Latest / Project Podcast / What artificial intelligence means for the future of project management

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What artificial intelligence means for the future of project management · Project Podcast

0:08
Welcome to the APM podcast. APM is the chartered body for
0:11
the project profession. My name is Emma De Vita and I'm
0:14
the editor of Project at OPM's Quarterly Journal.
0:17
And your Host and this podcast. I'm speaking to three project
0:20
professionals with a deep interest in artificial
0:22
intelligence to consider what impact this technology is having
0:25
right now on projects, what it might hold for the future, and
0:28
what project managers should be doing to adapt to this brave new
0:31
world. I'm delighted to have with me
0:33
today Professor Antonio Nieto Rodriguez, Founder of the
0:36
Strategy Implementation Institute and author of the
0:38
Harvard Business Review Project Management Handbook, James
0:42
Garner, Global Head of Data and Intelligence at Gleed and Chair
0:45
of the Project Data Analytics Task Force and Martin Paver, CEO
0:49
of Consultancy Projecting Success.
0:52
The Autumn 2023 issue of APM's Project Journal also covers this
0:55
topic, so please do make sure you grab a copy.
1:02
Welcome everyone. I just wanted to thank you for
1:05
joining me today. I'm really looking forward to
1:06
our discussion on AI and its impacts on the world of
1:10
projects. I think a good place to start
1:12
would be for each of you to just tell us a little bit about
1:16
yourself and what fuels your interest in AI, or even better,
1:22
what gets you excited about it. So, Sherrie, begin with you,
1:26
Antonio. Well, myself, I'm, I'm, I'm
1:31
coming more from the PMI world. I was the chairman of PMI a few
1:34
years ago I launched the Brightline initiative.
1:37
My my goal is always to close the gap between the senior
1:40
leaders, the business, the strategies and projects and
1:43
project management. So I'm a practitioner as well.
1:46
I'm I'm part of a big sustainability transformation in
1:50
a corporate in the pharma sector, but also an academic.
1:54
I like to research. I publish.
1:56
I published several books, one of them with Harvard Business
1:59
Review, which has been my my goal because that's the way to
2:03
switch the people mindset around projects and project management
2:08
to appreciate it. And what drives me on a I what
2:11
makes me excited is something that is in my mind, it's around
2:15
delivering more value. I think there's very few
2:17
professions that are challenged about their success rates.
2:23
If you look any research around project management, 60 to 70% of
2:27
the project fails and and you can challenge that but that's
2:32
not acceptable. So we're we're wasting value
2:36
money impact and I think AI is a unique opportunity to close that
2:42
gap to say OK, out of 10 projects we deliver 8
2:45
successfully and we are here like a.
2:48
Some some that we we can bring value fast for, for
2:52
organisation. So that's for me the biggest,
2:54
biggest driver on ICE, increased success rate of this profession
2:58
that we love so much. OK.
3:01
Thank you and Martin. So in terms of me personally,
3:05
I've been a project professional for about 30 years, so I'm a
3:08
fellow of the APM and back in about 2014, fifteen I started to
3:13
look into things like lessons learned.
3:15
I pulled together about 20,000 lessons learned and from that I
3:18
realised that we're not learning from those lessons, we're just
3:21
repeating the same things time and time again.
3:23
So what I try to do is to apply advanced data analytics to that
3:27
and I realised that it's the wrong journey.
3:30
So we're not collecting the right evidence at the moment.
3:33
So we can start to use those techniques and really start to
3:36
drive it. So back in 2017 and then set up
3:39
the project data analytics community that's grown to over
3:42
10,000 people and I start to run hackathons, et cetera.
3:45
So for me personally, I see this is the way of leveraging that
3:49
vast experience that we've got as professionals that it's all
3:52
codified in data. So back to Antonio's part about,
3:55
you know, we're not doing good enough at the moment as a
3:58
profession. We could do a lot better.
4:00
It's for me, data is right at the heart of that.
4:02
So I don't see this as a tweak. I see it as a transformation.
4:06
It's a completely different way of thinking.
4:09
How about you, James? Where does your interest in AI
4:12
stem from? Eastern I've been interested in
4:15
from the very beginning of my my journey Even back at university
4:18
I was looking at sort of digitization in the construction
4:22
industry back in sort of 2000 or even before then with this
4:26
overwhelming kind of needs to to understand that there must be a
4:31
better way of doing things. That's really where it comes
4:33
from. Comparing ourselves to other
4:34
industries and thinking, you know, the way we do these things
4:38
just inherently seems so inefficient and a lot of that
4:42
was because of the very nature of the industry.
4:44
But the as Antonio races, we have this unique opportunity
4:48
right now in that the the technology has kind of finally
4:51
caught up with our aspirations in terms of what we can do.
4:54
And we we need to grasp it So we'll talk about it I'm sure
4:58
later in the podcast this kind of balance between the
5:00
opportunities and the risks and and we've got this very very
5:04
small window of opportunity to make this a I work for the
5:09
profession rather than for for other interested parties.
5:13
I guess my other kind of inspiration is other people have
5:16
inspired me and you know Martin was one of the people who really
5:19
put me on this journey and really inspired me to to follow
5:22
this follow my passion. I I I come from a QS background.
5:25
You know, there's so many people in this field who are really
5:29
passionate about this that there's there's great hope for
5:32
the future. We can all sort of come.
5:33
Given to to make it work for the better of the profession,
5:38
there's been a lot of hype around AI should really be
5:42
listening to the people are saying this is the end of
5:44
humanity and no one's gonna have a job or should we be feeling
5:48
excited about it. Probably like to put that to you
5:51
too, Antonio first. The question of whether it's a
5:55
hype or not, I don't think so. It's a reality and people like
5:59
Martin has been working on this space way before many people
6:02
started to even think about it. So I think it's not a hype, it's
6:07
a reality. It's happening.
6:09
And I think in the profession we have not embraced very quickly
6:12
new disruptions. We kind of always very cautious.
6:16
We we talk about change, but we're not quick at adapting to
6:19
change. And this is an opportunity where
6:21
we can drive, we can embrace these changes.
6:25
The fact that there will be parts of our work that are
6:28
automated, it's a blessing for I think for anyone that wants to
6:33
develop. More executive kind of career.
6:36
If you want to stick to your spreadsheets and your dashboards
6:40
and and just chasing people. Yeah, your job is at stake for
6:44
sure because you are not adding very much value.
6:47
I don't know. I've been working in PMO's
6:49
project management for 25 years. I can't wait to automate all the
6:53
the the 50% of our work that we do and PMO it's 80%.
6:57
So bring it tomorrow and and let's embrace it, right and and
7:01
let's start focusing on on where we can bring more value, which
7:04
is that alignment, stakeholder engagement, team kind of
7:09
engagement and commitment where I think that's what in the end
7:12
what Martin? Was saying we know why projects
7:15
fail. I hate when people ask me what
7:18
you need to do for a project to be successful.
7:20
We know that it's everybody knows that people even that
7:23
don't know projects know why projects fail.
7:25
So we have an opportunity to use technology and AI to get those
7:30
things out of the way and focus on those important matters which
7:35
I cannot solve, which is the cultural aspect, the
7:38
organisational aspects, the leadership aspects.
7:41
So there will be change definitely in the skills that we
7:44
need to build up, but I see it as a huge opportunity, exciting
7:49
time for for our profession. Is there anything that concerns
7:53
you that needs to be thought about and dealt with and planned
7:57
for right now? I think there's concerns of
8:00
course, but they're really far off.
8:02
I think the project management profession, these are there's
8:05
something bad around, yeah, we cannot influence this.
8:09
I think what concerns me is that we don't take action.
8:12
And when I see chaps, GDP and some other of these AI early
8:18
developments, I questioned the role of the project manager.
8:21
I could see self-driving projects if we don't step up.
8:26
So I think it's more the concerns is more that the
8:29
profession and the can be extremely disrupted that
8:32
somebody else can start doing our job because they've embraced
8:36
AI. So that is the immediate.
8:38
Is that how can we move fast so that we take that spot, which is
8:42
the integration of product managers, agile project
8:45
management, change management, which if we don't step up,
8:49
somebody else will do very quickly.
8:51
And that's what worries me. The bigger picture of course,
8:54
but I just don't know these fears.
8:56
I'm very far off. Entirely agree.
8:59
I mean, and the question about hype, I mean the Garden, the
9:02
height cycle refers to where we are, this peak of inflated
9:05
expectations right now, thinking it's going to, you know, change
9:08
everything. And then you go through what's
9:11
called the through of disillusionment and that there
9:13
will be this kind of period coming up where ohh OK, it
9:15
didn't totally change the world, but that doesn't mean the hype
9:18
is not there. And actually we will go on this
9:20
journey towards what's called a jive.
9:21
It's artificial general intelligence and some people
9:25
think it's 30 years away, some people think it's 100 years
9:27
away, but we will get there eventually.
9:28
So we're on the journey. Towards that reality.
9:31
So it's not hyper in that sense, but sometimes I think maybe
9:34
people think where the technology is right now and they
9:37
get disappointed when it can't do these incredible things at
9:40
this moment in time that some people predicting that there
9:44
shouldn't lose faith. You know that it's only a matter
9:46
of time it keeps improving the the improvement is huge.
9:49
But I agree with what Antonio says in terms of and this goes
9:52
back to what we said at the introduction, You know, we have
9:55
this opportunity and if we don't carve this technology for
9:59
ourselves and make it work. Of ourselves and control the
10:02
data, then we risk giving it away or given away control of
10:06
our industry to other people and other third parties who may not
10:09
have the best interests of the industry at heart.
10:12
And that's why we're, I think three of us are pushing so hard
10:15
to say, look, this isn't something to be afraid of.
10:18
Yes, the hype is something that is going to change your
10:20
workflows. It's going to change the way you
10:22
do think. But embrace it, because the
10:24
other option is we start giving away control slowly but surely
10:27
to third parties. Which third parties are you
10:29
talking about? It effectively third party
10:32
vendors or software companies. So it it it could be small
10:36
startups, it could be the big giants, you know the Googles,
10:39
the Amazons, the Autodesk. In this world, you know you can
10:42
see a future very clearly if we don't intervene and and protect
10:46
our industry where we we slowly but surely have that kind of
10:49
Netflix moment. I suppose is the best way of
10:51
describing it where, you know before we know it clients are
10:54
paying subscription something and not using project
10:56
professionals. OK, Thanks Martin.
10:59
What are your thoughts? Love to hear what you think
11:01
about all of this. So it's back to that point
11:04
really about, you know, is this an an apocalypse, You know is it
11:08
going to be the end of the world?
11:09
And I think we've got separate out, you know, what's the impact
11:11
of this on future wars and all that sort of stuff.
11:14
Like that's an issue for the state.
11:15
It's not something I can control as a project professional.
11:18
So I bring it back to project delivery.
11:20
Is this an existential threat to our profession?
11:23
And personally I think it is. If we don't respond to this,
11:26
others will. And these data people start to
11:28
come into our jobs and start to automate a lot of it.
11:30
So it's a case of moving ourselves up the value chain,
11:33
and it's not just. Artificial intelligence.
11:35
So something I like to say is if you can describe something in
11:38
logic, you can probably code it. So if you can code it using
11:41
Python And various other tools, then you can do that fairly
11:44
simplistically and fairly cheaply.
11:46
A lot of that is available today that just put it into Microsoft
11:49
XLS well. So you can now code in Python as
11:51
part of Excel. If it's more intuition based
11:54
than what you need to do is to grow the data set so you can
11:57
look at the patterns in the data so you can extract some of that
12:00
intuition. And that's when it starts to get
12:01
a bit magical, right? And that's when we start to get
12:03
some really cool insights coming out of it.
12:05
If we layer on top of that, things like the large language
12:07
models, so it's not just ChatGPT, there's larger and
12:10
various other ones out there. So what we can do now we can
12:13
take a model that's being, say, trained on English and then
12:16
teach it to speak with a Yorkshire accent.
12:18
But it's that sort of approach. So what we're doing at the
12:21
moment. So I've got a call tomorrow with
12:23
Adrian Dooley who created the practise framework.
12:26
So instead of having a body of knowledge or a book, it's now
12:29
online. So Adrian's taking it all
12:30
online, There's templates and there's various collateral on
12:33
there if we can now train a model on all of that.
12:36
And then train it on all of the National Audit Office reports,
12:40
the GAO reports in the US etcetera, We start to get a
12:43
large language model that is trained on project delivery.
12:46
So it becomes very project delivery.
12:48
Nuance, we're going to have that in about two or three weeks
12:52
time. So that's going to be available
12:54
and it's all going to be open source.
12:56
We can open source it for people.
12:57
So just imagine if we push that further and further and further,
13:00
if you can now start to train it on data, on schedules of risks,
13:03
on compensation events, on technical queries.
13:06
That's when it becomes even better.
13:08
It's starting to predict where we're going to end up, right?
13:11
So it's not just about forecasting envelopes.
13:14
It's now starting to say to me, what is my call to action?
13:17
And people who don't understand this stuff will get replaced by
13:21
the people who do understand this stuff.
13:22
So if you've got a choice of two project professionals, one
13:25
person who really understands this and can leverage it and can
13:28
make the most out of it and get better at making decisions and
13:31
somebody who doesn't, which person would you choose?
13:34
Which? Which person's going to get
13:35
promoted? I think that's the issue for the
13:37
profession. How do we share knowledge?
13:42
How do we share this experience? How do how do we learn from the
13:45
first movers if we're not one ourselves?
13:47
And also, I'll be really pleased if any of you want to just talk
13:52
about a couple of practical examples of how AI is being used
13:56
in the world of projects today so that we can move away from
14:00
the kind of theory and the hype. So, Antonio.
14:04
Yeah, I think the sharing is great, but somehow we say I to
14:09
me it feels that there's a bit more strategic advantage.
14:13
So why should I share something where I'm using an AI tool with
14:18
my competitions, they can adapt it and do better.
14:22
So yeah, I was talking earlier with the podcast in Australia
14:26
and we were talking about the PPM market as well.
14:29
We which we know and we collaborate with some of these
14:33
big players that have been there for. 20 years, But discussing
14:37
that, you can see that they're they're struggling to adopt AI
14:41
and there's one big big player which is Microsoft whose open AI
14:46
and has charged BT who might just take everything away in
14:50
some niche players around. But for me the the sharing of
14:55
AI, there's still the issue of IP and whether they can use all
14:58
the information for free or not, but that's a bigger kind of
15:01
discussion. We don't know where it's going,
15:03
but for me using AI. Can be a strategic tool.
15:08
It's not like we're using Excel or we're using Microsoft
15:10
Project. No, no, there's much more
15:13
intelligence that you can use it to to do your projects better,
15:17
faster more successfully. So I don't have an answer it's
15:21
just thinking in my head of can you share or should keep it bit
15:26
more confidential to make it a competitive edge right.
15:32
So that is in the the point I wanted to to share in terms of.
15:37
Some parts, I think in my article with Ricardo Vargas, we
15:40
talk about Walmart using AI for managing their supply chains and
15:46
data analytics to manage the demand and the streamlining.
15:50
Another example, I think we talk about Siemens that they've
15:53
implemented system to streamline manufacturing process.
15:57
This is already well known general electrics applications
16:02
on AI and project management in the energy sector.
16:05
So there are already. Companies that are using pieces
16:10
of Asia to some extent. So I I would not replicate what
16:14
is in the in the article which you published in the APM
16:19
magazine. So, but yeah, I think there is
16:22
more and more and I recommend people to just follow some of
16:25
these newsletters around and I because it's just amazing how
16:29
quickly it changes. Martin, any practical examples
16:34
you want to talk about or how we can learn from each other?
16:38
I personally see we've got choices to make here.
16:40
So we can either compete around this artificial intelligence or
16:43
can work together and to accelerate.
16:46
If we work together, we started to pool our data.
16:48
We start to sort of open source some of these solutions.
16:51
Now in terms of the solutions, a lot of them aren't actually
16:55
intellectual property. It's more about configuration of
16:59
Python libraries or it's a configuration of Microsoft tool
17:02
or a low code app or something like that.
17:05
So what we've been doing, so this is for the past five years
17:08
or so. So we've been running these
17:10
hackathons, so the last one in Manchester City Football Stadium
17:14
and we had about 230 people come along to it.
17:19
So we Co create solutions to shared problems.
17:22
So we're then open source and James can tell you about some of
17:24
the solutions because he's been pulling them through into gleeds
17:27
as well. It's now part of his product
17:29
portfolio. So those are all the scenes of
17:31
ideas that people can now fork and take in a different
17:34
direction. So if we do that, we're learning
17:36
on the fly, we're learning from each other.
17:38
We're starting to accelerate. So I'll give you an example.
17:41
Instead of 100 people all working on a health and safety
17:43
app, because every company wants a health and safety app, if we
17:46
just develop one or two of these health and safety apps and then
17:50
share them, it means we can do the other 98 things individually
17:54
as well. So we go 100 times quicker.
17:57
And I think that's the difference is that we shouldn't
17:59
be competing. On some of these low value sort
18:03
of aspects of data-driven project delivery, which should
18:06
be worth together, get us to the data volumes, get us the data
18:10
quality as well. And that's one of the biggest
18:12
issues at the moment. So artificial intelligence needs
18:14
lots of data, needs good quality data.
18:17
We'll never get to that point unless we work together on
18:20
things like the data model, the ontology about how this data all
18:23
fits together and it's not flat data.
18:26
You can't just look at things like cost data by itself, the
18:30
cost data and the work package and the.
18:32
Now is it a Greenfield or brownfield site?
18:34
All these other things, they become the features in this
18:37
artificial intelligence model and then we can look at the
18:40
correlation between those features and that drives the
18:42
artificial intelligence engine basically.
18:45
We're APM, the only chartered membership organisation for the
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Plus, you'll access exclusive training and benefits to support
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your ongoing career development. Find out how we can help you
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reach your potential by visiting apm.org.uk, because when
19:11
projects succeed, society benefits James.
19:16
Any practical examples of AI that you've that have inspired
19:20
you or you would like to share with listeners?
19:24
Before I talk about some examples, I think Antonio picked
19:27
a really important point around this.
19:29
Some people think that AI or data more generally some kind of
19:33
competitive advantage. And I think what I'm trying to
19:36
do is challenge that to say that actually we're not in
19:39
competition with each other as consultants or or contractors or
19:43
whatever we are. We're actually in competition,
19:44
like I said with the big tech company.
19:47
So we we got to change our thinking and innovate ourselves
19:50
above that. There's enough to go around,
19:52
there's enough projects to go around and if we can use AI.
19:55
As a way of improving project delivery, it will clients
19:58
effectively less reason to start looking for moving, moving to
20:03
vendors. The other bit before I go into
20:06
some practical examples is is around the messaging and how we
20:11
get this out there. Now I think all three of us
20:13
probably say that's probably the hardest bit, because you can,
20:17
you could, you could have every single project professional in a
20:20
room and you could tell them about this, but it would still
20:23
sometimes not necessarily go over people's heads because
20:25
people are people intelligent. To understand it, I think
20:28
there's a there's a fear people have different motivations for
20:31
not wanting to pick up AI. Some of it is fear, some of it
20:35
is ohh, it's, you know, it's not gonna affect my profession, it's
20:39
not gonna affect what I'm doing. My bit is somehow unique from
20:42
everything else, so it's not going to affect that.
20:45
That's where I think the skills need to come in and sort of
20:48
upskilling people. And Martin does a lot of that
20:50
with his various data academies. We also do training to upskill
20:55
people and we've been working with the APM on the positive
20:58
dramatics deals guide document. So you know some of the examples
21:02
of things we've done is like training our own large language
21:05
models. So for years, one of the things
21:07
that we do in Leeds is published market reports about what's
21:10
happening in the project world and those have been issued as
21:15
traditional reports. We've actually trained a large
21:18
language model, ChatGPT basically of our market report
21:22
so that clients don't have to interact and read reports, they
21:27
can literally just talk to these reports.
21:29
That's a very simple example. We've been also using it to sort
21:34
of track material prices and what's happening in the market,
21:37
how kind of micro level almost in a daily so we can see what
21:41
trends are happening, you know even within a week or so prices,
21:45
steel or prices of labour in a certain region.
21:48
And that all of that is just giving people the tools so they
21:52
can become more efficient. Antonio, in the article you
21:56
wrote for us you you covered off some of the challenges and how
21:59
to overcome them when it comes to AI and and projects.
22:02
One might want to pick up on is the lack of skilled personnel.
22:06
So if you're speaking to individuals right now, what
22:11
advice would you give to them about adapting themselves to
22:16
this new reality? Yeah, that's something.
22:20
Again, one of these topics where there's no black or white
22:23
answer, I say things are moving really fast.
22:27
It's more like a mindset of continuous learning and trying.
22:31
I think you learn technology by trying, not by just listening.
22:34
And I think this is something that maybe we don't dedicate
22:38
enough time. But for me, it's about getting
22:42
involved in some of the work that both James and Martin are
22:46
doing, and and others and and and get connected with the
22:50
people who are in that. Breach of of this AI and and and
22:54
project management but also trying.
22:56
I think first of all everybody should have used already ChatGPT
23:00
or or bar to see what we have. What are the risks in in
23:06
projects like mine any like knowledge management you go to
23:09
chat and you can ask what kind of projects that in this sector
23:13
we've seen in the past and what are the key learnings you get
23:16
that immediately So that curiosity to experiment with the
23:19
tools I think it's. Is more important than saying
23:23
you need to develop the kind of design thinking or critical
23:27
thinking which is a bit more abstract.
23:29
We are not going to be developers for sure, but I think
23:33
that kind of constant checking and trying and new tools and see
23:37
how can make me better. How can I see that this
23:40
technology will help me to get to deliver value better.
23:45
I think that's where I I would put my focus.
23:47
That's what I'm doing. Of course, going through AI
23:51
courses. And and learning.
23:53
And I think one point. I did a survey with Ricardo
23:56
about this. Area is great, but don't forget
23:59
that AI is an enabler. So don't forget about if you're
24:03
working on sustainability, diversity, growth that should be
24:08
connected to. So don't just one thing, but see
24:11
how this connects to other bigger topics that are priority
24:15
in organisations that will make you even more special and
24:19
unique. So connecting AI with all these
24:21
other topics, I think something that I would recommend to do as
24:25
well, but the most important is try.
24:28
Don't be afraid just you said turn you'll get mistakes, you'd
24:32
struggle. But this is the best way to
24:33
embrace new technologies and and most of the softwares you have
24:38
the most uh that you can just watch and think about how do
24:42
they could benefit your your projects.
24:45
But I cannot give you one specific skill.
24:49
I think there's so many skills that we need to develop.
24:53
Thinking more strategically about one's career, should you
24:57
be thinking about the very human side of work?
25:00
So when it comes to decision making, So if all the AI
25:03
eventually is going to give you all the information you need and
25:07
flag up the risk areas of a project or suggest ways that
25:11
seems to be become better, what does that leave the project
25:14
manager needing to do? So if you're thinking five years
25:17
ahead of or 10 years ahead, what is it about the project
25:21
manager's role that will change from now until?
25:24
Kind of short term future, what should you be cultivating or
25:27
developing in within yourself to ensure that you?
25:31
Still going to be necessary and needed in a five years time.
25:36
In the skills, for me it's it's what we talk about is soft
25:40
skills, but in on a higher level is that alignment between
25:44
different groups of priorities and projects and critical
25:49
thinking of strategic thinking. These are things that a I will
25:54
for sure not do very soon. So engaging on stakeholders,
25:58
talking about the value of your projects, learning to speak the
26:03
language of the organisation, connecting with the right
26:06
people. I think this is where we should
26:10
have done that before anyway, because these are essential
26:12
skills. But now AI is going to make that
26:15
more obvious that if you want to have a job in this space in five
26:19
years, you need to develop that really acceleration there.
26:23
Because it's about also following empower, feeling the
26:27
confident, feeling confident to talk to our CEO of your company.
26:31
This kind of mindset we've never heard and it's the moment to
26:35
step up. And I always say it's more of a
26:37
mindset. Issue then the skills and the
26:40
mindset says you're in power to do these things to deliver your
26:44
project benefits and this is something that is built based on
26:48
confidence on on looking another examples and and trying this
26:53
kind of new new approaches to to delivering projects.
26:57
I think if there's something I would recommend it's the
27:00
mindset. Feel empowered to drive your
27:03
project. You're the owner.
27:04
You should be able to say let's stop it.
27:05
You should be able to go to the CEO, say I need.
27:08
Resources or this is not going to happen unless that in that.
27:12
So that's something that I always push in in conferences
27:16
and workshops. Martin, what are your thoughts
27:20
around this? So how project professionals
27:21
should be thinking about upskilling themselves or any
27:24
advice you'd pass on? So basically we saw this back in
27:30
2017 and the first point was about just awareness.
27:34
So that's why we set up the project data into its community
27:37
and that's why we start the meet ups.
27:38
We ran monthly meet ups and then pre COVID we were going to
27:41
Birmingham and we're going to Bristol and Manchester up to
27:45
Aberdeen as well and start to spread the word and share these
27:48
experiences as well. So we get guest speakers in.
27:50
So a chat came and talked about the Tour de France and he played
27:53
it back through a lens, which was brilliant, right, Really,
27:56
really brilliant. So what we then start to do is
27:58
to run these hackathons. Because it gave people some
28:00
practical experience and it took away some of the barriers.
28:03
It's not as scary if someone sat next to you and showing you what
28:06
to do and showing in two days what can be done.
28:09
So we run that and then people said to us that's not enough.
28:12
We need some practical experience.
28:14
You know, we need to be shown how to do it.
28:15
So we then started training, so we run the Project Data
28:18
Analytics Academy, So that's funded through the government's
28:22
Apprenticeship training levy. So it's basically free for most
28:25
organisations and that enables people to get up skills in data.
28:30
Even project delivery for nothing and a byproduct of that
28:33
process is each person has to do three or four projects as part
28:36
of the course. So there's no exams anymore.
28:37
It's all about your portfolio that that you develop.
28:40
If we do that, we start to develop this collateral then.
28:45
So we start to develop these little modules of capability,
28:48
these Lego bricks that we can all start to piece together.
28:51
And just imagine if you had 1000 people with three or four
28:55
projects seats, that's 4000 Lego pieces of capability.
28:59
If we start to pull along that like won't put it on cause some
29:02
of it will be commercially sensitive and et cetera.
29:04
But if we could pull half of it, we will transform the industry.
29:09
So that training is available now, so finders on
29:11
projectingsuccess.co.uk and look at the training page as well.
29:15
So it's all in there and it's funded by government.
29:18
So government is trying to help you to get these skills so you
29:21
can remain. At competitive and we start to
29:24
drive up UK PLC productivity. So back to one children's point,
29:30
you know what will project managers do in the future.
29:33
And for me there is a phase shift that's coming, a massive
29:36
phase shift as in spite of as spending loads of time, you know
29:41
looking backwards through reporting, doing assurance
29:43
etcetera, a load of that it's just going to be automated.
29:46
That doesn't need artificial intelligence, it just need needs
29:49
code and some of these sort of modules of capability that we
29:53
can develop through things like Power Automate and and Power BI
29:56
and that sort of stuff, right. So that's fairly easy to do, and
29:58
it's all accessible today. Well, this is 10 years away.
30:02
It's all available today. So what we then start to do as
30:05
project managers or project professionals, we start to say
30:09
where do I need to focus my laser beam so I can deliver
30:13
these projects more cost effectively and with greater
30:16
delivery confidence. So if you look at HS2 at the
30:18
moment, is it the politicians fault that it may get turned off
30:22
from Birmingham up to Manchester?
30:23
So is that a political decision or is it a case that IT cost
30:27
growth is so extreme is it's no longer investable?
30:30
So is that a project delivery issue?
30:32
Right. I think the jury is out on that
30:34
argument. But if projects are becoming an
30:36
investable once they get to a certain size, then that's a
30:40
massive issue for society and we can help to solve that through
30:44
advanced data analytics. So we start to understand which
30:47
parts of a project are predictable and which ones
30:50
aren't and why and what we need to do to sense that and and to
30:53
understand it. So the role of a project
30:56
delivery professional is going to evolve at pace.
30:59
So it's all about the superpowers.
31:01
Of individuals. So if you can see what's in the
31:04
future, you can predict where something's going to go wrong,
31:07
you will outperform your peers. And if you really, really good
31:09
at it and you're going to get paid a lot of money because you
31:12
can save the country and absolute fortune, you can save
31:14
your employees of fortune. The problem with this is, so
31:17
I've been posting a lot of things on LinkedIn at the moment
31:20
and people have said we need to take everybody on this journey
31:22
with us. So let's not scare the horses.
31:25
And let's not put out there that you know there's going to be
31:28
various people losing their jobs and plugged into this as I see a
31:32
lot of the time about all of this sort of efficiency
31:35
improvement and productivity improvement through project
31:38
delivery and some organisations will just cash that productivity
31:42
improvement in. So it will be heads, it'll be
31:45
headcount loss. It's the more visionary
31:47
organisations will transfer those people and start to move
31:51
them up the value chain. So they've got these superpowers
31:55
and they can deliver projects a lot more cost effectively.
31:59
So for me it's not necessarily about taking everyone with us
32:02
because they'll always be sceptics, It's about taking the
32:05
five or 10% who's really going to start to accelerate this,
32:08
this journey. We're going to pull a load of
32:11
other people along with them in their wake because we'll start
32:14
to develop the evidence and we'll start to demonstrate this.
32:17
That pace. Any last thoughts before we
32:22
finish? We run out of time already and I
32:24
could talk to you for another two hours.
32:26
Yeah, I think we cover a great questions and I think nothing to
32:31
what I think it's a great opportunity for the profession.
32:34
Let's step up, let's take this opportunity, follow Martin James
32:40
all the work that you are doing in PMG to focus on on a I and
32:44
and I think the future it's very right for for us in this
32:48
profession finally. So now I'm very excited and
32:51
looking forward to connecting with with everyone who's
32:55
listening. I think the big point for me,
32:58
it's about vision and leadership.
33:01
So with project professionals, we're all busy people.
33:04
We're stuck in the knife fight over the here and now we're
33:06
trying to deliver a project. It's probably going pear shaped,
33:09
you know, in most cases. Ohh, that's what the start
33:11
filling us. So we don't have the headspace
33:13
for this, we just don't have the head space.
33:16
And that's what I'm finding from speaking to organisations is
33:19
this is a job for tomorrow, it's not for a job for today and I've
33:23
been facing that for the past six years, so it's always a job
33:26
for tomorrow. So it needs client leadership,
33:29
it needs professional leadership, it needs all of us
33:31
to pull together to start to drive that.
33:34
I don't think that's where we need to be.
33:35
We need to be showing people the path.
33:38
And showing them the way to take this forward.
33:42
So some of the work we've done work with James as well and and
33:45
the project data on it, it's task force.
33:47
So James is the chair of the project date on it.
33:49
It's task force. So it's worth reaching out to
33:51
James if you're interested in some of this is the visions for
33:54
2025. So we said let's start to
33:57
reimagine things like risk management, let's start to
34:00
reimagine benefits management. Could that look like in about 18
34:06
months from now? All right.
34:09
If we can start to imagine it and break it down into some sort
34:12
of modules of capability to get us there, it becomes deliverable
34:16
and we can all do that if we unite around it.
34:20
And and James will probably share with you some of the pain
34:23
to try and motivate the community to line up around
34:26
those visions. And it's normally because it's
34:29
getting access to those thought leaders and those visionaries
34:32
and the people who's got the ambition to make this happen.
34:36
And I think that is changing in places now.
34:38
I think we are starting to see it in ChatGPT has brought this
34:41
into the imagination, right? It's made it a lot more
34:43
accessible for people. So I do sense that changing.
34:48
And I think people like Adam Bonnesen and we've got Andy
34:50
Murray as well from the major projects association.
34:53
Then I really started to understand this and get all over
34:55
it and start as well. So I do think we'll see a major
34:59
change probably this year. And James, any any last thoughts
35:04
from you? Yeah, so Martin mentioned the
35:07
Project Data Analytics Task Force.
35:08
So Martin set that up how to how many years ago probably four or
35:13
five years ago and he's passed the championship on to me.
35:15
We've got our first meeting this week actually.
35:19
And you know, I I think what out of the objectives, if I could
35:23
think of 1 objective that would move the goalpost most rapidly
35:28
is all the institutions started to work together.
35:30
And if I see that as my role as helping create this kind of
35:34
overlay. So you've got you know what you
35:36
guys do which is brilliant. You know these podcasts and
35:39
stuff and in in the journals is brilliant.
35:42
PMI is doing some of the things. Ohh ICS are doing things.
35:45
But you know if we rather than all we're doing little bits
35:49
ourselves if we can bring it under one umbrella.
35:51
And that's what I hope the project they genetics task force
35:53
can become a kind of a way of putting all of this information
35:57
like an overlay on top of it all so that people can see there's a
36:01
very clear direction that the profession is, is moving in and
36:05
then all the institutions and government these are doing their
36:08
bit to drive towards that vision.
36:10
So I am optimistic there is that there is with all these kind of
36:14
things, there's dangers and there's, there's risks and we
36:17
have to control that and who's better than to do that, than
36:19
project professionals, you know that's part of our part of our
36:22
role. All right.
36:23
Just leaves me to say thank you to everyone.
36:26
Thank you for your time and your thoughts.
36:28
Could talk another two hours about this, but we just can't do
36:31
that, I'm afraid. But thank you so much.
36:34
It's brilliant. Thanks again to Antonio, Martin
36:39
and James for joining us, and to you for listening to the APM
36:42
podcast. Don't forget to look out for
36:44
more episodes or to rate and review us wherever you get your
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37:02
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