OK, so let's just have a think about what this AI thing is, very, very briefly. So everyone thinks that artificial intelligence actually means artificial intelligence, OK. Artificial doesn't mean it's not human. You know that, don't you? OK, What it means is, do you know how inside your brain, I talked about your brain, there are these long, stringy, stringy things, OK, they're called neurons. Have you come across this? So what happens is these bits listen out. Hey, how's it going?
OK, And then they find that it's warm or it's noisy and they tell the nucleus move the foot. So the nucleus goes bang and sends a message which goes and then your foot moves, blah, blah. That's basically it. OK, so that's how a neuron works. So somebody had an idea like in the 1950s forties, something like that. Hey, why don't we make computers like that? Why don't they set them up so they have lots of inputs like that, OK. And then when you put the inputs in, we'll see this input's
really important. We'll give it a weight, and then we'll add up all the numbers. And then there's a little box here, and this box then says go and do X, OK. And that basically is what AI is. It's just a neural network. That was Eddie opening talking about AI at this year's APM conference. We'll hear more from Eddie shortly as we take a deep dive into the future of the project profession with a compilation of some of our favourite insights from the 2024 conference.
This is the APM podcast brought to you by the chartered body for the project profession. My name is Emma Devita and I'm the editor of Apms quarterly journal Project and your host held in Coventry in June. The theme for this year's APM Conference was Navigating Tomorrow, Future Skills for Project Professionals. The event invited project leaders and experts on future trends to unpack the rapidly changing landscape that projects are being delivered in.
AI and data literacy were among the hot topics, including both the opportunities and threats of new technology for project managers employment prospects. The conference also considered whether there is a skills gap in the profession and there must have competencies to future proof your career. So listen on as our expert speakers explore how to navigate tomorrow. First, let's hear more from Eddie. Opening session on developing your emotional intelligence in an ever more digital world.
Eddie is an educator, innovator, and digital pioneer. He explained what AI really is and why it has a tendency to hallucinate. And that basically is what AI is. It's just a neural network. All it's doing is 4 by 4 matrix calculations.
That's all it's doing. The reason it's now popular because it's been knocking around for a long time, it's because of gamers, you know, gamers, you know, when you're doing 3D gaming, the type of computer chip you need in your machine comes from a company called NVIDIA. Too late to buy their stock, OK. And it got really cheap. So suddenly we could do masses of calculations like that. So that's basically what it is. At the base of it, there is no intelligence. All it's doing is churning
through information. So the stuff you'd have come across is what's called generative AI. What they do is they feed in a whole bunch of numbers here. So let's say they've got 100 bits of cases about people being I'll, OK, they feed in, I don't know, eighty of them. And say the person was tall, they were long, they were hungry, they had a blood factor A they feed those in and then they get the machine to do the calculations. And then they say, send them to hospital.
And if that's the correct answer, it goes, great. Then they feed the next day. If it's the wrong answer, then they adjust the weights until for those 80 things it's approximately correct. Then they check with the last 20 and it's trained. So you have heard about training on data, training on large language, language models. That's all it is. But of course, if you're selling, you know the trick. If you're buying stuff, you make it simple. When you're selling it, as they
say, bullshit baffles brains. OK, so you put, you guys are so serious. OK, so, so you make it so. Oh, we're training it with our LLM. Oh yes, our LLM has data which is within the GDP. You know, they'll say all these things and then you take your past and give it to them. But it's really very simple. It works out patterns from huge amounts of data fast. It takes text which you've written painstakingly and it corrects all the grammar and makes it readable.
OK, It's marvellous. It helps your AI self driving car work its way and not fall into potholes. It's brilliant, am I right? And remember what I said about the computers, all those advantages. So what's the challenge? It's an interpolation machine. It feeds on the data you've given it. So when you get a report and input back from any AI, you feed
it into. If you look at this and your beautiful brain makes sense of it and you give it all the attributes because you anthropomorphize it, you think it's human, you think it's smart. This is the trick you should always do with any API you play with. Ask you something about something general, like how many walls have there been or what's the growth of our market within real estate? OK, and you'll get this brilliant answer.
Then ask it a question about something where you're the world expert or you're pretty damn good. What do you think happens? It's gibberish, because when you use a large model, you know the shape of the world is generally like that. So there's Einstein over here. Then there's the large long language model over here. For most of Einstein's life, he was lucky. He was a bit of a Nutter. The leading edge, cutting edge, best ideas, best ways of doing things are not the average.
Does it make sense? OK, so that's one thing. The 2nd is there's no human body involved. So AI, although it's very fast, it doesn't know anything. So it does this thing called hallucination. Have you come across this? Hallucinations are particularly easy to show with pictures. But what I came across the other day, which made me laugh is if you go to say Snapchat TBT and you type in I have a problem, it says yes, can I help? It says I've got a really complicated problem.
I've got a man with who's by a river with a with a boat. Can you help? It says yes, first send the cabbage across, then the lion. It answers a question you haven't asked it because so many people have put that thing in that it has learned the answer to a question you haven't asked. Isn't that just brilliant? That's a hallucination. So it's going to help us because it's fast, it's cheap, but it's not. A human being has no ethics.
It can't be fixed. Next, we turn to Laura Ellis, Head of Technology Forecasting at the BBC, who spoke about the balance between analytics and people skills and project decisions. In the following clip, she discusses her fascination with AI, the potential crisis in the workplace, and the fight to preserve human creativity.
What I think we're learning in the BBC at the moment about this, integrating humans, data and AI, and what we say to people when they ask us about this is learn as much as you can. This is something which if you are a remotely interested in technology, it is fascinating. If you don't like technology and you find it scary, it's still fascinating. And I would say do everything you can to overcome that fear because I had it once and now
I'm obsessed. And I think the more you know about it and the more you know about things like how these data sets are trained, who's making the decisions, where they're being made and how they're going to affect our businesses, the better keep coming back to this. But, you know, take great care with how data is used. I am inordinately fascinated with Co pilot. Does anybody use Microsoft Co
pilot in the room? So Co pilot is one of the first forays we've made in the BBC into essentially kind of, you know, really starting to use this stuff for real. And the first demo we went to, we one of our tech reporters wrote it up and it was quite chilling because she said the person that was sitting in the room with her looked at what Microsoft Co pilot could do. And it summarised meetings and
it said who'd said what. And then it gave a kind of 30% of people agreed with this point of view. Then it made a chart about it. And then it, you know, created an action list. It was absolutely amazing. And she looked at it and said, oh, that's three jobs I don't need to appoint. And there is a, you know, a, a potential pending crisis about what we value in the workplace
and, and valuing our colleagues. Microsoft at their most recent event, which was last week, said we've generated you an AI colleague as well, and you can give it a personality and have it in the room with you. Do we really want that? You know, do we really want that? And do we want to lose that really valuable entry level of professional, you know, who comes in and does some of the things that we're going to be ceding to AI? Do we do we want to lose them? I don't think we do.
So how do we keep them? How do we make sure that we're keeping them in the loop and we don't just throw away that effort, but then we perhaps use, use them to, to feed in, in data and to understand it and to sort of train the AI. I think we need to be really careful about our talent management as this as this happens, because we risk cutting out, you know, the, the, the nursery slopes of a lot of our professions if we're not careful.
And finally, I just think we need to fight for human creativity, human agency, rather than creativity to be, to be preserved. So when I think of journalism and I think of creativity, there is something incredibly special about a human looking at something and giving a representation of that, whether it's in journalism and art, whether it's input into a project, whether it's, you know, that understanding of human nature. All of those things are not available to us yet in AI.
And I think if we start to create too much of an AI driven economy and lose those things, we'll be much the poorer for it. So I think we need to be incredibly careful that we fight and we will have to fight, hopefully not in a kind of Terminator way, but we will have to fight to make sure that, you know, as we move forward. The old quote about, you know, your job isn't going to be taken by AI. It's going to be taken by somebody who uses AI better than
you. It's a horrible quote probably, and there's a grain of truth in it. But I think it's designed to scare us. And we don't have to be scared. We can push back and say, OK, fine, you know, we will understand AI, but we that doesn't mean we have to seed all of our sort of, you know, lower level jobs to it. It doesn't mean we have to see your creativity to it, and it doesn't mean that we have to end up doing the dishes when it writes us a song.
As AI matures, will project managers find themselves out of a job? It's the question on everyone's mind, and it's when our conference audience put to Daniel Armanios, BT, Professor of Major Programme Management at SCII Business School. Here's Daniel's response to that conundrum, followed by another audience members question about the readiness of the profession to embrace AII.
Think project management is going to become more crucial than ever, to be honest, because AI right now where the, if you see where the kind of targeting of AI is, it's optimising your Gantt chart and scheduling, optimising things with a lot of clear discrete tasks. And that's because to kind of break it down further, the most powerful aspects of AI right now is known as supervised learning. Supervised learning base is
dependent on you labelling data. So even to know what to label, how to use it, what's the bigger contextual picture, That's where project management spends a lot of time. So essentially what I think AI is doing, at least in the project management world, I'm not saying it's, it's, it's universally great for everyone. But what I'm saying is, is that right now, at least we can think about later what's going to
happen. AI is really focused on the execution, discrete, advertised kind of tasks, but even how to label it, what to inform it, what kind of information you need, it still requires a tremendous amount of project management. So I think actually it's going to make some aspects that are taking a lot of time easier, but it cannot possibly, at least at this moment that from a scene in the trend, replace what a project manager does.
Hi, I'm Ricky Hanson. I'm a programme manager, actually outside Business School. I'm wondering. I mean, my impression is that most of us aren't even ready. For AI, what is your perception of how big? Of a challenge it's going to be and how we begin to sort of. Address the issue of maturity in terms of. Having the basics right, you know most of. Us. I don't. Think have complete or even accurate data and if we sort of start getting out and buying all sorts. Of stuff.
How do we? How do we get to? Address that challenge. Yeah. So I think this is, it's really interesting your question to bring this up because the salary trend service seems to directly reflect that there's this excitement of what AI can do and the same time real worry about what are the skills I need to be able to use this effectively. I would say, OK, so there's a question about maturity. One is kind of you have to think about it, is this technology going to continue advancing?
I can say one trend we see, because I know the biggest one people are using our language learning models, which is Chachi, BT, etcetera. The belief in the consensus later on is that it's going to get so good about understanding you that you won't even have to prompt it in a particular way. So that's kind of to think through it. I think in terms of data, I think where the most promising trends are is to use public data.
So I've seen some really interesting work using satellite data, for example, and using that and trying to kind of use AI to kind of map the gaps between real interesting trend on that front is what's going on with Unreal Engine. So Unreal it, the AI algorithm are being so good at even training artificial data that they're now using physical objects like autonomous vehicles. They're training them on artificial data. Now it's not even on physical because AI is filling the gaps
in, in geographic things. So I think start with the public data. In fact, you don't want to give it, I think right now corporate data, it's unclear what that is going to happen with, but public data and even maybe satellite data is a way to start at least to, to build that. I'm happy to talk further. It's a more in depth question.
The skills gap in the project profession is cause for concern, but is the scale of the problem overstated and has the skill set to the project manager needs changed? These questions were tackled in a panel session at the conference. Let's hear now from Lorraine Bellinger of Bird and Bird, Derek Allen from Shell and Karen Skinner of Life ARC, who were joined by moderator Michelle Richmond, MBE and APM Trustee. Is there a skill shortage or are we just looking in the wrong place?
Myself, I came from APA background and I just naturally transferred into a project management role because I was quite lucky where I worked. They recognised that actually this was a role, it was needed, but it didn't really exist in legal. So I I kind of moved across into it. They recognised skills in me that I didn't see in myself. I was kind of doing this role anyway. So I've taken that with me in my career and I've now started looking elsewhere.
So don't just look in the legal world, don't just look in the project management world. There are those fundamental skill sets, There are those organisational skills, communication skills that exist in people that are all transferable if you have the right attitude. There's a lot of those skills you can teach. So personally I think maybe we might just be looking in the
wrong place. If you, if you look at some of the data that we're getting, you see these, these figures of all the projects that the world is planning to do over the next 5 to 10 years, the trillion dollars the UK government are planning to spend on projects. It's just unviable that we're actually going to do all those projects. And there's a, there's a skill shortage right across the patch to do all the work that we're planning to do.
So I don't, I don't believe that the project manager is the, is a deciding factor. In fact, I've never, ever seen a project not going ahead because we've not got a project manager. They go ahead. We either find a project manager or we find somebody to be the project manager, which is what you're trying to say. Yeah. So we, we, we never struggle to to find somebody to put in that space. Whether it's the right person or not, that's another story. But I don't believe we see a
skills shortage. It's maybe a competence question rather than can we fill a box with a project person? There's huge number of transferable skills I think, which is great. But I would say certainly in the in the life science sector, technology is moving on at such a rate that you end up having a bit of an experience gap. So I think, you know, if people think of medicines, they think of tablets and capsules, but actually the new wave is cell
therapies, gene therapies. It's, it's just a very different space of technology. So people that have the experience in that area and actually digital and data in our sector is growing at such a rate that actually having the people to kind of keep up with that, that's a particular challenge I would say because the scientists are moving so fast and the technologies are moving so fast. So it's project management actually trying to keep up with that.
So that we're, and I think that's all about continuous learning all the time because I think you can learn you, you just need to kind of keep up with the trend. So looking a little futuristic, we've talked a little about the skills you would look for when you recruit, when you recruit. But what new? Skills are you sort of looking for in your. Project managers. I don't think it's new skills. I think it's a real change in
emphasis. Traditionally, we think of project managers as somebody that delivers a technical piece of scope, somebody that builds a bridge or somebody that builds a house or an IT, an IT package or something from, from the, the health health industry. And with the way the world is, the way people want to live their lives and the technology, the project manager coming up needs to be more of a, a mini CEO. He needs to think of more of the
people aspects of the business. He needs to be more of a visionary, leading a team, communicating and, and it's a much more on the people side. I often say to people project managers are in the people business. And I think that's no more than ever the case that we need to be
in the people business. And I think that's where we need to evolve that let other people deal with the technical and let the new project managers deal with with with a broad spectrum of what it takes to take people on a journey. The the way we do this, like PMS are expected to have a wide range of skill sets. That's not going away for the way we operate in my firm, we've actually split the skills out.
So we have a separate continuous improvement team, we have a separate tech team, and everybody works together. There's invariably overlap, but actually separating those skill sets out means that people can focus on what it is they are there to deliver. And you do have to evolve with the changing market. Like Karen mentioned earlier, tech is continually evolving in the legal space.
It's quite saturated, frankly. So you have to really assess what you're using, what is actually the right tool for the job. But in terms of project management, I think it's just growing those skills, developing those skills, but new skills, not necessarily. The conference closed with an inspirational keynote from Doctor Anne Marie Maffedon, MBE, a mathematician and STEM
advocate. Anne Marie is Co founder of the award-winning social enterprise STEMET, a respected thought leader in the tech space and Trustee at the Institute for the Future of Work. She gave attendees 3 tips for leaning into the opportunity of new technologies. So I think there's a great opportunity for us as we navigate tomorrow now to maybe do three kind of buckets of
things. So I wanted to leave you kind of three mindsets of how we prepare for this, how we're not fearful, but actually we lean into the opportunity that we have in working with these technologies, deploying them and making decisions about them. But also being humans here at the beginning of this revolution for the change in the relationship we might have with work, but the relationships we might have with all manner of other things in life as a result
of good use of this technology. And we have this concept of good work at the Institute for the Future of Work. And you know, actually, if we if we set these milestones, if we set these guidelines, then this doesn't have to be a disruptive change. This can be something that can be really good actually for all of us if we're intentional about it from the beginning. So the first concept, first mindset is that of growing. And I think you're all doing
really well by being here. So you can pat yourselves on the back. You've already done your first part of homework. By knowing that this is something we have to continue to learn about and having a growth mindset on all of these things. As you will know if you're a technologist, there's always something new on the horizon. If we were doing this conference two years ago, it would have been Web 3 and blockchain. We're doing it now, it's AI. Two years time, it'll be Googoo Gaga.
Two years after, it'll be Higgy Hagger. There's always something on the horizon, right? I don't know if there's anyone here that would proudly proclaim themselves as a HTM O3 expert. No, you'd be a sucker. We're on HTML5 now, right? It's always continually changing and moving on. And so you have to adopt A growth mindset. The point isn't really to be an expert in all these AI things and data things and quantum things and Google Gaga things and Higgy Hagy things.
But the point is just to know more this week than you knew last week, more next month than you knew last month, and more this year than you knew last year. Just be heading in that direction, continually growing in that knowledge, going just outside your comfort zone because that's where the magic happens.
And actually, there are a lot of folks who know or proclaim to know quite a lot about the technology itself, but they don't know about the life and the experiences and the projects and what's going on around it and where that technology is going to be deployed. And that information is just as important as the core of that technology itself. Secondly, one of the best ways that we have to learn we can take from the Agile framework and that is of experimentation
and of making mistakes. It's important for us to be incredibly iterative and, and vigilant of the lessons that we're learning and the results of the experiments as we run them. We have this at the at the institute. There's lots of tools that we have for audits and assessments that folks can do on tools that they're bringing them in so they don't end up, you know, inadvertently making poor decisions or helping managers
make really bad decisions. But there's a lot of folks that we see end up doing the audit and kind of put it on the shelf and don't come and reflect that then in the work or in the next iteration or in the next set of decisions that they're making. So for goodness sake, please use it as part of your iterative processes, right? Continue to recheck because they're changing the underlying technology, the data sets, all of those things all the time. So keep an eye on that and act
accordingly. But also as if, especially if you're a leader and if this is something within your gift build environments where folks are able to experiment and iterate and make mistakes and then make higher quality mistakes and then continue to increase in the quality of the mistake that they're making. And let's all work in this experimentation and know that there will be mistakes, but what we have to do is learn the lessons from those mistakes as we go. The third one is impact.
And this one I think ends up not feeling less technical, but it's, it's still an important qualitative part of us ensuring that we can have different folks be a part of this journey and we can ensure the safety of what we're deploying. As project managers, you have to recognise and use the influence that you have. But this isn't just in the
project. This is in the way that you do business, the way that you run your projects, the way you have your social norms set around it. That concludes our wrap up of this year's APM Conference. The annual event will be returning to Coventry on the 11th and 12th of June 2025. To register your interest, follow the link in the episode description. APM runs more than 200 events every year, ranging from webinars and award ceremonies to day long conferences. To find out more, head to
apm.org.uk/events. If you want to get in touch with your feedback, suggestions or ideas for topics we should cover, e-mail us at apmpodcast@thinkpublishing.co.uk. Spotify users can also leave us a comment directly within the Spotify app. That's it for this episode. Thanks for listening.