Heath Gascoigne 04:39

Okay, so we'll kick it off. You know how it works? Right? We usually, I... Uh oh, then the background of who you are. Might as well cover it. Now that you've got, you've been in transformation. And as I just alluded to, the beginning, you started in in government, in the Senate, in I think it was, it like 2013 or something in Arizona. So you have really long experience of exposure to the government. And if we talk about the subject government transformation, that's what we're going to discuss. And then you move, you did some time in consulting Big Four, ey, and then and transformation ever since, since 2013 so you've got a few years on the clock there.

Heath Gascoigne 05:38

Yes, sir, yeah, it's been a it's been a ride, the way you you put it that way, it doesn't seem like it's been that long. And yeah, it's been in various capacities. I wouldn't say it's all it's been all government, but definitely having that, I would say that that expertise, that background in government, public sector, for sure, but have worked in various sectors. And yeah, it's interesting to the types of levels of transformation in and among businesses and government and organizations these days. You know, particularly with AI has...

Heath Gascoigne 06:09

oh, yeah, we'll get into that. It is the old it's so funny that even the people that harp on about it so much about technology, Gartner being one of them, and they themselves have their own Gartner Hype curve. You know, it's like, Well, if that the guys that published so much research around technology, and they themselves have a graph, some visualization to represent what happens of general trends. You know, you just have the, you know, the normal distribution, the early adopters, the laggards, you know, through the 95% saturation rate is like. So technology goes through the same kind of thing. The, I can't remember the terms, but there was I wore. The was it the trough of despair where reality kicks in, and the hype dies down. And oh, AI is oh, well, one time we were talking data lake, and then it was, now it's taking its data house, pool house. So at the moment, the new kid on the block is AI, Gen, AI, and then it's going to be agentic AI. Well, that's probably more so here, now and then the next is something else, again, yeah, and again, again, yeah. So it's cycle, right? Okay, so we'll get into that. So, so we usually cover 3.1 of them, the first is, and from your perspective, if we your niche, your where you cover the most will most comfortable, most liked from your perspective, what is the industry doing right or wrong right now? What are they learning? Any major like, I see it a lot. And even with the last guest, Larry Mendel was talking about mandelberg was talking about from his perspective, he sees that, you know, I will talk about, and the research will say the failure rate and transformation programs is at least 70, sometimes higher and and he'll say, actually, 85% of most transformations fail on their core objective. All that's been very specific, and said, yeah, the core objective, why they started in the first place, 85% of them don't hit it. They achieve benefits and other things. But the number one reason they got the winner to do this, transformation never got achieved. So from your perspective, what are they doing? Right, the wrong, lessons learned, repeated mistakes. Second one is when you go into your clients, now, done it 2030, years then is there a particular approach you follow any So, if there is, Could you step us through it? What does it look like? And thirdly, Now, having done this for 20 odd years to couple of decades, if you could do it all again, what would you do differently, which is really the takeaways, like, ah, you know what I like to like, I would go into big projects, and my colleagues, as business architects and business transformation are banging around with the business Canvas model. And I'm going, you know, you're doing a transformation, and that's a useful tool, probably, where you don't know your business model to start with, or there's lack of consensus, or you're a startup, has been fantastic, you know, get everyone on the same page, but yeah, the right tool for the right job. And so is there any aha moments? There you go. Oh yeah. Look, definitely use that as a go to, and I would probably avoid that. It's, you know, it's a red herring. Market teacher looks good, doesn't do anything.

Heath Gascoigne 09:34

Yeah, happy to speak to all about it. And I think definitely have some big, interesting insights I can share just in various experiences. But going back to what you were talking about in terms of like that, the trough of despair, just just to quickly tie in, because I think the term you were maybe looking for was like innovation, the innovation curve. Yes, you're kind of like you said. It's a normal distribution. Where are we? Right? Now, and in that sort of like a agentic AI generative, AI innovation in the broader markets, impacting various industries. So it is, it is something that, in terms of transformation, right? Like folks who are in transformation, what does that look like? How do we measure where we are in the innovation curve? And how can we use that to our advantage when we're performing transform, transformation activities for different clients or customers? Yes, it's something to start thinking about and kind of get a get ahead of the game here, as things really pick up with this, with the curve of innovation. So just to touch upon, I think we'll come back to, that's a great point. It's great point there...

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Heath Gascoigne 1:12:18

And absolutely my pleasure. Okay, thank you very much, my man. Okay, standby, okay, Cheers, bye, see ya.