Tech Talks Daily
00:07:58 · Ben Grinnell
[00:07:58] S1: Yeah. And the the biggest problem is the world's changing so fast. So if you do take eighteen months to do a solution, even if you got the requirements right at the start, it's definitely wrong by the time it comes out. So we have to be in a world where we deliver iterative value very, very fast. And the AI can speed up all bits of that. So I know we've seen live coding and some of that work speed up the development cycle. And we have got tools like Blitzy that allow legacy code bases to be looked at and to bring that context in. So a lot of the early problems with via coding have now been solved. But I was with a group in San Jose in April where we were writing some thought leadership papers on how you automate the testing bit of that work and how you can get models set up to challenge the the code that's been written by a different model and make that bit faster as well. And whereas a year ago, maybe six months ago, you couldn't really ask the model about the architecture or to get the architecture right. Now you've got a very good architecture coach in a lot of those models. So I think the the speed's brilliant. AI can... AI AI AI can speed up any element of that pipeline, but only if the person controlling it or directing it knows what questions to ask. And that's where it all goes wrong. If you don't know to ask the right questions, if you don't know to ask about what nonfunctional requirements should I consider, if you don't need... Know know to ask, how should I make this a modular architecture so it's easy to maintain? Then you won't get that.
00:09:31 · Neil C. Hughes
[00:09:31] S0: Yeah. And looking back, if we go about, what, ten, fifteen years, we had BYOD, then shadow IT. Now we're hinted at shadow AI. Just the thought of employees creating their own AI tools or workflows ten years ago would have been unthinkable, but this is where we are. So what kind of guardrails are needed now so experimentation doesn't accidentally drift into introducing security data or or even governance problems.
00:09:57 · Ben Grinnell
[00:09:57] S1: There are some interesting parallels, aren't there? Because we had all employees creating Excel spreadsheets with macros, which are, I would say, more difficult to maintain than a lot of the solutions people live by coding in Python now, particularly access databases. So we had the shadow IT sort of wars back then. I think we are a place where we can vibe code things that are much more testable and maintainable than some of the previous versions, but we do need to make sure that we've got IT knowledge aware of them. I think for a long time, I've been working with clients to help democratize IT into the business and bring IT closer to the business because coming out of the days of offshoring a lot of that because IT is just a cost center, which was was... We were in that world for a long time. Yeah. If IT is business critical and if it's not, I have a question which business you're in, but I think it's business critical for a lot of businesses now, then you want to keep some of that capability in house and some of it very close to the business. So stop thinking it as a cost center. And if I'm in the IT function, I need to be working very closely with the business so I'm closer to the the profit and the revenue because, otherwise, I'm going to get stopped. A lot of the projects that start off in IT run quite well, and then there's a downturn in the market or something, and they get mothballed for a period of time. Or they go too big and they build a lot of momentum, and then they can't keep that up. So I think having IT in the business with the people that know IT, actually working closely with their business counterparts and delivering value early and iteratively is the way we should all all been going for a long time. So this is just making that something that you have to do faster because otherwise, you're gonna lose control of it in entirety.
00:11:54 · Neil C. Hughes
[00:11:54] S0: And just to bring to life some of what we're talking about here, especially for people listening. Let's let's try and walk through something here. Why can starting with one narrow, very real problem, how can that create more momentum than launching a a large company wide AI transformation program where everyone tries to boil the ocean? Well, if we start with that one real problem, tell... Walk me through what could happen there.
00:12:16 · Ben Grinnell
[00:12:16] S1: So the momentum is exactly the thing to focus on. So you can build a lot of momentum by starting a great big project. Like, let's do an enterprise data or enterprise AI project, but you won't be able to sustain that momentum. If you create a very narrow single use case where you can demonstrate something and it produces business value, so it impacts the p and l and people can see it and you can deliver that very quickly, then you create a smaller amount of momentum. But the thing that transformations need to do is they need to continually increase the momentum. Otherwise, they die. So I think starting small, learning fast, and growing is the right way to go. And if you create business value, you create FOMO as well to other parts of the business. Everyone eventually is measured on business value. So if you're creating, as John Sparrow say, better value sooner, safer, happier, then a lot of people are gonna be looking at that and going, well, how do I grab some of that? And that's where you get the the pull, not the push. And in organizations, I think, creating a pull for your services is massively important and pushing against an organization which has generally got too many big projects that it can possibly deliver is an uphill battle. So you have to create the pull, and I think grow that momentum and keep it growing throughout the life cycle.
00:13:42 · S2, Neil C. Hughes
[00:13:42] S2: Your agents aren't producing accurate answers because they don't have a complete semantic understanding of your data, and Denodo is solving this and solving it through semantic consistency. Through semantic consistency, your agents can start making accurate predictions in real time. So see what else Denodo can do by visiting denodo dot com to learn more. [00:14:06] S0: And I think if we... Again, if we look back, tech projects traditionally failed when changing company culture, for example, was neglected and ensuring that people were ready to adopt any new solution. So when we fast forward to AI, what does successful adoption look like, especially when employees begin pulling AI through the organization because it genuinely makes it... Makes their work easier?