Latest / Project Podcast / Navigating tomorrow: 5 things we learned at the 2024 APM Conference

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Navigating tomorrow: 5 things we learned at the 2024 APM Conference · Project Podcast

• 0:00
  OK, so let's just have a think about what this AI thing is,
• 0:03
  very, very briefly. So everyone thinks that
• 0:06
  artificial intelligence actually means artificial intelligence,
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  OK. Artificial doesn't mean it's not
• 0:11
  human. You know that, don't you?
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  OK, What it means is, do you know how inside your brain, I
• 0:16
  talked about your brain, there are these long, stringy, stringy
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  things, OK, they're called neurons.
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  Have you come across this? So what happens is these bits
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  listen out. Hey, how's it going?
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  OK, And then they find that it's warm or it's noisy and they tell
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  the nucleus move the foot. So the nucleus goes bang and
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  sends a message which goes and then your foot moves, blah,
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  blah. That's basically it.
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  OK, so that's how a neuron works.
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  So somebody had an idea like in the 1950s forties, something
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  like that. Hey, why don't we make computers
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  like that? Why don't they set them up so
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  they have lots of inputs like that, OK.
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  And then when you put the inputs in, we'll see this input's
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  really important. We'll give it a weight, and then
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  we'll add up all the numbers. And then there's a little box
• 0:56
  here, and this box then says go and do X, OK.
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  And that basically is what AI is.
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  It's just a neural network. That was Eddie opening talking
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  about AI at this year's APM conference.
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  We'll hear more from Eddie shortly as we take a deep dive
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  into the future of the project profession with a compilation of
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  some of our favourite insights from the 2024 conference.
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  This is the APM podcast brought to you by the chartered body for
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  the project profession. My name is Emma Devita and I'm
• 1:31
  the editor of Apms quarterly journal Project and your host
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  held in Coventry in June. The theme for this year's APM
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  Conference was Navigating Tomorrow, Future Skills for
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  Project Professionals. The event invited project
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  leaders and experts on future trends to unpack the rapidly
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  changing landscape that projects are being delivered in.
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  AI and data literacy were among the hot topics, including both
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  the opportunities and threats of new technology for project
• 1:57
  managers employment prospects. The conference also considered
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  whether there is a skills gap in the profession and there must
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  have competencies to future proof your career.
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  So listen on as our expert speakers explore how to navigate
• 2:11
  tomorrow. First, let's hear more from
• 2:17
  Eddie. Opening session on developing
• 2:19
  your emotional intelligence in an ever more digital world.
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  Eddie is an educator, innovator, and digital pioneer.
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  He explained what AI really is and why it has a tendency to
• 2:29
  hallucinate. And that basically is what AI
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  is. It's just a neural network.
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  All it's doing is 4 by 4 matrix calculations.
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  That's all it's doing. The reason it's now popular
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  because it's been knocking around for a long time, it's
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  because of gamers, you know, gamers, you know, when you're
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  doing 3D gaming, the type of computer chip you need in your
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  machine comes from a company called NVIDIA.
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  Too late to buy their stock, OK. And it got really cheap.
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  So suddenly we could do masses of calculations like that.
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  So that's basically what it is. At the base of it, there is no
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  intelligence. All it's doing is churning
• 3:04
  through information. So the stuff you'd have come
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  across is what's called generative AI.
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  What they do is they feed in a whole bunch of numbers here.
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  So let's say they've got 100 bits of cases about people being
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  I'll, OK, they feed in, I don't know, eighty of them.
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  And say the person was tall, they were long, they were
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  hungry, they had a blood factor A they feed those in and then
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  they get the machine to do the calculations.
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  And then they say, send them to hospital.
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  And if that's the correct answer, it goes, great.
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  Then they feed the next day. If it's the wrong answer, then
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  they adjust the weights until for those 80 things it's
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  approximately correct. Then they check with the last 20
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  and it's trained. So you have heard about training
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  on data, training on large language, language models.
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  That's all it is. But of course, if you're
• 3:44
  selling, you know the trick. If you're buying stuff, you make
• 3:47
  it simple. When you're selling it, as they
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  say, bullshit baffles brains. OK, so you put, you guys are so
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  serious. OK, so, so you make it so.
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  Oh, we're training it with our LLM.
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  Oh yes, our LLM has data which is within the GDP.
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  You know, they'll say all these things and then you take your
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  past and give it to them. But it's really very simple.
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  It works out patterns from huge amounts of data fast.
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  It takes text which you've written painstakingly and it
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  corrects all the grammar and makes it readable.
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  OK, It's marvellous. It helps your AI self driving
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  car work its way and not fall into potholes.
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  It's brilliant, am I right? And remember what I said about
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  the computers, all those advantages.
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  So what's the challenge? It's an interpolation machine.
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  It feeds on the data you've given it.
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  So when you get a report and input back from any AI, you feed
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  it into. If you look at this and your
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  beautiful brain makes sense of it and you give it all the
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  attributes because you anthropomorphize it, you think
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  it's human, you think it's smart.
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  This is the trick you should always do with any API you play
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  with. Ask you something about
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  something general, like how many walls have there been or what's
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  the growth of our market within real estate?
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  OK, and you'll get this brilliant answer.
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  Then ask it a question about something where you're the world
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  expert or you're pretty damn good.
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  What do you think happens? It's gibberish, because when you
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  use a large model, you know the shape of the world is generally
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  like that. So there's Einstein over here.
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  Then there's the large long language model over here.
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  For most of Einstein's life, he was lucky.
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  He was a bit of a Nutter. The leading edge, cutting edge,
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  best ideas, best ways of doing things are not the average.
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  Does it make sense? OK, so that's one thing.
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  The 2nd is there's no human body involved.
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  So AI, although it's very fast, it doesn't know anything.
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  So it does this thing called hallucination.
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  Have you come across this? Hallucinations are particularly
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  easy to show with pictures. But what I came across the other
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  day, which made me laugh is if you go to say Snapchat TBT and
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  you type in I have a problem, it says yes, can I help?
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  It says I've got a really complicated problem.
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  I've got a man with who's by a river with a with a boat.
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  Can you help? It says yes, first send the
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  cabbage across, then the lion. It answers a question you
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  haven't asked it because so many people have put that thing in
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  that it has learned the answer to a question you haven't asked.
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  Isn't that just brilliant? That's a hallucination.
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  So it's going to help us because it's fast, it's cheap, but it's
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  not. A human being has no ethics.
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  It can't be fixed. Next, we turn to Laura Ellis,
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  Head of Technology Forecasting at the BBC, who spoke about the
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  balance between analytics and people skills and project
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  decisions. In the following clip, she
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  discusses her fascination with AI, the potential crisis in the
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  workplace, and the fight to preserve human creativity.
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  What I think we're learning in the BBC at the moment about
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  this, integrating humans, data and AI, and what we say to
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  people when they ask us about this is learn as much as you
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  can. This is something which if you
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  are a remotely interested in technology, it is fascinating.
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  If you don't like technology and you find it scary, it's still
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  fascinating. And I would say do everything
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  you can to overcome that fear because I had it once and now
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  I'm obsessed. And I think the more you know
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  about it and the more you know about things like how these data
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  sets are trained, who's making the decisions, where they're
• 7:28
  being made and how they're going to affect our businesses, the
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  better keep coming back to this. But, you know, take great care
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  with how data is used. I am inordinately fascinated
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  with Co pilot. Does anybody use Microsoft Co
• 7:42
  pilot in the room? So Co pilot is one of the first
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  forays we've made in the BBC into essentially kind of, you
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  know, really starting to use this stuff for real.
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  And the first demo we went to, we one of our tech reporters
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  wrote it up and it was quite chilling because she said the
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  person that was sitting in the room with her looked at what
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  Microsoft Co pilot could do. And it summarised meetings and
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  it said who'd said what. And then it gave a kind of 30%
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  of people agreed with this point of view.
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  Then it made a chart about it. And then it, you know, created
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  an action list. It was absolutely amazing.
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  And she looked at it and said, oh, that's three jobs I don't
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  need to appoint. And there is a, you know, a, a
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  potential pending crisis about what we value in the workplace
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  and, and valuing our colleagues. Microsoft at their most recent
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  event, which was last week, said we've generated you an AI
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  colleague as well, and you can give it a personality and have
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  it in the room with you. Do we really want that?
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  You know, do we really want that?
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  And do we want to lose that really valuable entry level of
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  professional, you know, who comes in and does some of the
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  things that we're going to be ceding to AI?
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  Do we do we want to lose them? I don't think we do.
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  So how do we keep them? How do we make sure that we're
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  keeping them in the loop and we don't just throw away that
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  effort, but then we perhaps use, use them to, to feed in, in data
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  and to understand it and to sort of train the AI.
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  I think we need to be really careful about our talent
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  management as this as this happens, because we risk cutting
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  out, you know, the, the, the nursery slopes of a lot of our
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  professions if we're not careful.
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  And finally, I just think we need to fight for human
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  creativity, human agency, rather than creativity to be, to be
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  preserved. So when I think of journalism
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  and I think of creativity, there is something incredibly special
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  about a human looking at something and giving a
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  representation of that, whether it's in journalism and art,
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  whether it's input into a project, whether it's, you know,
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  that understanding of human nature.
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  All of those things are not available to us yet in AI.
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  And I think if we start to create too much of an AI driven
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  economy and lose those things, we'll be much the poorer for it.
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  So I think we need to be incredibly careful that we fight
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  and we will have to fight, hopefully not in a kind of
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  Terminator way, but we will have to fight to make sure that, you
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  know, as we move forward. The old quote about, you know,
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  your job isn't going to be taken by AI.
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  It's going to be taken by somebody who uses AI better than
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  you. It's a horrible quote probably,
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  and there's a grain of truth in it.
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  But I think it's designed to scare us.
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  And we don't have to be scared. We can push back and say, OK,
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  fine, you know, we will understand AI, but we that
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  doesn't mean we have to seed all of our sort of, you know, lower
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  level jobs to it. It doesn't mean we have to see
• 10:09
  your creativity to it, and it doesn't mean that we have to end
• 10:11
  up doing the dishes when it writes us a song.
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  As AI matures, will project managers find themselves out of
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  a job? It's the question on everyone's
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  mind, and it's when our conference audience put to
• 10:25
  Daniel Armanios, BT, Professor of Major Programme Management at
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  SCII Business School. Here's Daniel's response to that
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  conundrum, followed by another audience members question about
• 10:36
  the readiness of the profession to embrace AII.
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  Think project management is going to become more crucial
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  than ever, to be honest, because AI right now where the, if you
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  see where the kind of targeting of AI is, it's optimising your
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  Gantt chart and scheduling, optimising things with a lot of
• 10:55
  clear discrete tasks. And that's because to kind of
• 10:58
  break it down further, the most powerful aspects of AI right now
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  is known as supervised learning. Supervised learning base is
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  dependent on you labelling data. So even to know what to label,
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  how to use it, what's the bigger contextual picture, That's where
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  project management spends a lot of time.
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  So essentially what I think AI is doing, at least in the
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  project management world, I'm not saying it's, it's, it's
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  universally great for everyone. But what I'm saying is, is that
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  right now, at least we can think about later what's going to
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  happen. AI is really focused on the
• 11:27
  execution, discrete, advertised kind of tasks, but even how to
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  label it, what to inform it, what kind of information you
• 11:32
  need, it still requires a tremendous amount of project
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  management. So I think actually it's going
• 11:37
  to make some aspects that are taking a lot of time easier, but
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  it cannot possibly, at least at this moment that from a scene in
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  the trend, replace what a project manager does.
• 11:47
  Hi, I'm Ricky Hanson. I'm a programme manager,
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  actually outside Business School.
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  I'm wondering. I mean, my impression is that
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  most of us aren't even ready. For AI, what is your perception
• 12:00
  of how big? Of a challenge it's going to be
• 12:03
  and how we begin to sort of. Address the issue of maturity in
• 12:07
  terms of. Having the basics right, you
• 12:10
  know most of. Us.
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  I don't. Think have complete or even
• 12:12
  accurate data and if we sort of start getting out and buying all
• 12:17
  sorts. Of stuff.
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  How do we? How do we get to?
• 12:20
  Address that challenge. Yeah.
• 12:22
  So I think this is, it's really interesting your question to
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  bring this up because the salary trend service seems to directly
• 12:27
  reflect that there's this excitement of what AI can do and
• 12:30
  the same time real worry about what are the skills I need to be
• 12:32
  able to use this effectively. I would say, OK, so there's a
• 12:36
  question about maturity. One is kind of you have to think
• 12:39
  about it, is this technology going to continue advancing?
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  I can say one trend we see, because I know the biggest one
• 12:44
  people are using our language learning models, which is
• 12:46
  Chachi, BT, etcetera. The belief in the consensus
• 12:50
  later on is that it's going to get so good about understanding
• 12:53
  you that you won't even have to prompt it in a particular way.
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  So that's kind of to think through it.
• 12:58
  I think in terms of data, I think where the most promising
• 13:01
  trends are is to use public data.
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  So I've seen some really interesting work using satellite
• 13:06
  data, for example, and using that and trying to kind of use
• 13:10
  AI to kind of map the gaps between real interesting trend
• 13:13
  on that front is what's going on with Unreal Engine.
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  So Unreal it, the AI algorithm are being so good at even
• 13:20
  training artificial data that they're now using physical
• 13:23
  objects like autonomous vehicles.
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  They're training them on artificial data.
• 13:26
  Now it's not even on physical because AI is filling the gaps
• 13:28
  in, in geographic things. So I think start with the public
• 13:31
  data. In fact, you don't want to give
• 13:33
  it, I think right now corporate data, it's unclear what that is
• 13:36
  going to happen with, but public data and even maybe satellite
• 13:39
  data is a way to start at least to, to build that.
• 13:41
  I'm happy to talk further. It's a more in depth question.
• 13:46
  The skills gap in the project profession is cause for concern,
• 13:49
  but is the scale of the problem overstated and has the skill set
• 13:53
  to the project manager needs changed?
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  These questions were tackled in a panel session at the
• 13:58
  conference. Let's hear now from Lorraine
• 14:01
  Bellinger of Bird and Bird, Derek Allen from Shell and Karen
• 14:04
  Skinner of Life ARC, who were joined by moderator Michelle
• 14:08
  Richmond, MBE and APM Trustee. Is there a skill shortage or are
• 14:15
  we just looking in the wrong place?
• 14:17
  Myself, I came from APA background and I just naturally
• 14:21
  transferred into a project management role because I was
• 14:24
  quite lucky where I worked. They recognised that actually
• 14:26
  this was a role, it was needed, but it didn't really exist in
• 14:29
  legal. So I I kind of moved across into
• 14:32
  it. They recognised skills in me
• 14:34
  that I didn't see in myself. I was kind of doing this role
• 14:37
  anyway. So I've taken that with me in my
• 14:39
  career and I've now started looking elsewhere.
• 14:42
  So don't just look in the legal world, don't just look in the
• 14:45
  project management world. There are those fundamental
• 14:47
  skill sets, There are those organisational skills,
• 14:50
  communication skills that exist in people that are all
• 14:54
  transferable if you have the right attitude.
• 14:56
  There's a lot of those skills you can teach.
• 14:58
  So personally I think maybe we might just be looking in the
• 15:01
  wrong place. If you, if you look at some of
• 15:04
  the data that we're getting, you see these, these figures of all
• 15:08
  the projects that the world is planning to do over the next 5
• 15:11
  to 10 years, the trillion dollars the UK government are
• 15:14
  planning to spend on projects. It's just unviable that we're
• 15:18
  actually going to do all those projects.
• 15:19
  And there's a, there's a skill shortage right across the patch
• 15:23
  to do all the work that we're planning to do.
• 15:25
  So I don't, I don't believe that the project manager is the, is a
• 15:30
  deciding factor. In fact, I've never, ever seen a
• 15:32
  project not going ahead because we've not got a project manager.
• 15:37
  They go ahead. We either find a project manager
• 15:41
  or we find somebody to be the project manager, which is what
• 15:45
  you're trying to say. Yeah.
• 15:46
  So we, we, we never struggle to to find somebody to put in that
• 15:50
  space. Whether it's the right person or
• 15:52
  not, that's another story. But I don't believe we see a
• 15:54
  skills shortage. It's maybe a competence question
• 15:58
  rather than can we fill a box with a project person?
• 16:02
  There's huge number of transferable skills I think,
• 16:04
  which is great. But I would say certainly in the
• 16:07
  in the life science sector, technology is moving on at such
• 16:11
  a rate that you end up having a bit of an experience gap.
• 16:15
  So I think, you know, if people think of medicines, they think
• 16:18
  of tablets and capsules, but actually the new wave is cell
• 16:23
  therapies, gene therapies. It's, it's just a very different
• 16:26
  space of technology. So people that have the
• 16:28
  experience in that area and actually digital and data in our
• 16:32
  sector is growing at such a rate that actually having the people
• 16:35
  to kind of keep up with that, that's a particular challenge I
• 16:38
  would say because the scientists are moving so fast and the
• 16:41
  technologies are moving so fast. So it's project management
• 16:44
  actually trying to keep up with that.
• 16:45
  So that we're, and I think that's all about continuous
• 16:49
  learning all the time because I think you can learn you, you
• 16:52
  just need to kind of keep up with the trend.
• 16:54
  So looking a little futuristic, we've talked a little about the
• 16:57
  skills you would look for when you recruit, when you recruit.
• 17:01
  But what new? Skills are you sort of looking
• 17:03
  for in your. Project managers.
• 17:06
  I don't think it's new skills. I think it's a real change in
• 17:08
  emphasis. Traditionally, we think of
• 17:10
  project managers as somebody that delivers a technical piece
• 17:13
  of scope, somebody that builds a bridge or somebody that builds a
• 17:16
  house or an IT, an IT package or something from, from the, the
• 17:21
  health health industry. And with the way the world is,
• 17:25
  the way people want to live their lives and the technology,
• 17:31
  the project manager coming up needs to be more of a, a mini
• 17:34
  CEO. He needs to think of more of the
• 17:37
  people aspects of the business. He needs to be more of a
• 17:39
  visionary, leading a team, communicating and, and it's a
• 17:44
  much more on the people side. I often say to people project
• 17:47
  managers are in the people business.
• 17:49
  And I think that's no more than ever the case that we need to be
• 17:53
  in the people business. And I think that's where we need
• 17:55
  to evolve that let other people deal with the technical and let
• 17:58
  the new project managers deal with with with a broad spectrum
• 18:01
  of what it takes to take people on a journey.
• 18:05
  The the way we do this, like PMS are expected to have a wide
• 18:09
  range of skill sets. That's not going away for the
• 18:12
  way we operate in my firm, we've actually split the skills out.
• 18:15
  So we have a separate continuous improvement team, we have a
• 18:18
  separate tech team, and everybody works together.
• 18:20
  There's invariably overlap, but actually separating those skill
• 18:24
  sets out means that people can focus on what it is they are
• 18:26
  there to deliver. And you do have to evolve with
• 18:29
  the changing market. Like Karen mentioned earlier,
• 18:31
  tech is continually evolving in the legal space.
• 18:35
  It's quite saturated, frankly. So you have to really assess
• 18:38
  what you're using, what is actually the right tool for the
• 18:40
  job. But in terms of project
• 18:43
  management, I think it's just growing those skills, developing
• 18:45
  those skills, but new skills, not necessarily.
• 18:51
  The conference closed with an inspirational keynote from
• 18:54
  Doctor Anne Marie Maffedon, MBE, a mathematician and STEM
• 18:58
  advocate. Anne Marie is Co founder of the
• 19:01
  award-winning social enterprise STEMET, a respected thought
• 19:04
  leader in the tech space and Trustee at the Institute for the
• 19:07
  Future of Work. She gave attendees 3 tips for
• 19:11
  leaning into the opportunity of new technologies.
• 19:16
  So I think there's a great opportunity for us as we
• 19:17
  navigate tomorrow now to maybe do three kind of buckets of
• 19:21
  things. So I wanted to leave you kind of
• 19:22
  three mindsets of how we prepare for this, how we're not fearful,
• 19:26
  but actually we lean into the opportunity that we have in
• 19:30
  working with these technologies, deploying them and making
• 19:32
  decisions about them. But also being humans here at
• 19:34
  the beginning of this revolution for the change in the
• 19:38
  relationship we might have with work, but the relationships we
• 19:41
  might have with all manner of other things in life as a result
• 19:43
  of good use of this technology. And we have this concept of good
• 19:46
  work at the Institute for the Future of Work.
• 19:48
  And you know, actually, if we if we set these milestones, if we
• 19:51
  set these guidelines, then this doesn't have to be a disruptive
• 19:55
  change. This can be something that can
• 19:56
  be really good actually for all of us if we're intentional about
• 19:59
  it from the beginning. So the first concept, first
• 20:01
  mindset is that of growing. And I think you're all doing
• 20:04
  really well by being here. So you can pat yourselves on the
• 20:06
  back. You've already done your first
• 20:07
  part of homework. By knowing that this is
• 20:10
  something we have to continue to learn about and having a growth
• 20:12
  mindset on all of these things. As you will know if you're a
• 20:15
  technologist, there's always something new on the horizon.
• 20:18
  If we were doing this conference two years ago, it would have
• 20:20
  been Web 3 and blockchain. We're doing it now, it's AI.
• 20:24
  Two years time, it'll be Googoo Gaga.
• 20:26
  Two years after, it'll be Higgy Hagger.
• 20:28
  There's always something on the horizon, right?
• 20:29
  I don't know if there's anyone here that would proudly proclaim
• 20:32
  themselves as a HTM O3 expert. No, you'd be a sucker.
• 20:36
  We're on HTML5 now, right? It's always continually changing
• 20:39
  and moving on. And so you have to adopt A
• 20:41
  growth mindset. The point isn't really to be an
• 20:44
  expert in all these AI things and data things and quantum
• 20:47
  things and Google Gaga things and Higgy Hagy things.
• 20:50
  But the point is just to know more this week than you knew
• 20:54
  last week, more next month than you knew last month, and more
• 20:57
  this year than you knew last year.
• 20:59
  Just be heading in that direction, continually growing
• 21:02
  in that knowledge, going just outside your comfort zone
• 21:05
  because that's where the magic happens.
• 21:06
  And actually, there are a lot of folks who know or proclaim to
• 21:10
  know quite a lot about the technology itself, but they
• 21:12
  don't know about the life and the experiences and the projects
• 21:16
  and what's going on around it and where that technology is
• 21:17
  going to be deployed. And that information is just as
• 21:19
  important as the core of that technology itself.
• 21:23
  Secondly, one of the best ways that we have to learn we can
• 21:26
  take from the Agile framework and that is of experimentation
• 21:30
  and of making mistakes. It's important for us to be
• 21:32
  incredibly iterative and, and vigilant of the lessons that
• 21:36
  we're learning and the results of the experiments as we run
• 21:39
  them. We have this at the at the
• 21:40
  institute. There's lots of tools that we
• 21:42
  have for audits and assessments that folks can do on tools that
• 21:45
  they're bringing them in so they don't end up, you know,
• 21:47
  inadvertently making poor decisions or helping managers
• 21:49
  make really bad decisions. But there's a lot of folks that
• 21:52
  we see end up doing the audit and kind of put it on the shelf
• 21:55
  and don't come and reflect that then in the work or in the next
• 21:57
  iteration or in the next set of decisions that they're making.
• 22:00
  So for goodness sake, please use it as part of your iterative
• 22:02
  processes, right? Continue to recheck because
• 22:05
  they're changing the underlying technology, the data sets, all
• 22:08
  of those things all the time. So keep an eye on that and act
• 22:11
  accordingly. But also as if, especially if
• 22:14
  you're a leader and if this is something within your gift build
• 22:17
  environments where folks are able to experiment and iterate
• 22:20
  and make mistakes and then make higher quality mistakes and then
• 22:26
  continue to increase in the quality of the mistake that
• 22:29
  they're making. And let's all work in this
• 22:31
  experimentation and know that there will be mistakes, but what
• 22:34
  we have to do is learn the lessons from those mistakes as
• 22:36
  we go. The third one is impact.
• 22:39
  And this one I think ends up not feeling less technical, but
• 22:43
  it's, it's still an important qualitative part of us ensuring
• 22:47
  that we can have different folks be a part of this journey and we
• 22:51
  can ensure the safety of what we're deploying.
• 22:53
  As project managers, you have to recognise and use the influence
• 22:56
  that you have. But this isn't just in the
• 22:58
  project. This is in the way that you do
• 23:00
  business, the way that you run your projects, the way you have
• 23:02
  your social norms set around it. That concludes our wrap up of
• 23:15
  this year's APM Conference. The annual event will be
• 23:18
  returning to Coventry on the 11th and 12th of June 2025.
• 23:22
  To register your interest, follow the link in the episode
• 23:25
  description. APM runs more than 200 events
• 23:29
  every year, ranging from webinars and award ceremonies to
• 23:32
  day long conferences. To find out more, head to
• 23:35
  apm.org.uk/events. If you want to get in touch with
• 23:40
  your feedback, suggestions or ideas for topics we should
• 23:43
  cover, e-mail us at apmpodcast@thinkpublishing.co.uk.
• 23:49
  Spotify users can also leave us a comment directly within the
• 23:52
  Spotify app. That's it for this episode.
• 23:55
  Thanks for listening.