1 00:00:00,040 --> 00:00:03,800 OK, so let's just have a think about what this AI thing is, 2 00:00:03,800 --> 00:00:06,720 very, very briefly. So everyone thinks that 3 00:00:06,760 --> 00:00:09,400 artificial intelligence actually means artificial intelligence, 4 00:00:09,720 --> 00:00:11,320 OK. Artificial doesn't mean it's not 5 00:00:11,320 --> 00:00:12,520 human. You know that, don't you? 6 00:00:12,720 --> 00:00:16,960 OK, What it means is, do you know how inside your brain, I 7 00:00:16,960 --> 00:00:19,960 talked about your brain, there are these long, stringy, stringy 8 00:00:19,960 --> 00:00:21,520 things, OK, they're called neurons. 9 00:00:21,520 --> 00:00:24,120 Have you come across this? So what happens is these bits 10 00:00:24,120 --> 00:00:25,920 listen out. Hey, how's it going? 11 00:00:25,920 --> 00:00:29,160 OK, And then they find that it's warm or it's noisy and they tell 12 00:00:29,160 --> 00:00:32,320 the nucleus move the foot. So the nucleus goes bang and 13 00:00:32,320 --> 00:00:35,240 sends a message which goes and then your foot moves, blah, 14 00:00:35,240 --> 00:00:36,120 blah. That's basically it. 15 00:00:36,360 --> 00:00:38,840 OK, so that's how a neuron works. 16 00:00:39,040 --> 00:00:42,280 So somebody had an idea like in the 1950s forties, something 17 00:00:42,280 --> 00:00:44,400 like that. Hey, why don't we make computers 18 00:00:44,400 --> 00:00:45,880 like that? Why don't they set them up so 19 00:00:45,880 --> 00:00:47,800 they have lots of inputs like that, OK. 20 00:00:48,080 --> 00:00:51,800 And then when you put the inputs in, we'll see this input's 21 00:00:51,800 --> 00:00:53,720 really important. We'll give it a weight, and then 22 00:00:53,720 --> 00:00:56,040 we'll add up all the numbers. And then there's a little box 23 00:00:56,040 --> 00:00:59,800 here, and this box then says go and do X, OK. 24 00:01:00,280 --> 00:01:02,120 And that basically is what AI is. 25 00:01:02,240 --> 00:01:12,240 It's just a neural network. That was Eddie opening talking 26 00:01:12,240 --> 00:01:14,600 about AI at this year's APM conference. 27 00:01:15,280 --> 00:01:18,040 We'll hear more from Eddie shortly as we take a deep dive 28 00:01:18,040 --> 00:01:21,280 into the future of the project profession with a compilation of 29 00:01:21,280 --> 00:01:24,520 some of our favourite insights from the 2024 conference. 30 00:01:25,200 --> 00:01:28,480 This is the APM podcast brought to you by the chartered body for 31 00:01:28,480 --> 00:01:31,760 the project profession. My name is Emma Devita and I'm 32 00:01:31,760 --> 00:01:35,160 the editor of Apms quarterly journal Project and your host 33 00:01:36,000 --> 00:01:38,720 held in Coventry in June. The theme for this year's APM 34 00:01:38,720 --> 00:01:41,720 Conference was Navigating Tomorrow, Future Skills for 35 00:01:41,720 --> 00:01:44,840 Project Professionals. The event invited project 36 00:01:44,840 --> 00:01:48,160 leaders and experts on future trends to unpack the rapidly 37 00:01:48,160 --> 00:01:50,760 changing landscape that projects are being delivered in. 38 00:01:51,480 --> 00:01:54,640 AI and data literacy were among the hot topics, including both 39 00:01:54,640 --> 00:01:57,720 the opportunities and threats of new technology for project 40 00:01:57,720 --> 00:02:01,600 managers employment prospects. The conference also considered 41 00:02:01,600 --> 00:02:04,560 whether there is a skills gap in the profession and there must 42 00:02:04,560 --> 00:02:07,240 have competencies to future proof your career. 43 00:02:07,680 --> 00:02:11,400 So listen on as our expert speakers explore how to navigate 44 00:02:11,400 --> 00:02:17,360 tomorrow. First, let's hear more from 45 00:02:17,360 --> 00:02:19,000 Eddie. Opening session on developing 46 00:02:19,000 --> 00:02:22,280 your emotional intelligence in an ever more digital world. 47 00:02:22,760 --> 00:02:26,080 Eddie is an educator, innovator, and digital pioneer. 48 00:02:26,360 --> 00:02:29,920 He explained what AI really is and why it has a tendency to 49 00:02:29,920 --> 00:02:33,400 hallucinate. And that basically is what AI 50 00:02:33,400 --> 00:02:35,280 is. It's just a neural network. 51 00:02:35,520 --> 00:02:39,200 All it's doing is 4 by 4 matrix calculations. 52 00:02:39,720 --> 00:02:42,280 That's all it's doing. The reason it's now popular 53 00:02:42,280 --> 00:02:44,080 because it's been knocking around for a long time, it's 54 00:02:44,080 --> 00:02:46,400 because of gamers, you know, gamers, you know, when you're 55 00:02:46,400 --> 00:02:49,320 doing 3D gaming, the type of computer chip you need in your 56 00:02:49,320 --> 00:02:51,600 machine comes from a company called NVIDIA. 57 00:02:51,600 --> 00:02:55,560 Too late to buy their stock, OK. And it got really cheap. 58 00:02:55,800 --> 00:02:58,400 So suddenly we could do masses of calculations like that. 59 00:02:58,880 --> 00:03:01,800 So that's basically what it is. At the base of it, there is no 60 00:03:01,800 --> 00:03:04,320 intelligence. All it's doing is churning 61 00:03:04,320 --> 00:03:06,040 through information. So the stuff you'd have come 62 00:03:06,040 --> 00:03:07,760 across is what's called generative AI. 63 00:03:07,880 --> 00:03:10,600 What they do is they feed in a whole bunch of numbers here. 64 00:03:10,600 --> 00:03:13,600 So let's say they've got 100 bits of cases about people being 65 00:03:13,600 --> 00:03:15,880 I'll, OK, they feed in, I don't know, eighty of them. 66 00:03:16,040 --> 00:03:18,440 And say the person was tall, they were long, they were 67 00:03:18,800 --> 00:03:22,280 hungry, they had a blood factor A they feed those in and then 68 00:03:22,280 --> 00:03:24,040 they get the machine to do the calculations. 69 00:03:24,240 --> 00:03:25,840 And then they say, send them to hospital. 70 00:03:25,920 --> 00:03:27,680 And if that's the correct answer, it goes, great. 71 00:03:27,840 --> 00:03:30,120 Then they feed the next day. If it's the wrong answer, then 72 00:03:30,120 --> 00:03:34,480 they adjust the weights until for those 80 things it's 73 00:03:34,480 --> 00:03:37,240 approximately correct. Then they check with the last 20 74 00:03:37,440 --> 00:03:39,920 and it's trained. So you have heard about training 75 00:03:39,920 --> 00:03:42,640 on data, training on large language, language models. 76 00:03:42,840 --> 00:03:44,840 That's all it is. But of course, if you're 77 00:03:44,840 --> 00:03:47,360 selling, you know the trick. If you're buying stuff, you make 78 00:03:47,360 --> 00:03:49,280 it simple. When you're selling it, as they 79 00:03:49,280 --> 00:03:53,760 say, bullshit baffles brains. OK, so you put, you guys are so 80 00:03:53,880 --> 00:03:56,560 serious. OK, so, so you make it so. 81 00:03:56,600 --> 00:03:58,600 Oh, we're training it with our LLM. 82 00:03:58,760 --> 00:04:01,800 Oh yes, our LLM has data which is within the GDP. 83 00:04:01,800 --> 00:04:04,320 You know, they'll say all these things and then you take your 84 00:04:04,320 --> 00:04:06,720 past and give it to them. But it's really very simple. 85 00:04:07,320 --> 00:04:11,200 It works out patterns from huge amounts of data fast. 86 00:04:12,200 --> 00:04:15,960 It takes text which you've written painstakingly and it 87 00:04:15,960 --> 00:04:18,560 corrects all the grammar and makes it readable. 88 00:04:19,040 --> 00:04:24,200 OK, It's marvellous. It helps your AI self driving 89 00:04:24,200 --> 00:04:27,320 car work its way and not fall into potholes. 90 00:04:27,400 --> 00:04:31,560 It's brilliant, am I right? And remember what I said about 91 00:04:31,560 --> 00:04:33,680 the computers, all those advantages. 92 00:04:35,280 --> 00:04:40,040 So what's the challenge? It's an interpolation machine. 93 00:04:40,440 --> 00:04:42,480 It feeds on the data you've given it. 94 00:04:43,440 --> 00:04:46,800 So when you get a report and input back from any AI, you feed 95 00:04:46,800 --> 00:04:49,000 it into. If you look at this and your 96 00:04:49,000 --> 00:04:52,040 beautiful brain makes sense of it and you give it all the 97 00:04:52,040 --> 00:04:54,720 attributes because you anthropomorphize it, you think 98 00:04:54,720 --> 00:04:55,960 it's human, you think it's smart. 99 00:04:56,680 --> 00:04:59,440 This is the trick you should always do with any API you play 100 00:04:59,440 --> 00:05:01,400 with. Ask you something about 101 00:05:01,400 --> 00:05:05,160 something general, like how many walls have there been or what's 102 00:05:05,160 --> 00:05:07,480 the growth of our market within real estate? 103 00:05:07,520 --> 00:05:09,320 OK, and you'll get this brilliant answer. 104 00:05:09,760 --> 00:05:12,760 Then ask it a question about something where you're the world 105 00:05:12,760 --> 00:05:15,640 expert or you're pretty damn good. 106 00:05:15,640 --> 00:05:19,320 What do you think happens? It's gibberish, because when you 107 00:05:19,320 --> 00:05:24,360 use a large model, you know the shape of the world is generally 108 00:05:24,360 --> 00:05:27,680 like that. So there's Einstein over here. 109 00:05:28,280 --> 00:05:30,600 Then there's the large long language model over here. 110 00:05:31,200 --> 00:05:33,480 For most of Einstein's life, he was lucky. 111 00:05:33,880 --> 00:05:37,800 He was a bit of a Nutter. The leading edge, cutting edge, 112 00:05:37,800 --> 00:05:41,880 best ideas, best ways of doing things are not the average. 113 00:05:41,880 --> 00:05:46,120 Does it make sense? OK, so that's one thing. 114 00:05:46,360 --> 00:05:49,120 The 2nd is there's no human body involved. 115 00:05:49,280 --> 00:05:52,120 So AI, although it's very fast, it doesn't know anything. 116 00:05:52,280 --> 00:05:54,160 So it does this thing called hallucination. 117 00:05:54,160 --> 00:05:57,040 Have you come across this? Hallucinations are particularly 118 00:05:57,040 --> 00:05:59,320 easy to show with pictures. But what I came across the other 119 00:05:59,320 --> 00:06:02,320 day, which made me laugh is if you go to say Snapchat TBT and 120 00:06:02,320 --> 00:06:05,200 you type in I have a problem, it says yes, can I help? 121 00:06:05,200 --> 00:06:07,240 It says I've got a really complicated problem. 122 00:06:07,440 --> 00:06:11,280 I've got a man with who's by a river with a with a boat. 123 00:06:12,560 --> 00:06:15,320 Can you help? It says yes, first send the 124 00:06:15,320 --> 00:06:19,360 cabbage across, then the lion. It answers a question you 125 00:06:19,360 --> 00:06:22,680 haven't asked it because so many people have put that thing in 126 00:06:22,840 --> 00:06:25,240 that it has learned the answer to a question you haven't asked. 127 00:06:25,240 --> 00:06:27,000 Isn't that just brilliant? That's a hallucination. 128 00:06:27,920 --> 00:06:34,400 So it's going to help us because it's fast, it's cheap, but it's 129 00:06:34,400 --> 00:06:36,680 not. A human being has no ethics. 130 00:06:37,320 --> 00:06:43,080 It can't be fixed. Next, we turn to Laura Ellis, 131 00:06:43,080 --> 00:06:46,440 Head of Technology Forecasting at the BBC, who spoke about the 132 00:06:46,440 --> 00:06:49,600 balance between analytics and people skills and project 133 00:06:49,600 --> 00:06:51,960 decisions. In the following clip, she 134 00:06:51,960 --> 00:06:55,720 discusses her fascination with AI, the potential crisis in the 135 00:06:55,720 --> 00:06:59,160 workplace, and the fight to preserve human creativity. 136 00:07:01,480 --> 00:07:03,720 What I think we're learning in the BBC at the moment about 137 00:07:03,720 --> 00:07:06,800 this, integrating humans, data and AI, and what we say to 138 00:07:06,800 --> 00:07:08,840 people when they ask us about this is learn as much as you 139 00:07:08,840 --> 00:07:10,800 can. This is something which if you 140 00:07:10,800 --> 00:07:13,640 are a remotely interested in technology, it is fascinating. 141 00:07:13,880 --> 00:07:16,640 If you don't like technology and you find it scary, it's still 142 00:07:16,640 --> 00:07:18,680 fascinating. And I would say do everything 143 00:07:18,680 --> 00:07:21,120 you can to overcome that fear because I had it once and now 144 00:07:21,120 --> 00:07:23,560 I'm obsessed. And I think the more you know 145 00:07:23,560 --> 00:07:25,840 about it and the more you know about things like how these data 146 00:07:25,840 --> 00:07:28,560 sets are trained, who's making the decisions, where they're 147 00:07:28,560 --> 00:07:31,440 being made and how they're going to affect our businesses, the 148 00:07:31,440 --> 00:07:35,520 better keep coming back to this. But, you know, take great care 149 00:07:35,520 --> 00:07:40,440 with how data is used. I am inordinately fascinated 150 00:07:40,440 --> 00:07:42,520 with Co pilot. Does anybody use Microsoft Co 151 00:07:42,520 --> 00:07:46,920 pilot in the room? So Co pilot is one of the first 152 00:07:46,920 --> 00:07:50,480 forays we've made in the BBC into essentially kind of, you 153 00:07:50,480 --> 00:07:52,560 know, really starting to use this stuff for real. 154 00:07:53,040 --> 00:07:56,400 And the first demo we went to, we one of our tech reporters 155 00:07:56,400 --> 00:07:59,040 wrote it up and it was quite chilling because she said the 156 00:07:59,040 --> 00:08:00,800 person that was sitting in the room with her looked at what 157 00:08:00,800 --> 00:08:03,680 Microsoft Co pilot could do. And it summarised meetings and 158 00:08:03,680 --> 00:08:06,560 it said who'd said what. And then it gave a kind of 30% 159 00:08:06,560 --> 00:08:07,800 of people agreed with this point of view. 160 00:08:07,800 --> 00:08:10,080 Then it made a chart about it. And then it, you know, created 161 00:08:10,080 --> 00:08:11,760 an action list. It was absolutely amazing. 162 00:08:11,760 --> 00:08:14,520 And she looked at it and said, oh, that's three jobs I don't 163 00:08:14,520 --> 00:08:18,000 need to appoint. And there is a, you know, a, a 164 00:08:18,080 --> 00:08:23,200 potential pending crisis about what we value in the workplace 165 00:08:23,200 --> 00:08:26,200 and, and valuing our colleagues. Microsoft at their most recent 166 00:08:26,200 --> 00:08:28,640 event, which was last week, said we've generated you an AI 167 00:08:28,640 --> 00:08:30,600 colleague as well, and you can give it a personality and have 168 00:08:30,600 --> 00:08:33,799 it in the room with you. Do we really want that? 169 00:08:33,840 --> 00:08:35,000 You know, do we really want that? 170 00:08:35,080 --> 00:08:39,080 And do we want to lose that really valuable entry level of 171 00:08:39,080 --> 00:08:41,159 professional, you know, who comes in and does some of the 172 00:08:41,159 --> 00:08:42,880 things that we're going to be ceding to AI? 173 00:08:43,200 --> 00:08:45,160 Do we do we want to lose them? I don't think we do. 174 00:08:45,160 --> 00:08:46,960 So how do we keep them? How do we make sure that we're 175 00:08:47,360 --> 00:08:49,400 keeping them in the loop and we don't just throw away that 176 00:08:49,400 --> 00:08:53,040 effort, but then we perhaps use, use them to, to feed in, in data 177 00:08:53,040 --> 00:08:54,920 and to understand it and to sort of train the AI. 178 00:08:55,280 --> 00:08:57,120 I think we need to be really careful about our talent 179 00:08:57,120 --> 00:08:59,320 management as this as this happens, because we risk cutting 180 00:08:59,320 --> 00:09:02,120 out, you know, the, the, the nursery slopes of a lot of our 181 00:09:02,120 --> 00:09:03,480 professions if we're not careful. 182 00:09:04,720 --> 00:09:06,840 And finally, I just think we need to fight for human 183 00:09:06,840 --> 00:09:10,640 creativity, human agency, rather than creativity to be, to be 184 00:09:10,640 --> 00:09:13,080 preserved. So when I think of journalism 185 00:09:13,080 --> 00:09:15,960 and I think of creativity, there is something incredibly special 186 00:09:15,960 --> 00:09:17,520 about a human looking at something and giving a 187 00:09:17,520 --> 00:09:20,120 representation of that, whether it's in journalism and art, 188 00:09:20,120 --> 00:09:22,880 whether it's input into a project, whether it's, you know, 189 00:09:23,040 --> 00:09:24,680 that understanding of human nature. 190 00:09:25,080 --> 00:09:29,000 All of those things are not available to us yet in AI. 191 00:09:29,000 --> 00:09:32,960 And I think if we start to create too much of an AI driven 192 00:09:32,960 --> 00:09:35,960 economy and lose those things, we'll be much the poorer for it. 193 00:09:35,960 --> 00:09:39,840 So I think we need to be incredibly careful that we fight 194 00:09:39,880 --> 00:09:42,600 and we will have to fight, hopefully not in a kind of 195 00:09:42,880 --> 00:09:46,000 Terminator way, but we will have to fight to make sure that, you 196 00:09:46,000 --> 00:09:49,280 know, as we move forward. The old quote about, you know, 197 00:09:49,280 --> 00:09:50,560 your job isn't going to be taken by AI. 198 00:09:50,560 --> 00:09:53,040 It's going to be taken by somebody who uses AI better than 199 00:09:53,040 --> 00:09:55,320 you. It's a horrible quote probably, 200 00:09:55,640 --> 00:09:56,800 and there's a grain of truth in it. 201 00:09:57,040 --> 00:09:58,560 But I think it's designed to scare us. 202 00:09:58,800 --> 00:10:01,240 And we don't have to be scared. We can push back and say, OK, 203 00:10:01,240 --> 00:10:04,200 fine, you know, we will understand AI, but we that 204 00:10:04,200 --> 00:10:07,280 doesn't mean we have to seed all of our sort of, you know, lower 205 00:10:07,280 --> 00:10:09,440 level jobs to it. It doesn't mean we have to see 206 00:10:09,440 --> 00:10:11,840 your creativity to it, and it doesn't mean that we have to end 207 00:10:11,840 --> 00:10:14,560 up doing the dishes when it writes us a song. 208 00:10:18,240 --> 00:10:21,480 As AI matures, will project managers find themselves out of 209 00:10:21,480 --> 00:10:23,480 a job? It's the question on everyone's 210 00:10:23,480 --> 00:10:25,920 mind, and it's when our conference audience put to 211 00:10:25,920 --> 00:10:29,880 Daniel Armanios, BT, Professor of Major Programme Management at 212 00:10:29,880 --> 00:10:33,160 SCII Business School. Here's Daniel's response to that 213 00:10:33,160 --> 00:10:36,840 conundrum, followed by another audience members question about 214 00:10:36,840 --> 00:10:39,840 the readiness of the profession to embrace AII. 215 00:10:43,040 --> 00:10:45,640 Think project management is going to become more crucial 216 00:10:45,640 --> 00:10:49,280 than ever, to be honest, because AI right now where the, if you 217 00:10:49,280 --> 00:10:52,760 see where the kind of targeting of AI is, it's optimising your 218 00:10:52,760 --> 00:10:55,600 Gantt chart and scheduling, optimising things with a lot of 219 00:10:55,600 --> 00:10:58,840 clear discrete tasks. And that's because to kind of 220 00:10:58,840 --> 00:11:02,000 break it down further, the most powerful aspects of AI right now 221 00:11:02,000 --> 00:11:05,160 is known as supervised learning. Supervised learning base is 222 00:11:05,160 --> 00:11:09,000 dependent on you labelling data. So even to know what to label, 223 00:11:09,040 --> 00:11:12,480 how to use it, what's the bigger contextual picture, That's where 224 00:11:12,480 --> 00:11:14,520 project management spends a lot of time. 225 00:11:14,520 --> 00:11:17,400 So essentially what I think AI is doing, at least in the 226 00:11:17,400 --> 00:11:19,680 project management world, I'm not saying it's, it's, it's 227 00:11:19,800 --> 00:11:23,120 universally great for everyone. But what I'm saying is, is that 228 00:11:23,240 --> 00:11:25,640 right now, at least we can think about later what's going to 229 00:11:25,640 --> 00:11:27,480 happen. AI is really focused on the 230 00:11:27,520 --> 00:11:30,600 execution, discrete, advertised kind of tasks, but even how to 231 00:11:30,600 --> 00:11:32,920 label it, what to inform it, what kind of information you 232 00:11:32,920 --> 00:11:35,120 need, it still requires a tremendous amount of project 233 00:11:35,120 --> 00:11:37,800 management. So I think actually it's going 234 00:11:37,800 --> 00:11:41,920 to make some aspects that are taking a lot of time easier, but 235 00:11:41,920 --> 00:11:44,560 it cannot possibly, at least at this moment that from a scene in 236 00:11:44,560 --> 00:11:46,600 the trend, replace what a project manager does. 237 00:11:47,520 --> 00:11:49,560 Hi, I'm Ricky Hanson. I'm a programme manager, 238 00:11:49,560 --> 00:11:50,760 actually outside Business School. 239 00:11:52,640 --> 00:11:55,400 I'm wondering. I mean, my impression is that 240 00:11:55,400 --> 00:12:00,480 most of us aren't even ready. For AI, what is your perception 241 00:12:00,480 --> 00:12:03,520 of how big? Of a challenge it's going to be 242 00:12:03,520 --> 00:12:07,000 and how we begin to sort of. Address the issue of maturity in 243 00:12:07,000 --> 00:12:10,240 terms of. Having the basics right, you 244 00:12:10,240 --> 00:12:10,840 know most of. Us. 245 00:12:10,840 --> 00:12:12,520 I don't. Think have complete or even 246 00:12:12,520 --> 00:12:17,080 accurate data and if we sort of start getting out and buying all 247 00:12:17,080 --> 00:12:17,880 sorts. Of stuff. 248 00:12:18,880 --> 00:12:20,200 How do we? How do we get to? 249 00:12:20,200 --> 00:12:22,080 Address that challenge. Yeah. 250 00:12:22,080 --> 00:12:24,560 So I think this is, it's really interesting your question to 251 00:12:24,560 --> 00:12:27,160 bring this up because the salary trend service seems to directly 252 00:12:27,160 --> 00:12:30,120 reflect that there's this excitement of what AI can do and 253 00:12:30,120 --> 00:12:32,640 the same time real worry about what are the skills I need to be 254 00:12:32,640 --> 00:12:36,200 able to use this effectively. I would say, OK, so there's a 255 00:12:36,200 --> 00:12:39,360 question about maturity. One is kind of you have to think 256 00:12:39,360 --> 00:12:42,120 about it, is this technology going to continue advancing? 257 00:12:42,120 --> 00:12:44,480 I can say one trend we see, because I know the biggest one 258 00:12:44,480 --> 00:12:46,840 people are using our language learning models, which is 259 00:12:46,920 --> 00:12:50,800 Chachi, BT, etcetera. The belief in the consensus 260 00:12:50,800 --> 00:12:53,760 later on is that it's going to get so good about understanding 261 00:12:53,760 --> 00:12:56,680 you that you won't even have to prompt it in a particular way. 262 00:12:56,680 --> 00:12:58,600 So that's kind of to think through it. 263 00:12:58,600 --> 00:13:01,800 I think in terms of data, I think where the most promising 264 00:13:01,800 --> 00:13:03,440 trends are is to use public data. 265 00:13:03,440 --> 00:13:06,000 So I've seen some really interesting work using satellite 266 00:13:06,000 --> 00:13:10,160 data, for example, and using that and trying to kind of use 267 00:13:10,280 --> 00:13:13,600 AI to kind of map the gaps between real interesting trend 268 00:13:13,600 --> 00:13:16,360 on that front is what's going on with Unreal Engine. 269 00:13:16,640 --> 00:13:20,120 So Unreal it, the AI algorithm are being so good at even 270 00:13:20,120 --> 00:13:22,880 training artificial data that they're now using physical 271 00:13:23,360 --> 00:13:24,640 objects like autonomous vehicles. 272 00:13:24,640 --> 00:13:26,160 They're training them on artificial data. 273 00:13:26,160 --> 00:13:28,680 Now it's not even on physical because AI is filling the gaps 274 00:13:28,680 --> 00:13:31,640 in, in geographic things. So I think start with the public 275 00:13:31,640 --> 00:13:33,560 data. In fact, you don't want to give 276 00:13:33,560 --> 00:13:36,320 it, I think right now corporate data, it's unclear what that is 277 00:13:36,320 --> 00:13:39,480 going to happen with, but public data and even maybe satellite 278 00:13:39,480 --> 00:13:41,520 data is a way to start at least to, to build that. 279 00:13:41,520 --> 00:13:44,040 I'm happy to talk further. It's a more in depth question. 280 00:13:46,760 --> 00:13:49,520 The skills gap in the project profession is cause for concern, 281 00:13:49,920 --> 00:13:53,600 but is the scale of the problem overstated and has the skill set 282 00:13:53,600 --> 00:13:55,800 to the project manager needs changed? 283 00:13:56,320 --> 00:13:58,560 These questions were tackled in a panel session at the 284 00:13:58,560 --> 00:14:01,360 conference. Let's hear now from Lorraine 285 00:14:01,360 --> 00:14:04,960 Bellinger of Bird and Bird, Derek Allen from Shell and Karen 286 00:14:04,960 --> 00:14:08,120 Skinner of Life ARC, who were joined by moderator Michelle 287 00:14:08,120 --> 00:14:15,000 Richmond, MBE and APM Trustee. Is there a skill shortage or are 288 00:14:15,000 --> 00:14:16,400 we just looking in the wrong place? 289 00:14:17,040 --> 00:14:20,760 Myself, I came from APA background and I just naturally 290 00:14:21,080 --> 00:14:24,200 transferred into a project management role because I was 291 00:14:24,200 --> 00:14:26,520 quite lucky where I worked. They recognised that actually 292 00:14:26,520 --> 00:14:29,760 this was a role, it was needed, but it didn't really exist in 293 00:14:29,760 --> 00:14:32,920 legal. So I I kind of moved across into 294 00:14:32,920 --> 00:14:34,560 it. They recognised skills in me 295 00:14:34,800 --> 00:14:37,040 that I didn't see in myself. I was kind of doing this role 296 00:14:37,040 --> 00:14:39,240 anyway. So I've taken that with me in my 297 00:14:39,240 --> 00:14:41,640 career and I've now started looking elsewhere. 298 00:14:42,120 --> 00:14:45,160 So don't just look in the legal world, don't just look in the 299 00:14:45,160 --> 00:14:47,840 project management world. There are those fundamental 300 00:14:47,840 --> 00:14:50,560 skill sets, There are those organisational skills, 301 00:14:50,560 --> 00:14:54,040 communication skills that exist in people that are all 302 00:14:54,040 --> 00:14:56,360 transferable if you have the right attitude. 303 00:14:56,360 --> 00:14:57,840 There's a lot of those skills you can teach. 304 00:14:58,240 --> 00:15:01,000 So personally I think maybe we might just be looking in the 305 00:15:01,000 --> 00:15:04,440 wrong place. If you, if you look at some of 306 00:15:04,440 --> 00:15:08,720 the data that we're getting, you see these, these figures of all 307 00:15:08,720 --> 00:15:11,320 the projects that the world is planning to do over the next 5 308 00:15:11,320 --> 00:15:14,680 to 10 years, the trillion dollars the UK government are 309 00:15:14,680 --> 00:15:18,240 planning to spend on projects. It's just unviable that we're 310 00:15:18,240 --> 00:15:19,760 actually going to do all those projects. 311 00:15:19,960 --> 00:15:23,320 And there's a, there's a skill shortage right across the patch 312 00:15:23,800 --> 00:15:25,640 to do all the work that we're planning to do. 313 00:15:25,840 --> 00:15:30,200 So I don't, I don't believe that the project manager is the, is a 314 00:15:30,200 --> 00:15:32,960 deciding factor. In fact, I've never, ever seen a 315 00:15:32,960 --> 00:15:37,160 project not going ahead because we've not got a project manager. 316 00:15:37,840 --> 00:15:40,920 They go ahead. We either find a project manager 317 00:15:41,120 --> 00:15:45,280 or we find somebody to be the project manager, which is what 318 00:15:45,280 --> 00:15:46,240 you're trying to say. Yeah. 319 00:15:46,640 --> 00:15:50,200 So we, we, we never struggle to to find somebody to put in that 320 00:15:50,200 --> 00:15:52,320 space. Whether it's the right person or 321 00:15:52,320 --> 00:15:54,840 not, that's another story. But I don't believe we see a 322 00:15:54,840 --> 00:15:58,000 skills shortage. It's maybe a competence question 323 00:15:58,000 --> 00:16:01,680 rather than can we fill a box with a project person? 324 00:16:02,400 --> 00:16:04,720 There's huge number of transferable skills I think, 325 00:16:04,720 --> 00:16:07,640 which is great. But I would say certainly in the 326 00:16:07,640 --> 00:16:11,760 in the life science sector, technology is moving on at such 327 00:16:11,760 --> 00:16:15,080 a rate that you end up having a bit of an experience gap. 328 00:16:15,080 --> 00:16:18,040 So I think, you know, if people think of medicines, they think 329 00:16:18,040 --> 00:16:23,280 of tablets and capsules, but actually the new wave is cell 330 00:16:23,280 --> 00:16:26,400 therapies, gene therapies. It's, it's just a very different 331 00:16:26,400 --> 00:16:28,720 space of technology. So people that have the 332 00:16:28,720 --> 00:16:32,320 experience in that area and actually digital and data in our 333 00:16:32,320 --> 00:16:35,800 sector is growing at such a rate that actually having the people 334 00:16:35,800 --> 00:16:38,280 to kind of keep up with that, that's a particular challenge I 335 00:16:38,320 --> 00:16:41,560 would say because the scientists are moving so fast and the 336 00:16:41,560 --> 00:16:44,280 technologies are moving so fast. So it's project management 337 00:16:44,280 --> 00:16:45,960 actually trying to keep up with that. 338 00:16:45,960 --> 00:16:49,760 So that we're, and I think that's all about continuous 339 00:16:49,760 --> 00:16:52,160 learning all the time because I think you can learn you, you 340 00:16:52,160 --> 00:16:54,080 just need to kind of keep up with the trend. 341 00:16:54,520 --> 00:16:57,320 So looking a little futuristic, we've talked a little about the 342 00:16:57,320 --> 00:17:00,040 skills you would look for when you recruit, when you recruit. 343 00:17:01,080 --> 00:17:03,760 But what new? Skills are you sort of looking 344 00:17:03,760 --> 00:17:05,280 for in your. Project managers. 345 00:17:06,359 --> 00:17:08,640 I don't think it's new skills. I think it's a real change in 346 00:17:08,640 --> 00:17:10,680 emphasis. Traditionally, we think of 347 00:17:10,680 --> 00:17:13,119 project managers as somebody that delivers a technical piece 348 00:17:13,119 --> 00:17:16,839 of scope, somebody that builds a bridge or somebody that builds a 349 00:17:16,839 --> 00:17:21,440 house or an IT, an IT package or something from, from the, the 350 00:17:21,440 --> 00:17:25,400 health health industry. And with the way the world is, 351 00:17:25,920 --> 00:17:29,880 the way people want to live their lives and the technology, 352 00:17:31,440 --> 00:17:34,720 the project manager coming up needs to be more of a, a mini 353 00:17:34,720 --> 00:17:37,240 CEO. He needs to think of more of the 354 00:17:37,240 --> 00:17:39,920 people aspects of the business. He needs to be more of a 355 00:17:39,920 --> 00:17:44,080 visionary, leading a team, communicating and, and it's a 356 00:17:44,080 --> 00:17:47,560 much more on the people side. I often say to people project 357 00:17:47,560 --> 00:17:49,080 managers are in the people business. 358 00:17:49,360 --> 00:17:53,000 And I think that's no more than ever the case that we need to be 359 00:17:53,000 --> 00:17:55,160 in the people business. And I think that's where we need 360 00:17:55,160 --> 00:17:58,400 to evolve that let other people deal with the technical and let 361 00:17:58,400 --> 00:18:01,960 the new project managers deal with with with a broad spectrum 362 00:18:01,960 --> 00:18:04,280 of what it takes to take people on a journey. 363 00:18:05,120 --> 00:18:09,560 The the way we do this, like PMS are expected to have a wide 364 00:18:09,560 --> 00:18:12,280 range of skill sets. That's not going away for the 365 00:18:12,280 --> 00:18:15,000 way we operate in my firm, we've actually split the skills out. 366 00:18:15,000 --> 00:18:18,280 So we have a separate continuous improvement team, we have a 367 00:18:18,280 --> 00:18:20,720 separate tech team, and everybody works together. 368 00:18:20,720 --> 00:18:24,240 There's invariably overlap, but actually separating those skill 369 00:18:24,240 --> 00:18:26,760 sets out means that people can focus on what it is they are 370 00:18:26,760 --> 00:18:29,320 there to deliver. And you do have to evolve with 371 00:18:29,320 --> 00:18:31,320 the changing market. Like Karen mentioned earlier, 372 00:18:31,320 --> 00:18:35,240 tech is continually evolving in the legal space. 373 00:18:35,240 --> 00:18:38,440 It's quite saturated, frankly. So you have to really assess 374 00:18:38,440 --> 00:18:40,560 what you're using, what is actually the right tool for the 375 00:18:40,560 --> 00:18:43,040 job. But in terms of project 376 00:18:43,040 --> 00:18:45,640 management, I think it's just growing those skills, developing 377 00:18:45,640 --> 00:18:48,680 those skills, but new skills, not necessarily. 378 00:18:51,920 --> 00:18:54,720 The conference closed with an inspirational keynote from 379 00:18:54,720 --> 00:18:58,680 Doctor Anne Marie Maffedon, MBE, a mathematician and STEM 380 00:18:58,680 --> 00:19:01,320 advocate. Anne Marie is Co founder of the 381 00:19:01,320 --> 00:19:04,880 award-winning social enterprise STEMET, a respected thought 382 00:19:04,880 --> 00:19:07,680 leader in the tech space and Trustee at the Institute for the 383 00:19:07,680 --> 00:19:11,160 Future of Work. She gave attendees 3 tips for 384 00:19:11,160 --> 00:19:13,760 leaning into the opportunity of new technologies. 385 00:19:16,160 --> 00:19:17,680 So I think there's a great opportunity for us as we 386 00:19:17,680 --> 00:19:21,160 navigate tomorrow now to maybe do three kind of buckets of 387 00:19:21,160 --> 00:19:22,240 things. So I wanted to leave you kind of 388 00:19:22,240 --> 00:19:26,160 three mindsets of how we prepare for this, how we're not fearful, 389 00:19:26,200 --> 00:19:29,760 but actually we lean into the opportunity that we have in 390 00:19:30,000 --> 00:19:32,200 working with these technologies, deploying them and making 391 00:19:32,200 --> 00:19:34,800 decisions about them. But also being humans here at 392 00:19:34,800 --> 00:19:38,160 the beginning of this revolution for the change in the 393 00:19:38,160 --> 00:19:41,040 relationship we might have with work, but the relationships we 394 00:19:41,040 --> 00:19:43,360 might have with all manner of other things in life as a result 395 00:19:43,360 --> 00:19:46,840 of good use of this technology. And we have this concept of good 396 00:19:46,840 --> 00:19:48,600 work at the Institute for the Future of Work. 397 00:19:48,600 --> 00:19:51,600 And you know, actually, if we if we set these milestones, if we 398 00:19:51,600 --> 00:19:55,400 set these guidelines, then this doesn't have to be a disruptive 399 00:19:55,400 --> 00:19:56,800 change. This can be something that can 400 00:19:56,800 --> 00:19:59,040 be really good actually for all of us if we're intentional about 401 00:19:59,040 --> 00:20:01,400 it from the beginning. So the first concept, first 402 00:20:01,400 --> 00:20:04,760 mindset is that of growing. And I think you're all doing 403 00:20:04,760 --> 00:20:06,760 really well by being here. So you can pat yourselves on the 404 00:20:06,760 --> 00:20:07,720 back. You've already done your first 405 00:20:07,720 --> 00:20:10,520 part of homework. By knowing that this is 406 00:20:10,520 --> 00:20:12,840 something we have to continue to learn about and having a growth 407 00:20:12,840 --> 00:20:15,640 mindset on all of these things. As you will know if you're a 408 00:20:15,640 --> 00:20:18,240 technologist, there's always something new on the horizon. 409 00:20:18,240 --> 00:20:20,640 If we were doing this conference two years ago, it would have 410 00:20:20,680 --> 00:20:24,520 been Web 3 and blockchain. We're doing it now, it's AI. 411 00:20:24,800 --> 00:20:26,520 Two years time, it'll be Googoo Gaga. 412 00:20:26,800 --> 00:20:28,200 Two years after, it'll be Higgy Hagger. 413 00:20:28,200 --> 00:20:29,880 There's always something on the horizon, right? 414 00:20:29,880 --> 00:20:32,160 I don't know if there's anyone here that would proudly proclaim 415 00:20:32,160 --> 00:20:36,000 themselves as a HTM O3 expert. No, you'd be a sucker. 416 00:20:36,000 --> 00:20:39,240 We're on HTML5 now, right? It's always continually changing 417 00:20:39,240 --> 00:20:41,120 and moving on. And so you have to adopt A 418 00:20:41,120 --> 00:20:44,360 growth mindset. The point isn't really to be an 419 00:20:44,360 --> 00:20:47,640 expert in all these AI things and data things and quantum 420 00:20:47,640 --> 00:20:50,520 things and Google Gaga things and Higgy Hagy things. 421 00:20:50,840 --> 00:20:54,040 But the point is just to know more this week than you knew 422 00:20:54,040 --> 00:20:57,920 last week, more next month than you knew last month, and more 423 00:20:57,920 --> 00:20:59,080 this year than you knew last year. 424 00:20:59,320 --> 00:21:02,680 Just be heading in that direction, continually growing 425 00:21:02,680 --> 00:21:05,480 in that knowledge, going just outside your comfort zone 426 00:21:05,480 --> 00:21:06,920 because that's where the magic happens. 427 00:21:06,920 --> 00:21:10,400 And actually, there are a lot of folks who know or proclaim to 428 00:21:10,400 --> 00:21:12,200 know quite a lot about the technology itself, but they 429 00:21:12,200 --> 00:21:15,720 don't know about the life and the experiences and the projects 430 00:21:16,000 --> 00:21:17,760 and what's going on around it and where that technology is 431 00:21:17,760 --> 00:21:19,960 going to be deployed. And that information is just as 432 00:21:19,960 --> 00:21:23,000 important as the core of that technology itself. 433 00:21:23,800 --> 00:21:26,920 Secondly, one of the best ways that we have to learn we can 434 00:21:26,920 --> 00:21:30,120 take from the Agile framework and that is of experimentation 435 00:21:30,120 --> 00:21:32,880 and of making mistakes. It's important for us to be 436 00:21:32,880 --> 00:21:36,200 incredibly iterative and, and vigilant of the lessons that 437 00:21:36,200 --> 00:21:39,120 we're learning and the results of the experiments as we run 438 00:21:39,120 --> 00:21:40,960 them. We have this at the at the 439 00:21:40,960 --> 00:21:42,640 institute. There's lots of tools that we 440 00:21:42,640 --> 00:21:45,640 have for audits and assessments that folks can do on tools that 441 00:21:45,640 --> 00:21:47,320 they're bringing them in so they don't end up, you know, 442 00:21:47,320 --> 00:21:49,840 inadvertently making poor decisions or helping managers 443 00:21:49,840 --> 00:21:52,240 make really bad decisions. But there's a lot of folks that 444 00:21:52,240 --> 00:21:54,640 we see end up doing the audit and kind of put it on the shelf 445 00:21:55,160 --> 00:21:57,480 and don't come and reflect that then in the work or in the next 446 00:21:57,480 --> 00:21:59,600 iteration or in the next set of decisions that they're making. 447 00:22:00,080 --> 00:22:02,560 So for goodness sake, please use it as part of your iterative 448 00:22:02,560 --> 00:22:05,240 processes, right? Continue to recheck because 449 00:22:05,240 --> 00:22:08,280 they're changing the underlying technology, the data sets, all 450 00:22:08,280 --> 00:22:11,680 of those things all the time. So keep an eye on that and act 451 00:22:11,680 --> 00:22:14,720 accordingly. But also as if, especially if 452 00:22:14,720 --> 00:22:17,360 you're a leader and if this is something within your gift build 453 00:22:17,360 --> 00:22:20,320 environments where folks are able to experiment and iterate 454 00:22:20,840 --> 00:22:26,800 and make mistakes and then make higher quality mistakes and then 455 00:22:26,800 --> 00:22:29,240 continue to increase in the quality of the mistake that 456 00:22:29,240 --> 00:22:31,040 they're making. And let's all work in this 457 00:22:31,040 --> 00:22:34,320 experimentation and know that there will be mistakes, but what 458 00:22:34,320 --> 00:22:36,760 we have to do is learn the lessons from those mistakes as 459 00:22:36,760 --> 00:22:38,600 we go. The third one is impact. 460 00:22:39,000 --> 00:22:43,760 And this one I think ends up not feeling less technical, but 461 00:22:43,760 --> 00:22:47,640 it's, it's still an important qualitative part of us ensuring 462 00:22:47,640 --> 00:22:51,000 that we can have different folks be a part of this journey and we 463 00:22:51,000 --> 00:22:53,240 can ensure the safety of what we're deploying. 464 00:22:53,440 --> 00:22:56,960 As project managers, you have to recognise and use the influence 465 00:22:56,960 --> 00:22:58,640 that you have. But this isn't just in the 466 00:22:58,640 --> 00:23:00,480 project. This is in the way that you do 467 00:23:00,480 --> 00:23:02,880 business, the way that you run your projects, the way you have 468 00:23:02,880 --> 00:23:15,000 your social norms set around it. That concludes our wrap up of 469 00:23:15,000 --> 00:23:18,200 this year's APM Conference. The annual event will be 470 00:23:18,200 --> 00:23:22,160 returning to Coventry on the 11th and 12th of June 2025. 471 00:23:22,880 --> 00:23:25,280 To register your interest, follow the link in the episode 472 00:23:25,280 --> 00:23:29,040 description. APM runs more than 200 events 473 00:23:29,040 --> 00:23:32,080 every year, ranging from webinars and award ceremonies to 474 00:23:32,080 --> 00:23:35,320 day long conferences. To find out more, head to 475 00:23:35,400 --> 00:23:40,320 apm.org.uk/events. If you want to get in touch with 476 00:23:40,320 --> 00:23:43,200 your feedback, suggestions or ideas for topics we should 477 00:23:43,200 --> 00:23:48,520 cover, e-mail us at apmpodcast@thinkpublishing.co.uk. 478 00:23:49,360 --> 00:23:52,720 Spotify users can also leave us a comment directly within the 479 00:23:52,720 --> 00:23:55,360 Spotify app. That's it for this episode. 480 00:23:55,560 --> 00:23:56,520 Thanks for listening.