Tech Talks Daily
00:17:58 · Ben Grinnell
[00:17:58] S1: Yeah. I mean, that was based on my early experiments where AI and looking at how you can use it to replatform an existing system. Yeah. So you can replatform a system with all the bugs the original system had. And a lot of people say, well, why would you do that? Well, AI can do that. Humans can't because you just can't get developers to write those bugs in. But with AI, you can mirror what was originally there. And that came from my experience of trying to replay. I was the IT director for the Hyboth for the border agency for 2007, 2010 when we were putting in the e gates and things like that. And I was trying to replace one of the large legacy systems at the home office. And what I realized was that the system had been built and had evolved, and it hadn't matched user requirements. So I had teams in lots of different cities, teams of caseworkers who had all worked out a different way of working around the system. Yeah. So everyone's got their own way of working around the bits of the system that don't work. Now if you try and replace that system, one, you've got a massive technical engineering task to do. But two, the change management is almost impossible because whatever you do is wrong for everyone but one of those teams. But if you actually want to divorce the massive change management problem from the technical problem of this systems on a platform I can't support anymore, you could just get AI to rewrite it with all the bugs so that it performs exactly the same. So all those teams can carry on doing what they were doing, and then it becomes much easier when you've got a stable platform to do the other bit, which is the change management in a probably a piecemeal way. So I think that's one of the use cases that was one of the earliest ones, which is now getting really advanced very quickly putting a platform into a more... It might be something that's more defendable, more secure. It might be something that's in warranty, or it might be just a coding language that you've got engineers to actually know rather than one that everyone's retired. So lots of lots of systems can benefit from that at the moment without having to wait for the change management effort that goes with them.
00:19:56 · Neil C. Hughes
[00:19:56] S0: And for any leader listening that is feeling somewhat inspired by what you're doing here, how should they decide which problems they should attack first? So which belong with the frontline teams, which need a central AI function, and which should still firmly remain with the traditional tech teams. A bit of a balancing act there, but any tips or advice on how to make that or make that decision a little easier?
00:20:19 · Ben Grinnell
[00:20:19] S1: So I think I'd see it as an opportunity because a lot of companies have got a lot of projects running that are the traditional. They'll have a portfolio, and they'll be the traditional IT projects, which might be I'm building in a big enterprise service bus or I'm building a big ontology for my data, so I've got one version of the truth, which you never quite get to. Yeah. And there... There's there's a lot of white elephants in that space. But you've generally got three or four of those big corporate projects running that are struggling to get that momentum or maintain that momentum or get that business engagement. And then you've got a lot of projects in the business that might be doing the shadow IT or trying to do things faster and they can't get the support. There's a wonderful opportunity with AI to work with the the business delivering the value. So take the... I'd take some of the biggest problems that that, you know, the CEO is keeping awake at night and say, okay. Well, can we address that problem? And if we can do a fast iteration on that, but we can actually... Because we can write in any particular code stack or language you like because AI is brilliant at helping us with that, we can actually say, well, let's look at those big corporate projects and find which of those can we actually utilize to deliver this solution rather than building shadow IT. Let's make the back office IT project famous by using it to do this narrow slice. And then suddenly, we found a way of joining up what we were trying to do here, which we were never gonna get enough momentum around with what's the biggest problem in the business here. And the two are mutually beneficial. This one's not shadow IT, and this one's no longer a big white elephant in the center, but it's actually driving value. And if you just carry on doing that with other vertical sizes, but solving significant problems that impact the business and involving the... And joining them up with the back office projects, then you've solved a lot of the organizational friction... Organizational friction that you have with those competitions. They shouldn't be competing with each other. They should be helping each other.
00:22:22 · Neil C. Hughes
[00:22:22] S0: 100% with you. And I think if we look back, I think it's, what, eighteen months ago, that infamous MIT study that that found that lack of ROI from some of those big AI tech projects. There's been a big focus on securing ROI and measuring progress there, but knowing what to measure. We all know you can only improve what you measure, but knowing what to measure, That can prove difficult. So, again, for... Give leaders listening a valuable takeaway. What kind of measures will tell an organization that a small AI solution is actually delivering real value and ready to scale beyond its original team?
00:23:01 · Ben Grinnell
[00:23:01] S1: I think it's a really difficult question, which is why you end up with studies like that one. And Yeah. I didn't like that much because it actually said that, if I remember rightly, 95% of AI projects are not delivering a tangible difference to p and l. And if if I was in an organization and one of my 20 experiments delivered this value of p and l, I'd be quite pleased with that metric because if you're not experimenting at that rate, then you're probably not doing enough experimentation and innovation Yeah. To really stay current in your market. So we... That... There's a headline grab, but it's not really what we should be measuring. I think when I look at the difficulty is because if you look at doing a study and doing a baseline, by the time you finish doing the baseline so you can show improvement in a mathematically proper way, the tools have all changed. So you never get a chance to do the same experiment again because all the tools have changed under you, which is why it's really difficult. But I think where we've got AI being used, we need to look at some of the measures that aren't just about efficiency. I think there's been a big focus on let's measure how many heads we can save. And I think that's the wrong way to do it. So if you're doing it, any transformation project that's focused that that gets a reputation for being about removing heads from the organization is gonna get a lot of tissue rejection from the organization. Because at least for now, when you pit humans against AI, the humans win. So you will not get any adoption, and it will be some they will set it up to fail. So we're describing the amazing new impact you can have if you use AI and how your job can be different before we're describing how fewer people we can have doing the job. So what is the next level job and getting the the people who are more keen to jump on that bandwagon before everyone jumps on their, this is gonna be a a cost saving dragon. So we've really got to look at where is AI producing an increase in revenue, better services for our clients, more profitability, or just removing the toil from from some of our work so we can deliver those things ourselves. But it's gotta be the positive measures rather than the ones that are generally perceived as negative because we won't get the adoption from doing that.
00:25:28 · Neil C. Hughes
[00:25:28] S0: And we are recording this at that time of the year where we're drinking pumpkin spice lattes, preparing our inboxes for Black Friday emails and Cyber Monday with one eye on 2027. So finally, what... What's keeping you busy at Newton, and what can we expect next year and and beyond? What are you working on?