So, my name's Stuart, Stuart Easton.
Um, thank you for joining us.
This is, uh, if you've been on our December sessions
before, you'll know that they're,
that they're not terribly structured.
They're not terribly, you know, the goal is to have fun,
hang out with smart people, people that we like.
Um, and so I've assembled some of my favorites here,
but we figured the theme to close out the year is a theme,
something we've been talking about all year long,
which is ai, and everybody is learning, right?
Everybody's experimenting.
So the goal is to share those experiences.
What have we learned, what's working, what's not working?
And, uh, everyone on the panel is here for a reason.
Um, uh, so, you know, apart from just being a super,
super nice guy, uh,
and a super smart guy, uh, Joe also is one of the, the,
uh, the, the, the
most accomplished and knowledgeable PMO experts out there.
And, um, is working, you know, works all the time with PMOs,
uh, across the US primarily, um,
and is seeing lots of the stuff that's going on, the things
that are working and the things that aren't working.
Um, so Joe is bringing that sort of wide PMO e experience.
David is here. David wears several hats.
Um, and I'll let each
of you introduce yourselves as we go along.
But David wears several hats.
Um, one of which is that he runs, uh,
uh, community interest company, that's,
that's building a whole, uh, building
and promoting a whole body of knowledge around how
to do governance, but at the, at the enterprise level.
So it's called big Business integrated Governance.
It's a great framework, David.
You can drop the link in the chat whenever you feel like it,
but, uh, it's a, a super thing.
I was actually on a webinar that, uh,
that the big team were doing yesterday
where Steve Jenner was talking about,
about portfolio management, which was, it's always awesome
to hear Steve talk.
Um, uh, but,
but for the purpose of this, uh, David is also, uh, uh,
the founder of, uh, uh, a company called CPS,
and they are Microsoft ai,
super partner, partner of the year.
I think it was, wasn't it, David? Something like that.
Something like that, yeah.
We're, uh, a Microsoft, um, finalist.
So one of four companies, I believe, that we're, uh, that
by Microsoft as being, uh, outstanding with, with, uh,
copilot and AI type stuff.
Uh, the only one, I think, in the uk. So, which is awesome.
We got, we got some very clever people that work for us.
No, it, it's, uh, no, I'm,
I'm not a clever one, but, uh, and
David's been very kind joining us today
because he's, he's sitting in a hotel room in,
in the Netherlands, uh,
and as soon as we finish,
he's running off to a football match.
So, uh, uh, really appreciate you rejigging your priorities
for a few minutes to join us.
Thank you for that. Um, uh, an anana again, has,
has a lot of experience in and around ai.
You know, those of you were on a few minutes ago heard this
story about how she stood up a really, you know,
a fantastic workshop in no time flat, uh, last year, uh,
around ai, uh, based in, uh, based in, in Dubai.
So it is hopefully bringing a slightly different, um,
geographic slant to this.
And James is, um, uh, PMO leader within the NHS.
So for those of you who don't know what the NHS is,
if you don't live in the uk, there's no reason you should.
It's our national health service in the uk.
To give you an idea of the scale, the NHS employs about 10%
of, uh, the workforce in the uk, right?
So this is a huge undertaking.
Um, uh, it's a, it's a, an organization
that we're all really, really proud of.
Um, but, uh, uh, but despite that, they still employ James.
So, so James is really kind of the nucleus
of the PMO community for the NHS in Wales.
Um, does a lot of speaking.
He, he just published a chapter in a book with Dawn Mahan,
uh, and Joe, Joe, you had a chapter in there as well, right?
Yeah. I should have. Men should have mentioned that. Mm-hmm.
Um, uh, and Jo, uh, James
and I were talking the other day about some of the, uh, some
of the AI experiments they've been doing across the NHS.
So we figured it would be fun to, to share some of those.
So thank you, everybody.
Um, and what I'd love to do again,
is challenge the audience, challenge the audience time
to ask questions, make suggestions, make jokes, um,
uh, but just get involved, right?
It'll be more fun if you do that.
Um, and, um, uh, we'll, we'll all get more out of it.
And again, it's end of year, it's December,
we're all just trying to chill out a bit
with some smart people and enjoy a cup of tea
or a glass of old wine
or whatever it is that you happen to have.
So, I, I'm gonna kick off, um, and just ask this question.
Um, and what I'd love you to do is just whip along
and give me one example each of an AI disaster
that you've seen, or something that just didn't work well.
Um, and, uh,
and while you do that, just, you know,
introduce yourselves a little bit as well,
a bit more detail than I did.
So shall we start with, uh, David?
Yeah, sure. Um, so I'm David.
Um, uh, as, as, uh, uh, Stuart says, um, I'm founder
of a, an IT company that's got a, a capability
and specialism in, in ai.
Um, the, the, the one, uh, disaster I would, uh, think of,
it's not really anything particular that's happened, but it,
but for me, there's a disaster that's caused a whole lot
of people I speak to, to, um, recoil at the thought of,
of including AI in their capability
because, uh, they don't like the idea
that AI is getting involved in their capability.
Um, I, I'm just scared that, um, uh, there is, there is, uh,
uh, reservation with use of AI from people,
but I think, I think in, in many quarters,
it's kind of going the other way.
So for me, the disaster is, is that, uh, people are,
are still, um, on, on the brink of ignoring this stuff and,
and trying not to, to exploit it and leverage it.
So, um, not nothing, not a particular case, but, but a tick.
But a particular attitude to my mind is, is, uh, is,
is gonna be quite traumatic.
And I think people like us need to try
and, um, uh, uh, encourage people not to, to still be,
uh, a little bit ludi and, and in denial of this stuff.
Um, that, that's, that's my takeaway.
So let's, let's ask a question into, into chat,
if you please everybody, uh,
I wanna say everybody participating.
So who would, who, who has, uh, gimme a, gimme a, a, a,
a, yes, no, maybe, right?
So yes is, yes, I'm experimenting with AI
and excited, no,
is I haven't touched it, and I'm not really interested.
And maybe is, yeah, I'm just on the edge.
So let's have a look, see, see where the,
where the audience is.
We've got some good yeses.
Hey, Annette, didn't see you there. Good to see you.
Excellent. So we've got lots of yeses.
I guess there's a, a, a,
a selection bias on this session, right?
People are in the NOC camp. Probably wouldn't turn up.
Um, excellent. Oh, Fadi. Hey, Fadi, how are you?
Um, so, um, so an let's come to you next.
Yeah. Thank you. Thanks, Stuart. Glad to be here today.
So, um, I manage portfolios, different portfolios,
and also for those who doesn't know, I'm a founder
of Little Project Manager.
It's now became the framework of teaching project management
for the kids, uh, about the disaster.
I think the disastrous project I've been involved,
and, uh, I think I'm happy that I'm not part of
that project anymore, is when you're trying to embed AI
to the legacy system, to a system that is dying,
that is very old, not user friendly
and just terribly, terribly designed, that should just die.
And you are trying to kind of recover it by embedding
and trying to figure out how to plug AI into it
and say that it's now with AI kind of solution.
Excellent. Yeah.
Excellent. Shall we move on to James?
Uh, hi everyone. I'm James.
As, uh, Stuart mentioned, um, I lead a PMO, win Wales,
and also get involved in a number of other, uh, PMO
and project capability based work across, um,
the Welsh Network, um, disaster.
I think the, the main thing I've seen, um,
quite recently actually, is where people have started to try
and embrace AI and really start to, to lean into it,
but they've gone too far.
Um, and they've actually kind
of just put everything into AI and gone, right?
Well, we don't have to actually quality check it.
We don't have the thing, if we wanted to write a report,
we're just gonna trust the AI or do it for us.
Whatever it spits out, we're gonna give
that into the board meeting next week.
And actually, they've kind of done themselves a bit
of a disservice then,
because they're relying too much on the ai, doing the work
for them, thinking it's basically another human coming in
to do it, instead of working with the AI
to pro provide the outcomes.
So we instead putting the AI into a bad light when it's not
delivering what they're expecting it to.
Um, I think that's the, the sort
of main thing I'm seeing a lot of at the moment.
Awesome. Awesome. Love it. And finally, Joe PMO.
Joe, should I get,
I should get the guitar down, shouldn't I? Hey, Joe,
That'd be great, right? We
could sing Jingle Bells
or something together, you know, AI for me is kind of like,
is it the Grinch or is it Frosty the Snowman, right?
There's no happy medium right now.
It's the horror stories or the successes.
Um, so we're talking about disasters here to start, and,
and we've been focusing at the PMO squad lately this past
year a lot on people and on staffing and on resources.
Um, and, and two horror stories for those who are unemployed
looking for work.
Um, hiring managers are telling me they can tell when
you're reading AI answers back to them
during the interview process,
and they're immediately removing you from consideration.
So you've got chat GPT
or something up on your other screen,
you get asked a question,
you don't really have the experience.
You type something in the chat, GPT, it spits it out,
and you give it back to the hiring manager.
Don't do that. That's a disaster.
And, and you're in trouble
in your interview, isn't it? People are
Doing that, I didn't realize. Wow. Yeah.
And then the other is candidates who've been interviewed
by ai, like an actual robot head, is interviewing them.
And, um, there's no emotional connection
during the interview process.
It's very cut and dry.
Uh, it's not a dialogue going back and forth.
It's a set of questions, whether answer and then a question
and then an answer, right?
There's no reaction to that.
So we're evolving into non-human interviews, uh,
and right now they're not quite where they need to be,
probably to get a thorough betting of the candidates,
but they're out there, and I think we all need
to be aware that they're coming.
It's going to be, um, more common probably in 26,
and it's just becoming part of life, right?
It's just, it's integrated in, in places sometimes
where we don't even know it's there.
And sometimes it's very obvious to us.
Yeah. Yeah. Well, so it was, it was interesting.
So, so the disaster, I'll share a disaster
that is an internal one.
It wasn't a total disaster, but we, a little while ago,
we decided we'd run a hackathon hackathon day,
and the goal was to pick a small piece of functionality
for our product, new functionality,
and use AI to interview the bus,
the product owner, and the, the,
the people on the front line who interact with customers
to compile requirements, turn that into, into
domains, epics, stories, and so forth.
And then pick a couple of stories and generate some code
and try and do that using AI.
And, uh, what, what, so what we learned was that the, um,
the second half of that was kind of success,
fairly successful, but the first half was a total disaster
because it was completely unstructured.
And, uh,
and as a result, we, we found
that there just wasn't consistent.
AI wasn't able to take that
and then turn that into a structure
that it could consistently hold onto.
So the learning for us was that, you know,
if you're gonna try and do something like that,
you probably need to give it a bit more structure, uh,
and not expect the ai.
So I don't think it was a failure of ai.
I think it was a failure of us to understand
what AI was capable of.
Um, and I, I, I think I see a lot of
that going on right now.
So, so James, you, you, you were sharing some,
some good stories the other day,
so why don't we do the opposite question, right?
Let's hear, let's hear a success and,
and what I, what I'd really love to hear is
how did you make it a success?
What, and, and do you know what? I'm gonna take
this hat off 'cause it's too hot.
Um, so, so let's hear about a success and,
and you know, what, what you did to make it a success.
'cause that's, that's the big learning here, right?
I can't actually remember which, uh,
which ring I was talking to about the other day.
Um, but in terms of AI use, where we are using it, well,
it's, we're,
we're finding success is coming from helping us support
with some of the tasks that we're taking a lot of time
for our PMO to work through.
Um, and this isn't just in the organization I'm working in
now, but previously, um, resource constraints,
budget constraints, um, it's pulling a lot of, um,
capacity out of our PMOs, um, and the typical work we do.
So AI has really helped start to support that now
to actually take away some of those more administrative, um,
sort of day-to-day menial tasks that we're taking up a lot
of our capacity than now.
AI has been able to be programmed
or prompted to be able to do for us, um,
which has freed up quite a bit of capacity, um, for a team.
That's usually the last couple of PMOs.
Um, I've led have been anywhere between five people big,
um, compared up to 18.
Uh, but it's quite rare to get that now, um, an 18, um,
strength PMO, uh, where I work.
So to have the AI being able to pick up some
of those administrative functions
and actually, um, start managing our calendars,
start booking in the meeting snap for us, uh,
even building the agendas
or in the loose format that we then just have
to quality as assure and build over.
And even I think the biggest help,
which is something really small,
but in the grand scheme of things, some of us are
so busy having, um, co-pilot
and have AI to be able to go through
and pull through information from all of our emails, all
of our teams chats,
and any other sort of formats we're using,
and condense that into a summary at the end of each day so
that we know our actions and plans for the coming weeks
and months going forwards.
That has been a game changer for us
and has saved so much capacity within our,
within the way that we work.
Awesome. Awesome.
Um, uh, so it's great to hear some, some,
uh, some successes.
So in doing that, James, what were the, what were the things
that you felt contributed to being successful there?
Because I've, I've, I've also heard people who've tried
to automate some of that stuff and failed.
I think it was the, the bit
that made it a real success was listening to
where the problems were coming from.
So we almost removed the, the solution from it at first.
So it wasn't just immediately, let's go,
AI's gonna fix everything.
It was actually listening to where the problems were.
So, you know, stuff's getting lost in emails,
we're losing track, there's too much stuff coming in.
We haven't got time to go in and put together the, the notes
or the packs for the project boards.
We haven't got this time.
So when we started to put together all of these tasks
that were causing the problems, when we started
to look at the solutions to solve this, it,
it came down to two things.
We could go back to what traditionally we would've done,
and try and get a business case to get more staff
and try and bring that in.
But then we knew that well, actually
resourcing budgetary constraints,
that's not going to happen.
However, we've got this system here that's not being used.
Could this be the answer is, is AI the answer in this space?
Now, I'm not an AI expert. No one in my team was AI experts.
So we basically had to leverage the, the internet
to teach us, and we had to use AI to teach us how to use ai,
which was quite a, quite a nuance as well.
Um, but when we just started to do basic automations
with it, or just asking it to do certain things,
and I find some of the ai, those quite intuitive in
how you use it, um, that was enough for us to start
and to test our appetite.
And as we've got more familiar with it
and better, we've started to meet people along the, the way
as well on those, on that journey
and start to get more advice, more guidance.
And it's now become something that we've sort of
lent on quite heavily.
So my entire team now operate with, um, a form
of ai, um, in their everyday tasks.
Um, but we are very conscious that we won't slip into the,
the horror story I started with at the beginning,
and that we, we work with the AI
and not have the AI replace, um, any sort
of personal function that it was doing.
Excellent. So I like that. So I heard, uh, one
of the key themes that I suspect will come again,
come up again, is start with a business problem.
Don't start with the ai. Mm-hmm. Right?
Make sure we're solving, first of all,
solving a problem that's actually a problem.
And then secondly,
using the right solution for that problem.
Um, David, uh, same question to you, example of something
that went well and,
and what were the key enablers that may
that enabled it to go well?
Oh, you're on mute, David.
Yeah. Uh, me, me again.
Um, so, uh, all, all I can talk about really is, um,
having, having seen, I've got a, a, a company that's got a,
a whole group of folks that do lots of this AI stuff,
and they're all clever folks that, that, um,
can produce solution this or that.
Uh, but what I, what I think, uh, uh, needs to go well with,
with these examples is, is not trying to work out
what the problem is, uh, or what the solution might be, but,
but I think it's actually, and,
and Steve Jana will love me for saying this,
it's actually trying to work out, uh,
what the business case is and what the benefit profile is.
Uh, so if, if we, um, take this, um, uh, uh, uh,
solution for, for, for example, something that's gonna, uh,
make, uh, make, uh, quicker
and simpler, uh, h HR queries happen so that we,
we can get response times
and shortened amount of time that, that HR queries are, are,
are gonna take, for example, in the NHS, um, how do we,
how do we put together a business case which says we can
justify doing this?
Uh, I think, I think, um, for, for my experience that, um,
that that's where a lot of the, the thinking needs to go.
Not, not to what the problem is
and what the answer might be, but how I can,
how can I put this together into a case
that will persuade somebody to engage?
Um, that, that to me is, um, a huge learning point and a,
and a huge thing that I didn't, I didn't really expect.
I, I thought it would be easy to,
to show some technical stuff and excite people
and to, to, uh, show we understood the problem.
But no, the, the, the real challenge, I think is to, is to,
um, convince people that it's actually the,
a solution is actually viable and it actually works.
So I'd say that people don't underestimate what, uh,
what the business case build, uh, need is gonna be,
because there are very many cynical people out there.
Um, but if that works for you guys,
Does that, does that resonate with you, Joe?
Yeah, absolutely.
Uh, you know, a, a good example, I think that kinda relates
to that, uh, PMO squad's main kind of approach to building
and helping with PMOs is something we call OPD
Organizational Project Delivery.
And a key component of
that is it's built on system dynamics out of MIT,
and that's on feedback loops, right?
It's closed loop, uh, instead of open loop,
which traditional project management is.
And, and with a closed loop mindset, you have
to receive feedback throughout the life of the project.
And after a project throughout your organization on how
to deliver projects, how to fulfill the business case,
we collectively, as an industry have said,
let's have retrospectives and lessons learned at the end,
and then we store them and SharePoint or somewhere
and never see them again.
So what we've done recently is with our, our use of OPD is
let's get all of those lessons learned loaded up into AI
and see what those, what does AI tell us about
what we learned from all of our projects?
And then we're not spending,
because people don't spend time retroactively and go back
and look at projects as they should.
So you're still not spending any time,
you're letting the technology go do that, give that time
that you should be spending and aren't to ai.
Let it analyze what you're getting out of the projects.
What consistencies are you finding?
What variances are you finding?
And then make those changes throughout the organization
or in your PMO or your portfolio going forward.
It's all about the business case.
Everything we're doing is supposed to be delivering impact
and value back to the organization.
If our lessons learned said
we weren't measuring value at the end,
and we do that project after project
after project, we're not needed, right?
We're not, organizations are gonna find us, uh,
as overhead, and that's what we hear.
The, these stories all over the place
that we just are overhead
and we can remove those, bring it back to the functions,
let this be a project delivery at the functional level.
That's our fault. It's not the business's fault.
That's our fault for, for not giving
what they've asked us to deliver.
So for me, the, the success story is watching that in action
to let AI produce these, uh, answers
and solutions that came out of retrospectives
that we already knew.
We just ignored them and hid them and,
and stopped acting on them.
That's how I think we start to see the power of AI come in
to not disrupt us,
but to make us better than what we really could be.
Awesome. Awesome. An you're back with us? Yeah.
Excellent. I'm back. You're the, you're
the only one with, this is still like that.
My hat was too hot. I'm taking it off.
I have an AC here, right? I'm in Dubai.
It actually looks more like a
pair of slippers, doesn't it?
But there you go. It's a hat.
Um, so an, the, the, the question was, was basically let's,
let's hear a good story, a success story
and focus on what, what made it successful?
What was the key to making it a successful
uh, yeah, AI story.
I have a good example. Uh, very practical example.
Uh, I've been involved with one of the teams
that's been experimenting and, uh, trying to adapt ai.
So we, uh, put together a sort of the scorecard
on measuring which AI
and how much it helps for the team, for the PMO team,
and then the team being experimenting with different ais
for the different use cases.
So it wasn't more on, um, kind of,
we have a problem, let's solve it.
With ai, it was more on the adventurous side.
It's kind of, there are a lot of AI tools and let's research
and let's test and let's see which one works best for us.
Uh, so they've been taking different AI tools, testing them,
they've been putting together the scorecard,
and then we've been assessing which AI helps
to which instance, and, uh, for how much.
And then they've been deciding whether they needed
further for the work or not.
That's been pretty, kind of interesting to observe
and also to work with them.
Excellent. Did you come to the conclusion that any tool,
any particular tool was at the top consistently?
Oh,
Consistently, yeah.
We found out the good AI tool for the note taking.
Uh, also they've been experimenting quite a lot
with different, uh, building up different agents
and the workflows in the way.
So they will be trying to automate the kind
of the boring work that they've been having
and then trying by different tools
and different combinations.
They've been putting that together.
Yeah, as I'm saying, it was very practical.
Yeah, I've been following up with them,
but yeah, they, they've been pretty happy
with the results that they've achieved.
PR practical is good. Uh, so there you go.
That was, that was, uh, the inevitable question has come
through once she said that.
An which is, what's your recommendation for the note taker?
Ai?
Yeah. We've been, uh, I think the top one was the read ai.
Read ai. Mm-hmm. Yeah.
Because it wasn't only just taking the notes pretty in a
good quality, but it's also been observing, uh, behavior
of the audience if the camera is on
and recommending kind of how to better, uh, kind
of giving a report on who is more engaged,
who is less engaged, I
Think. Ooh,
yeah. Excellent. Big brother is watching you.
Yeah. Nice. That's
Exactly the point.
So, so one of the things that we did recently that was,
uh, that was really kind of cool just
to share another fun story is, um, so there was a,
there's a process on our marketing team,
which is about writing the newsletter.
And, you know, a newsletter sounds like
it's just a newsletter, right?
But it, it's historically taken someone a couple of days
to create and, and organize and everything, uh,
but over, over two week period.
And primarily the reason it would take them that long is
because they had to keep chasing me to write the CEO letter,
the welcome letter, and the newsletter, uh,
you know, the note from the CEO.
And, um, so what we,
what we did was we built an agent to do that.
So the, so we actually, so one of the success, you know, one
of the success drivers is the fact
that we actually have an ecosystem of agents,
and I think this is one of the things people
sometimes don't do.
Um, so we actually have an ecosystem of agents,
and each agent does one thing well, and then,
and then the, the, the core agent
that the marketing team uses talks
to all those different agents
and gets them to do their bit of the job well.
And that way you, what, what we found is
that you can control what data each of those agents uses.
So we've got one agent, for example, that we built
to scour our website
and to recommend interesting content
for a particular reader, right?
So, so in the newsletter,
the newsletter writing agent will go to that one
and say, this is my topic.
Give me four things on our website
that we should include in the newsletter.
And it, it returns that,
and then it goes somewhere else, get some more information.
Then it brings it all together in one newsletter.
So what, what was a two day piece of work over a couple
of weeks is now, at least in first draft form,
it's about a five minute piece of work.
And of course, the caveat, I think everybody said it so far,
is that you don't just take the AI stuff
and say, that's it, it's done.
'cause it's not that good. Um, you still have
to sprinkle human fairy dust on it, but, um,
but it, it's massively, you know, it's freed up one, one
and a half, two, you know, almost two person days a month.
Uh, and building that agent, put it in context,
took about half an hour, right?
So, um, uh, so, so when it goes well,
it's, it's great, right?
It's fantastic. But coming back to the themes,
the business case, right?
The business case was we need to be able to do it faster.
We need, right?
We, we need better quality,
and we need, uh, we need to get Stuart outta the, the,
the first draft loop, right?
So now Stuart's in the final review tweak
and, you know, add my own particular sense of sarcasm, um,
which the AI hasn't quite got yet, can't quite pull off
that last little bit of a sarcastic, um, uh, knives
and the ribs kind of fun that, that we tend to have.
Um, so that's, so that's awesome.
So now what I would like is for the audience
to start throwing some questions out, right?
So we've got a panel of people here who have,
have some experience, some more technical than others,
some more PMO centric than others.
Um, so, you know, I, I can keep going with questions, um,
uh, but, uh, I'd love to, I'd love to put some
of your questions to the, to the audience as well.
So do jump into the note, into the, uh, into the,
the chat with questions.
I've got it open on if I keep,
if you wonder why I'm looking over here, I've got 'em on a,
I've got the, the chat on a different screen over here,
so I'm keeping an eye on that.
Um, uh, so, so Stuart, that's an excellent question.
How long has it taken so far
to feel comfortable in using ai?
I'm gonna come to James on that one first,
because James, it sounds like you
and your team are, are relatively new at experiencing,
you're still going through that process.
So how long does that take?
Um, that's a really good question.
Um, so I think, I think from the sort of initial onset
of actually playing about with it
and then actually getting comfortable into using it
for business purposes
and having the confidence that you can then go
and use it for your day to day work,
I think it's been a probably about a journey of
around about 12 to 18 months to really get to
that sort of level.
Um, the, the first sort of four, four
to six months was very much, um, playing with it, um, seeing
what was in the realms of possible.
I think you can imagine
that when you give the team a tool like this,
everyone's first thing they had to do on there was go
and create a little action figure of themselves using ai
that wa that was the first,
first thing they had to go and do. And see and
See your action figure come on.
We wanna, we wanna see Jason,
My, my looks nothing like my, um, so that was the sort
of thing, and everyone started going,
all these little PMO action figures that they generated
of themselves was great.
But then when they got C vs
and people were starting to go, well, actually, if I start
to ask the AI to summarize this meeting or summarize that
and starting to get used to it, that's
where it started to come in.
But it took about, it's taken about 18 months to get 'em
to a point now where they are comfortable
that they can use it and know its limits.
The limitations part is, um, the bit
that's taken the longest, but we're still learning
and the hoofing around the agents is
that's the conversation we're moving into now.
Can we generate our own agent
or PMO agent that is from the AI part?
And that's, that's where I'm interested in going next.
Yeah. Yeah. It's a good step when you get into the agents,
it takes a while to get your head wrapped around now
to make them useful, uh,
and how big they need to be and all that kind of stuff.
We may, we may come onto that later on.
Um, uh, Joe, I'm gonna, I'm gonna ask you
that same question actually.
So how long do you think, you know,
what are you hearing people saying when
you're going around PMOs?
How long has it taken them to get their arms wrapped
around this as a, and and make it productive?
Because you talk to loads of people, right? All the time.
Yeah. Yeah. I, I think it's still happening.
I, I, I don't think it's gotten comfortable yet.
Um, the kind of, the consensus I feel is I,
people are doing more at home
than they are at work still with it.
'cause they're kind of experimenting
and playing around with it, uh, within their families
and friends and, and, and neighborhoods
and different associations.
Their parts of, um, I, I'd liken it to, you know,
20 years ago when, when nobody had one of these, right?
The phones, and then when you got one of these, you had two
of them because you had one for work
and you had one for home.
You, you couldn't have your work email on, I mean,
your personal email on your work phone.
Um, and then finally companies got okay with that
and said, it's okay.
There's technology now to make that be all right.
I think that's what's happening right now.
But the feedback I'm getting from PMOs all over the US is
we're experimenting.
We're not comfortable yet.
We're getting comfortable, um, our IT organizations
and our compliance, there was a question about
compliance in here as well.
They're getting comfortable with it.
Uh, because remember, it only really works if
you give it data.
If, if you're giving AI something,
it can give you something back.
So, uh, people are still a little hesitant with that at,
you know, PMO squad, for instance.
We're, we're a PMO consulting firm.
We, we don't have any technologists on our team.
And one of the things we do is an OPD assessment.
I would love to put that on our website
and have people self assess to see where they're at.
I went out and just had AI write to code for it.
Uh, we're now iterating through that to be able
to take non-technical people on our team
who do the in-person assessments
and have AI be able to do those assessments
as a self-assessment tool on the web, just
as well as if we did in person.
And that's gonna save an organization thousands
and thousands of dollars if they trust those results.
So we're, we're able to take non-technical people,
but we're trying to get comfortable with it
that the output's gonna match what we could do as people.
And so that's where, you know,
from a very personalized struggle,
what we're seeing out there.
But I think PE companies are still
easing their way in that that's what we're hearing.
Nobody is kind of all in yet, right?
They're, they're trying to figure out how to make it work.
Excellent. So I'm gonna come back to that,
that data governance question next.
And David, I'm gonna come to you with that one first
and then an, um, on the, I'm giving you warning
so you can think about it.
Um, but before we do that, um, I thought I would share one
of the other experiments that our marketing team has done
recently with ai, um, uh, which,
which hopefully you will enjoy.
Uh, it'll only take a minute, assuming I can work Zoom.
Here we go. Um, so we are in the process of relaunching
our, um, uh, YouTube channel,
or reviga reinvigorating our YouTube channel.
Uh, so, uh, where's it gone?
Oh, I thought we, I thought we were there. There we go.
Let's go to the, the transparent choice YouTube channel.
There we go. So if you have, if you are not subscribed
to the YouTube channel, please go and subscribe now.
Um, we would very, very much appreciate it.
Um, but the AI piece was, uh, this is, this is our, uh,
our first experiment, uh, with ai.
Um, uh, where we, we took
the announcement about PMBOK
and turned it into a video. It's like
Wandering around a room with a blindfold on.
You'll find the filing cabinet with your face.
So here's the big reveal.
PMBOK eight finally gives PMOs their own appendix
for the first time.
PMOs are officially essential to delivering value.
They're not admin, they're your portfolio satnav,
making sure the work backs your strategy,
not someone's pet project.
And they give decision makers the insight
and support to spot the dead ends fund.
The winners fix the bottlenecks.
So outcomes actually improve delivering value without PMO.
That's like baking a cake without a recipe or flour
or an oven without a strong PMO.
You're just guessing.
Wanna see how a real PMO delivers value?
Well check out our PMO page, the links in the description
below, no blindfolds required for a mix of PMO insight
and fun, and to stop the dancing, like,
comment, and subscribe.
So this is, this is a great example of
where we are small.
We're a small company, right?
And we just produce something, um,
that we just could not have project any other way, right?
So it's not about, in this case,
it's not about being more efficient.
We just couldn't produce something like that before.
And, uh, so, you know, so my lesson from that though was
that, um, uh, the first one that we did
took us quite a long time.
And it took us a long time
'cause we had to figure out what was the right tool to use.
Uh, it took us a long time
'cause we had to figure out how to use the tool.
It took us a long time to figure out, uh, you know,
how do you put a story together in a way
that AI can understand it?
So that video that you just saw was a minute long.
It took us three quarters of a person day to do.
The second video that we did was a minute long.
It took about an hour. And so,
you know, one of the, one of, so for, so my learning from
that is, you know, there's, there's a piece here.
Let's celebrate the success, right? Yay. It looked great.
We, you know, we, we, we were able
to do something we couldn't do before.
Um, but there's a, there's a,
there is absolutely an economy of learning here.
So if your first experiment doesn't
work, that's not a failure.
That's a learning, right?
That's a learning that's moving you forward.
Every time you fail, that moves you forward.
So I'm gonna share another, I'm gonna share a quick fail
with you before I come to David.
And then, and then an hour to talk about governance sake.
So the failure is
that I think organizations right now are often failing.
So there's a, a pharmaceutical, large global pharmaceutical,
one of the world's biggest pharmaceutical companies.
I know, uh, a few people who work there.
And, uh, they have set aside a few tens of millions
of dollars specifically to fund AI projects,
to improve effectiveness, especially in the whole, you know,
drug discovery, uh, part of their business.
At the exact same time, the IT governance team
have put a moratorium on any new AI projects.
So you've got the executives, you've got the strategy part
of the business saying, this is the one
of the most important things we have to do.
And at the same time, the governance guys have just put a
big stop sign on the entire company, right?
So there's a lot of that kind of thing going on
as well at the minute.
So, so David, that brings us to, to Liz's question about,
uh, uh, data governance.
How are we handling data, data governance?
Uh, you're on mute still.
Alright, sorry. Um, I'll give you an example.
One of the, one of the solutions I've seen people, uh,
use is a, a bid a bid preparation solution.
So that in a sales team, um, if, if you are fed up
of answering tenders with, uh, uh, an answer that you, you,
you've recreated millions of, millions of times, can,
can we take, uh, questions from a tender
and, uh, ask, ask AI to go and answer all this stuff?
Uh, really quickly, really quickly
and simply from what we've answered
before, um, the obvious thing there is, well,
what have we answered before?
Where is that information? And who says it's good?
So it, it must be glaringly obvious that, that, that, um,
if we're gonna use AI to do anything,
then the information we, uh, ask it to do it with has
to be good quality stuff.
And it needs to be under control.
We need to be, have confidence that it's usable.
Um, so, so that's, that's the challenge.
I think the challenge is, is when people say they want AI
to, to do this or that, um, they can't do this all
that based on all, all the junk and sit chat and waffle
and fluff that, that we all create in our business lives,
that isn't all that brilliant.
We, we, we have to find a way to, to point AI at,
at information, at data
and information that is, is solid stuff.
And I don't think many organizations or,
or certainly most organizations are, are truly on top of
what's good information
and what's, what's the right information.
We, we have copies on OneDrive, we have copies in email,
we have copies in folders.
Um, I, I think there's a whole lot of, um, personal
internal organizational hygiene we need to get on top of
with respect to our information and data
before we can truly leverage the AI
to, to really work for us.
Awesome. Um, an do you have any thoughts on that?
That data governance thing?
How do you, you know, how do you get that lined up to so
that you can actually get out and execute?
Yeah, I have a good example that I've been observing.
And the bad example, so I'll start with the bad one.
That's more fun, isn't it?
Yeah, it's more fun
and especially when you see it's happening
and, uh, then the level of the ignorance of the security
and uh, intellectual property is just insane.
So the bad example is when, uh, you assume that your
employees know, uh, that all the data that you have
and you create your company is, uh, assets of the company.
And you can't just fit AI with that data
and expect that it'll be good,
because then that data actually leaked right to the AI
and, uh, that,
that ignorance level unfortunately is pretty
high. And, uh, so they put
Things into the AI that shouldn't have gone into the ai.
No, no, absolutely not.
And then there is quite a lot of education that need
to be done before you would allow ai, uh, the users
to use ai, uh, regardless of your subscription level,
whether you have in the enterprise level that you assume
that, uh, your data is secured
and the AI is not trained on your data
or it's just the general access that you're getting
to the AI solutions that there, um, human nature is the way
that if you can't simplify your life, you would do that
and you wouldn't care much about what you're doing, right?
So, um, that's, that's a bad example.
The good example is that very recently, it was last week,
uh, I've been involved in the discussion
of the good solution.
And that solution creates a dashboard
for the data governance.
So you have an AI on the backend, you have on the front end,
uh, the kind of the front end where users interact
with the AI and uh, write a prompt and get an answers.
It's exactly the example of I think, joy you used
for the lessons learned.
But then in the between, you have a dashboard
that accumulates the data that been going back and forth
and the providing for a data governance, what's been used
and what kind of prompts been written.
And, uh, it's inner level alarms.
Uh, the situations when it kind of sense, uh,
based on the training that that, that,
or this data should not been kind of used
or operated with the ai.
And I'm pleased that there are this sort of the solutions
and, uh, teams are working on those solutions
because then it gives the clarity on what, what kind
of data users are feeding DA
and what kind of answers are coming as well.
So it's kind of two-sided dashboard, which is really nice.
Love it, love it. Um,
and I, I, I've seen another similar idea to that error
as well where, um, I, I've seen teams building agents
to be the enforcer, right?
To basically just keep an eye on everything
and make sure that, that, uh,
people are doing the right things.
That the agents are doing the right things to watch
for agen drift to do all those sorts of fun things.
Now, I, I'm actually going to skip over, uh, Gina's question
'cause I, I think David gave you one line answer.
You deserve more than that.
If we've got time, we'll come back to it.
But I, I want to get to,
I wanna jump ahead to Joe's question.
Uh, so Joe's also in the NHS, um,
and so Joe's question is, I'm a, I'm a total novice on a ai.
Uh, so when you say ai, what, what systems
and what approach, um, uh, they're, they're just beginning
to experiment with copilot.
So, so I guess the question is,
if you're just getting started, what tool would you,
you know, what tools would you use?
What would you do? How would you go
about learning and starting?
And Joe, I'm gonna,
I'm actually, no, I'm gonna pick on James.
'cause you've gone through this relatively recently.
So, so how did you go about just starting the process
and getting to know you,
you talked about creating these little, these little, um,
figures and things like that.
So how did you, how did you go about that?
I think a lot of it came from, um, just sort
of things people had picked up from other conversations.
Like, I've heard AI can do this, I heard it can do that.
And we actually had a similar conversation internally,
whereas what, what AI systems are, are people using?
How, how does it work?
And the,
what I'm seeing across NHS rails at the moment is this
varying levels of, um, confidence
and actually wanting to go to use ai.
So one of the health boards is basically locked off
every AI system.
You can't do it until you've signed something
to say you understand that you can't go
and share confidential information in there.
You can't put patient information in there
because that would be a nightmare.
Um, and you've gotta use in a certain way.
Um, so they've started to put those barriers in.
But in terms of the, what systems to use, um, it,
it came down to two for us.
Um, a lot of people had heard of chat, GBT
'cause that was all over the news.
Everyone was using it, it was all over social media.
That was the sort of big one that everyone was drawn to.
But for us, um, our ICT were basically saying, no,
it's too, too risky, um, to there.
But because we had our Microsoft, um,
to copilot was built into that,
there was a lot more confidence towards that.
But a lot of people, um, had started experimenting
with chat GBT in their homes
and starting to learn how to get the prompts right
and working with it there.
But in terms of how we've worked, it's largely been, um,
co-pilots, um, for internal ways of working.
And in terms of how we've taught it, then it,
it has very much come from, um, videos,
self-teaching each other.
Um, getting the AI to teach you, um, is quite, uh, uh,
fun way of trying to do it.
So asking the ai, how can you help me solve this?
Or how can you help me do this?
And it's actually guided us through.
And some of it has been going, right,
we're doing this at the moment, we need you to act
as a member of the PMO team, and you tell us.
And it, it's guiding us that way.
And it, it has been very much, um, just experiment,
um, see how we can do it.
I think we are a long way off actually nailing it
and actually being in that area
of using it to its full advantage.
But compared to a lot of people,
I think we are very far along the journey compared to
what I've seen in other organizations around us.
Joe, what have you seen working well
for teams early in the adoption process?
Yeah, I think as, as James said, uh,
co-pilot's the easy win
because there's so many Microsoft shops out there
and they're, they're able to experiment.
Um, you know, we're, we're technology partners
with several PPM providers
and each of those tools are doing their own version
of AI experimentation.
So, as an example, Asana, you can go in
and have, um, AI rate your status reports for you.
Um, I still don't recommend that quite yet
because you're reliant on all
of your team members inputting the data to make
that status report be accurate
before you distribute it out to your executives.
You better confirm, uh, what you've collected, um,
you know, people doing portfolio management prioritization
based on resource availability using AI tools,
uh, to be able to do that. Uh, US
Folks. Woo.
Yeah. As, as, as you guys do, right? Of course.
Um, so to me, it's, uh, in PMO squad we've gone in and,
and we're a chat GPT open AI organization.
So we've got a business account in there and,
and we've upload all our data into that, uh, tenant.
So we can go in
and talk as if we're talking to a coworker, um,
and be able to say, go look at these documents
and tell me what we should do based on that.
Um, so we do get feedback directly
from our own organization.
Plus what we're seeing out there in PMOs across the us uh,
right now it's experiment with what you have.
Don't feel like you need to go out and get something
because everybody is experimenting.
If you have technology in your organization,
that provider is experimenting with ai,
go use what you already have.
You don't need to go get anything new.
Um, and then at home, experiment with everything.
Get comfortable, the prompts, understanding how
to ask the questions, uh,
and how to be able to get, uh, AI to be able
to react the way you, you're thinking one thing.
And what we say and type doesn't match.
And it takes a long time to get your thoughts actually out
so that an inanimate object can
understand what you're thinking.
And, and that's not intuitive.
'cause we've never had to do that before.
Um, so I think use what you have and then practice at home
before you, you bring it in, in-house
and really try to leverage it.
So we, we have, um, you know,
we're probably fairly typical, right?
We've got some people who are further along the curve, some
who are really not comfortable with ai.
Um, and one of the things that we do to try
and support the team in, in these early days for some
of the users is we have a, we're only a small company,
so once a week we bring the whole team together, um,
and we just have a share your experiments
session, right?
Where people show, this is what I did,
and it messed up colossally, right?
It was just a disaster, right?
And other people come and say, look,
I did this and it was really cool.
And, and so we all learned from that, right?
Uh, and we, we did, we, I had one
of the guys come up with a competition.
So he came up, we had, he gave,
he gave us over half an hour, we had 10 challenges.
And, uh, you had to, you had
to use AI to solve the challenge.
Uh, but they were all written in a way
that if you just copied the question into ai,
it would give you a ridiculous answer, right?
And so the, the, the training there was, you know,
you've gotta think about the prompt.
You've gotta actually read the answer.
'cause, you know, one
or two people on, on the team
actually just took the answer it gave them
and said, I've got the answer.
And it's like, no, you're an idiot.
You didn't read the answer, did you?
Um, so, so, so you can turn it to little games like that to,
to encourage people to experiment, to play with
what those prompts are,
because the prompts are really important.
It's a conversation with the tool.
Um, if you just ask generic questions,
you'll get poor quality answers.
If you, if you give it lots of context
and, uh, then it is generally quite good.
David, do you wanna jump in?
'cause I know you wanna say something about Microsoft
and how amazing they are.
Uh, I probably need to, um, just, oh, I'm on, on mute.
Well, there's all kinds of, uh, providers out there.
I mean, I would say this
because I own a Microsoft solution providers,
but, um, the, the, there is, there is, um,
I think there's a lot to learn basically.
And, uh, you, we can experiment for ourselves,
but we can experiment for ourselves
what we see on the coalface, what we don't, um, and,
and we can experiment as to how to use, um, the AI to go
and, uh, research something
or produce us a, a, um, an analysis
or rewrite this email that's all low level tactical stuff.
But, but at some, at some stage, we, uh, the,
the next level is to start using AI
to build into our applications, to build into our workflows,
to build into our business applications.
And that, that's, that's a whole different,
different level entirely.
And, um, uh, us sitting around, um, chatting about
how we've used ai, um, to rewrite my CV
or to, to create a set of questions for,
for this quiz I'm running next week, that's,
that's all, all small beers.
Um, the, the, the, the, what can you do with this stuff to,
to improve our, our, our business capability,
to reduce our business problems.
Um, the, the only way you're gonna, uh, really get to grips
with that is, is, is actually speaking to people about
what they've done, why they've done it.
And then you, you've gotta get past the,
what the hocus pocus factor from,
from people in your organization thinking, well,
you've just come up with a lot of waffle.
That that's absolute nonsense.
Uh, how do I believe that what you've, that
that lovely story you've just told me is it's all possible.
Give me some kind of, uh, business story that,
that can show benefit.
Mm-hmm. And for me, that,
and mentioned it earlier, to me, it's not just about knowing
what, what the thing, what this capability does
or what's possible with it.
It's about making cases that persuade other people
that's actual benefit here.
And that's, that's the key point.
Can you make cases to get people to do things?
That that's what's gonna enable you
to use AI in your organization or not in, in my opinion.
Awesome. So, so, um, uh, uh,
yes, we've got just a couple minutes left.
Uh, thank you an for spotting that.
Um, uh, so, uh, what I would love to do is just go
around the, the panelists
and see if we could just get one last thought.
Um, there's, there's a few questions we haven't got
to, uh, sorry about that.
So one last thought each perhaps, um, I,
I'm gonna throw mine out, which is, um, to think about
when you're building, when
you're trying to automate the process.
So this isn't so much necessarily within the PMO,
it's when you're delivering projects for the organization,
leveraging ai.
Um, remember
that most processes in organizations are set up so that humans can do them.
And if you're gonna try
and leverage AI to automate the process,
it might just be worth thinking about how to
redefine the process so that AI can do it.
So it's built for AI to do, not built for humans to do.
So we learned that when we did that hackathon, that was,
that I mentioned earlier, that was one of the lessons
that we came away, the bit that really failed.
Totally. We just tried to replicate the human process
and we realized that we needed to change that process,
that it was AI friendly before we tried to automate it.
Um, so so that would,
that would be my one last little thought.
Um, James, what about you?
I think for me it's, um, it's what I'm experiencing now
that ai, AI is absolutely fantastic, you hear,
but there is two sides to it.
And at all of the recent events I've gone to,
it always goes one or two things.
You hear the horror stories, you hear the, the real things.
You hear the things that AI is gonna take everyone's job.
You hear all of these different things.
I think none of that is true.
I think if there's something right down the middle that
AI is evolving the way PMOs
or any sort of profession is going to be working, working
with AI going forward as the future,
and actually everyone can actually work with AI in a way to,
that will actually upskill
and benefit the way that they actually deliver whatever
their jobs are at the moment.
But what it won't ever do, in my opinion,
is I don't think it will ever remove the human factor
from any type of delivery.
And the one thing that AI can't do at the moment,
and I'm gonna say at the moment,
'cause we dunno what the future holds is,
it can't actually read human emotional gut feeling.
And that's something that the human
will always bring to the table.
However, a human using a robot
or AI to support them, I think is absolutely unstoppable.
You got it. You got it. Um, an
Yeah, I like the question that, uh,
Gina wrote about the data,
and I would say that your, your AI is as good as your data.
Oh, big one. So if you have a good data, then AI is helpful.
If your data is rubbish
or it's in, not in the, on the paper,
and it was never put on the digital solution, um,
AI will not be that much helpful.
Most of the projects fail
because they are not ready to do this AI
because of the data and the data quality.
So to give you a real world example of that, um, our sales
and marketing team have gone a little bit agent actually.
So they're building agents to all kinds of things.
Um, and one of the things that we found was
that we were training and retraining these agents again
and again with different data.
And so one of the things that we did was we created a
SharePoint FO folder that is, this is
the gold standard training data when you're training up
for sales or a marketing agent, right?
It's got all our story in it,
it's got all our positioning in, it's got documentation in
it, it's got all this stuff that is the, the body
of knowledge that, that, that an agent would need.
And so now all we do when we're standing up a new
agent, we just point it there.
It does two things. First of all, it's really fast,
but most importantly, we know what's in the folder.
We know it's the right story,
and someone's not using the wrong
documentation to train the agent.
So da you know, good data. Really important.
Uh, David, and then we'll finish with Joe.
We'll finish strong with Joe.
You're still on mute, David.
We're gonna have to teach how to use, is
that an AI button by any chance?
An AI mute.
What a what a chie.
Um, uh, well, I lost my train of thought there.
Um, all, all I was gonna say is, is that I,
I hear people talk about AI as a thing.
Um, AI this, AI that it, it's, it's like,
I dunno how to describe it.
It's, it's like salt, um, it's, it's in ev it's every part.
It's gonna be in every part of your food.
So you just gotta get used
to it being in every part of your food.
And if you, if you don't like
salt, then you're gonna go hungry.
Um, so it, it's, it's just a case of embracing it work.
Can you work out how to leverage it
to improve your information management
better than those around you?
If you can, you'll get a competitive
advantage and survival thrive.
If you ignore, if you don't leverage it, then you, you'll,
you'll not survive and thrive as much as the people.
I think. I think it's that, that, um, that simple. Really.
Wow. That dramatic even.
I, I think we're getting dangerously close
to wisdom there, David.
That was very good. I like that one, Joe. Follow that.
Yeah. Thanks. Um, for me, we're at a generational shift.
Um, project management
and PMOs, for the most part are mature employees
and organizations, right?
We are in our forties and fifties and, and sixties
and beyond, and we didn't grow up with this.
My kids are in school at, at high schools and universities,
and this is what they're learning every day.
What is foreign to us is natural to them.
And the 25-year-old project manager running million dollar
projects didn't exist now
or 10 years ago, right?
In 10 years, all 25 year olds will be running multimillion
dollar projects because they're gonna grow up with the tools
to make them capable beyond just what their experience has.
It's gonna be up to us to embrace that change
and have the courage to step back
and say, we don't have the answers anymore.
Uh, the next generation, we look at them
and say, man, they just, they're lazy, they're slow.
They don't, they're not committed the way we are.
They job hop. They, there's a difference to that generation.
You're right, there is a difference to that generation,
and they're growing up with tools
that we never had available to us.
So embrace that, encourage that, and, and,
and empower your people to go gain this knowledge
that just really is foreign to us that we're trying
to figure out that they've just had forever.
It's just part of their life now.
Yeah. Yeah.
And so I, I'll ju I'm just gonna finish on this.
So Joe, you
and I are, I think probably the old men on
the panel here, right?
Uh, we, Joe
and I are almost exactly the same age
we discovered the other day.
Um, and, uh, so, you know, Joe
and I have lived through, uh, we lived
through the.com crash.
We lived through the Asian crisis.
We lived through big data coming to eat up the world.
We've lived through the 20 2008 crash.
We've lived through all these things.
We've seen, uh, we've seen expert systems come and go.
We've seen all these, right? All these different things.
So AI is probably bigger than all of those put together,
but at the same time, it's no different to all
of those things in the, you know, when big data came out,
that was, that was, you know,
that was gonna completely change the world.
But you know what, you know, if, if AI's like salt,
big data is like sugar, right?
It's also in everything, and it's used everywhere, right?
Um, so 10 years from now, we'll look back
and we'll say, ai, what was the big deal?
Right? It's just gonna be part of life.
And if it's just gonna be part of life, you might
as well just dive in and learn how to use it
and get used to it, right?
Because it's here, it's not going anywhere.
Um, and the earlier you get used to it, the,
the easier the transition's gonna be.
So, you know, don't fight it.
Just get out there, enjoy it,
have some fun, go make some videos.
Um, there are some amazing, you know, get it to write jokes,
create memes, whatever it is that you do, uh,
write poetry with it.
Um, uh, I love Shannon's thing, by the way. Uh, oh.
I I do, you know what, I I take it back, David.
You're the old man. Um,
uh, what was I gonna say there?
Yeah, so, you know, you know, get it to write poetry.
Whatever it is you're fa that you're passionate about, use
that to experiment and play, right?
And just get comfortable chatting to it.
Um, and, uh, and you'll very quickly learn that.
You know, the more explicit you are with your instructions,
the more interactive you are, the better.
I love what Shannon said.
She uses it as a, as a, a sparring partner, right?
And I, I had this conversation with one
of my co-founders earlier today.
That's what he does. He, he comes up with an idea, and
before he implements the idea, he goes to, he goes
to Gemini chat, GBT and copilot,
and he asks, he asks Gemini, what do you think of this idea?
And he takes the answer from Gemini and feeds it into GBT.
And he takes the answer from GBT
and he feeds it into copilot.
And by the time he's gone through that loop twice,
he's got 15 different perspectives on his idea,
and he understands it way better than he did at the
beginning, right?
So it's really, really powerful just for brainstorming,
for challenging your ideas,
refining ideas, all that kind of stuff.
But it all hinges about just getting out there and doing it.
So if you're not already, I'm sure
most of the people here are.
If you're not already, dive in. Take a swim.
The water's great. Brilliant.
I want to thank everyone for, uh,
thank the panelists for joining.
I want to thank Anana for wearing the
thank Christmas hat the whole way through.
My, my hat was too hot. I had to take it off.
Um, uh, but thank you to the, to the panel for coming.
David, thank you for postponing your
first beer of the evening.
Uh, we didn't postpone the first beer of the evening.
That's good. Uh, but go enjoy the football.
Uh, good, good luck with the game.
Uh, thanks to you all for turning up.
I wish you all a very merry Christmas, if that's your thing.
Uh, happy holidays, if not,
and if you're in a region
where you don't have holidays at this time of year, too bad.
Uh, fantastic. Uh, thank you everyone.
Uh, and here's wishing you all a very prosperous
and happy New Year.