WEBVTT

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So, my name's Stuart, Stuart Easton.

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Um, thank you for joining us.

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This is, uh, if you've been on our December sessions

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before, you'll know that they're,

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that they're not terribly structured.

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They're not terribly, you know, the goal is to have fun,

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hang out with smart people, people that we like.

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Um, and so I've assembled some of my favorites here,

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but we figured the theme to close out the year is a theme,

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something we've been talking about all year long,

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which is ai, and everybody is learning, right?

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Everybody's experimenting.

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So the goal is to share those experiences.

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What have we learned, what's working, what's not working?

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And, uh, everyone on the panel is here for a reason.

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Um, uh, so, you know, apart from just being a super,

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super nice guy, uh,

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and a super smart guy, uh, Joe also is one of the, the,

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uh, the, the, the

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most accomplished and knowledgeable PMO experts out there.

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And, um, is working, you know, works all the time with PMOs,

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uh, across the US primarily, um,

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and is seeing lots of the stuff that's going on, the things

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that are working and the things that aren't working.

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Um, so Joe is bringing that sort of wide PMO e experience.

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David is here. David wears several hats.

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Um, and I'll let each

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of you introduce yourselves as we go along.

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But David wears several hats.

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Um, one of which is that he runs, uh,

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uh, community interest company, that's,

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that's building a whole, uh, building

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and promoting a whole body of knowledge around how

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to do governance, but at the, at the enterprise level.

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So it's called big Business integrated Governance.

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It's a great framework, David.

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You can drop the link in the chat whenever you feel like it,

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but, uh, it's a, a super thing.

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I was actually on a webinar that, uh,

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that the big team were doing yesterday

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where Steve Jenner was talking about,

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about portfolio management, which was, it's always awesome

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to hear Steve talk.

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Um, uh, but,

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but for the purpose of this, uh, David is also, uh, uh,

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the founder of, uh, uh, a company called CPS,

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and they are Microsoft ai,

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super partner, partner of the year.

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I think it was, wasn't it, David? Something like that.

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Something like that, yeah.

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We're, uh, a Microsoft, um, finalist.

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So one of four companies, I believe, that we're, uh, that

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by Microsoft as being, uh, outstanding with, with, uh,

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copilot and AI type stuff.

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Uh, the only one, I think, in the uk. So, which is awesome.

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We got, we got some very clever people that work for us.

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No, it, it's, uh, no, I'm,

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I'm not a clever one, but, uh, and

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David's been very kind joining us today

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because he's, he's sitting in a hotel room in,

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in the Netherlands, uh,

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and as soon as we finish,

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he's running off to a football match.

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So, uh, uh, really appreciate you rejigging your priorities

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for a few minutes to join us.

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Thank you for that. Um, uh, an anana again, has,

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has a lot of experience in and around ai.

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You know, those of you were on a few minutes ago heard this

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story about how she stood up a really, you know,

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a fantastic workshop in no time flat, uh, last year, uh,

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around ai, uh, based in, uh, based in, in Dubai.

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So it is hopefully bringing a slightly different, um,

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geographic slant to this.

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And James is, um, uh, PMO leader within the NHS.

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So for those of you who don't know what the NHS is,

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if you don't live in the uk, there's no reason you should.

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It's our national health service in the uk.

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To give you an idea of the scale, the NHS employs about 10%

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of, uh, the workforce in the uk, right?

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So this is a huge undertaking.

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Um, uh, it's a, it's a, an organization

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that we're all really, really proud of.

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Um, but, uh, uh, but despite that, they still employ James.

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So, so James is really kind of the nucleus

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of the PMO community for the NHS in Wales.

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Um, does a lot of speaking.

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He, he just published a chapter in a book with Dawn Mahan,

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uh, and Joe, Joe, you had a chapter in there as well, right?

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Yeah. I should have. Men should have mentioned that. Mm-hmm.

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Um, uh, and Jo, uh, James

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and I were talking the other day about some of the, uh, some

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of the AI experiments they've been doing across the NHS.

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So we figured it would be fun to, to share some of those.

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So thank you, everybody.

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Um, and what I'd love to do again,

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is challenge the audience, challenge the audience time

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to ask questions, make suggestions, make jokes, um,

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uh, but just get involved, right?

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It'll be more fun if you do that.

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Um, and, um, uh, we'll, we'll all get more out of it.

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And again, it's end of year, it's December,

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we're all just trying to chill out a bit

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with some smart people and enjoy a cup of tea

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or a glass of old wine

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or whatever it is that you happen to have.

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So, I, I'm gonna kick off, um, and just ask this question.

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Um, and what I'd love you to do is just whip along

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and give me one example each of an AI disaster

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that you've seen, or something that just didn't work well.

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Um, and, uh,

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and while you do that, just, you know,

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introduce yourselves a little bit as well,

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a bit more detail than I did.

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So shall we start with, uh, David?

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Yeah, sure. Um, so I'm David.

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Um, uh, as, as, uh, uh, Stuart says, um, I'm founder

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of a, an IT company that's got a, a capability

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and specialism in, in ai.

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Um, the, the, the one, uh, disaster I would, uh, think of,

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it's not really anything particular that's happened, but it,

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but for me, there's a disaster that's caused a whole lot

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of people I speak to, to, um, recoil at the thought of,

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of including AI in their capability

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because, uh, they don't like the idea

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that AI is getting involved in their capability.

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Um, I, I'm just scared that, um, uh, there is, there is, uh,

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uh, reservation with use of AI from people,

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but I think, I think in, in many quarters,

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it's kind of going the other way.

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So for me, the disaster is, is that, uh, people are,

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are still, um, on, on the brink of ignoring this stuff and,

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and trying not to, to exploit it and leverage it.

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So, um, not nothing, not a particular case, but, but a tick.

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But a particular attitude to my mind is, is, uh, is,

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is gonna be quite traumatic.

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And I think people like us need to try

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and, um, uh, uh, encourage people not to, to still be,

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uh, a little bit ludi and, and in denial of this stuff.

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Um, that, that's, that's my takeaway.

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So let's, let's ask a question into, into chat,

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if you please everybody, uh,

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I wanna say everybody participating.

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So who would, who, who has, uh, gimme a, gimme a, a, a,

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a, yes, no, maybe, right?

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So yes is, yes, I'm experimenting with AI

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and excited, no,

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is I haven't touched it, and I'm not really interested.

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And maybe is, yeah, I'm just on the edge.

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So let's have a look, see, see where the,

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where the audience is.

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We've got some good yeses.

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Hey, Annette, didn't see you there. Good to see you.

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Excellent. So we've got lots of yeses.

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I guess there's a, a, a,

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a selection bias on this session, right?

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People are in the NOC camp. Probably wouldn't turn up.

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Um, excellent. Oh, Fadi. Hey, Fadi, how are you?

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Um, so, um, so an let's come to you next.

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Yeah. Thank you. Thanks, Stuart. Glad to be here today.

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So, um, I manage portfolios, different portfolios,

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and also for those who doesn't know, I'm a founder

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of Little Project Manager.

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It's now became the framework of teaching project management

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for the kids, uh, about the disaster.

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I think the disastrous project I've been involved,

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and, uh, I think I'm happy that I'm not part of

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that project anymore, is when you're trying to embed AI

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to the legacy system, to a system that is dying,

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that is very old, not user friendly

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and just terribly, terribly designed, that should just die.

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And you are trying to kind of recover it by embedding

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and trying to figure out how to plug AI into it

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and say that it's now with AI kind of solution.

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Excellent. Yeah.

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Excellent. Shall we move on to James?

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Uh, hi everyone. I'm James.

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As, uh, Stuart mentioned, um, I lead a PMO, win Wales,

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and also get involved in a number of other, uh, PMO

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and project capability based work across, um,

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the Welsh Network, um, disaster.

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I think the, the main thing I've seen, um,

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quite recently actually, is where people have started to try

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and embrace AI and really start to, to lean into it,

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but they've gone too far.

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Um, and they've actually kind

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of just put everything into AI and gone, right?

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Well, we don't have to actually quality check it.

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We don't have the thing, if we wanted to write a report,

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we're just gonna trust the AI or do it for us.

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Whatever it spits out, we're gonna give

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that into the board meeting next week.

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And actually, they've kind of done themselves a bit

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of a disservice then,

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because they're relying too much on the ai, doing the work

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for them, thinking it's basically another human coming in

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to do it, instead of working with the AI

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to pro provide the outcomes.

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So we instead putting the AI into a bad light when it's not

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delivering what they're expecting it to.

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Um, I think that's the, the sort

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of main thing I'm seeing a lot of at the moment.

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Awesome. Awesome. Love it. And finally, Joe PMO.

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Joe, should I get,

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I should get the guitar down, shouldn't I? Hey, Joe,

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That'd be great, right? We

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could sing Jingle Bells

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or something together, you know, AI for me is kind of like,

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is it the Grinch or is it Frosty the Snowman, right?

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There's no happy medium right now.

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It's the horror stories or the successes.

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Um, so we're talking about disasters here to start, and,

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and we've been focusing at the PMO squad lately this past

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year a lot on people and on staffing and on resources.

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Um, and, and two horror stories for those who are unemployed

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looking for work.

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Um, hiring managers are telling me they can tell when

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you're reading AI answers back to them

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during the interview process,

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and they're immediately removing you from consideration.

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So you've got chat GPT

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or something up on your other screen,

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you get asked a question,

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you don't really have the experience.

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You type something in the chat, GPT, it spits it out,

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and you give it back to the hiring manager.

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Don't do that. That's a disaster.

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And, and you're in trouble

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in your interview, isn't it? People are

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Doing that, I didn't realize. Wow. Yeah.

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And then the other is candidates who've been interviewed

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by ai, like an actual robot head, is interviewing them.

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And, um, there's no emotional connection

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during the interview process.

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It's very cut and dry.

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Uh, it's not a dialogue going back and forth.

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It's a set of questions, whether answer and then a question

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and then an answer, right?

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There's no reaction to that.

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So we're evolving into non-human interviews, uh,

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and right now they're not quite where they need to be,

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probably to get a thorough betting of the candidates,

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but they're out there, and I think we all need

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to be aware that they're coming.

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It's going to be, um, more common probably in 26,

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00:11:50.435 --> 00:11:52.775
and it's just becoming part of life, right?

246
00:11:52.805 --> 00:11:55.685
It's just, it's integrated in, in places sometimes

247
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where we don't even know it's there.

248
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And sometimes it's very obvious to us.

249
00:11:59.985 --> 00:12:02.915
Yeah. Yeah. Well, so it was, it was interesting.

250
00:12:03.015 --> 00:12:05.675
So, so the disaster, I'll share a disaster

251
00:12:05.675 --> 00:12:07.035
that is an internal one.

252
00:12:07.035 --> 00:12:10.595
It wasn't a total disaster, but we, a little while ago,

253
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we decided we'd run a hackathon hackathon day,

254
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and the goal was to pick a small piece of functionality

255
00:12:16.615 --> 00:12:18.315
for our product, new functionality,

256
00:12:19.415 --> 00:12:23.475
and use AI to interview the bus,

257
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the product owner, and the, the,

258
00:12:25.175 --> 00:12:27.195
the people on the front line who interact with customers

259
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to compile requirements, turn that into, into

260
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domains, epics, stories, and so forth.

261
00:12:38.125 --> 00:12:41.385
And then pick a couple of stories and generate some code

262
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and try and do that using AI.

263
00:12:44.775 --> 00:12:49.035
And, uh, what, what, so what we learned was that the, um,

264
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the second half of that was kind of success,

265
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fairly successful, but the first half was a total disaster

266
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because it was completely unstructured.

267
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And, uh,

268
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and as a result, we, we found

269
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that there just wasn't consistent.

270
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AI wasn't able to take that

271
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and then turn that into a structure

272
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that it could consistently hold onto.

273
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So the learning for us was that, you know,

274
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if you're gonna try and do something like that,

275
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you probably need to give it a bit more structure, uh,

276
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and not expect the ai.

277
00:13:16.855 --> 00:13:18.355
So I don't think it was a failure of ai.

278
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I think it was a failure of us to understand

279
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what AI was capable of.

280
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Um, and I, I, I think I see a lot of

281
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that going on right now.

282
00:13:26.735 --> 00:13:29.995
So, so James, you, you, you were sharing some,

283
00:13:30.345 --> 00:13:31.675
some good stories the other day,

284
00:13:31.675 --> 00:13:33.955
so why don't we do the opposite question, right?

285
00:13:34.005 --> 00:13:36.435
Let's hear, let's hear a success and,

286
00:13:36.435 --> 00:13:38.275
and what I, what I'd really love to hear is

287
00:13:38.735 --> 00:13:40.075
how did you make it a success?

288
00:13:40.105 --> 00:13:41.435
What, and, and do you know what? I'm gonna take

289
00:13:41.435 --> 00:13:42.515
this hat off 'cause it's too hot.

290
00:13:45.175 --> 00:13:48.115
Um, so, so let's hear about a success and,

291
00:13:48.175 --> 00:13:50.955
and you know, what, what you did to make it a success.

292
00:13:51.235 --> 00:13:53.115
'cause that's, that's the big learning here, right?

293
00:13:54.915 --> 00:13:56.195
I can't actually remember which, uh,

294
00:13:56.285 --> 00:13:58.595
which ring I was talking to about the other day.

295
00:13:58.655 --> 00:14:03.275
Um, but in terms of AI use, where we are using it, well,

296
00:14:03.505 --> 00:14:04.835
it's, we're,

297
00:14:04.955 --> 00:14:08.275
we're finding success is coming from helping us support

298
00:14:08.275 --> 00:14:11.635
with some of the tasks that we're taking a lot of time

299
00:14:11.695 --> 00:14:13.155
for our PMO to work through.

300
00:14:13.375 --> 00:14:15.995
Um, and this isn't just in the organization I'm working in

301
00:14:15.995 --> 00:14:19.515
now, but previously, um, resource constraints,

302
00:14:19.575 --> 00:14:23.395
budget constraints, um, it's pulling a lot of, um,

303
00:14:23.795 --> 00:14:27.515
capacity out of our PMOs, um, and the typical work we do.

304
00:14:27.575 --> 00:14:30.555
So AI has really helped start to support that now

305
00:14:30.615 --> 00:14:34.755
to actually take away some of those more administrative, um,

306
00:14:35.305 --> 00:14:37.835
sort of day-to-day menial tasks that we're taking up a lot

307
00:14:37.835 --> 00:14:39.515
of our capacity than now.

308
00:14:39.775 --> 00:14:43.195
AI has been able to be programmed

309
00:14:43.195 --> 00:14:45.555
or prompted to be able to do for us, um,

310
00:14:45.565 --> 00:14:49.595
which has freed up quite a bit of capacity, um, for a team.

311
00:14:49.735 --> 00:14:52.485
That's usually the last couple of PMOs.

312
00:14:53.145 --> 00:14:57.285
Um, I've led have been anywhere between five people big,

313
00:14:57.465 --> 00:14:58.965
um, compared up to 18.

314
00:14:59.585 --> 00:15:03.045
Uh, but it's quite rare to get that now, um, an 18, um,

315
00:15:03.405 --> 00:15:05.805
strength PMO, uh, where I work.

316
00:15:05.865 --> 00:15:08.525
So to have the AI being able to pick up some

317
00:15:08.525 --> 00:15:10.085
of those administrative functions

318
00:15:10.105 --> 00:15:13.445
and actually, um, start managing our calendars,

319
00:15:13.455 --> 00:15:16.125
start booking in the meeting snap for us, uh,

320
00:15:16.125 --> 00:15:17.445
even building the agendas

321
00:15:17.445 --> 00:15:19.645
or in the loose format that we then just have

322
00:15:19.645 --> 00:15:21.085
to quality as assure and build over.

323
00:15:21.625 --> 00:15:23.685
And even I think the biggest help,

324
00:15:23.685 --> 00:15:25.125
which is something really small,

325
00:15:25.745 --> 00:15:27.965
but in the grand scheme of things, some of us are

326
00:15:27.965 --> 00:15:30.405
so busy having, um, co-pilot

327
00:15:30.465 --> 00:15:32.325
and have AI to be able to go through

328
00:15:32.325 --> 00:15:35.645
and pull through information from all of our emails, all

329
00:15:35.645 --> 00:15:36.725
of our teams chats,

330
00:15:37.105 --> 00:15:39.685
and any other sort of formats we're using,

331
00:15:39.905 --> 00:15:43.765
and condense that into a summary at the end of each day so

332
00:15:43.765 --> 00:15:46.365
that we know our actions and plans for the coming weeks

333
00:15:46.505 --> 00:15:47.565
and months going forwards.

334
00:15:47.755 --> 00:15:49.365
That has been a game changer for us

335
00:15:49.365 --> 00:15:51.405
and has saved so much capacity within our,

336
00:15:52.025 --> 00:15:53.325
within the way that we work.

337
00:15:55.795 --> 00:15:57.125
Awesome. Awesome.

338
00:15:57.905 --> 00:16:00.565
Um, uh, so it's great to hear some, some,

339
00:16:00.945 --> 00:16:02.245
uh, some successes.

340
00:16:02.245 --> 00:16:05.805
So in doing that, James, what were the, what were the things

341
00:16:05.805 --> 00:16:08.925
that you felt contributed to being successful there?

342
00:16:08.925 --> 00:16:11.085
Because I've, I've, I've also heard people who've tried

343
00:16:11.105 --> 00:16:13.525
to automate some of that stuff and failed.

344
00:16:14.925 --> 00:16:16.765
I think it was the, the bit

345
00:16:16.765 --> 00:16:18.805
that made it a real success was listening to

346
00:16:18.805 --> 00:16:21.375
where the problems were coming from.

347
00:16:21.515 --> 00:16:25.295
So we almost removed the, the solution from it at first.

348
00:16:25.315 --> 00:16:27.335
So it wasn't just immediately, let's go,

349
00:16:27.685 --> 00:16:28.855
AI's gonna fix everything.

350
00:16:29.315 --> 00:16:31.535
It was actually listening to where the problems were.

351
00:16:31.675 --> 00:16:34.015
So, you know, stuff's getting lost in emails,

352
00:16:34.105 --> 00:16:36.215
we're losing track, there's too much stuff coming in.

353
00:16:36.515 --> 00:16:39.775
We haven't got time to go in and put together the, the notes

354
00:16:39.775 --> 00:16:41.575
or the packs for the project boards.

355
00:16:41.835 --> 00:16:42.895
We haven't got this time.

356
00:16:42.995 --> 00:16:45.775
So when we started to put together all of these tasks

357
00:16:45.805 --> 00:16:48.575
that were causing the problems, when we started

358
00:16:48.595 --> 00:16:51.415
to look at the solutions to solve this, it,

359
00:16:51.435 --> 00:16:52.735
it came down to two things.

360
00:16:52.995 --> 00:16:56.175
We could go back to what traditionally we would've done,

361
00:16:56.635 --> 00:16:58.935
and try and get a business case to get more staff

362
00:16:59.075 --> 00:17:00.495
and try and bring that in.

363
00:17:00.755 --> 00:17:02.695
But then we knew that well, actually

364
00:17:03.455 --> 00:17:04.655
resourcing budgetary constraints,

365
00:17:04.655 --> 00:17:05.775
that's not going to happen.

366
00:17:06.325 --> 00:17:09.895
However, we've got this system here that's not being used.

367
00:17:10.845 --> 00:17:14.315
Could this be the answer is, is AI the answer in this space?

368
00:17:14.695 --> 00:17:18.995
Now, I'm not an AI expert. No one in my team was AI experts.

369
00:17:19.935 --> 00:17:23.435
So we basically had to leverage the, the internet

370
00:17:23.435 --> 00:17:26.235
to teach us, and we had to use AI to teach us how to use ai,

371
00:17:26.235 --> 00:17:28.955
which was quite a, quite a nuance as well.

372
00:17:29.175 --> 00:17:32.475
Um, but when we just started to do basic automations

373
00:17:32.475 --> 00:17:34.635
with it, or just asking it to do certain things,

374
00:17:34.735 --> 00:17:39.515
and I find some of the ai, those quite intuitive in

375
00:17:39.515 --> 00:17:42.875
how you use it, um, that was enough for us to start

376
00:17:42.875 --> 00:17:44.435
and to test our appetite.

377
00:17:44.495 --> 00:17:46.595
And as we've got more familiar with it

378
00:17:46.615 --> 00:17:49.290
and better, we've started to meet people along the, the way

379
00:17:49.290 --> 00:17:50.725
as well on those, on that journey

380
00:17:50.785 --> 00:17:52.925
and start to get more advice, more guidance.

381
00:17:53.665 --> 00:17:55.845
And it's now become something that we've sort of

382
00:17:56.715 --> 00:17:57.725
lent on quite heavily.

383
00:17:57.985 --> 00:18:02.525
So my entire team now operate with, um, a form

384
00:18:02.525 --> 00:18:04.405
of ai, um, in their everyday tasks.

385
00:18:05.225 --> 00:18:09.365
Um, but we are very conscious that we won't slip into the,

386
00:18:09.705 --> 00:18:12.245
the horror story I started with at the beginning,

387
00:18:12.865 --> 00:18:16.045
and that we, we work with the AI

388
00:18:16.185 --> 00:18:19.605
and not have the AI replace, um, any sort

389
00:18:19.605 --> 00:18:21.285
of personal function that it was doing.

390
00:18:24.455 --> 00:18:26.535
Excellent. So I like that. So I heard, uh, one

391
00:18:26.535 --> 00:18:28.895
of the key themes that I suspect will come again,

392
00:18:29.005 --> 00:18:31.655
come up again, is start with a business problem.

393
00:18:31.745 --> 00:18:34.415
Don't start with the ai. Mm-hmm. Right?

394
00:18:34.645 --> 00:18:36.095
Make sure we're solving, first of all,

395
00:18:36.095 --> 00:18:37.375
solving a problem that's actually a problem.

396
00:18:37.395 --> 00:18:38.415
And then secondly,

397
00:18:38.415 --> 00:18:40.295
using the right solution for that problem.

398
00:18:40.875 --> 00:18:44.655
Um, David, uh, same question to you, example of something

399
00:18:44.655 --> 00:18:45.655
that went well and,

400
00:18:45.675 --> 00:18:47.775
and what were the key enablers that may

401
00:18:47.775 --> 00:18:49.095
that enabled it to go well?

402
00:18:52.405 --> 00:18:53.405
Oh, you're on mute, David.

403
00:18:56.695 --> 00:18:58.225
Yeah. Uh, me, me again.

404
00:18:58.485 --> 00:19:03.145
Um, so, uh, all, all I can talk about really is, um,

405
00:19:03.565 --> 00:19:06.505
having, having seen, I've got a, a, a company that's got a,

406
00:19:06.585 --> 00:19:09.505
a whole group of folks that do lots of this AI stuff,

407
00:19:09.845 --> 00:19:12.265
and they're all clever folks that, that, um,

408
00:19:12.445 --> 00:19:14.265
can produce solution this or that.

409
00:19:14.725 --> 00:19:17.985
Uh, but what I, what I think, uh, uh, needs to go well with,

410
00:19:17.985 --> 00:19:20.265
with these examples is, is not trying to work out

411
00:19:20.265 --> 00:19:24.345
what the problem is, uh, or what the solution might be, but,

412
00:19:24.405 --> 00:19:25.985
but I think it's actually, and,

413
00:19:25.985 --> 00:19:27.625
and Steve Jana will love me for saying this,

414
00:19:27.815 --> 00:19:29.425
it's actually trying to work out, uh,

415
00:19:29.455 --> 00:19:31.945
what the business case is and what the benefit profile is.

416
00:19:32.405 --> 00:19:36.945
Uh, so if, if we, um, take this, um, uh, uh, uh,

417
00:19:37.305 --> 00:19:39.945
solution for, for, for example, something that's gonna, uh,

418
00:19:40.055 --> 00:19:41.905
make, uh, make, uh, quicker

419
00:19:42.005 --> 00:19:46.065
and simpler, uh, h HR queries happen so that we,

420
00:19:46.085 --> 00:19:47.265
we can get response times

421
00:19:47.365 --> 00:19:51.105
and shortened amount of time that, that HR queries are, are,

422
00:19:51.205 --> 00:19:55.945
are gonna take, for example, in the NHS, um, how do we,

423
00:19:56.085 --> 00:19:58.625
how do we put together a business case which says we can

424
00:19:58.625 --> 00:19:59.825
justify doing this?

425
00:20:00.245 --> 00:20:03.265
Uh, I think, I think, um, for, for my experience that, um,

426
00:20:03.375 --> 00:20:05.865
that that's where a lot of the, the thinking needs to go.

427
00:20:06.005 --> 00:20:07.105
Not, not to what the problem is

428
00:20:07.105 --> 00:20:08.905
and what the answer might be, but how I can,

429
00:20:08.965 --> 00:20:11.105
how can I put this together into a case

430
00:20:11.105 --> 00:20:12.825
that will persuade somebody to engage?

431
00:20:13.565 --> 00:20:16.265
Um, that, that to me is, um, a huge learning point and a,

432
00:20:16.265 --> 00:20:19.185
and a huge thing that I didn't, I didn't really expect.

433
00:20:19.305 --> 00:20:21.145
I, I thought it would be easy to,

434
00:20:21.365 --> 00:20:23.945
to show some technical stuff and excite people

435
00:20:24.445 --> 00:20:26.745
and to, to, uh, show we understood the problem.

436
00:20:26.925 --> 00:20:30.065
But no, the, the, the real challenge, I think is to, is to,

437
00:20:30.205 --> 00:20:32.545
um, convince people that it's actually the,

438
00:20:32.625 --> 00:20:34.425
a solution is actually viable and it actually works.

439
00:20:34.925 --> 00:20:37.265
So I'd say that people don't underestimate what, uh,

440
00:20:37.415 --> 00:20:40.425
what the business case build, uh, need is gonna be,

441
00:20:40.535 --> 00:20:42.825
because there are very many cynical people out there.

442
00:20:43.645 --> 00:20:45.505
Um, but if that works for you guys,

443
00:20:46.255 --> 00:20:48.315
Does that, does that resonate with you, Joe?

444
00:20:49.985 --> 00:20:51.515
Yeah, absolutely.

445
00:20:51.735 --> 00:20:55.435
Uh, you know, a, a good example, I think that kinda relates

446
00:20:55.455 --> 00:21:00.115
to that, uh, PMO squad's main kind of approach to building

447
00:21:00.175 --> 00:21:02.795
and helping with PMOs is something we call OPD

448
00:21:02.795 --> 00:21:04.475
Organizational Project Delivery.

449
00:21:06.225 --> 00:21:07.485
And a key component of

450
00:21:07.485 --> 00:21:10.885
that is it's built on system dynamics out of MIT,

451
00:21:11.105 --> 00:21:12.925
and that's on feedback loops, right?

452
00:21:12.925 --> 00:21:17.085
It's closed loop, uh, instead of open loop,

453
00:21:17.085 --> 00:21:18.965
which traditional project management is.

454
00:21:19.585 --> 00:21:22.645
And, and with a closed loop mindset, you have

455
00:21:22.645 --> 00:21:25.125
to receive feedback throughout the life of the project.

456
00:21:25.265 --> 00:21:27.765
And after a project throughout your organization on how

457
00:21:27.765 --> 00:21:30.885
to deliver projects, how to fulfill the business case,

458
00:21:32.395 --> 00:21:34.375
we collectively, as an industry have said,

459
00:21:34.375 --> 00:21:38.495
let's have retrospectives and lessons learned at the end,

460
00:21:39.155 --> 00:21:41.775
and then we store them and SharePoint or somewhere

461
00:21:41.795 --> 00:21:42.975
and never see them again.

462
00:21:43.675 --> 00:21:47.135
So what we've done recently is with our, our use of OPD is

463
00:21:47.785 --> 00:21:51.655
let's get all of those lessons learned loaded up into AI

464
00:21:52.355 --> 00:21:54.935
and see what those, what does AI tell us about

465
00:21:55.005 --> 00:21:56.735
what we learned from all of our projects?

466
00:21:58.135 --> 00:21:59.275
And then we're not spending,

467
00:21:59.275 --> 00:22:02.715
because people don't spend time retroactively and go back

468
00:22:02.715 --> 00:22:04.195
and look at projects as they should.

469
00:22:04.775 --> 00:22:06.355
So you're still not spending any time,

470
00:22:06.455 --> 00:22:10.235
you're letting the technology go do that, give that time

471
00:22:10.305 --> 00:22:13.235
that you should be spending and aren't to ai.

472
00:22:13.295 --> 00:22:16.115
Let it analyze what you're getting out of the projects.

473
00:22:16.115 --> 00:22:17.635
What consistencies are you finding?

474
00:22:17.635 --> 00:22:18.955
What variances are you finding?

475
00:22:19.655 --> 00:22:22.595
And then make those changes throughout the organization

476
00:22:22.655 --> 00:22:25.155
or in your PMO or your portfolio going forward.

477
00:22:26.675 --> 00:22:28.005
It's all about the business case.

478
00:22:28.005 --> 00:22:30.645
Everything we're doing is supposed to be delivering impact

479
00:22:30.645 --> 00:22:32.165
and value back to the organization.

480
00:22:33.105 --> 00:22:34.485
If our lessons learned said

481
00:22:35.185 --> 00:22:37.285
we weren't measuring value at the end,

482
00:22:38.535 --> 00:22:40.355
and we do that project after project

483
00:22:40.445 --> 00:22:44.145
after project, we're not needed, right?

484
00:22:44.145 --> 00:22:46.465
We're not, organizations are gonna find us, uh,

485
00:22:46.525 --> 00:22:48.505
as overhead, and that's what we hear.

486
00:22:48.605 --> 00:22:50.305
The, these stories all over the place

487
00:22:50.305 --> 00:22:51.625
that we just are overhead

488
00:22:51.625 --> 00:22:54.025
and we can remove those, bring it back to the functions,

489
00:22:54.025 --> 00:22:56.745
let this be a project delivery at the functional level.

490
00:22:57.565 --> 00:22:59.825
That's our fault. It's not the business's fault.

491
00:22:59.825 --> 00:23:01.585
That's our fault for, for not giving

492
00:23:01.585 --> 00:23:02.985
what they've asked us to deliver.

493
00:23:03.845 --> 00:23:07.785
So for me, the, the success story is watching that in action

494
00:23:08.325 --> 00:23:11.345
to let AI produce these, uh, answers

495
00:23:11.445 --> 00:23:13.665
and solutions that came out of retrospectives

496
00:23:14.175 --> 00:23:15.465
that we already knew.

497
00:23:15.845 --> 00:23:18.185
We just ignored them and hid them and,

498
00:23:18.245 --> 00:23:19.505
and stopped acting on them.

499
00:23:20.125 --> 00:23:23.105
That's how I think we start to see the power of AI come in

500
00:23:23.365 --> 00:23:24.625
to not disrupt us,

501
00:23:24.965 --> 00:23:27.185
but to make us better than what we really could be.

502
00:23:29.255 --> 00:23:34.215
Awesome. Awesome. An you're back with us? Yeah.

503
00:23:34.495 --> 00:23:35.855
Excellent. I'm back. You're the, you're

504
00:23:35.855 --> 00:23:36.895
the only one with, this is still like that.

505
00:23:36.895 --> 00:23:39.775
My hat was too hot. I'm taking it off.

506
00:23:40.295 --> 00:23:42.295
I have an AC here, right? I'm in Dubai.

507
00:23:42.955 --> 00:23:44.015
It actually looks more like a

508
00:23:44.015 --> 00:23:45.170
pair of slippers, doesn't it?

509
00:23:45.505 --> 00:23:47.285
But there you go. It's a hat.

510
00:23:47.705 --> 00:23:51.285
Um, so an, the, the, the question was, was basically let's,

511
00:23:51.285 --> 00:23:53.285
let's hear a good story, a success story

512
00:23:53.625 --> 00:23:58.525
and focus on what, what made it successful?

513
00:23:58.525 --> 00:24:00.365
What was the key to making it a successful

514
00:24:01.185 --> 00:24:02.285
uh, yeah, AI story.

515
00:24:03.045 --> 00:24:05.885
I have a good example. Uh, very practical example.

516
00:24:06.745 --> 00:24:08.965
Uh, I've been involved with one of the teams

517
00:24:09.065 --> 00:24:12.565
that's been experimenting and, uh, trying to adapt ai.

518
00:24:13.265 --> 00:24:17.405
So we, uh, put together a sort of the scorecard

519
00:24:18.265 --> 00:24:19.965
on measuring which AI

520
00:24:20.145 --> 00:24:23.485
and how much it helps for the team, for the PMO team,

521
00:24:23.985 --> 00:24:26.885
and then the team being experimenting with different ais

522
00:24:26.945 --> 00:24:28.085
for the different use cases.

523
00:24:28.905 --> 00:24:32.365
So it wasn't more on, um, kind of,

524
00:24:32.545 --> 00:24:34.365
we have a problem, let's solve it.

525
00:24:34.365 --> 00:24:36.765
With ai, it was more on the adventurous side.

526
00:24:37.195 --> 00:24:41.605
It's kind of, there are a lot of AI tools and let's research

527
00:24:41.665 --> 00:24:44.685
and let's test and let's see which one works best for us.

528
00:24:45.265 --> 00:24:49.445
Uh, so they've been taking different AI tools, testing them,

529
00:24:49.555 --> 00:24:51.605
they've been putting together the scorecard,

530
00:24:51.665 --> 00:24:53.845
and then we've been assessing which AI helps

531
00:24:53.905 --> 00:24:56.525
to which instance, and, uh, for how much.

532
00:24:56.625 --> 00:24:58.965
And then they've been deciding whether they needed

533
00:24:59.155 --> 00:25:00.765
further for the work or not.

534
00:25:01.225 --> 00:25:04.005
That's been pretty, kind of interesting to observe

535
00:25:04.105 --> 00:25:05.405
and also to work with them.

536
00:25:07.495 --> 00:25:10.415
Excellent. Did you come to the conclusion that any tool,

537
00:25:10.515 --> 00:25:14.255
any particular tool was at the top consistently?

538
00:25:14.255 --> 00:25:15.255
Oh,

539
00:25:15.455 --> 00:25:16.615
Consistently, yeah.

540
00:25:16.635 --> 00:25:20.335
We found out the good AI tool for the note taking.

541
00:25:21.155 --> 00:25:24.055
Uh, also they've been experimenting quite a lot

542
00:25:24.055 --> 00:25:26.415
with different, uh, building up different agents

543
00:25:26.435 --> 00:25:28.655
and the workflows in the way.

544
00:25:28.835 --> 00:25:31.295
So they will be trying to automate the kind

545
00:25:31.295 --> 00:25:33.135
of the boring work that they've been having

546
00:25:33.155 --> 00:25:35.175
and then trying by different tools

547
00:25:35.175 --> 00:25:36.175
and different combinations.

548
00:25:36.175 --> 00:25:38.255
They've been putting that together.

549
00:25:39.595 --> 00:25:41.805
Yeah, as I'm saying, it was very practical.

550
00:25:42.555 --> 00:25:44.365
Yeah, I've been following up with them,

551
00:25:44.505 --> 00:25:47.005
but yeah, they, they've been pretty happy

552
00:25:47.005 --> 00:25:48.485
with the results that they've achieved.

553
00:25:49.065 --> 00:25:51.965
PR practical is good. Uh, so there you go.

554
00:25:51.965 --> 00:25:55.805
That was, that was, uh, the inevitable question has come

555
00:25:55.805 --> 00:25:57.005
through once she said that.

556
00:25:57.025 --> 00:26:00.405
An which is, what's your recommendation for the note taker?

557
00:26:00.585 --> 00:26:01.585
Ai?

558
00:26:02.395 --> 00:26:06.085
Yeah. We've been, uh, I think the top one was the read ai.

559
00:26:06.845 --> 00:26:08.255
Read ai. Mm-hmm. Yeah.

560
00:26:08.255 --> 00:26:12.775
Because it wasn't only just taking the notes pretty in a

561
00:26:12.775 --> 00:26:16.415
good quality, but it's also been observing, uh, behavior

562
00:26:16.555 --> 00:26:18.335
of the audience if the camera is on

563
00:26:19.075 --> 00:26:23.255
and recommending kind of how to better, uh, kind

564
00:26:23.255 --> 00:26:25.335
of giving a report on who is more engaged,

565
00:26:25.355 --> 00:26:26.495
who is less engaged, I

566
00:26:26.495 --> 00:26:27.495
Think. Ooh,

567
00:26:27.495 --> 00:26:29.375
yeah. Excellent. Big brother is watching you.

568
00:26:29.805 --> 00:26:32.015
Yeah. Nice. That's

569
00:26:32.015 --> 00:26:33.015
Exactly the point.

570
00:26:33.995 --> 00:26:36.495
So, so one of the things that we did recently that was,

571
00:26:36.635 --> 00:26:38.335
uh, that was really kind of cool just

572
00:26:38.335 --> 00:26:41.695
to share another fun story is, um, so there was a,

573
00:26:41.695 --> 00:26:43.645
there's a process on our marketing team,

574
00:26:43.645 --> 00:26:45.165
which is about writing the newsletter.

575
00:26:45.985 --> 00:26:48.325
And, you know, a newsletter sounds like

576
00:26:48.325 --> 00:26:49.565
it's just a newsletter, right?

577
00:26:49.625 --> 00:26:52.885
But it, it's historically taken someone a couple of days

578
00:26:53.465 --> 00:26:57.645
to create and, and organize and everything, uh,

579
00:26:57.745 --> 00:26:59.805
but over, over two week period.

580
00:26:59.945 --> 00:27:02.325
And primarily the reason it would take them that long is

581
00:27:02.325 --> 00:27:06.685
because they had to keep chasing me to write the CEO letter,

582
00:27:06.825 --> 00:27:09.565
the welcome letter, and the newsletter, uh,

583
00:27:09.625 --> 00:27:10.845
you know, the note from the CEO.

584
00:27:11.705 --> 00:27:13.885
And, um, so what we,

585
00:27:13.955 --> 00:27:16.285
what we did was we built an agent to do that.

586
00:27:17.145 --> 00:27:21.125
So the, so we actually, so one of the success, you know, one

587
00:27:21.125 --> 00:27:23.765
of the success drivers is the fact

588
00:27:23.765 --> 00:27:26.485
that we actually have an ecosystem of agents,

589
00:27:26.745 --> 00:27:28.205
and I think this is one of the things people

590
00:27:29.515 --> 00:27:31.965
sometimes don't do.

591
00:27:32.465 --> 00:27:34.645
Um, so we actually have an ecosystem of agents,

592
00:27:34.645 --> 00:27:38.165
and each agent does one thing well, and then,

593
00:27:38.305 --> 00:27:40.445
and then the, the, the core agent

594
00:27:40.555 --> 00:27:42.965
that the marketing team uses talks

595
00:27:42.965 --> 00:27:44.005
to all those different agents

596
00:27:44.025 --> 00:27:47.045
and gets them to do their bit of the job well.

597
00:27:47.225 --> 00:27:49.365
And that way you, what, what we found is

598
00:27:49.365 --> 00:27:52.845
that you can control what data each of those agents uses.

599
00:27:52.865 --> 00:27:54.805
So we've got one agent, for example, that we built

600
00:27:55.385 --> 00:27:56.965
to scour our website

601
00:27:57.225 --> 00:27:59.365
and to recommend interesting content

602
00:27:59.545 --> 00:28:01.765
for a particular reader, right?

603
00:28:01.865 --> 00:28:03.085
So, so in the newsletter,

604
00:28:03.185 --> 00:28:05.525
the newsletter writing agent will go to that one

605
00:28:05.525 --> 00:28:07.205
and say, this is my topic.

606
00:28:07.515 --> 00:28:09.285
Give me four things on our website

607
00:28:09.285 --> 00:28:10.765
that we should include in the newsletter.

608
00:28:11.465 --> 00:28:12.925
And it, it returns that,

609
00:28:12.945 --> 00:28:15.205
and then it goes somewhere else, get some more information.

610
00:28:15.205 --> 00:28:17.685
Then it brings it all together in one newsletter.

611
00:28:17.785 --> 00:28:21.245
So what, what was a two day piece of work over a couple

612
00:28:21.245 --> 00:28:24.445
of weeks is now, at least in first draft form,

613
00:28:24.835 --> 00:28:26.325
it's about a five minute piece of work.

614
00:28:27.855 --> 00:28:31.435
And of course, the caveat, I think everybody said it so far,

615
00:28:31.435 --> 00:28:32.995
is that you don't just take the AI stuff

616
00:28:32.995 --> 00:28:34.435
and say, that's it, it's done.

617
00:28:35.475 --> 00:28:38.035
'cause it's not that good. Um, you still have

618
00:28:38.035 --> 00:28:41.715
to sprinkle human fairy dust on it, but, um,

619
00:28:42.015 --> 00:28:45.715
but it, it's massively, you know, it's freed up one, one

620
00:28:45.715 --> 00:28:49.315
and a half, two, you know, almost two person days a month.

621
00:28:49.735 --> 00:28:53.475
Uh, and building that agent, put it in context,

622
00:28:53.705 --> 00:28:56.435
took about half an hour, right?

623
00:28:56.855 --> 00:29:00.475
So, um, uh, so, so when it goes well,

624
00:29:00.585 --> 00:29:01.915
it's, it's great, right?

625
00:29:01.945 --> 00:29:04.835
It's fantastic. But coming back to the themes,

626
00:29:05.415 --> 00:29:06.915
the business case, right?

627
00:29:07.095 --> 00:29:09.755
The business case was we need to be able to do it faster.

628
00:29:10.495 --> 00:29:11.795
We need, right?

629
00:29:11.895 --> 00:29:13.715
We, we need better quality,

630
00:29:14.335 --> 00:29:17.795
and we need, uh, we need to get Stuart outta the, the,

631
00:29:17.895 --> 00:29:19.955
the first draft loop, right?

632
00:29:20.455 --> 00:29:23.395
So now Stuart's in the final review tweak

633
00:29:23.455 --> 00:29:27.875
and, you know, add my own particular sense of sarcasm, um,

634
00:29:29.445 --> 00:29:33.035
which the AI hasn't quite got yet, can't quite pull off

635
00:29:33.035 --> 00:29:36.435
that last little bit of a sarcastic, um, uh, knives

636
00:29:36.455 --> 00:29:38.795
and the ribs kind of fun that, that we tend to have.

637
00:29:39.095 --> 00:29:41.165
Um, so that's, so that's awesome.

638
00:29:41.545 --> 00:29:44.045
So now what I would like is for the audience

639
00:29:44.105 --> 00:29:46.045
to start throwing some questions out, right?

640
00:29:46.105 --> 00:29:49.525
So we've got a panel of people here who have,

641
00:29:50.235 --> 00:29:52.485
have some experience, some more technical than others,

642
00:29:52.635 --> 00:29:54.445
some more PMO centric than others.

643
00:29:55.025 --> 00:29:58.485
Um, so, you know, I, I can keep going with questions, um,

644
00:29:59.065 --> 00:30:01.725
uh, but, uh, I'd love to, I'd love to put some

645
00:30:01.725 --> 00:30:04.045
of your questions to the, to the audience as well.

646
00:30:04.465 --> 00:30:06.885
So do jump into the note, into the, uh, into the,

647
00:30:07.505 --> 00:30:09.045
the chat with questions.

648
00:30:09.045 --> 00:30:10.365
I've got it open on if I keep,

649
00:30:10.385 --> 00:30:12.965
if you wonder why I'm looking over here, I've got 'em on a,

650
00:30:12.995 --> 00:30:14.965
I've got the, the chat on a different screen over here,

651
00:30:14.965 --> 00:30:16.045
so I'm keeping an eye on that.

652
00:30:16.945 --> 00:30:20.965
Um, uh, so, so Stuart, that's an excellent question.

653
00:30:22.025 --> 00:30:23.405
How long has it taken so far

654
00:30:23.405 --> 00:30:25.245
to feel comfortable in using ai?

655
00:30:25.425 --> 00:30:27.045
I'm gonna come to James on that one first,

656
00:30:27.675 --> 00:30:29.285
because James, it sounds like you

657
00:30:29.285 --> 00:30:31.885
and your team are, are relatively new at experiencing,

658
00:30:31.885 --> 00:30:33.165
you're still going through that process.

659
00:30:33.945 --> 00:30:35.365
So how long does that take?

660
00:30:37.125 --> 00:30:39.425
Um, that's a really good question.

661
00:30:39.805 --> 00:30:44.635
Um, so I think, I think from the sort of initial onset

662
00:30:44.635 --> 00:30:46.915
of actually playing about with it

663
00:30:46.915 --> 00:30:49.955
and then actually getting comfortable into using it

664
00:30:49.975 --> 00:30:51.235
for business purposes

665
00:30:51.235 --> 00:30:54.965
and having the confidence that you can then go

666
00:30:54.965 --> 00:30:56.925
and use it for your day to day work,

667
00:30:57.725 --> 00:30:59.885
I think it's been a probably about a journey of

668
00:30:59.885 --> 00:31:02.445
around about 12 to 18 months to really get to

669
00:31:02.445 --> 00:31:03.725
that sort of level.

670
00:31:04.505 --> 00:31:08.405
Um, the, the first sort of four, four

671
00:31:08.405 --> 00:31:13.345
to six months was very much, um, playing with it, um, seeing

672
00:31:13.345 --> 00:31:14.745
what was in the realms of possible.

673
00:31:15.665 --> 00:31:17.105
I think you can imagine

674
00:31:17.105 --> 00:31:19.025
that when you give the team a tool like this,

675
00:31:19.785 --> 00:31:21.625
everyone's first thing they had to do on there was go

676
00:31:21.625 --> 00:31:24.065
and create a little action figure of themselves using ai

677
00:31:24.095 --> 00:31:25.305
that wa that was the first,

678
00:31:25.715 --> 00:31:27.625
first thing they had to go and do. And see and

679
00:31:27.625 --> 00:31:28.705
See your action figure come on.

680
00:31:28.705 --> 00:31:30.025
We wanna, we wanna see Jason,

681
00:31:30.565 --> 00:31:35.225
My, my looks nothing like my, um, so that was the sort

682
00:31:35.225 --> 00:31:36.745
of thing, and everyone started going,

683
00:31:36.745 --> 00:31:39.265
all these little PMO action figures that they generated

684
00:31:39.525 --> 00:31:40.705
of themselves was great.

685
00:31:40.845 --> 00:31:42.665
But then when they got C vs

686
00:31:42.665 --> 00:31:45.265
and people were starting to go, well, actually, if I start

687
00:31:45.285 --> 00:31:48.785
to ask the AI to summarize this meeting or summarize that

688
00:31:49.125 --> 00:31:51.825
and starting to get used to it, that's

689
00:31:51.825 --> 00:31:53.065
where it started to come in.

690
00:31:53.065 --> 00:31:55.705
But it took about, it's taken about 18 months to get 'em

691
00:31:55.705 --> 00:31:57.825
to a point now where they are comfortable

692
00:31:57.825 --> 00:31:59.505
that they can use it and know its limits.

693
00:31:59.965 --> 00:32:02.905
The limitations part is, um, the bit

694
00:32:02.905 --> 00:32:04.585
that's taken the longest, but we're still learning

695
00:32:04.885 --> 00:32:07.345
and the hoofing around the agents is

696
00:32:07.565 --> 00:32:09.825
that's the conversation we're moving into now.

697
00:32:10.285 --> 00:32:12.105
Can we generate our own agent

698
00:32:12.325 --> 00:32:16.145
or PMO agent that is from the AI part?

699
00:32:16.205 --> 00:32:19.305
And that's, that's where I'm interested in going next.

700
00:32:19.695 --> 00:32:22.225
Yeah. Yeah. It's a good step when you get into the agents,

701
00:32:22.245 --> 00:32:24.105
it takes a while to get your head wrapped around now

702
00:32:24.105 --> 00:32:25.825
to make them useful, uh,

703
00:32:25.965 --> 00:32:28.065
and how big they need to be and all that kind of stuff.

704
00:32:28.205 --> 00:32:30.225
We may, we may come onto that later on.

705
00:32:30.965 --> 00:32:34.465
Um, uh, Joe, I'm gonna, I'm gonna ask you

706
00:32:34.465 --> 00:32:35.585
that same question actually.

707
00:32:35.645 --> 00:32:37.705
So how long do you think, you know,

708
00:32:37.705 --> 00:32:39.290
what are you hearing people saying when

709
00:32:39.290 --> 00:32:40.485
you're going around PMOs?

710
00:32:40.905 --> 00:32:42.845
How long has it taken them to get their arms wrapped

711
00:32:42.845 --> 00:32:45.205
around this as a, and and make it productive?

712
00:32:45.595 --> 00:32:48.125
Because you talk to loads of people, right? All the time.

713
00:32:48.875 --> 00:32:52.565
Yeah. Yeah. I, I think it's still happening.

714
00:32:52.725 --> 00:32:54.925
I, I, I don't think it's gotten comfortable yet.

715
00:32:55.745 --> 00:32:59.285
Um, the kind of, the consensus I feel is I,

716
00:32:59.285 --> 00:33:00.765
people are doing more at home

717
00:33:01.555 --> 00:33:03.405
than they are at work still with it.

718
00:33:03.645 --> 00:33:04.925
'cause they're kind of experimenting

719
00:33:04.925 --> 00:33:07.645
and playing around with it, uh, within their families

720
00:33:07.665 --> 00:33:10.365
and friends and, and, and neighborhoods

721
00:33:10.465 --> 00:33:11.605
and different associations.

722
00:33:11.605 --> 00:33:16.045
Their parts of, um, I, I'd liken it to, you know,

723
00:33:16.205 --> 00:33:19.165
20 years ago when, when nobody had one of these, right?

724
00:33:19.165 --> 00:33:23.575
The phones, and then when you got one of these, you had two

725
00:33:23.575 --> 00:33:25.175
of them because you had one for work

726
00:33:25.175 --> 00:33:26.175
and you had one for home.

727
00:33:26.275 --> 00:33:28.535
You, you couldn't have your work email on, I mean,

728
00:33:28.535 --> 00:33:30.095
your personal email on your work phone.

729
00:33:31.035 --> 00:33:34.095
Um, and then finally companies got okay with that

730
00:33:34.235 --> 00:33:35.255
and said, it's okay.

731
00:33:35.255 --> 00:33:37.295
There's technology now to make that be all right.

732
00:33:38.095 --> 00:33:39.415
I think that's what's happening right now.

733
00:33:39.475 --> 00:33:42.815
But the feedback I'm getting from PMOs all over the US is

734
00:33:43.465 --> 00:33:44.575
we're experimenting.

735
00:33:45.105 --> 00:33:47.135
We're not comfortable yet.

736
00:33:47.425 --> 00:33:51.935
We're getting comfortable, um, our IT organizations

737
00:33:51.935 --> 00:33:54.055
and our compliance, there was a question about

738
00:33:54.055 --> 00:33:55.215
compliance in here as well.

739
00:33:55.805 --> 00:33:57.335
They're getting comfortable with it.

740
00:33:57.875 --> 00:34:00.255
Uh, because remember, it only really works if

741
00:34:00.255 --> 00:34:01.375
you give it data.

742
00:34:01.675 --> 00:34:03.575
If, if you're giving AI something,

743
00:34:03.715 --> 00:34:05.135
it can give you something back.

744
00:34:06.075 --> 00:34:08.855
So, uh, people are still a little hesitant with that at,

745
00:34:09.395 --> 00:34:10.975
you know, PMO squad, for instance.

746
00:34:11.025 --> 00:34:13.495
We're, we're a PMO consulting firm.

747
00:34:13.515 --> 00:34:16.015
We, we don't have any technologists on our team.

748
00:34:17.515 --> 00:34:20.095
And one of the things we do is an OPD assessment.

749
00:34:20.975 --> 00:34:22.855
I would love to put that on our website

750
00:34:23.595 --> 00:34:25.855
and have people self assess to see where they're at.

751
00:34:27.165 --> 00:34:29.365
I went out and just had AI write to code for it.

752
00:34:29.825 --> 00:34:32.885
Uh, we're now iterating through that to be able

753
00:34:32.885 --> 00:34:35.085
to take non-technical people on our team

754
00:34:35.865 --> 00:34:38.245
who do the in-person assessments

755
00:34:38.705 --> 00:34:41.445
and have AI be able to do those assessments

756
00:34:41.825 --> 00:34:44.445
as a self-assessment tool on the web, just

757
00:34:44.465 --> 00:34:45.765
as well as if we did in person.

758
00:34:46.425 --> 00:34:48.805
And that's gonna save an organization thousands

759
00:34:48.805 --> 00:34:51.685
and thousands of dollars if they trust those results.

760
00:34:52.265 --> 00:34:54.645
So we're, we're able to take non-technical people,

761
00:34:55.305 --> 00:34:56.885
but we're trying to get comfortable with it

762
00:34:56.885 --> 00:34:59.085
that the output's gonna match what we could do as people.

763
00:35:00.115 --> 00:35:02.215
And so that's where, you know,

764
00:35:02.215 --> 00:35:03.695
from a very personalized struggle,

765
00:35:03.695 --> 00:35:04.695
what we're seeing out there.

766
00:35:04.835 --> 00:35:07.415
But I think PE companies are still

767
00:35:08.845 --> 00:35:10.525
easing their way in that that's what we're hearing.

768
00:35:10.525 --> 00:35:13.735
Nobody is kind of all in yet, right?

769
00:35:13.735 --> 00:35:16.015
They're, they're trying to figure out how to make it work.

770
00:35:17.095 --> 00:35:18.815
Excellent. So I'm gonna come back to that,

771
00:35:18.845 --> 00:35:20.535
that data governance question next.

772
00:35:20.535 --> 00:35:22.335
And David, I'm gonna come to you with that one first

773
00:35:22.715 --> 00:35:25.415
and then an, um, on the, I'm giving you warning

774
00:35:25.475 --> 00:35:26.615
so you can think about it.

775
00:35:27.275 --> 00:35:30.975
Um, but before we do that, um, I thought I would share one

776
00:35:30.975 --> 00:35:33.375
of the other experiments that our marketing team has done

777
00:35:33.615 --> 00:35:37.095
recently with ai, um, uh, which,

778
00:35:37.375 --> 00:35:39.285
which hopefully you will enjoy.

779
00:35:39.945 --> 00:35:42.645
Uh, it'll only take a minute, assuming I can work Zoom.

780
00:35:42.875 --> 00:35:47.645
Here we go. Um, so we are in the process of relaunching

781
00:35:48.505 --> 00:35:53.015
our, um, uh, YouTube channel,

782
00:35:53.275 --> 00:35:55.855
or reviga reinvigorating our YouTube channel.

783
00:35:56.635 --> 00:35:58.975
Uh, so, uh, where's it gone?

784
00:35:59.115 --> 00:36:01.615
Oh, I thought we, I thought we were there. There we go.

785
00:36:01.615 --> 00:36:03.895
Let's go to the, the transparent choice YouTube channel.

786
00:36:04.225 --> 00:36:07.095
There we go. So if you have, if you are not subscribed

787
00:36:07.095 --> 00:36:09.375
to the YouTube channel, please go and subscribe now.

788
00:36:09.955 --> 00:36:12.495
Um, we would very, very much appreciate it.

789
00:36:12.875 --> 00:36:17.255
Um, but the AI piece was, uh, this is, this is our, uh,

790
00:36:17.355 --> 00:36:19.815
our first experiment, uh, with ai.

791
00:36:20.635 --> 00:36:24.215
Um, uh, where we, we took

792
00:36:24.795 --> 00:36:26.215
the announcement about PMBOK

793
00:36:26.355 --> 00:36:27.975
and turned it into a video. It's like

794
00:36:27.975 --> 00:36:29.735
Wandering around a room with a blindfold on.

795
00:36:30.795 --> 00:36:33.375
You'll find the filing cabinet with your face.

796
00:36:33.995 --> 00:36:35.095
So here's the big reveal.

797
00:36:35.865 --> 00:36:38.735
PMBOK eight finally gives PMOs their own appendix

798
00:36:39.075 --> 00:36:40.095
for the first time.

799
00:36:41.165 --> 00:36:43.495
PMOs are officially essential to delivering value.

800
00:36:43.925 --> 00:36:46.935
They're not admin, they're your portfolio satnav,

801
00:36:46.935 --> 00:36:49.095
making sure the work backs your strategy,

802
00:36:49.555 --> 00:36:50.575
not someone's pet project.

803
00:36:51.355 --> 00:36:53.615
And they give decision makers the insight

804
00:36:53.615 --> 00:36:56.255
and support to spot the dead ends fund.

805
00:36:56.255 --> 00:36:57.935
The winners fix the bottlenecks.

806
00:36:57.935 --> 00:37:02.135
So outcomes actually improve delivering value without PMO.

807
00:37:02.515 --> 00:37:05.535
That's like baking a cake without a recipe or flour

808
00:37:05.755 --> 00:37:08.655
or an oven without a strong PMO.

809
00:37:08.655 --> 00:37:09.655
You're just guessing.

810
00:37:10.905 --> 00:37:13.015
Wanna see how a real PMO delivers value?

811
00:37:13.605 --> 00:37:17.015
Well check out our PMO page, the links in the description

812
00:37:17.015 --> 00:37:21.215
below, no blindfolds required for a mix of PMO insight

813
00:37:21.215 --> 00:37:24.095
and fun, and to stop the dancing, like,

814
00:37:24.125 --> 00:37:25.695
comment, and subscribe.

815
00:37:30.755 --> 00:37:34.845
So this is, this is a great example of

816
00:37:34.855 --> 00:37:36.405
where we are small.

817
00:37:36.405 --> 00:37:37.765
We're a small company, right?

818
00:37:38.585 --> 00:37:41.405
And we just produce something, um,

819
00:37:41.715 --> 00:37:45.125
that we just could not have project any other way, right?

820
00:37:45.125 --> 00:37:46.445
So it's not about, in this case,

821
00:37:46.445 --> 00:37:48.165
it's not about being more efficient.

822
00:37:48.785 --> 00:37:51.045
We just couldn't produce something like that before.

823
00:37:52.105 --> 00:37:56.925
And, uh, so, you know, so my lesson from that though was

824
00:37:57.195 --> 00:38:00.165
that, um, uh, the first one that we did

825
00:38:00.675 --> 00:38:02.245
took us quite a long time.

826
00:38:03.745 --> 00:38:04.805
And it took us a long time

827
00:38:04.965 --> 00:38:07.325
'cause we had to figure out what was the right tool to use.

828
00:38:08.645 --> 00:38:09.905
Uh, it took us a long time

829
00:38:10.225 --> 00:38:12.225
'cause we had to figure out how to use the tool.

830
00:38:13.615 --> 00:38:17.075
It took us a long time to figure out, uh, you know,

831
00:38:17.075 --> 00:38:18.835
how do you put a story together in a way

832
00:38:18.835 --> 00:38:19.955
that AI can understand it?

833
00:38:20.855 --> 00:38:23.795
So that video that you just saw was a minute long.

834
00:38:25.015 --> 00:38:29.645
It took us three quarters of a person day to do.

835
00:38:31.395 --> 00:38:33.535
The second video that we did was a minute long.

836
00:38:34.235 --> 00:38:39.165
It took about an hour. And so,

837
00:38:39.305 --> 00:38:42.245
you know, one of the, one of, so for, so my learning from

838
00:38:42.245 --> 00:38:44.325
that is, you know, there's, there's a piece here.

839
00:38:44.375 --> 00:38:46.565
Let's celebrate the success, right? Yay. It looked great.

840
00:38:46.625 --> 00:38:48.205
We, you know, we, we, we were able

841
00:38:48.205 --> 00:38:49.565
to do something we couldn't do before.

842
00:38:50.025 --> 00:38:51.245
Um, but there's a, there's a,

843
00:38:51.245 --> 00:38:53.605
there is absolutely an economy of learning here.

844
00:38:54.705 --> 00:38:56.845
So if your first experiment doesn't

845
00:38:56.845 --> 00:38:58.165
work, that's not a failure.

846
00:38:58.345 --> 00:39:00.415
That's a learning, right?

847
00:39:00.955 --> 00:39:02.775
That's a learning that's moving you forward.

848
00:39:02.945 --> 00:39:05.895
Every time you fail, that moves you forward.

849
00:39:06.475 --> 00:39:08.695
So I'm gonna share another, I'm gonna share a quick fail

850
00:39:08.695 --> 00:39:10.535
with you before I come to David.

851
00:39:10.755 --> 00:39:12.975
And then, and then an hour to talk about governance sake.

852
00:39:13.195 --> 00:39:14.255
So the failure is

853
00:39:14.255 --> 00:39:17.015
that I think organizations right now are often failing.

854
00:39:17.875 --> 00:39:20.975
So there's a, a pharmaceutical, large global pharmaceutical,

855
00:39:21.035 --> 00:39:22.895
one of the world's biggest pharmaceutical companies.

856
00:39:22.975 --> 00:39:25.735
I know, uh, a few people who work there.

857
00:39:26.195 --> 00:39:29.175
And, uh, they have set aside a few tens of millions

858
00:39:29.175 --> 00:39:31.975
of dollars specifically to fund AI projects,

859
00:39:33.075 --> 00:39:36.695
to improve effectiveness, especially in the whole, you know,

860
00:39:36.725 --> 00:39:39.495
drug discovery, uh, part of their business.

861
00:39:41.725 --> 00:39:45.025
At the exact same time, the IT governance team

862
00:39:45.535 --> 00:39:48.905
have put a moratorium on any new AI projects.

863
00:39:50.885 --> 00:39:53.665
So you've got the executives, you've got the strategy part

864
00:39:53.665 --> 00:39:55.265
of the business saying, this is the one

865
00:39:55.265 --> 00:39:56.905
of the most important things we have to do.

866
00:39:57.365 --> 00:40:01.265
And at the same time, the governance guys have just put a

867
00:40:01.265 --> 00:40:04.145
big stop sign on the entire company, right?

868
00:40:04.285 --> 00:40:06.585
So there's a lot of that kind of thing going on

869
00:40:07.045 --> 00:40:08.065
as well at the minute.

870
00:40:08.485 --> 00:40:11.705
So, so David, that brings us to, to Liz's question about,

871
00:40:12.005 --> 00:40:13.705
uh, uh, data governance.

872
00:40:13.705 --> 00:40:15.465
How are we handling data, data governance?

873
00:40:18.585 --> 00:40:19.685
Uh, you're on mute still.

874
00:40:20.515 --> 00:40:23.605
Alright, sorry. Um, I'll give you an example.

875
00:40:23.905 --> 00:40:26.605
One of the, one of the solutions I've seen people, uh,

876
00:40:26.865 --> 00:40:30.765
use is a, a bid a bid preparation solution.

877
00:40:31.065 --> 00:40:35.045
So that in a sales team, um, if, if you are fed up

878
00:40:35.045 --> 00:40:39.125
of answering tenders with, uh, uh, an answer that you, you,

879
00:40:39.125 --> 00:40:41.605
you've recreated millions of, millions of times, can,

880
00:40:41.705 --> 00:40:43.765
can we take, uh, questions from a tender

881
00:40:43.985 --> 00:40:47.885
and, uh, ask, ask AI to go and answer all this stuff?

882
00:40:48.425 --> 00:40:51.085
Uh, really quickly, really quickly

883
00:40:51.105 --> 00:40:53.005
and simply from what we've answered

884
00:40:53.005 --> 00:40:56.285
before, um, the obvious thing there is, well,

885
00:40:56.515 --> 00:40:57.725
what have we answered before?

886
00:40:57.935 --> 00:41:00.405
Where is that information? And who says it's good?

887
00:41:00.945 --> 00:41:04.205
So it, it must be glaringly obvious that, that, that, um,

888
00:41:04.205 --> 00:41:05.685
if we're gonna use AI to do anything,

889
00:41:05.685 --> 00:41:09.565
then the information we, uh, ask it to do it with has

890
00:41:09.565 --> 00:41:11.165
to be good quality stuff.

891
00:41:11.305 --> 00:41:12.685
And it needs to be under control.

892
00:41:12.745 --> 00:41:14.845
We need to be, have confidence that it's usable.

893
00:41:15.505 --> 00:41:17.405
Um, so, so that's, that's the challenge.

894
00:41:17.525 --> 00:41:20.765
I think the challenge is, is when people say they want AI

895
00:41:20.785 --> 00:41:23.285
to, to do this or that, um, they can't do this all

896
00:41:23.285 --> 00:41:28.045
that based on all, all the junk and sit chat and waffle

897
00:41:28.185 --> 00:41:31.565
and fluff that, that we all create in our business lives,

898
00:41:31.565 --> 00:41:33.285
that isn't all that brilliant.

899
00:41:33.505 --> 00:41:36.445
We, we, we have to find a way to, to point AI at,

900
00:41:36.505 --> 00:41:38.205
at information, at data

901
00:41:38.225 --> 00:41:40.205
and information that is, is solid stuff.

902
00:41:40.425 --> 00:41:42.565
And I don't think many organizations or,

903
00:41:42.785 --> 00:41:46.085
or certainly most organizations are, are truly on top of

904
00:41:46.625 --> 00:41:47.805
what's good information

905
00:41:47.805 --> 00:41:50.085
and what's, what's the right information.

906
00:41:50.545 --> 00:41:53.645
We, we have copies on OneDrive, we have copies in email,

907
00:41:53.745 --> 00:41:55.165
we have copies in folders.

908
00:41:55.865 --> 00:41:58.525
Um, I, I think there's a whole lot of, um, personal

909
00:41:59.045 --> 00:42:01.525
internal organizational hygiene we need to get on top of

910
00:42:01.755 --> 00:42:03.405
with respect to our information and data

911
00:42:03.585 --> 00:42:05.485
before we can truly leverage the AI

912
00:42:05.485 --> 00:42:06.845
to, to really work for us.

913
00:42:09.255 --> 00:42:12.665
Awesome. Um, an do you have any thoughts on that?

914
00:42:12.695 --> 00:42:13.985
That data governance thing?

915
00:42:14.005 --> 00:42:16.705
How do you, you know, how do you get that lined up to so

916
00:42:16.705 --> 00:42:18.425
that you can actually get out and execute?

917
00:42:19.385 --> 00:42:22.075
Yeah, I have a good example that I've been observing.

918
00:42:22.075 --> 00:42:24.995
And the bad example, so I'll start with the bad one.

919
00:42:25.975 --> 00:42:26.995
That's more fun, isn't it?

920
00:42:27.265 --> 00:42:28.355
Yeah, it's more fun

921
00:42:28.575 --> 00:42:31.235
and especially when you see it's happening

922
00:42:31.575 --> 00:42:35.275
and, uh, then the level of the ignorance of the security

923
00:42:36.255 --> 00:42:39.475
and uh, intellectual property is just insane.

924
00:42:39.975 --> 00:42:43.755
So the bad example is when, uh, you assume that your

925
00:42:44.275 --> 00:42:48.035
employees know, uh, that all the data that you have

926
00:42:48.095 --> 00:42:52.955
and you create your company is, uh, assets of the company.

927
00:42:53.655 --> 00:42:56.555
And you can't just fit AI with that data

928
00:42:56.815 --> 00:42:59.435
and expect that it'll be good,

929
00:42:59.545 --> 00:43:03.115
because then that data actually leaked right to the AI

930
00:43:03.815 --> 00:43:05.035
and, uh, that,

931
00:43:05.345 --> 00:43:08.395
that ignorance level unfortunately is pretty

932
00:43:08.465 --> 00:43:10.035
high. And, uh, so they put

933
00:43:10.035 --> 00:43:12.915
Things into the AI that shouldn't have gone into the ai.

934
00:43:13.335 --> 00:43:14.835
No, no, absolutely not.

935
00:43:14.855 --> 00:43:17.715
And then there is quite a lot of education that need

936
00:43:17.715 --> 00:43:22.275
to be done before you would allow ai, uh, the users

937
00:43:22.295 --> 00:43:26.315
to use ai, uh, regardless of your subscription level,

938
00:43:26.395 --> 00:43:29.155
whether you have in the enterprise level that you assume

939
00:43:29.155 --> 00:43:30.555
that, uh, your data is secured

940
00:43:30.555 --> 00:43:32.355
and the AI is not trained on your data

941
00:43:32.495 --> 00:43:35.555
or it's just the general access that you're getting

942
00:43:35.655 --> 00:43:39.715
to the AI solutions that there, um, human nature is the way

943
00:43:39.715 --> 00:43:42.275
that if you can't simplify your life, you would do that

944
00:43:42.335 --> 00:43:45.835
and you wouldn't care much about what you're doing, right?

945
00:43:46.655 --> 00:43:49.795
So, um, that's, that's a bad example.

946
00:43:49.855 --> 00:43:53.955
The good example is that very recently, it was last week,

947
00:43:54.295 --> 00:43:56.755
uh, I've been involved in the discussion

948
00:43:56.815 --> 00:43:58.075
of the good solution.

949
00:43:58.695 --> 00:44:01.595
And that solution creates a dashboard

950
00:44:01.695 --> 00:44:02.835
for the data governance.

951
00:44:03.695 --> 00:44:07.315
So you have an AI on the backend, you have on the front end,

952
00:44:07.655 --> 00:44:11.725
uh, the kind of the front end where users interact

953
00:44:11.725 --> 00:44:15.045
with the AI and uh, write a prompt and get an answers.

954
00:44:15.355 --> 00:44:18.205
It's exactly the example of I think, joy you used

955
00:44:18.205 --> 00:44:19.285
for the lessons learned.

956
00:44:19.825 --> 00:44:22.405
But then in the between, you have a dashboard

957
00:44:22.435 --> 00:44:25.685
that accumulates the data that been going back and forth

958
00:44:26.305 --> 00:44:29.565
and the providing for a data governance, what's been used

959
00:44:29.585 --> 00:44:31.245
and what kind of prompts been written.

960
00:44:31.865 --> 00:44:34.645
And, uh, it's inner level alarms.

961
00:44:35.225 --> 00:44:38.925
Uh, the situations when it kind of sense, uh,

962
00:44:38.925 --> 00:44:40.885
based on the training that that, that,

963
00:44:40.945 --> 00:44:43.525
or this data should not been kind of used

964
00:44:43.525 --> 00:44:44.965
or operated with the ai.

965
00:44:45.345 --> 00:44:48.725
And I'm pleased that there are this sort of the solutions

966
00:44:48.825 --> 00:44:51.805
and, uh, teams are working on those solutions

967
00:44:51.805 --> 00:44:55.045
because then it gives the clarity on what, what kind

968
00:44:55.045 --> 00:44:56.605
of data users are feeding DA

969
00:44:57.345 --> 00:44:59.405
and what kind of answers are coming as well.

970
00:45:00.265 --> 00:45:03.125
So it's kind of two-sided dashboard, which is really nice.

971
00:45:04.065 --> 00:45:05.235
Love it, love it. Um,

972
00:45:05.255 --> 00:45:08.755
and I, I, I've seen another similar idea to that error

973
00:45:08.815 --> 00:45:12.675
as well where, um, I, I've seen teams building agents

974
00:45:13.935 --> 00:45:16.315
to be the enforcer, right?

975
00:45:16.495 --> 00:45:18.475
To basically just keep an eye on everything

976
00:45:18.575 --> 00:45:21.235
and make sure that, that, uh,

977
00:45:21.235 --> 00:45:22.915
people are doing the right things.

978
00:45:22.915 --> 00:45:26.275
That the agents are doing the right things to watch

979
00:45:26.295 --> 00:45:29.915
for agen drift to do all those sorts of fun things.

980
00:45:30.295 --> 00:45:34.315
Now, I, I'm actually going to skip over, uh, Gina's question

981
00:45:34.515 --> 00:45:36.795
'cause I, I think David gave you one line answer.

982
00:45:37.295 --> 00:45:38.475
You deserve more than that.

983
00:45:38.735 --> 00:45:40.155
If we've got time, we'll come back to it.

984
00:45:40.175 --> 00:45:41.235
But I, I want to get to,

985
00:45:41.275 --> 00:45:42.715
I wanna jump ahead to Joe's question.

986
00:45:43.455 --> 00:45:47.115
Uh, so Joe's also in the NHS, um,

987
00:45:47.615 --> 00:45:51.075
and so Joe's question is, I'm a, I'm a total novice on a ai.

988
00:45:51.735 --> 00:45:53.955
Uh, so when you say ai, what, what systems

989
00:45:54.015 --> 00:45:57.555
and what approach, um, uh, they're, they're just beginning

990
00:45:57.555 --> 00:45:59.035
to experiment with copilot.

991
00:45:59.575 --> 00:46:01.275
So, so I guess the question is,

992
00:46:02.095 --> 00:46:06.605
if you're just getting started, what tool would you,

993
00:46:06.705 --> 00:46:07.965
you know, what tools would you use?

994
00:46:08.355 --> 00:46:10.365
What would you do? How would you go

995
00:46:10.365 --> 00:46:12.525
about learning and starting?

996
00:46:12.625 --> 00:46:13.605
And Joe, I'm gonna,

997
00:46:13.665 --> 00:46:15.205
I'm actually, no, I'm gonna pick on James.

998
00:46:15.405 --> 00:46:17.245
'cause you've gone through this relatively recently.

999
00:46:17.625 --> 00:46:20.405
So, so how did you go about just starting the process

1000
00:46:20.625 --> 00:46:22.765
and getting to know you,

1001
00:46:22.765 --> 00:46:25.245
you talked about creating these little, these little, um,

1002
00:46:25.835 --> 00:46:26.925
figures and things like that.

1003
00:46:27.025 --> 00:46:28.765
So how did you, how did you go about that?

1004
00:46:30.085 --> 00:46:33.645
I think a lot of it came from, um, just sort

1005
00:46:33.645 --> 00:46:36.805
of things people had picked up from other conversations.

1006
00:46:37.035 --> 00:46:39.725
Like, I've heard AI can do this, I heard it can do that.

1007
00:46:40.385 --> 00:46:43.325
And we actually had a similar conversation internally,

1008
00:46:43.325 --> 00:46:46.845
whereas what, what AI systems are, are people using?

1009
00:46:47.025 --> 00:46:48.125
How, how does it work?

1010
00:46:48.425 --> 00:46:49.565
And the,

1011
00:46:49.875 --> 00:46:53.565
what I'm seeing across NHS rails at the moment is this

1012
00:46:53.595 --> 00:46:56.245
varying levels of, um, confidence

1013
00:46:56.265 --> 00:46:58.325
and actually wanting to go to use ai.

1014
00:46:58.385 --> 00:47:01.445
So one of the health boards is basically locked off

1015
00:47:01.445 --> 00:47:02.525
every AI system.

1016
00:47:02.665 --> 00:47:05.725
You can't do it until you've signed something

1017
00:47:05.725 --> 00:47:08.325
to say you understand that you can't go

1018
00:47:08.425 --> 00:47:10.925
and share confidential information in there.

1019
00:47:10.925 --> 00:47:12.685
You can't put patient information in there

1020
00:47:12.755 --> 00:47:14.165
because that would be a nightmare.

1021
00:47:14.585 --> 00:47:16.325
Um, and you've gotta use in a certain way.

1022
00:47:16.625 --> 00:47:19.605
Um, so they've started to put those barriers in.

1023
00:47:19.945 --> 00:47:23.565
But in terms of the, what systems to use, um, it,

1024
00:47:23.585 --> 00:47:24.885
it came down to two for us.

1025
00:47:25.225 --> 00:47:27.325
Um, a lot of people had heard of chat, GBT

1026
00:47:27.485 --> 00:47:28.765
'cause that was all over the news.

1027
00:47:29.245 --> 00:47:31.565
Everyone was using it, it was all over social media.

1028
00:47:31.755 --> 00:47:34.845
That was the sort of big one that everyone was drawn to.

1029
00:47:34.905 --> 00:47:39.395
But for us, um, our ICT were basically saying, no,

1030
00:47:39.585 --> 00:47:42.235
it's too, too risky, um, to there.

1031
00:47:42.575 --> 00:47:44.515
But because we had our Microsoft, um,

1032
00:47:44.735 --> 00:47:46.635
to copilot was built into that,

1033
00:47:47.045 --> 00:47:49.155
there was a lot more confidence towards that.

1034
00:47:49.415 --> 00:47:53.315
But a lot of people, um, had started experimenting

1035
00:47:53.315 --> 00:47:54.515
with chat GBT in their homes

1036
00:47:54.895 --> 00:47:57.195
and starting to learn how to get the prompts right

1037
00:47:57.295 --> 00:47:58.715
and working with it there.

1038
00:47:59.375 --> 00:48:02.275
But in terms of how we've worked, it's largely been, um,

1039
00:48:03.025 --> 00:48:06.395
co-pilots, um, for internal ways of working.

1040
00:48:07.255 --> 00:48:09.675
And in terms of how we've taught it, then it,

1041
00:48:09.855 --> 00:48:13.795
it has very much come from, um, videos,

1042
00:48:14.665 --> 00:48:15.835
self-teaching each other.

1043
00:48:16.495 --> 00:48:21.275
Um, getting the AI to teach you, um, is quite, uh, uh,

1044
00:48:21.375 --> 00:48:22.515
fun way of trying to do it.

1045
00:48:22.515 --> 00:48:26.195
So asking the ai, how can you help me solve this?

1046
00:48:26.255 --> 00:48:27.715
Or how can you help me do this?

1047
00:48:28.415 --> 00:48:30.195
And it's actually guided us through.

1048
00:48:30.455 --> 00:48:32.035
And some of it has been going, right,

1049
00:48:32.035 --> 00:48:35.525
we're doing this at the moment, we need you to act

1050
00:48:35.525 --> 00:48:38.045
as a member of the PMO team, and you tell us.

1051
00:48:38.665 --> 00:48:40.125
And it, it's guiding us that way.

1052
00:48:40.145 --> 00:48:45.085
And it, it has been very much, um, just experiment,

1053
00:48:45.465 --> 00:48:47.445
um, see how we can do it.

1054
00:48:47.685 --> 00:48:50.965
I think we are a long way off actually nailing it

1055
00:48:50.985 --> 00:48:52.725
and actually being in that area

1056
00:48:52.785 --> 00:48:54.645
of using it to its full advantage.

1057
00:48:54.865 --> 00:48:56.805
But compared to a lot of people,

1058
00:48:56.965 --> 00:48:59.805
I think we are very far along the journey compared to

1059
00:48:59.805 --> 00:49:02.445
what I've seen in other organizations around us.

1060
00:49:03.665 --> 00:49:05.205
Joe, what have you seen working well

1061
00:49:05.225 --> 00:49:07.405
for teams early in the adoption process?

1062
00:49:09.105 --> 00:49:10.955
Yeah, I think as, as James said, uh,

1063
00:49:11.085 --> 00:49:12.475
co-pilot's the easy win

1064
00:49:12.475 --> 00:49:14.995
because there's so many Microsoft shops out there

1065
00:49:14.995 --> 00:49:16.435
and they're, they're able to experiment.

1066
00:49:17.215 --> 00:49:21.715
Um, you know, we're, we're technology partners

1067
00:49:21.825 --> 00:49:24.035
with several PPM providers

1068
00:49:24.815 --> 00:49:27.915
and each of those tools are doing their own version

1069
00:49:28.015 --> 00:49:29.555
of AI experimentation.

1070
00:49:29.695 --> 00:49:33.275
So, as an example, Asana, you can go in

1071
00:49:33.275 --> 00:49:36.115
and have, um, AI rate your status reports for you.

1072
00:49:36.655 --> 00:49:39.475
Um, I still don't recommend that quite yet

1073
00:49:39.475 --> 00:49:41.355
because you're reliant on all

1074
00:49:41.355 --> 00:49:43.635
of your team members inputting the data to make

1075
00:49:43.635 --> 00:49:45.075
that status report be accurate

1076
00:49:45.855 --> 00:49:47.875
before you distribute it out to your executives.

1077
00:49:47.875 --> 00:49:51.715
You better confirm, uh, what you've collected, um,

1078
00:49:52.695 --> 00:49:55.475
you know, people doing portfolio management prioritization

1079
00:49:55.605 --> 00:49:58.155
based on resource availability using AI tools,

1080
00:49:58.495 --> 00:50:00.555
uh, to be able to do that. Uh, US

1081
00:50:00.555 --> 00:50:01.195
Folks. Woo.

1082
00:50:01.385 --> 00:50:03.635
Yeah. As, as, as you guys do, right? Of course.

1083
00:50:04.575 --> 00:50:08.835
Um, so to me, it's, uh, in PMO squad we've gone in and,

1084
00:50:08.835 --> 00:50:11.755
and we're a chat GPT open AI organization.

1085
00:50:11.815 --> 00:50:13.675
So we've got a business account in there and,

1086
00:50:14.535 --> 00:50:18.275
and we've upload all our data into that, uh, tenant.

1087
00:50:18.455 --> 00:50:20.075
So we can go in

1088
00:50:20.075 --> 00:50:22.595
and talk as if we're talking to a coworker, um,

1089
00:50:22.775 --> 00:50:25.115
and be able to say, go look at these documents

1090
00:50:25.115 --> 00:50:26.555
and tell me what we should do based on that.

1091
00:50:27.345 --> 00:50:29.285
Um, so we do get feedback directly

1092
00:50:29.285 --> 00:50:30.445
from our own organization.

1093
00:50:30.475 --> 00:50:34.045
Plus what we're seeing out there in PMOs across the us uh,

1094
00:50:34.045 --> 00:50:36.045
right now it's experiment with what you have.

1095
00:50:36.095 --> 00:50:38.245
Don't feel like you need to go out and get something

1096
00:50:38.515 --> 00:50:41.165
because everybody is experimenting.

1097
00:50:41.165 --> 00:50:43.165
If you have technology in your organization,

1098
00:50:44.705 --> 00:50:46.915
that provider is experimenting with ai,

1099
00:50:47.805 --> 00:50:49.025
go use what you already have.

1100
00:50:49.025 --> 00:50:50.625
You don't need to go get anything new.

1101
00:50:51.285 --> 00:50:53.945
Um, and then at home, experiment with everything.

1102
00:50:54.365 --> 00:50:56.985
Get comfortable, the prompts, understanding how

1103
00:50:56.985 --> 00:50:59.225
to ask the questions, uh,

1104
00:50:59.405 --> 00:51:02.665
and how to be able to get, uh, AI to be able

1105
00:51:02.665 --> 00:51:05.545
to react the way you, you're thinking one thing.

1106
00:51:05.545 --> 00:51:07.785
And what we say and type doesn't match.

1107
00:51:08.525 --> 00:51:12.105
And it takes a long time to get your thoughts actually out

1108
00:51:13.125 --> 00:51:15.105
so that an inanimate object can

1109
00:51:15.105 --> 00:51:16.185
understand what you're thinking.

1110
00:51:16.975 --> 00:51:18.435
And, and that's not intuitive.

1111
00:51:18.635 --> 00:51:19.995
'cause we've never had to do that before.

1112
00:51:20.815 --> 00:51:24.035
Um, so I think use what you have and then practice at home

1113
00:51:24.035 --> 00:51:25.715
before you, you bring it in, in-house

1114
00:51:25.735 --> 00:51:26.995
and really try to leverage it.

1115
00:51:27.615 --> 00:51:30.365
So we, we have, um, you know,

1116
00:51:30.365 --> 00:51:31.605
we're probably fairly typical, right?

1117
00:51:31.605 --> 00:51:34.045
We've got some people who are further along the curve, some

1118
00:51:34.045 --> 00:51:36.925
who are really not comfortable with ai.

1119
00:51:37.665 --> 00:51:39.885
Um, and one of the things that we do to try

1120
00:51:39.885 --> 00:51:42.965
and support the team in, in these early days for some

1121
00:51:42.965 --> 00:51:45.885
of the users is we have a, we're only a small company,

1122
00:51:45.945 --> 00:51:49.445
so once a week we bring the whole team together, um,

1123
00:51:50.065 --> 00:51:53.965
and we just have a share your experiments

1124
00:51:54.235 --> 00:51:55.605
session, right?

1125
00:51:55.605 --> 00:51:58.205
Where people show, this is what I did,

1126
00:51:58.745 --> 00:52:01.565
and it messed up colossally, right?

1127
00:52:01.585 --> 00:52:03.885
It was just a disaster, right?

1128
00:52:03.945 --> 00:52:05.405
And other people come and say, look,

1129
00:52:05.485 --> 00:52:06.725
I did this and it was really cool.

1130
00:52:07.705 --> 00:52:10.325
And, and so we all learned from that, right?

1131
00:52:10.985 --> 00:52:13.205
Uh, and we, we did, we, I had one

1132
00:52:13.205 --> 00:52:14.885
of the guys come up with a competition.

1133
00:52:14.885 --> 00:52:16.285
So he came up, we had, he gave,

1134
00:52:16.345 --> 00:52:19.565
he gave us over half an hour, we had 10 challenges.

1135
00:52:20.425 --> 00:52:22.885
And, uh, you had to, you had

1136
00:52:22.885 --> 00:52:24.405
to use AI to solve the challenge.

1137
00:52:25.305 --> 00:52:26.885
Uh, but they were all written in a way

1138
00:52:26.885 --> 00:52:29.125
that if you just copied the question into ai,

1139
00:52:29.345 --> 00:52:31.925
it would give you a ridiculous answer, right?

1140
00:52:32.505 --> 00:52:35.605
And so the, the, the training there was, you know,

1141
00:52:35.605 --> 00:52:37.125
you've gotta think about the prompt.

1142
00:52:37.465 --> 00:52:38.805
You've gotta actually read the answer.

1143
00:52:39.285 --> 00:52:40.365
'cause, you know, one

1144
00:52:40.365 --> 00:52:41.645
or two people on, on the team

1145
00:52:42.645 --> 00:52:44.005
actually just took the answer it gave them

1146
00:52:44.005 --> 00:52:45.045
and said, I've got the answer.

1147
00:52:45.105 --> 00:52:46.285
And it's like, no, you're an idiot.

1148
00:52:46.345 --> 00:52:47.805
You didn't read the answer, did you?

1149
00:52:48.665 --> 00:52:52.805
Um, so, so, so you can turn it to little games like that to,

1150
00:52:52.905 --> 00:52:55.605
to encourage people to experiment, to play with

1151
00:52:55.875 --> 00:52:57.085
what those prompts are,

1152
00:52:57.595 --> 00:52:59.565
because the prompts are really important.

1153
00:52:59.835 --> 00:53:01.325
It's a conversation with the tool.

1154
00:53:01.985 --> 00:53:04.045
Um, if you just ask generic questions,

1155
00:53:04.045 --> 00:53:05.605
you'll get poor quality answers.

1156
00:53:06.305 --> 00:53:08.605
If you, if you give it lots of context

1157
00:53:09.785 --> 00:53:11.725
and, uh, then it is generally quite good.

1158
00:53:11.735 --> 00:53:13.045
David, do you wanna jump in?

1159
00:53:13.165 --> 00:53:15.205
'cause I know you wanna say something about Microsoft

1160
00:53:15.225 --> 00:53:16.805
and how amazing they are.

1161
00:53:19.095 --> 00:53:23.755
Uh, I probably need to, um, just, oh, I'm on, on mute.

1162
00:53:23.945 --> 00:53:26.925
Well, there's all kinds of, uh, providers out there.

1163
00:53:27.005 --> 00:53:28.245
I mean, I would say this

1164
00:53:28.245 --> 00:53:32.165
because I own a Microsoft solution providers,

1165
00:53:32.225 --> 00:53:34.765
but, um, the, the, there is, there is, um,

1166
00:53:35.125 --> 00:53:36.565
I think there's a lot to learn basically.

1167
00:53:36.665 --> 00:53:38.645
And, uh, you, we can experiment for ourselves,

1168
00:53:38.945 --> 00:53:40.325
but we can experiment for ourselves

1169
00:53:40.325 --> 00:53:43.565
what we see on the coalface, what we don't, um, and,

1170
00:53:43.705 --> 00:53:48.205
and we can experiment as to how to use, um, the AI to go

1171
00:53:48.205 --> 00:53:49.645
and, uh, research something

1172
00:53:49.745 --> 00:53:53.165
or produce us a, a, um, an analysis

1173
00:53:53.265 --> 00:53:56.525
or rewrite this email that's all low level tactical stuff.

1174
00:53:56.585 --> 00:53:58.805
But, but at some, at some stage, we, uh, the,

1175
00:53:58.805 --> 00:54:01.165
the next level is to start using AI

1176
00:54:01.225 --> 00:54:04.285
to build into our applications, to build into our workflows,

1177
00:54:04.545 --> 00:54:06.285
to build into our business applications.

1178
00:54:06.665 --> 00:54:08.725
And that, that's, that's a whole different,

1179
00:54:08.725 --> 00:54:10.045
different level entirely.

1180
00:54:10.585 --> 00:54:14.365
And, um, uh, us sitting around, um, chatting about

1181
00:54:14.425 --> 00:54:17.325
how we've used ai, um, to rewrite my CV

1182
00:54:17.385 --> 00:54:19.565
or to, to create a set of questions for,

1183
00:54:19.585 --> 00:54:21.845
for this quiz I'm running next week, that's,

1184
00:54:21.845 --> 00:54:23.165
that's all, all small beers.

1185
00:54:23.625 --> 00:54:28.405
Um, the, the, the, the, what can you do with this stuff to,

1186
00:54:28.465 --> 00:54:31.005
to improve our, our, our business capability,

1187
00:54:31.025 --> 00:54:32.325
to reduce our business problems.

1188
00:54:32.745 --> 00:54:35.325
Um, the, the only way you're gonna, uh, really get to grips

1189
00:54:35.325 --> 00:54:38.365
with that is, is, is actually speaking to people about

1190
00:54:38.555 --> 00:54:40.245
what they've done, why they've done it.

1191
00:54:40.585 --> 00:54:42.365
And then you, you've gotta get past the,

1192
00:54:42.395 --> 00:54:44.285
what the hocus pocus factor from,

1193
00:54:44.395 --> 00:54:46.325
from people in your organization thinking, well,

1194
00:54:46.325 --> 00:54:47.565
you've just come up with a lot of waffle.

1195
00:54:47.595 --> 00:54:48.965
That that's absolute nonsense.

1196
00:54:49.345 --> 00:54:51.525
Uh, how do I believe that what you've, that

1197
00:54:51.525 --> 00:54:53.605
that lovely story you've just told me is it's all possible.

1198
00:54:53.795 --> 00:54:57.565
Give me some kind of, uh, business story that,

1199
00:54:57.685 --> 00:54:58.685
that can show benefit.

1200
00:54:59.145 --> 00:55:00.285
Mm-hmm. And for me, that,

1201
00:55:00.705 --> 00:55:03.845
and mentioned it earlier, to me, it's not just about knowing

1202
00:55:03.845 --> 00:55:05.805
what, what the thing, what this capability does

1203
00:55:06.105 --> 00:55:07.285
or what's possible with it.

1204
00:55:07.435 --> 00:55:10.845
It's about making cases that persuade other people

1205
00:55:10.905 --> 00:55:12.165
that's actual benefit here.

1206
00:55:12.345 --> 00:55:14.165
And that's, that's the key point.

1207
00:55:14.625 --> 00:55:18.245
Can you make cases to get people to do things?

1208
00:55:18.675 --> 00:55:20.165
That that's what's gonna enable you

1209
00:55:20.165 --> 00:55:22.965
to use AI in your organization or not in, in my opinion.

1210
00:55:23.835 --> 00:55:28.565
Awesome. So, so, um, uh, uh,

1211
00:55:29.545 --> 00:55:31.805
yes, we've got just a couple minutes left.

1212
00:55:32.345 --> 00:55:34.165
Uh, thank you an for spotting that.

1213
00:55:34.985 --> 00:55:39.045
Um, uh, so, uh, what I would love to do is just go

1214
00:55:39.045 --> 00:55:40.125
around the, the panelists

1215
00:55:40.125 --> 00:55:42.285
and see if we could just get one last thought.

1216
00:55:43.025 --> 00:55:45.165
Um, there's, there's a few questions we haven't got

1217
00:55:45.185 --> 00:55:46.725
to, uh, sorry about that.

1218
00:55:47.025 --> 00:55:50.085
So one last thought each perhaps, um, I,

1219
00:55:50.145 --> 00:55:54.485
I'm gonna throw mine out, which is, um, to think about

1220
00:55:55.515 --> 00:55:56.525
when you're building, when

1221
00:55:56.525 --> 00:55:57.605
you're trying to automate the process.

1222
00:55:57.745 --> 00:55:59.925
So this isn't so much necessarily within the PMO,

1223
00:55:59.925 --> 00:56:02.125
it's when you're delivering projects for the organization,

1224
00:56:02.415 --> 00:56:03.485
leveraging ai.

1225
00:56:04.185 --> 00:56:05.855
Um, remember

1226
00:56:05.855 --> 00:56:09.095
that most processes in organizations are set up so that humans can do them.

1227
00:56:10.765 --> 00:56:12.505
And if you're gonna try

1228
00:56:12.585 --> 00:56:14.185
and leverage AI to automate the process,

1229
00:56:14.565 --> 00:56:17.145
it might just be worth thinking about how to

1230
00:56:18.185 --> 00:56:22.065
redefine the process so that AI can do it.

1231
00:56:22.065 --> 00:56:24.845
So it's built for AI to do, not built for humans to do.

1232
00:56:25.465 --> 00:56:27.885
So we learned that when we did that hackathon, that was,

1233
00:56:27.885 --> 00:56:29.605
that I mentioned earlier, that was one of the lessons

1234
00:56:29.605 --> 00:56:31.965
that we came away, the bit that really failed.

1235
00:56:31.965 --> 00:56:34.925
Totally. We just tried to replicate the human process

1236
00:56:35.705 --> 00:56:37.645
and we realized that we needed to change that process,

1237
00:56:37.645 --> 00:56:40.325
that it was AI friendly before we tried to automate it.

1238
00:56:41.025 --> 00:56:42.445
Um, so so that would,

1239
00:56:42.445 --> 00:56:43.925
that would be my one last little thought.

1240
00:56:44.145 --> 00:56:45.685
Um, James, what about you?

1241
00:56:47.045 --> 00:56:51.845
I think for me it's, um, it's what I'm experiencing now

1242
00:56:51.995 --> 00:56:55.365
that ai, AI is absolutely fantastic, you hear,

1243
00:56:55.985 --> 00:56:57.325
but there is two sides to it.

1244
00:56:57.325 --> 00:56:59.805
And at all of the recent events I've gone to,

1245
00:56:59.925 --> 00:57:01.405
it always goes one or two things.

1246
00:57:01.505 --> 00:57:04.805
You hear the horror stories, you hear the, the real things.

1247
00:57:04.805 --> 00:57:07.085
You hear the things that AI is gonna take everyone's job.

1248
00:57:07.265 --> 00:57:09.125
You hear all of these different things.

1249
00:57:09.765 --> 00:57:11.565
I think none of that is true.

1250
00:57:12.085 --> 00:57:14.205
I think if there's something right down the middle that

1251
00:57:14.785 --> 00:57:17.205
AI is evolving the way PMOs

1252
00:57:17.345 --> 00:57:20.605
or any sort of profession is going to be working, working

1253
00:57:20.635 --> 00:57:22.845
with AI going forward as the future,

1254
00:57:23.545 --> 00:57:27.365
and actually everyone can actually work with AI in a way to,

1255
00:57:27.365 --> 00:57:29.165
that will actually upskill

1256
00:57:29.165 --> 00:57:31.565
and benefit the way that they actually deliver whatever

1257
00:57:31.565 --> 00:57:32.725
their jobs are at the moment.

1258
00:57:33.305 --> 00:57:36.285
But what it won't ever do, in my opinion,

1259
00:57:36.785 --> 00:57:39.365
is I don't think it will ever remove the human factor

1260
00:57:39.365 --> 00:57:40.605
from any type of delivery.

1261
00:57:41.225 --> 00:57:44.085
And the one thing that AI can't do at the moment,

1262
00:57:44.265 --> 00:57:45.285
and I'm gonna say at the moment,

1263
00:57:45.525 --> 00:57:47.405
'cause we dunno what the future holds is,

1264
00:57:47.405 --> 00:57:50.005
it can't actually read human emotional gut feeling.

1265
00:57:50.385 --> 00:57:52.005
And that's something that the human

1266
00:57:52.005 --> 00:57:53.205
will always bring to the table.

1267
00:57:53.435 --> 00:57:55.805
However, a human using a robot

1268
00:57:55.945 --> 00:57:59.725
or AI to support them, I think is absolutely unstoppable.

1269
00:58:00.945 --> 00:58:04.405
You got it. You got it. Um, an

1270
00:58:06.675 --> 00:58:08.965
Yeah, I like the question that, uh,

1271
00:58:09.275 --> 00:58:10.565
Gina wrote about the data,

1272
00:58:10.945 --> 00:58:15.645
and I would say that your, your AI is as good as your data.

1273
00:58:16.715 --> 00:58:20.655
Oh, big one. So if you have a good data, then AI is helpful.

1274
00:58:20.915 --> 00:58:22.455
If your data is rubbish

1275
00:58:22.455 --> 00:58:24.255
or it's in, not in the, on the paper,

1276
00:58:24.715 --> 00:58:28.455
and it was never put on the digital solution, um,

1277
00:58:28.835 --> 00:58:30.095
AI will not be that much helpful.

1278
00:58:30.565 --> 00:58:32.735
Most of the projects fail

1279
00:58:32.735 --> 00:58:35.695
because they are not ready to do this AI

1280
00:58:36.475 --> 00:58:38.685
because of the data and the data quality.

1281
00:58:39.865 --> 00:58:42.805
So to give you a real world example of that, um, our sales

1282
00:58:42.805 --> 00:58:45.885
and marketing team have gone a little bit agent actually.

1283
00:58:45.885 --> 00:58:47.485
So they're building agents to all kinds of things.

1284
00:58:48.185 --> 00:58:50.325
Um, and one of the things that we found was

1285
00:58:50.325 --> 00:58:53.205
that we were training and retraining these agents again

1286
00:58:53.205 --> 00:58:54.565
and again with different data.

1287
00:58:55.345 --> 00:58:57.525
And so one of the things that we did was we created a

1288
00:58:57.525 --> 00:59:01.285
SharePoint FO folder that is, this is

1289
00:59:02.025 --> 00:59:04.925
the gold standard training data when you're training up

1290
00:59:04.925 --> 00:59:06.885
for sales or a marketing agent, right?

1291
00:59:06.885 --> 00:59:08.045
It's got all our story in it,

1292
00:59:08.045 --> 00:59:10.285
it's got all our positioning in, it's got documentation in

1293
00:59:10.285 --> 00:59:12.765
it, it's got all this stuff that is the, the body

1294
00:59:12.765 --> 00:59:14.725
of knowledge that, that, that an agent would need.

1295
00:59:15.265 --> 00:59:17.365
And so now all we do when we're standing up a new

1296
00:59:17.365 --> 00:59:18.525
agent, we just point it there.

1297
00:59:18.945 --> 00:59:20.685
It does two things. First of all, it's really fast,

1298
00:59:21.385 --> 00:59:24.045
but most importantly, we know what's in the folder.

1299
00:59:24.425 --> 00:59:25.885
We know it's the right story,

1300
00:59:26.225 --> 00:59:27.605
and someone's not using the wrong

1301
00:59:27.885 --> 00:59:29.165
documentation to train the agent.

1302
00:59:29.665 --> 00:59:31.845
So da you know, good data. Really important.

1303
00:59:32.465 --> 00:59:36.285
Uh, David, and then we'll finish with Joe.

1304
00:59:36.285 --> 00:59:37.525
We'll finish strong with Joe.

1305
00:59:41.025 --> 00:59:42.165
You're still on mute, David.

1306
00:59:43.175 --> 00:59:44.925
We're gonna have to teach how to use, is

1307
00:59:44.925 --> 00:59:46.485
that an AI button by any chance?

1308
00:59:46.625 --> 00:59:47.625
An AI mute.

1309
00:59:48.875 --> 00:59:49.885
What a what a chie.

1310
00:59:50.145 --> 00:59:53.605
Um, uh, well, I lost my train of thought there.

1311
00:59:53.865 --> 00:59:56.285
Um, all, all I was gonna say is, is that I,

1312
00:59:56.325 --> 00:59:57.965
I hear people talk about AI as a thing.

1313
00:59:58.465 --> 01:00:01.005
Um, AI this, AI that it, it's, it's like,

1314
01:00:01.045 --> 01:00:02.125
I dunno how to describe it.

1315
01:00:02.435 --> 01:00:06.245
It's, it's like salt, um, it's, it's in ev it's every part.

1316
01:00:06.315 --> 01:00:07.845
It's gonna be in every part of your food.

1317
01:00:08.305 --> 01:00:09.445
So you just gotta get used

1318
01:00:09.445 --> 01:00:10.725
to it being in every part of your food.

1319
01:00:10.745 --> 01:00:12.125
And if you, if you don't like

1320
01:00:12.155 --> 01:00:13.405
salt, then you're gonna go hungry.

1321
01:00:13.705 --> 01:00:17.045
Um, so it, it's, it's just a case of embracing it work.

1322
01:00:17.185 --> 01:00:19.205
Can you work out how to leverage it

1323
01:00:19.305 --> 01:00:20.925
to improve your information management

1324
01:00:21.065 --> 01:00:22.445
better than those around you?

1325
01:00:22.865 --> 01:00:24.565
If you can, you'll get a competitive

1326
01:00:24.565 --> 01:00:26.005
advantage and survival thrive.

1327
01:00:26.305 --> 01:00:29.285
If you ignore, if you don't leverage it, then you, you'll,

1328
01:00:29.285 --> 01:00:31.405
you'll not survive and thrive as much as the people.

1329
01:00:31.565 --> 01:00:34.285
I think. I think it's that, that, um, that simple. Really.

1330
01:00:34.905 --> 01:00:36.085
Wow. That dramatic even.

1331
01:00:36.445 --> 01:00:38.125
I, I think we're getting dangerously close

1332
01:00:38.125 --> 01:00:40.045
to wisdom there, David.

1333
01:00:40.485 --> 01:00:43.645
That was very good. I like that one, Joe. Follow that.

1334
01:00:44.635 --> 01:00:49.325
Yeah. Thanks. Um, for me, we're at a generational shift.

1335
01:00:49.905 --> 01:00:52.045
Um, project management

1336
01:00:52.045 --> 01:00:55.485
and PMOs, for the most part are mature employees

1337
01:00:55.645 --> 01:00:57.525
and organizations, right?

1338
01:00:57.625 --> 01:01:00.365
We are in our forties and fifties and, and sixties

1339
01:01:00.505 --> 01:01:04.085
and beyond, and we didn't grow up with this.

1340
01:01:05.575 --> 01:01:09.355
My kids are in school at, at high schools and universities,

1341
01:01:09.415 --> 01:01:12.115
and this is what they're learning every day.

1342
01:01:13.785 --> 01:01:16.955
What is foreign to us is natural to them.

1343
01:01:17.975 --> 01:01:21.315
And the 25-year-old project manager running million dollar

1344
01:01:21.315 --> 01:01:24.965
projects didn't exist now

1345
01:01:25.105 --> 01:01:26.405
or 10 years ago, right?

1346
01:01:26.705 --> 01:01:30.485
In 10 years, all 25 year olds will be running multimillion

1347
01:01:30.485 --> 01:01:33.685
dollar projects because they're gonna grow up with the tools

1348
01:01:33.745 --> 01:01:36.685
to make them capable beyond just what their experience has.

1349
01:01:38.035 --> 01:01:40.645
It's gonna be up to us to embrace that change

1350
01:01:41.585 --> 01:01:43.245
and have the courage to step back

1351
01:01:43.245 --> 01:01:45.005
and say, we don't have the answers anymore.

1352
01:01:45.905 --> 01:01:47.685
Uh, the next generation, we look at them

1353
01:01:47.685 --> 01:01:50.485
and say, man, they just, they're lazy, they're slow.

1354
01:01:50.515 --> 01:01:52.885
They don't, they're not committed the way we are.

1355
01:01:52.915 --> 01:01:55.965
They job hop. They, there's a difference to that generation.

1356
01:01:56.425 --> 01:01:59.005
You're right, there is a difference to that generation,

1357
01:01:59.425 --> 01:02:00.885
and they're growing up with tools

1358
01:02:00.885 --> 01:02:02.645
that we never had available to us.

1359
01:02:03.305 --> 01:02:06.845
So embrace that, encourage that, and, and,

1360
01:02:06.865 --> 01:02:11.325
and empower your people to go gain this knowledge

1361
01:02:11.325 --> 01:02:13.365
that just really is foreign to us that we're trying

1362
01:02:13.365 --> 01:02:15.325
to figure out that they've just had forever.

1363
01:02:15.635 --> 01:02:16.965
It's just part of their life now.

1364
01:02:18.655 --> 01:02:19.795
Yeah. Yeah.

1365
01:02:20.015 --> 01:02:22.355
And so I, I'll ju I'm just gonna finish on this.

1366
01:02:22.495 --> 01:02:23.875
So Joe, you

1367
01:02:23.875 --> 01:02:25.835
and I are, I think probably the old men on

1368
01:02:25.835 --> 01:02:26.955
the panel here, right?

1369
01:02:27.095 --> 01:02:28.155
Uh, we, Joe

1370
01:02:28.155 --> 01:02:29.675
and I are almost exactly the same age

1371
01:02:29.695 --> 01:02:30.955
we discovered the other day.

1372
01:02:31.615 --> 01:02:35.235
Um, and, uh, so, you know, Joe

1373
01:02:35.235 --> 01:02:39.235
and I have lived through, uh, we lived

1374
01:02:39.235 --> 01:02:40.275
through the.com crash.

1375
01:02:40.935 --> 01:02:42.195
We lived through the Asian crisis.

1376
01:02:42.415 --> 01:02:45.875
We lived through big data coming to eat up the world.

1377
01:02:45.965 --> 01:02:49.035
We've lived through the 20 2008 crash.

1378
01:02:49.045 --> 01:02:50.635
We've lived through all these things.

1379
01:02:51.325 --> 01:02:55.235
We've seen, uh, we've seen expert systems come and go.

1380
01:02:55.235 --> 01:02:57.875
We've seen all these, right? All these different things.

1381
01:02:58.175 --> 01:03:02.155
So AI is probably bigger than all of those put together,

1382
01:03:02.855 --> 01:03:05.155
but at the same time, it's no different to all

1383
01:03:05.155 --> 01:03:08.635
of those things in the, you know, when big data came out,

1384
01:03:08.665 --> 01:03:10.275
that was, that was, you know,

1385
01:03:10.275 --> 01:03:11.675
that was gonna completely change the world.

1386
01:03:11.675 --> 01:03:14.795
But you know what, you know, if, if AI's like salt,

1387
01:03:15.335 --> 01:03:17.235
big data is like sugar, right?

1388
01:03:17.345 --> 01:03:20.835
It's also in everything, and it's used everywhere, right?

1389
01:03:21.135 --> 01:03:24.475
Um, so 10 years from now, we'll look back

1390
01:03:24.575 --> 01:03:27.715
and we'll say, ai, what was the big deal?

1391
01:03:28.775 --> 01:03:30.645
Right? It's just gonna be part of life.

1392
01:03:31.145 --> 01:03:33.845
And if it's just gonna be part of life, you might

1393
01:03:33.845 --> 01:03:36.685
as well just dive in and learn how to use it

1394
01:03:37.025 --> 01:03:38.805
and get used to it, right?

1395
01:03:38.805 --> 01:03:40.645
Because it's here, it's not going anywhere.

1396
01:03:41.385 --> 01:03:44.045
Um, and the earlier you get used to it, the,

1397
01:03:44.145 --> 01:03:45.885
the easier the transition's gonna be.

1398
01:03:46.625 --> 01:03:48.045
So, you know, don't fight it.

1399
01:03:48.045 --> 01:03:49.165
Just get out there, enjoy it,

1400
01:03:49.275 --> 01:03:50.885
have some fun, go make some videos.

1401
01:03:51.425 --> 01:03:54.925
Um, there are some amazing, you know, get it to write jokes,

1402
01:03:54.945 --> 01:03:57.725
create memes, whatever it is that you do, uh,

1403
01:03:57.775 --> 01:03:59.085
write poetry with it.

1404
01:03:59.505 --> 01:04:03.285
Um, uh, I love Shannon's thing, by the way. Uh, oh.

1405
01:04:03.325 --> 01:04:05.325
I I do, you know what, I I take it back, David.

1406
01:04:05.325 --> 01:04:09.765
You're the old man. Um,

1407
01:04:10.945 --> 01:04:12.645
uh, what was I gonna say there?

1408
01:04:12.675 --> 01:04:15.885
Yeah, so, you know, you know, get it to write poetry.

1409
01:04:16.245 --> 01:04:18.765
Whatever it is you're fa that you're passionate about, use

1410
01:04:18.765 --> 01:04:21.245
that to experiment and play, right?

1411
01:04:21.425 --> 01:04:24.125
And just get comfortable chatting to it.

1412
01:04:24.945 --> 01:04:28.525
Um, and, uh, and you'll very quickly learn that.

1413
01:04:28.585 --> 01:04:31.845
You know, the more explicit you are with your instructions,

1414
01:04:32.425 --> 01:04:34.085
the more interactive you are, the better.

1415
01:04:34.685 --> 01:04:35.805
I love what Shannon said.

1416
01:04:35.865 --> 01:04:39.645
She uses it as a, as a, a sparring partner, right?

1417
01:04:39.905 --> 01:04:41.685
And I, I had this conversation with one

1418
01:04:41.685 --> 01:04:43.045
of my co-founders earlier today.

1419
01:04:43.305 --> 01:04:46.725
That's what he does. He, he comes up with an idea, and

1420
01:04:46.725 --> 01:04:49.445
before he implements the idea, he goes to, he goes

1421
01:04:49.445 --> 01:04:51.965
to Gemini chat, GBT and copilot,

1422
01:04:52.545 --> 01:04:55.325
and he asks, he asks Gemini, what do you think of this idea?

1423
01:04:55.465 --> 01:04:58.565
And he takes the answer from Gemini and feeds it into GBT.

1424
01:04:58.985 --> 01:05:00.525
And he takes the answer from GBT

1425
01:05:00.525 --> 01:05:01.765
and he feeds it into copilot.

1426
01:05:02.545 --> 01:05:04.445
And by the time he's gone through that loop twice,

1427
01:05:04.915 --> 01:05:07.805
he's got 15 different perspectives on his idea,

1428
01:05:07.805 --> 01:05:09.965
and he understands it way better than he did at the

1429
01:05:09.965 --> 01:05:11.005
beginning, right?

1430
01:05:11.065 --> 01:05:14.405
So it's really, really powerful just for brainstorming,

1431
01:05:14.865 --> 01:05:16.205
for challenging your ideas,

1432
01:05:16.685 --> 01:05:18.445
refining ideas, all that kind of stuff.

1433
01:05:18.825 --> 01:05:21.005
But it all hinges about just getting out there and doing it.

1434
01:05:21.005 --> 01:05:22.565
So if you're not already, I'm sure

1435
01:05:22.565 --> 01:05:23.605
most of the people here are.

1436
01:05:24.425 --> 01:05:26.805
If you're not already, dive in. Take a swim.

1437
01:05:26.825 --> 01:05:31.415
The water's great. Brilliant.

1438
01:05:31.615 --> 01:05:33.135
I want to thank everyone for, uh,

1439
01:05:33.135 --> 01:05:34.215
thank the panelists for joining.

1440
01:05:34.935 --> 01:05:36.855
I want to thank Anana for wearing the

1441
01:05:36.855 --> 01:05:38.615
thank Christmas hat the whole way through.

1442
01:05:38.715 --> 01:05:40.975
My, my hat was too hot. I had to take it off.

1443
01:05:41.755 --> 01:05:45.175
Um, uh, but thank you to the, to the panel for coming.

1444
01:05:45.305 --> 01:05:47.215
David, thank you for postponing your

1445
01:05:47.225 --> 01:05:48.495
first beer of the evening.

1446
01:05:49.155 --> 01:05:53.215
Uh, we didn't postpone the first beer of the evening.

1447
01:05:53.235 --> 01:05:56.455
That's good. Uh, but go enjoy the football.

1448
01:05:56.755 --> 01:05:58.335
Uh, good, good luck with the game.

1449
01:05:58.515 --> 01:06:00.255
Uh, thanks to you all for turning up.

1450
01:06:00.775 --> 01:06:03.335
I wish you all a very merry Christmas, if that's your thing.

1451
01:06:03.595 --> 01:06:05.095
Uh, happy holidays, if not,

1452
01:06:05.635 --> 01:06:06.815
and if you're in a region

1453
01:06:06.815 --> 01:06:09.575
where you don't have holidays at this time of year, too bad.

1454
01:06:10.435 --> 01:06:12.455
Uh, fantastic. Uh, thank you everyone.

1455
01:06:12.715 --> 01:06:14.975
Uh, and here's wishing you all a very prosperous

1456
01:06:14.975 --> 01:06:16.215
and happy New Year.