Tech Talks Daily
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?
00:25:47 · Ben Grinnell
[00:25:47] S1: So we have taken all of our work through through live coding hackathons. So everyone from the CEO to the most junior person just to make sure that everyone understands what we're working with because I think there's a there's a need to be a bit hands on, whatever level you are in the organization, to be able to effectively work with this technology. So that's been one thing. We've also been looking very closely at the the war for tokens, as you said. We had token maxing and now we're in... They're getting quite expensive. So we've we've got some sovereign compute. One, because tokens are expensive, and we want to be able to at least run inference ourselves. And two, because a lot of our work is in places where data shouldn't leave The UK, so we need to have that capability as well. And there's a... I'm predicting a big crunch in the inference market because there just isn't enough compute to go around. They can't build it fast in America at the moment, and I don't think we're doing any better in The UK. So, I saw Microsoft pull out of a contract this day, which, set one of our our main hopes of having a lot of infrastructure, go down a bit. So I'm really keen that we protect our ability to use AI in a safe and secure way. So that's been a big focus as well as making sure that, you know, I've got the right things in place to make sure all of my workforce can continue to use it in a... Not an unrestricted way, but a way that's giving our clients the biggest benefit. And one of the things I've done there is I've said, I I I do want people to have access to the latest models, and they might be the most expensive. But I'm looking at... The people who are using it most are already adopted, and they can look at how can we make it more efficient, how can we use more efficient models. The people that I try over the first time, I want them to be using the best model and getting the best experience, because otherwise, they won't become long term adopters.
00:27:48 · Neil C. Hughes, Ben Grinnell
[00:27:48] S0: I think that's a perfect moment to end on. And for anybody listening who just wants to find out more information about you, Newton, anything we talked about today, where where would you like me to point everyone? [00:27:59] S1: Yeah. Please. Newton's website, newtonimpact.com, is a great place to learn about what we're doing and sharing with clients. And, personally, I'm quite active on LinkedIn, so I'll be sharing a lot there about what I'm doing. [00:28:11] S0: Awesome. Well, I will have links to everything that you mentioned there and pop that in the show notes for people to check you out. I'd love to stay in touch with you and see how things evolve next year and see how many more marathons you're gonna be running as well, maybe. But more than anything, just thank you for sitting down with me talking about your story. Really appreciate your time. [00:28:26] S1: Great. Thank you, Neil. That was great.