Tech Talks Daily
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?
00:14:34 · Ben Grinnell
[00:14:34] S1: Yeah. So I think we only really move at the speed of trust. So I think getting trust across the organization and things we introduce is absolutely critical. And we're not doing a good job of that in the news at the moment, especially this week. So I think if I look at the way AI is used in most big organizations at the moment, it starts off being something that the people use behind them. So they use it to enhance themselves and then present the AI to the world because that's the way chatbots have started. So initially, everyone is using it to make their their back office, if you like, their personal back office more efficient. And I think that the adoption part that really gets a company transformation is when it moves beyond that. So when people wish you couldn't do a year ago very effectively, but when you just start to create agents. And I think there's a load of different types that you could do, but I think the first one most people try is to create the expert agent. So if I'm the expert that everyone phones up to know everyone about the company, everyone about a specific part of knowledge, can I create a bot that they can then ask when I'm on holiday so that I don't get the call? So that's the sort of the personal expert bot. And then I think we're now moving into an age where you can have bots that show off as members of the team. So if you're if you're a business that use Slack for your communications, then you've got dot in Slack that shows up, And you could have it as a member of the team that the whole team can share an agent with the context. I think that changes the the dynamic completely and massively drives adoption because then the whole team can see it happening, and they've all got to adopt. Whereas when you've got individuals, you'll have a lot of leaders and laggards. And then you've got agents that go across the teams and agents that are chief of staffs for people, which is another iteration, which I think is gonna become very common in the workplace over the next six months. And then I think the adoption will get faster and the trust will get faster as well. But we've got to make sure that we're implementing the guardrails around that as we go because there's a huge amount of power.
00:16:46 · Ben Grinnell, Neil C. Hughes
I mean, if you look at the the things we had recently where the bugs have always been there, but the AI has found them and made them more discoverable. In lots of large organizations, the data's always been there, but AI makes it really more findable. So on lots of SharePoint sites and organizations, the data that shouldn't be there. It's been there probably for ten years. But when AI gives you ability to have such fast access over such a broad scope, then suddenly that's a much bigger problem than it was before. And that's exactly the same as the vulnerabilities that we're seeing out in the news at the moment. Corporate data and the trust that we have in AI, gonna have to make sure that we we implement it sensibly, and we put guardrails in place so we only give access to the things that we are comfortable with. [00:17:37] S0: And before you join me on the show today, was doing a little research on you, and I was reading how you said that some of the best AI implementations, that should actually remain almost invisible behind familiar systems. So do you have an an example of technology changing substantially without forcing employees to to change how they work and go go somewhere else?
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?