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.