --> 0:07:34.620 out in case people haven't heard it or realised it's 0:07:34.620 --> 0:07:39.510 already being abbreviated. Um, it. If we discuss what it is, 0:07:39.510 --> 0:07:42.900 it'll show that it's more than just a fad, that 0:07:42.900 --> 0:07:44.550 it is a real thing. 0:07:45.720 --> 0:07:49.050 Yeah, absolutely. Um, and I think the easiest way to 0:07:49.050 --> 0:07:52.080 explain it to people that haven't come across it, um, 0:07:52.200 --> 0:07:55.020 before is, is that it kind of operates in the 0:07:55.020 --> 0:07:58.140 same way as our brains do. And in fact, the 0:07:58.140 --> 0:08:01.830 underlying technology that generative AI has been around for a 0:08:01.830 --> 0:08:04.710 long time, and it's the study of neural networks, and 0:08:04.710 --> 0:08:08.040 it's basically trying to mimic the way our brains work. Um, 0:08:08.040 --> 0:08:11.940 what's happened relatively recent though, and, and ChatGPT kind of 0:08:11.940 --> 0:08:13.860 made this popular, but there's a lot of others in 0:08:13.860 --> 0:08:18.300 the same space um, was the ability to also include 0:08:18.300 --> 0:08:21.120 what we call natural language processing, and that's to get 0:08:21.120 --> 0:08:24.330 this technology to think like a human, because you can 0:08:24.330 --> 0:08:28.020 understand the English language or Spanish or Hindi or whatever 0:08:28.020 --> 0:08:29.730 might be but the way we actually use it is 0:08:29.730 --> 0:08:32.100 very we don't we don't speak in the most efficient way. 0:08:32.100 --> 0:08:35.250 We've got colloquialisms, all those types of things. And so 0:08:35.250 --> 0:08:38.790 what happened recently was this ability to get this awesome technology, 0:08:39.270 --> 0:08:41.550 which has been improving at a rapid rate, to also 0:08:41.550 --> 0:08:46.320 understand natural language. And that's been the real game changer. Um, 0:08:46.770 --> 0:08:49.590 so something like ChatGPT. And again, I know that's just 0:08:49.590 --> 0:08:52.140 one of them, but it's probably the most well known. Um, 0:08:52.800 --> 0:08:57.150 it GPT stands for generative pre-trained transformer. And so what 0:08:57.150 --> 0:08:59.550 it means is that they took these neural networks, these 0:08:59.580 --> 0:09:02.640 these this piece of technology, and they basically got it 0:09:02.640 --> 0:09:05.760 to ingest a whole bunch of data and information from 0:09:05.760 --> 0:09:08.130 the internet. And it just let it loose. And this 0:09:08.130 --> 0:09:12.600 technology was then able to, as it ingested, start finding patterns and 0:09:12.600 --> 0:09:17.130 learn how to predict things. And that's really all Gen AI is. 0:09:17.130 --> 0:09:20.069 It's just a tool that's good at predicting what the 0:09:20.070 --> 0:09:23.010 next piece of text might be, or what you actually 0:09:23.010 --> 0:09:25.319 mean when you ask it to do something so that 0:09:25.320 --> 0:09:29.550 if it reads, for example, an article on Daniel Carter, um, 0:09:29.550 --> 0:09:31.860 it might predict that the next words are going to 0:09:31.860 --> 0:09:35.070 be All Black, and it'll then be able to test 0:09:35.070 --> 0:09:37.380 itself to figure out whether it gets it right or wrong. 0:09:37.380 --> 0:09:39.689 And it will know or store that in the memory bank, 0:09:39.690 --> 0:09:43.920 not the actual information, but its ability to predict. Um, 0:09:43.920 --> 0:09:46.949 and so every time it reads something new, it's predicting 0:09:46.950 --> 0:09:49.080 every single word and then figuring out whether it gets 0:09:49.080 --> 0:09:50.670 it right or wrong. And that's how it's able to 0:09:50.670 --> 0:09:52.559 really improve at such a rapid rate. 0:09:53.220 --> 0:09:55.950 So you touched on it briefly, then, um, in the 0:09:55.950 --> 0:10:01.020 sense of why accountants should be interested in that. Um, that, that, 0:10:01.020 --> 0:10:05.160 that is helpful in the sense of understanding. ChatGPT. But 0:10:05.160 --> 0:10:10.230 why does that apply to the profession? What other bits 0:10:10.230 --> 0:10:12.630 and bobs should we know about to show why we 0:10:12.630 --> 0:10:14.280 should be interested in this space? 0:10:14.910 --> 0:10:17.670 Yeah. So if I think about what we we would 0:10:17.670 --> 0:10:21.780 do as a chartered accountant advising businesses, um, you could 0:10:21.780 --> 0:10:24.300 probably and there's way more nuance in this, but at 0:10:24.300 --> 0:10:27.210 a high level you could probably separate it, separate out 0:10:27.210 --> 0:10:30.030 what we do into a couple of different fields. Um, 0:10:30.330 --> 0:10:33.120 we do some stuff where we build relationships with clients, 0:10:33.120 --> 0:10:35.820 and we understand their business and we understand them as 0:10:35.820 --> 0:10:38.550 a person, and we understand the market that they're in, 0:10:38.580 --> 0:10:41.430 their customers, all of those types of things. And that's 0:10:41.429 --> 0:10:44.400 really built on human relationships. And then off the back 0:10:44.400 --> 0:10:47.490 of that understanding, we will then help those businesses with 0:10:47.490 --> 0:10:50.670 certain tasks. Now it might be accounting and tax preparation. 0:10:50.670 --> 0:10:53.760 It could be things with applications for research and development grants, 0:10:53.760 --> 0:10:57.239 whatever that might be. The relationship building and all of 0:10:57.240 --> 0:10:59.969 those things at the moment requires us to have that 0:10:59.970 --> 0:11:02.970 human to human connection. But the task of doing the 0:11:02.970 --> 0:11:05.670 stuff off the back of that, that is all about 0:11:05.670 --> 0:11:09.960 generating text, generating data, understanding data to then generate more 0:11:09.960 --> 0:11:13.290 data or text. And the use of AI to do 0:11:13.290 --> 0:11:15.809 that will allow us, and it was already allowing us 0:11:15.809 --> 0:11:18.510 to be far more efficient at how we generate the 0:11:18.510 --> 0:11:21.360 data or the text, or understand what the data might 0:11:21.360 --> 0:11:23.460 mean to then be able to go back to our 0:11:23.460 --> 0:11:26.940 client and provide meaningful insights or, or just do what 0:11:26.940 --> 0:11:30.000 we've always done, but make it more efficient. And that's 0:11:30.000 --> 0:11:32.190 where the tool, these tools are playing in that space. 0:11:32.460 --> 0:11:36.300 So it's like they're an extra set of hands, but 0:11:36.300 --> 0:11:37.980 you would need to check their work. 0:11:38.820 --> 0:11:41.280 Absolutely need to check their work. And your listeners have 0:11:41.280 --> 0:11:44.640 probably heard around things like hallucinations where you've got, I 0:11:44.640 --> 0:11:47.579 go on ChatGPT to tell me that a fringe benefit text didn't exist 0:11:47.580 --> 0:11:50.819 in New Zealand, which is obviously very incorrect. So there's 0:11:50.850 --> 0:11:55.440 this absolutely a need to check. Um, and it's not perfect, but, um, 0:11:55.440 --> 0:11:58.530 the rate at which these technologies are improving is very, 0:11:58.530 --> 0:11:59.130 very quick. 0:11:59.820 --> 0:12:02.730 So what could this mean then for the accounting workforce? Um, 0:12:02.730 --> 0:12:05.250 and I'm thinking in particular we are talking to small 0:12:05.250 --> 0:12:06.990 and medium sized practice members here. 0:12:07.500 --> 0:12:11.820 Yeah. So I think, um, small practices and medium sized 0:12:11.820 --> 0:12:14.820 practices that are getting across this right now, what they're 0:12:14.820 --> 0:12:17.160 finding is that they're able to do the stuff they 0:12:17.160 --> 0:12:19.620 already doing, but they're doing it a little bit more efficiently, right? 0:12:20.640 --> 0:12:24.540 So if you get a tool like this to help 0:12:24.540 --> 0:12:26.880 you draft an email to a client, maybe you have 0:12:26.880 --> 0:12:30.120 to draft a challenging email around a client wants to reduce 0:12:30.120 --> 0:12:32.370 fees and you don't want to reduce fees. You've got 0:12:32.370 --> 0:12:35.340 to put some nuance in there. These tools can help 0:12:35.340 --> 0:12:38.430 you do that um, quite well and a lot quicker 0:12:38.429 --> 0:12:40.470 than you used to be able to do it. Maybe 0:12:40.470 --> 0:12:42.630 if you have to, if you've got Excel spreadsheets that 0:12:42.630 --> 0:12:44.280 you've got lots of data on and you want to 0:12:44.280 --> 0:12:47.160 manipulate that data to come out with some graphs or 0:12:47.160 --> 0:12:49.319 a report or whatever might be, you would do that anyway, 0:12:49.320 --> 0:12:51.030 but you can get it done faster. You can get 0:12:51.030 --> 0:12:54.179 it done a bit more quickly. And at the moment, 0:12:54.179 --> 0:12:56.699 those are where those use cases are helping me generate 0:12:56.700 --> 0:13:01.020 text or generate visual data visualizations or understand data help 0:13:01.020 --> 0:13:02.670 me do that quicker than I used to be able 0:13:02.670 --> 0:13:06.329 to do. At this point in time it's not yet 0:13:06.330 --> 0:13:08.370 at the level where I would see it saying, okay, 0:13:08.370 --> 0:13:10.470 I used to need a person to do this, and 0:13:10.470 --> 0:13:12.750 now I'll take that person out and, and replace it 0:13:13.770 --> 0:13:17.309 with this either single or multiple tools. It's more that 0:13:17.309 --> 0:13:19.530 we've got people in there just being more efficient at 0:13:19.530 --> 0:13:21.810 what they're already doing, and then being able to spend 0:13:21.809 --> 0:13:23.939 more time at the value add stuff, have those discussions 0:13:23.940 --> 0:13:26.010 with the clients, build those relationships, all the stuff we 0:13:26.010 --> 0:13:27.810 want to do we don't have the time to do 0:13:27.809 --> 0:13:30.690 because we're bogged down and in the data manipulation and 0:13:30.690 --> 0:13:31.470 the processing. 0:13:31.980 --> 0:13:34.230 What happens if you don't get across this info? If 0:13:34.230 --> 0:13:36.240 you're just like, I don't have time to get trained 0:13:36.240 --> 0:13:39.030 and have time to read, it's not something that's ever 0:13:39.030 --> 0:13:40.110 going to impact me. 0:13:40.880 --> 0:13:45.500 Yeah, well, see, my personal view is that, um, you know, 0:13:45.500 --> 0:13:49.309 the big next big paradigm shift with AI will be 0:13:49.309 --> 0:13:52.640 around the fact that humans with AI will replace humans 0:13:52.640 --> 0:13:55.910 without it. Now, that's not going to happen tomorrow. Um, 0:13:55.910 --> 0:13:58.670 but I think that's probably the next horizon that we're 0:13:58.670 --> 0:14:01.640 looking at. And so I think it's extremely important to 0:14:01.640 --> 0:14:04.429 get across this for two reasons. One, there's some real 0:14:04.429 --> 0:14:07.910 value that you can get right now. And we're seeing businesses, 0:14:07.910 --> 0:14:12.380 both accounting firms and um, and, and their clients seeing 0:14:12.380 --> 0:14:15.920 some real value right now with specific use cases that 0:14:15.920 --> 0:14:19.460 are very useful. But the second piece is this technology 0:14:19.460 --> 0:14:21.290 is only getting better and better. And I think your 0:14:21.290 --> 0:14:24.620 last guest on this topic, um, mentioned the fact that, 0:14:24.620 --> 0:14:26.359 you know, she's really looking forward to the time when 0:14:26.360 --> 0:14:29.270 AI is built into the existing tools she's already using. 0:14:29.840 --> 0:14:32.600 That's not that far from happening. It's happening right now, 0:14:32.600 --> 0:14:35.630 and it's going to continue to happen. And so if 0:14:35.630 --> 0:14:38.420 you're using these AI tools now, by the time they're 0:14:38.420 --> 0:14:41.150 integrated in the stuff you already use or the stuff 0:14:41.150 --> 0:14:43.940 you want to use, you'll already be across it. It's 0:14:43.940 --> 0:14:45.770 not going to be as steep a learning curve as it 0:14:45.770 --> 0:14:48.590 might be when you find that you have to interact 0:14:48.590 --> 0:14:50.840 with this stuff all the time, day in and day out, 0:14:50.840 --> 0:14:54.470 you can't escape it because it's just built into everything. Um, 0:14:54.470 --> 0:14:57.110 and the reason for that, and I think this might 0:14:57.110 --> 0:15:01.220 be something that people often miss, Gen AI doesn't work 0:15:01.220 --> 0:15:04.280 unless you know how to use it well, and in fact, 0:15:04.280 --> 0:15:05.990 it actually is worse if you don't know how to 0:15:05.990 --> 0:15:08.000 use it well. Like if you just if you ask 0:15:08.000 --> 0:15:10.610 open ended questions, you don't iterate with it. You're not 0:15:10.610 --> 0:15:13.010 specific enough with your type of prompting. You don't give 0:15:13.010 --> 0:15:15.830 it the right persona. It can provide you with a 0:15:15.830 --> 0:15:19.130 lot of material that is just irrelevant and will take 0:15:19.130 --> 0:15:22.490 you longer. And so understanding how to use it is important. 0:15:23.120 --> 0:15:25.340 I got two questions out of that, the first one 0:15:25.340 --> 0:15:27.920 is when you said humans with AI will replace humans 0:15:27.920 --> 0:15:32.030 without AI. Do you mean that those businesses or, you know, 0:15:32.030 --> 0:15:35.210 sole practitioners who who aren't, those clients who aren't using 0:15:35.210 --> 0:15:37.520 it will just fizzle out because they can't keep up? 0:15:37.520 --> 0:15:39.320 Is that what you mean essentially? 0:15:39.990 --> 0:15:42.210 Yeah, I think there will come a time where that 0:15:42.210 --> 0:15:46.140 is absolutely the case. Um, now, if you ask me 0:15:46.140 --> 0:15:47.940 to predict a timeline as to where that happened, I 0:15:47.940 --> 0:15:50.400 don't think there is. There's so many variables as to that. 0:15:50.760 --> 0:15:51.330 No. 0:15:51.600 --> 0:15:54.390 But I do think that, um, you know, in our 0:15:54.390 --> 0:15:59.280 industry particularly, there is a need right now to have 0:15:59.280 --> 0:16:02.430 advisors and chartered accountants spending more time on the relationship 0:16:02.430 --> 0:16:06.450 building and the value add. Every, every accounting firm, every 0:16:06.450 --> 0:16:10.290 accountant I see, they've been talking about that for years. Um, 0:16:10.470 --> 0:16:12.720 this is the opportunity for them to be able to 0:16:12.720 --> 0:16:15.240 do that. And I think the ones that are able 0:16:15.240 --> 0:16:19.110 to use AI to then do that processing, free them up 0:16:19.110 --> 0:16:20.670 on the value add, they're going to be the ones 0:16:20.670 --> 0:16:24.270 that are successful. Um, and therefore they're going to be 0:16:24.270 --> 0:16:26.910 the ones that are, that are more valuable in the future. 0:16:27.330 --> 0:16:31.560 So part two will go into some specific use cases. 0:16:31.560 --> 0:16:34.380 But before we get stuck into that, and obviously that 0:16:34.380 --> 0:16:37.350 episode's next, there will be people listening along going, okay, 0:16:37.350 --> 0:16:40.770 this is great. I do want to get some information. 0:16:40.800 --> 0:16:43.650 Where do you start? Where do you find that training 0:16:43.650 --> 0:16:45.780 that's relevant to you? 0:16:46.440 --> 0:16:50.220 Yeah, it's a good question. Um, and it probably depends 0:16:50.220 --> 0:16:53.220 on where you are on the, on the spectrum of what your, 0:16:53.220 --> 0:16:56.340 your ability to immerse yourself in this stuff. Um, I 0:16:56.340 --> 0:16:59.220 think to start with, it's really important to have a 0:16:59.220 --> 0:17:02.910 base level general understanding of what the what this is, 0:17:02.910 --> 0:17:05.250 the different options available to you and just how to 0:17:05.250 --> 0:17:08.970 use it just for text. Um, there's lots of different 0:17:08.970 --> 0:17:13.500 podcasts out there. There's lots of different YouTube videos out there. Um, and, 0:17:13.500 --> 0:17:18.389 and maybe we can provide some, um, some links or something like that. 0:17:18.390 --> 0:17:20.520 It's a good idea that. Yeah, we'll have a look 0:17:20.520 --> 0:17:22.320 at it. Yes. We'll have a look at some useful 0:17:22.320 --> 0:17:24.149 things to put in. And normally as well, people who 0:17:24.150 --> 0:17:26.700 listen to the podcast will know I love a plug. 0:17:26.700 --> 0:17:28.260 I love putting a link in the show notes. So 0:17:28.260 --> 0:17:30.480 we'll find some some relevant stuff that you can you 0:17:30.480 --> 0:17:32.850 can listen to. We've we've answered our own question. We 0:17:32.850 --> 0:17:34.260 will do that. I should have thought of that ahead 0:17:34.260 --> 0:17:35.939 of time, but continue as you were. 0:17:36.150 --> 0:17:38.520 Yeah. I just think that's a really good way. And 0:17:38.520 --> 0:17:40.709 there's some that I, that I'm quite partial to. And 0:17:40.710 --> 0:17:42.720 I think it's a really good way to just get 0:17:42.720 --> 0:17:44.730 up to speed. I think the best thing you can 0:17:44.730 --> 0:17:47.700 do is just understand how to use a ChatGPT or 0:17:47.700 --> 0:17:50.490 a Google Bard or a Microsoft Copilot. It doesn't matter which one, 0:17:50.490 --> 0:17:54.240 but just learn how to understand, how to interact with 0:17:54.240 --> 0:17:56.970 them in a way that is useful, as opposed to 0:17:56.970 --> 0:17:59.040 just treating these systems like Google, where you're asking it 0:17:59.040 --> 0:18:01.590 questions and expecting it to come out with a good 0:18:01.590 --> 0:18:03.359 email that sounds like you, it's not going to do that 0:18:03.359 --> 0:18:05.430 unless you know how to use it. So those general 0:18:05.430 --> 0:18:08.609 things I think are really, really important. And then after that, 0:18:08.609 --> 0:18:12.330 and what we'll cover in the next episode are some specific, um, 0:18:12.480 --> 0:18:15.660 some specific use cases. And then of course, it's specific 0:18:15.660 --> 0:18:16.979 ways that you can get up to speed on those 0:18:16.980 --> 0:18:20.010 specific use cases. So I think it's probably worth delving 0:18:20.010 --> 0:18:24.480 into the, the realm of, of data analytics and data visualization. 0:18:24.480 --> 0:18:27.780 Lots of accountants are helping to prepare board reports or 0:18:27.780 --> 0:18:31.050 management reports and and all of that data manipulation is 0:18:31.050 --> 0:18:35.580 quite a good use case. Um, things like drafting, you know, 0:18:35.580 --> 0:18:38.550 automated client reminders. There are a lot of us that 0:18:38.550 --> 0:18:42.810 are sending prov-tax reminders or whatever else it might be. Um, 0:18:42.810 --> 0:18:44.729 you know, there's some there's some real use cases in 0:18:44.730 --> 0:18:47.399 that space as well. So but for those there's specific 0:18:47.400 --> 0:18:49.710 places you can go for specific use cases. 0:18:49.830 --> 0:18:53.160 Hmm, now you and I are definitely excited about this. 0:18:53.160 --> 0:18:56.340 We sound excited about this. Uh, two questions in before 0:18:56.340 --> 0:18:58.800 we wrap up and then everyone make sure you're listening to part 0:18:58.800 --> 0:19:03.780 two. Are accountants excited enough about this. Are enough of us thinking 0:19:03.780 --> 0:19:05.700 about this. And if they're not, how do we get 0:19:05.700 --> 0:19:06.930 them excited about it? 0:19:07.080 --> 0:19:09.899 Yeah, it's a real range. Um, you know, when I 0:19:09.900 --> 0:19:12.240 go in and speak at conferences, there's there's definitely a 0:19:12.240 --> 0:19:14.820 group that are super excited, but there's also some that 0:19:14.820 --> 0:19:20.010 are apprehensive and fair enough. Like, um, I can totally 0:19:20.010 --> 0:19:24.390 understand how how a change in technology of this magnitude 0:19:24.420 --> 0:19:27.630 could be something that makes you apprehensive. I think on 0:19:27.630 --> 0:19:30.030 the whole in our industry, though, this technology is only 0:19:30.030 --> 0:19:31.830 going to be a good thing. And I know I've 0:19:31.830 --> 0:19:34.260 seen this probably four times now in this podcast, but 0:19:34.260 --> 0:19:37.949 like every accountant I have, I have talked to, they 0:19:37.950 --> 0:19:40.590 really want to spend time doing the fun stuff, the 0:19:40.590 --> 0:19:45.660 valuable stuff, um, the value add stuff with clients. And 0:19:45.660 --> 0:19:48.300 this is the best piece of technology that I've ever 0:19:48.300 --> 0:19:50.639 seen that can help us to spend time out of the 0:19:50.640 --> 0:19:54.030 weeds and into into that type of work that we 0:19:54.030 --> 0:19:57.240 really want to do. And it's just about understanding how 0:19:57.240 --> 0:20:01.469 to use it. And as the technology improves, we're improving 0:20:01.470 --> 0:20:03.840 our understanding of how to use it. And together we'll 0:20:03.840 --> 0:20:06.540 get to that level where we can we can really 0:20:06.540 --> 0:20:09.810 have and we already have a massive impact on on 0:20:09.810 --> 0:20:13.050 small and medium size business. I really think we do 0:20:13.050 --> 0:20:15.690 and we can. That's only going to improve. And that 0:20:15.690 --> 0:20:17.489 makes me really proud of the industry we're in. I 0:20:17.490 --> 0:20:19.649 think there's very few industries that can have such a 0:20:19.650 --> 0:20:24.119 direct impact on small, medium sized business. Um, and as 0:20:24.119 --> 0:20:26.130